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AIAE » E-LEARNING » INTERVIEW 8

AI technologies – AI & ethics

The following interview snippets were given by Emanuela Girardi and cover the development of AI technologies at different scales, the societal concerns and the ethical use of AI as well as the norms and regulations on AI.

transcript

Good morning. Today we are featuring Dr Emanuela Girardi in our interview. Emanuela Girardi is the founder of Pop-AI (Popular Artificial Intelligence). She is a member of the Artificial Intelligence Expert Group of the Ministry for Economic Development, which wrote the Italian Strategy for the Artificial Intelligence. She is member of the board of the Italian Association for Artificial Intelligence and of the Industry Task Force of CLEAR (Confederation of Laboratories in AI Research in Europe), of which she is the coordinator of the task force on Artificial Intelligence and Covid 19. She is also a member of the board of AI Data and Robotics (ADRA), which is the new European association that will work with the European Commission to implement the Horizon 2020 programme. Thus, she is an extremely relevant and extremely competent person who will guide us in this half-hour journey on some interesting things in the field of AI. I thank her again and I would proceed to ask her some questions.  

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The interviewee, Emanuela Girardi, is the founder of Pop Ai, which means Popular Artificial Intelligence and which is part of the group of AI experts belonging to the ministry of economic development. She is also the coordinator of the task force on AI and Covid19 and member of the board on AI, data and robotics.


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QUESTION (F. ANANASSO)

Let’s start with the most basic question. Given your experience, Emanuela, what do you know about global strategies in the field of artificial intelligence? What is the context? What are the international models? I hear particularly in America. What is the international context?  

 

ANWER (E. GIRARDI)

Yes, thank you Fulvio, and good morning everyone. Certainly right now, let’s say that artificial intelligence technology is considered like a strategic technology for the development of future societies. And a number of Countries have started addressing artificial intelligence, investing a lot of resources and – above all – developing a national strategy for artificial intelligence. At the moment, there are about 50 national strategies for artificial intelligence around the world and some are in development, so they will be published in the next few years or at even this year, and so all the Countries have seen these technologies as really strategic because of the disruptive aspect AI has on society, already today on our everyday life and more and more on our life in the future. InI particular, though, let’s say that although there are 50 different strategies already worldwide, we can basically speak of two-three development models for artificial intelligence globally, i.e. the American model, the Chinese model and, in between, the European model has recently entered. At first glance, the American and Chinese models, appear rather different, but there are indeed great similarities, because in both models, in reality, the development of artificial intelligence technologies is led by large private groups that have, among other things, a presence all over the world — not only in China and the United States. The two big differences between these groups are that while in China we have a tendency towards government-centric control of these technologies, in the United States there is a kind of tendency instead towards deregulation, hence a kind of lack of regulation of these technologies. Europe started a bit late in terms of investment in the development and adoption of these technologies, and in particular it started in 2016 with an agreement in 2018 ‘married’ with a cooperation agreement between all member states for the development of a coordinated plan for the development of artificial intelligence technologies. Starting a bit late, it decided, however, to focus on the ethical vision and therefore primarily on the definition of ethical guidelines for the development of artificial intelligence technologies. And from this research group that the European Commission convened, the European vision of artificial intelligence came up, which is a vision that is defined as ‘human-centric’, i.e. putting humans at the centre and promoting the use of artificial intelligence technologies to improve people’s lives. The second aspect, however, is that it promotes the use of ‘trustworthy’ technologies, i.e. they are reliable. This is somewhat at the heart of the European vision. Trustworthy because if we think of a very simplified definition of artificial intelligence technologies, where and with which we can identify systems that analyse their surroundings, i.e. collect data in order to achieve a certain objective and do so autonomously, by displaying a behaviour that if it were carried out by a human being, we could define as intelligent behaviour. Now, in order for them, when they reach this goal, perform some action that is defined by human being, for the human being to accept the outcome of this action or decision – which is taken by an autonomous system – and therefore trust both the use of these systems and the actions or decisions that are taken by these systems, they must be reliable (‘trustworthy’). And what does that mean, reliable / trustworthy? Let’s say that as far as the European version and definition of reliable technology is concerned, it means three things. Basically, it means that they must comply with European laws, with the ethical values that are included in the European Charter of Rights and they must be safe from a technical robustness point of view, they should not harm people. So, if these systems meet the three requirements, are ethically sound, then the European Commission says that they can be developed and used within the European Community. Then the European Commission went ahead and developed a set of two important documents a white paper, a European data strategy, but actually the document at the moment that is more controversial but also more important is the one that was presented in April 2021 and is the so-called AI Act,  which is a set of documents that are the first proposal in the world to regulate the use of artificial intelligence systems. This document is very important because first of all it uses a risk-based approach, i.e. it divides all artificial intelligence systems into four categories based on risk. The first one is considered the unacceptable risk ones, so these are the systems that are banned within the European Community. Then there are high-risk, medium-risk and low-risk systems. With regard to high and medium risk systems, a whole series of requirements are defined, the so-called assessment lists, that is, a series of requirements or certifications that these systems must have in order to be used within the single market of the European Community. So these aspects are very important and there is a lot of debate within the EU Commission, since on the one hand, it is said that the objective of the European Commission is to encourage the development and use of artificial intelligence technologies that in some way certainly protect people and protect European citizens, but at the same time that there is a kind of legal certainty for companies to be able to develop these systems in a safe environment from the legislative point of view, so as not to have risks when they introduce artificial intelligence technologies in their products and services, in their market offer. Clearly, the biggest limitation being discussed at the moment is that over-regulation could in some way restrict innovation. And this is especially true if we consider the two systems we talked about earlier, the Chinese one and the American one, where actually there is much less regulation, and especially the American one compared to the European market that we are trying to create. And so it is very difficult on the one hand to balance the need to regulate these systems, which if they are used maliciously can actually cause damage, and on the other hand also to try to encourage the development of European innovation and to ensure that there is also a real development of artificial intelligence technologies, but not only artificial intelligence, also in fact all the supporting technologies. So I’m thinking of blockchain, I’m thinking of cloud technologies, I’m thinking of performance computing because it’s actually very important that these technologies can also be developed at European level and that what is called European technological sovereignty in the coordinated plan is therefore realised. Let’s also think, for example, to the Gaia X project, that aims at  the same objective,  that of achieving a kind of European technological sovereignty. 

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Yes
No
 

AI technologies are strategic for the development of society in the future.


Only few resources have been invested in the field of AI.


At the moment, there are about 150 national strategies of AI all around the world.


All countries consider AI technologies as strategic and significant in today’s society that will become even more relevant in the future.


Globally, we can basically say there are two to three models for the development of AI technologies: the American model, the Chinese model and in between the European model.


The American AI model and the Chinese one are very different from each other.


Regarding investments on the development of AI technologies Europe is very much ahead of the rest of the world.



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1) What did all EU member states agree on in 2018? a coordinated plan for the development of AI technologies.
2) What did Europe decide to focus on? an ethical vision of AI.
3) What is the European vision based and focused on? humans
4) What does a reliable AI technology mean from an ethical point of view? It needs to respect the European laws, respect the European ethical values and be reliable, e.g. safe from a technical point of vue.
5) Which documents did the European Commission publish over the regulation of AI? A white paper, an European data strategy
6) What is the most important and controversial document proposed in April 2021 by the European Commission to regulate the use of AI systems? The AI Act
7) What do AI systems that are high and medium risk need to comply with to be used within the European community? with a series of requirements and certifications
8) What could an excess of regulations lead to? Limit innovation
9) Among the American and European markets, which one is more regulated? the European one


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QUESTION (F. ANANASSO)

So, if I understand correctly, beyond the American model and the Chinese model that with different aspects go in a different direction than ours, the European Commission one  is more human-centric and “trustworthy”, as you said. Then afterwards we’ll talk maybe about what’s going on in Italy. If I understand correctly ,the AI Act you’re talking about (the April 21, 2021 one) put various levels of risks. Is there anything you want to tell us on that, what are some relevant points? Besides the risk classification of the European proposal’s, are there relevant aspects? I’ve heard of 32 cases where one can intervene subjected to the fact that there are warrants of arrest. Is there something that can be said quickly about what are the essential points of the European proposal?

ANSWER (E. GIRARDI)

Yes. Actually, as far as the risks are concerned, what I can tell you are the aspects of artificial intelligence systems whose risk is considered unacceptable, for example social scoring, people remote biometric monitoring or systems that can somehow influence or manipulate the behaviour of people — especially vulnerable people. These features are considered unacceptable and are therefore prohibited at the moment within the European Community. In terms of high-risk systems, instead, in my opinion at the moment there are aspects still to be clarified, because we are talking about conformity assessment, a list of a set of criteria that will have to be met in order to be introduced and used within the European Community. But the problem is that there are also talks of a European certification, right now being not very clear. Among other things, we have to keep in mind that the value chain of artificial intelligence systems is extremely complex. If we think about the development of an artificial intelligence algorithm it goes, for example, from those who collect the data, those who do the training, those who develop the algorithm, those who then put it into use in their own systems within their own company and/or put it into the market, and then maybe the dataset gets changed — a re-update is needed and then one might need to change the system and in that case  monitor everything through use. We are really talking about the whole life cycle, the artificial intelligence system and then it makes all the different players in the supply chain constantly stay connected and constantly monitor the use of the artificial intelligence system that they have developed, trained and put into the market. So these aspects are still not well clarified because there is an aspect of supply chain that is very complex in my opinion. 

Another complex aspect concerns the fact that at the moment high-risk systems are defined, not in a very clear way, but they are defined nonetheless. But another aspect concerns the fact that the European Commission at the moment has, let’s say, ascribed to itself the right to change this list and then adding new systems, which is quite controversial because normally there should always be a division between the legislative power and the executive power, so in fact there are already many discussions about this, saying that it is not the European Commission that has the power to change an annex of a law. And so these aspects are quite controversial, for example I was reading the other day that the various CEN, CENELEC, … and most of the various standardization Bodies asked the reason for the above EU Commission position. 

There is also a very interesting aspect regarding just the definition of AI standards, which at the moment are still missing at the European level and so all the various standardization bodies are working on that. It’s one of the feedbacks they have requested since these days everybody is writing feedback to the European Commission, because 6 August 2021 was the last day to be able to submit feedbacks. A proposal for regulation is one of the feedbacks, which is just to delete Article 41, that is the fact that the European Commission can vary, let’s say, more or less as it likes (that’s not exactly the case, but it can vary discretionarily) this list of systems. So these are some of let’s say the most conflicting elements.

Another very conflicting element concerns the AI definition, since it is a very broad definition whereas it is actually supposed to be, let’s say, future-oriented and future proof. But from the other point of view at the moment systems, that I don’t know if they are AI, are also considered as AI systems, like advanced statistics systems, linear regression systems … that then make it – let’s say – much more complex the use of these systems that probably today are not artificial intelligence, so there are so many aspects of discussion. I think it will take two or three years before this regulation is applied in all member states.

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1) What unacceptable risks related to AI systems are there within the European community? social scoring, control of mass, control to influence and manipulate Europeans’ behaviour
2) As to high risk AI systems, how are requirements and criteria that need to be evaluated called? Conformity assessments
3) What is the value chain of AI systems like? extremely complex
4) What does monitoring of AI systems refer to? the life cycle of AI systems


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QUESTION (F. ANANASSO)

Thank you for your considerations, which in large part coincide with some of the doubts I had. I understand, however, that the essential thing that we have more than others is precisely the concept of the “human centric”, right? The famous “Human in the loop” that many people say “on the loop”, rightly so, that is, at the end / on top  there must be a “human”, and I am very concerned personally on what the European Commission has said explicitly it cannot deal with, that it is military topics, we already dealt with in other interviews, very worrying because there is no Human In the loop. However, this is a topic that has nothing to do with what we are addressing now, it is only a consideration between us who study and address these topics .

So we have seen the World and Europe. How about Italy? What is the situation of Italy? Among other things, In Italy, as well as the rest of Europe, but we in particular, lately there is also the term “sustainability” that we often refer to. What is the Italian approach, the Italian strategy and our approach, let’s say, to Sustainability and to the importance of using artificial intelligence and investing in artificial intelligence systems?

ANSWER (E. GIRARDI)

Italy has Developed different strategies. At the moment I was involved in the work that led to the development of the artificial intelligence strategy promoted by the Ministry of Economic Development (MISE). There were actually two other initiatives, so we have actually developed a lot of strategies, but to date we have not published a single one. Now the news of the last few days is that a group made up of three ministries has created a new task force that has to re-analyse the document we wrote at MISE, and I hope really they will propose the phase of execution, that is, to actually implement the strategy, to carry it out, the execution that is the fundamental thing, so we really hope so. That will be the turning point for Italy as well, it is a pity because in Italy we have so much excellence, in my opinion, especially as regards, e.g., the research and development of artificial intelligence, but unfortunately there is a lack of strategic vision at the moment, of what could be the applications and uses and developments of artificial intelligence applied in the various fields of the society. 

On the other hand, the proposed Italian strategy that we have made is based on three pillars. The first one was AI for the human being, so absolutely in line with the European Human-centric vision, the second one AI for a productive development sustainable and of excellence, and this is very much in line with the EU vision, with the European White Paper on artificial intelligence that was presented in February 2021 and which promotes precisely the creation of a system of excellence and trust at European level to promote the development of artificial intelligence technologies, and then to invest in education to bring them to companies, schools, society. The last point, however, is the point that I think is the most important and is also the most innovative as far as the Italian artificial intelligence strategy is concerned. And it is AI for sustainability, that is what we have proposed and that we think really needing a paradigm shift, in the sense that it is no longer enough to just put the human being at the centre, because he / she actually lives on the planet and within the ecosystem. And so to say that we only use technologies to improve human life is no longer enough, and we therefore proposed the use of artificial intelligence technologies to achieve the sustainable development goals (SDG) of the United Nations 2030 Agenda. And so just changing a little bit the paradigm and proposing this aspect of sustainability or an approach, let’s call it “planet-centric” if you like, this vision is  very innovative at the moment. In fact it has also been adopted then by the OECD, by the UN and I think it has also been included actually in the last coordinated plan of the European Commission, and I think it is probably the only possible vision. Even  because, if there are 156 Countries that decided that the UN 2030 Agenda is THE Agenda, as well as the SDG goals that, even if we think that 2030 is practically tomorrow, are the most shared goals that there are today at European level, then, in my opinion, once the goals to be reached are defined, as we were saying before, the definition of artificial intelligence are systems that in some autonomous way allow us to achieve certain goals. If we are clear about the goals we want to achieve, at this point, even if they are complex goals, we can use these technologies to achieve them. And so we then analysed for some SDG how to use technologies to achieve them. In particular, one aspect, let’s say very important, we have tried to devote to trying to increase the inclusion and accessibility of people with disabilities. And this is thanks to the use of artificial intelligence technologies, and so these aspects are very important, but they are also very important in helping us to counteract climate change or how to improve the impact on the environment. For example, in this regard, there is a very interesting project that has just been promoted by the European Commission, called “Destination Earth”, which has created a digital twin of the earth that makes it possible to monitor and assess climate change, the effects of climate change, and to test, let’s say, on this digital twin of the earth, the new environmental policies that we want to develop using artificial intelligence technologies. This is also to promote an optimisation and rationalisation of the scarce resources that there are on earth.

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Italy has been developing various strategies related to the deployment of AI technologies, however these haven’t been published yet. The Italian proposition of strategies is based on three pillars: AI for the human being, which refers to the European human-centred vision, AI for the productive and sustainable development (for example in companies, at schools, in education and in society) and AI for sustainability. The European Commission has recently promoted a very interesting project called “Destination Earth”, in which a digital simulation of Earth was created with a digital twin that will be used to better understand, monitor and evaluate the effects of climate change and environmental disasters and to test the new environmental policies on this digital twin by using AI technologies.


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QUESTION (F. ANANASSO)

Excellent, excellent, and without mentioning the newly established Institute for Artificial Intelligence in Turin, which has had initial funding, am I wrong? 

ANSWER (E. GIRARDI)

No, no, unfortunately you are wrong, It actually went a little differently, in the sense that yes, it was supposed to be in Turin, but now there is a bit of uncertainty in reality, since this decision, that had been taken by the previous government, was not followed up and then after various discussions, what was decided to do is a centre for automotive research and development that will be based in Turin and will also use artificial intelligence technologies, with funding, if I remember correctly, of 20 million euros. Therefore, at this stage, the Italian Institute for Artificial Intelligence has been put let’s say on hold, which in my opinion is a great pity because that would have allowed Italy to participate in the various international tables, let’s say with an institutional hat, because today, unfortunately, Italy is present at many international tables, but is represented only by individuals who are the excellences, not at institutional level, so it is, let’s say , a missed opportunity. 

Let us hope that this new task force will be able to propose it again.

QUESTION (F. ANANASSO)

Let’s say it’s a half a victory, let’s see it in a positive light. And look, we have seen the World, Europe and Italy, now, to conclude, there is a lot of need for training, obviously, because everyone is talking about artificial intelligence, that thing that a few really understand completely if one doesn’t go into it in some detail. Thus, I think we agree that training is crucial to understand what these technologies are and how to use them safely. Is there even the risk that without proper training – I am talking about the Italian system, since we are in this project of training for adults in artificial intelligence – there could be a sort of “digital divide” in the sector of artificial intelligence, compared to others? How do you see it, what is the importance and what do you think are the important things that should be done precisely to teach well and better the potential, pros and cons,  risks and whatever of artificial intelligence? 

ANSWER (E. GIRARDI)

In my opinion this is the key aspect and  the most important. Just think that at the beginning we were talking about over 50 countries that have already defined their national artificial intelligence strategy. Think that there are countries like Finland that have put as the number one priority of their artificial intelligence strategy the training, the education of their citizens in artificial intelligence technologies, so it’s really a key aspect. Also China, by the way, is investing so much precisely in the education and training of its citizens, since they  realised that this, let’s say, is the key to being able to actively participate to the society of the future. So in my opinion it is precisely the key aspect. That’s because if we think about us as workers or as citizens or as students, for example, then if we put on the workers’ shoes, the jobs of the future will certainly require the ability to use artificial intelligence technologies, and so this aspect is fundamental, training or upskilling is necessary to learn let’s say new job profiles or re-training or reskilling to really learning about totally new jobs. 

And this is, in my opinion, a key aspect, because if we think of a company that is introducing at this time perhaps robotic automation systems of processes and then somehow freeing up resources because there were tasks carried out by people that are now carried out by automated systems, therefore this time that is freed-up, these freed-up resources should, in my opinion, absolutely be reinvested in training for employees. This aspect is also important from an economic point of view, because it also means a redistribution of what are also the economic benefits of the introduction of automation systems in companies and therefore it is also a redistribution, let’s say, of what are the beneficial effects among the various stakeholders, i.e. investing in training, which should, in my opinion, be a right of all workers. 

The other aspect is that on these things it is really important to learn what are, let’s say, the digital skills, not only more basic, but advanced, to be able to know what the artificial intelligence technologies are and to be able to use them for one’s job, because all jobs will also use these technologies no matter what. In this regard it is very interesting, for example, an approach that concerns more, let’s say, the education, so the schools, like a very nice project developed at MIT in Boston, together with a project funded by Schwartzman, a private equity fund by Mr Schwartzman himself, founding a college called the Schwartzman College, which is a kind of bilingual school. That is, whatever one decides to study – philosophy, anthropology, medicine, law, … – he/she studies along with computational science and artificial intelligence, because the idea is that everybody in the future will need, no matter what, a solid base of computational science and the ability to use artificial intelligence technologies. These subjects should also be included as compulsory in our school, really from the time we are children, i.e. primary school, subjects which in theory are already there, but in reality, unfortunately, they are not taught  in depth. And so perhaps the most important thing is the training of trainers, so the project you are carrying out is also very important in my opinion, since it aims first to train trainers so that they can then clearly go and teach these subjects in schools. It is very important because if we think about, for example, the doctors who are now in hospitals, they are probably not able to use the new artificial intelligence systems and even the doctors who are now at university, who are not studying these technologies, when they go to work in hospitals will not be able to use them, and we have seen how useful these technologies were during the pandemic Covid-19, so if we could really train them to be able to use these technologies, it could really be a very useful tool in their work. 

And then there’s the other aspect to consider, that the jobs of the future will be different from presently, that is, the doctor of today will be very different from the doctor of tomorrow. Thus they really have to change as well the approach both to learn how to use these technologies and to have a mentality, a more fluid, more open mindset to be able to tackle even new jobs that probably don’t exist today and that will change a lot in their contents.

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True or false?

In Italy education and training with regards to AI technologies is the top priority.




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True or false?

The resources released thanks to AI systems should be reinvested and redistributed in workers’, students’ and citizens’ training, upskilling and reskilling.




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True or false?

All workers should have the right to profit from redistribution of saved resources through AI.




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True or false?

In the future workers will not need to be able to understand and use AI technologies.




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True or false?

A very interesting project has been implemented in Boston: It is called “MIT Schwarzman College of Computing” which is a bilingual college in which students study two subjects: one subject of their choice (like medicine, law etc) and computer sciences and AI, because everyone will need this knowledge in the future.




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True or false?

Computer sciences and AI should be integrated in school as well.




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AIAE » E-LEARNING » INTERVIEW 7

Machine learning

The following interview snippets were given by Fabio Del Frate and cover the definition of AI, the most important aspects of machine learning, the interpretations about deep learning, the topics of supervised and unsupervised learning and its relation with AI as well as the different types of machine learning.

transcript

Fabio Del Frate received from the University of Rome “Tor Vergata” a Master Degree in Electronics Engineering (1992) and the Ph.D. degree in Computer Science (1997). In 1995-1996 he was Visiting Scientist at Massachusetts Institute of Technology, and from 1998 to 1999 he was with European Space Agency. He then joined the University of Rome “Tor Vergata”, where he is currently Associate Professor of Remote Sensing and Applied Electromagnetism in various Master and PhD Programs. On those topics, he has been lecturer at several European Universities, as well as principal investigator / project manager in several European Space Agency (ESA) and Italian Space Agency (ASI) funded projects, leading research activities in the field of Artificial Intelligence applied to satellite data for Earth Observation (referred to as “EO” in the following).

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The interviewee, Fabio Del Frate, is a doctor of computer science, currently employed as a professor at the University of Rome Tor Vergata. He lectures about remote sensing and electromagnetics. In European and Italian Space Agencies he was leading research activity in the field of artificial intelligence applied to satellite data for Earth observations.


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Q1. Prof. Del Frate, let’s starts from basics. What is Artificial Intelligence?

Well, to shortly explain what AI is I often like to cite one of the fathers of AI, Prof Nils John Nilsson. For him  “Artificial intelligence is that activity devoted to making machines intelligent, and intelligence is that quality that enables an entity to function appropriately and with foresight in its environment.” 

So, we can say that we provide to the machines and computers more degrees of freedom. We’re asking them to make their own decisions to go beyond what has been happening with traditional computer programming, that is to basically execute a sequence, even if long and complex, of command lines. 

How do we realize this ? It basically consists in feeding the machines with data and in providing them the capability to look for relationships, even if very subtle, among these data. It is with these two elements: data and  models for interpretation, that AI develops and builds its own knowledge, its own experience

I think that in this context an historical perspective could be also interesting, so that we might be curious to know when the first artificial intelligence and machine learning algorithms have been proposed. There might be different answers but my opinion is that to find the roots of artificial intelligence we should go back to 1943 when the first mathematical model of neural networks was presented in the scientific paper “A logical calculus of the ideas immanent in nervous activity” by Walter Pitts and Warren McCulloch. In this paper for the first time a single neuron is presented as an elemental nonlinear processing unit and it is assumed to be connected to other neurons. In other words a first rough but effective mathematical representation of brain.

Now the point is: is all this dangerous ? I think the answer is “Yes, it might be”, but just as much as it would be, or it has been, every time we’re fascinated by a new technology and we want to let it grow. Actually, the issue of developing a “safe” AI is definitely to the attention of the communities of scientists and researchers. The key point is that human beings should always act as supervisors. The rules, the guidelines, but most of all, the data on which the machine, as we said before, builds its knowledge, on which it will rely when taking its decisions, are provided by human experts, so that the machine yes, behaves with a certain level of autonomy, but within a well fixed playground, and the borders of this playground are decided by human beings. 

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AI is providing machines and computers more degrees of freedom, meaning that machines are asked to go beyond the traditional computer programming. In order for machines to identify patterns, relationships, and build their own knowledge, it is needed to provide them data. Two important elements for interpretation of results in AI are data and models. AI has both positive and inspiring results, but it can also prove to be dangerous. Therefore, researchers must focus on safe development of artificial intelligence. The main principle must be that a human being is always in the role of a supervisor of rules and guidelines, but especially data.


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Q2. What is Machine Learning?

Well, I would say that machine learning is a possible, I guess at the moment the most used one, technical process to generate artificial intelligence inside a machine. We’ve seen already that there are  two main actors: the data and the model, more exactly a mathematical model. The challenge is to find the parameters of the mathematical model so that it can represent the relationships among the data. Everything relies on setting-up a learning process based on the data themselves. We may have different types of models, for example neural networks or support vector machines and, consequently different algorithms to learn the model. The final goal however is always the same: to extract knowledge from the training data used in the training process and to apply this extracted knowledge to new data. By the way, the evaluation of how good the performance of the model is on the new data, that is data not considered during the training phase, is very important. It says to us how much the model is applicable in the real world, how much it is able to generalize. 

In any case, the quality and the quantity of the data used during the learning phase are crucial factors for a successful performance. In particular, the quantity of data needs to be statistically significant to provide the relevant features of the phenomenon we want to investigate. Another important key issue is to decide how complex our mathematical model should be. It would be a serious problem if we choose a too simple model to look for very subtle relationships among the data but, at the same way, if the rules underlying the data are less complicated, also the structure of the model can be lighter, sometime…. less is more, as we say, where less is referred to the  number of parameters characterizing the model. 

By the way, the model complexity  has also to do with the time we need to train the model and with the amount of data which is necessary for the implementation of the complete learning process.

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What is machine learning?





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What does machine learning do?





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What are the results of different types of models?





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Why is it important to evaluate the performance of the model on new data?





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What are crucial factors for successful performance of AI?





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Q3. AI & ML are often associated to terms like “deep learning”. What is Deep Learning?

Well, we may say that the word deep in deep learning has two different meanings, although they are very much correlated. One has to do with the hierarchical representation of the scenario we’re interested in.  If the scenario is for example object recognition, deep has to do with the multiple stages in the process of recognizing the object: as an animal, as a dog, as a Dalmatian dog.

These multiple stages of representation find a correspondence with the different layers of the mathematical model that we want to use to represent it, so, to simplify, a layer of the mathematical model focuses on the animal, a layer on the dog, a layer on the type of dog.  This is what happens with a deep neural network. Neural Networks are the most used models for deep learning. So here we have the second meaning, which is a topological meaning, addressing the number of layers of the architecture. So we have deep neural networks, such as Convolutional Neural Networks, which involve many processing layers against shallow neural networks, such as conventional Multi-Layer Perceptrons, which are neural networks with a limited number of layers. To go back to what we said about the complexity of the models, deep learning architectures are undoubtedly very complex, which means high computational burden required, and availability of huge quantities of learning data. Indeed, the need of a very high number of data stimulated the development of new techniques for what is called “data augmentation”, which consists in increasing the number of data starting from a more limited data set.

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Yes
No
 

Can we say that deep learning has two meanings?


Is deep learning responsible for multiple stages in the process of recognising objects?


Does a mathematical model contain different layers of knowledge?


Is it true that neural networks are not the most used networks for deep learning?


Is one of the meanings of deep learning the topological one?


Neural networks can be shallow or convolutional?


Is deep learning a non-complex strategy?



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Q4. What is Supervised Learning? Can you give some examples of applications that use it?

Well I would say that Supervised learning is the most common sub-branch of machine learning today. Supervised learning is a form of learning where, for a given input, a “true” outcome is available and this truth can be taught to the model. In other words, as a teacher or a supervisor, for each input given to the model we know what the output of the model should be and we want the model to incorporate the general rule. Typically, this happens by minimizing what is called an error or a cost function, which represents the distance between the desired output values and the actual output values generated by the model during the training phase. Actually, supervised learning can be assumed to be successful when the final overall error over a set of examples, which have not been used during the training phase, is under a predefined threshold. The training phase can be very long, but once we have concluded it properly, we have a very powerful tool, basically operating in real time, for the processing of the new data. Supervised learning can be useful in many fields. A rather massive application regards image classification. In this case the true data are data labelled by human image-interpretation. The classification can be performed at various levels: pixel level, patch level, object level. The level of classification guides the choice of the input that we want to give to the model. More in particular, supervised learning is now very much applied in Earth Observation where we want to make estimation of bio-geophysical parameters from data collected by satellites. An interesting example regards precision farming in agriculture. If we want to monitor the biomass of a crop in order to optimize the amount of fertilizer to be used, we can exploit satellite measurements taken on that crop. However, especially at the microwave region of the electromagnetic spectrum, the extraction of vegetation parameters is not trivial because the measurement is also, let’s say, contaminated by the underlying soil, so AI supervised models can definitely be one of the tools which allow us to retrieve that part of the information in the measurement more connected to the vegetation, discarding the one more conditioned by the soil.

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True or false?

Supervised learning is not that common sub-branch of machine learning.




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True or false?

Error represents the distance between the decided and actual output values.




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True or false?

The training phase of supervised learning is short.




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True or false?

The end result of supervised learning is very powerful, because it can operate on new data in real time.




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True or false?

Image classification and object detection are examples of supervised learning.




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True or false?

Supervised learning is rarely applied to earth observations.




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True or false?

Biophysical parameters from data collected by satellites are included in supervised learning when earth observations are made.




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Q5. What is Unsupervised Learning? Can you give some examples of applications using it?

The main purpose of unsupervised learning is to find correlations among the data so as to regroup them in similar categories. In unsupervised learning we don’t need external labelling operations or a teacher providing target values. The entire training of the mathematical model is based only on the existent original data so in this case, with respect to supervised learning, the process is characterized by a higher level of automation. We can say that, at the end of the training, the model organizes an internal representation of the data which make them more understandable and easy to be interpreted. As for the other learning paradigms, also unsupervised learning can be implemented by means of  various mathematical models. Also in this case, we need always to keep an eye to the complexity of the model that should reflect the complexity of the internal representation that we’re looking for. In EO and in other fields unsupervised learning can be used for dimensionality reduction. If we consider an hyperspectral measurement, we have that the collected information may be distributed over hundreds of wavelengths. In many cases it would be desirable to have such information, or the most significant of it, compressed in a fewer number of components. This can be achieved with unsupervised learning techniques.

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1) What are we looking for in data with unsupervised learning? Correlation.
2) Based on what is regrouping of data done in unsupervised learning? Similarities.
3) Are external labelling operations needed in unsupervised learning? No.
4) What is the training of mathematical models based on? Original data.
5) What does the model do with the data at the end of the training in unsupervised learning? It organises it.
6) What do we need to pay attention to in unsupervised learning? Complexity of model.
7) For what in particular can unsupervised learning be used? Dimensionality reduction.


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Q6. There are various groupings of Machine Learning algorithms, e.g. regression, classification, and so forth. Can you explain their relevance and difference between them?

There are various possibilities to group machine learning algorithms. I think that the main one relies on the type of the mathematical model. More in particular, as mentioned before, we have models for supervised training and models for unsupervised training. Regarding the former, among the most important ones we have neural networks, deep or shallow, support vector machines, decision trees, ensemble techniques. For the unsupervised models we can mention k-means, principal component analysis, auto-associative neural networks, self-organized maps, and so forth. 

But as your question was pointing out, another possibility to group machine learning algorithms addresses the distinction between regression and classification. Once a certain input is provided to our mathematical model, the difference between regression and classification mainly relies on the type of output we’re interested in. In the case of classification, the output will be a label, a class. For example, let’s consider again a satellite application where remote sensing is used for meteorology. Let’s therefore assume we have to make a meteorological analysis, and we want to decide which pixel in an image of the sky has to be associated to a cloudy sky and which pixel of the image to clear sky, so that the final purpose is to produce a map where we represent with the blue color the clear sky pixels and with the grey color the cloudy pixels. We say in this case that we want to obtain a classification map. For the regression, basically, we want that our mathematical model, as a response of its data processing, computes, or estimates, a number, more in general a real value number. So, if we consider the same scenario set on meteorology, we want to know from the remotely sensed data how much is the humidity or the precipitation rate, real numbers, in a given area. However, it is interesting to note how a regression problem can be translated into a classification problem. This is what happens if I say: within this range of precipitation values, so between these two numbers, I have the class “low precipitation”, in this other interval or range I have “moderate precipitation”, and so on. In this case I map ranges of values into classes.

Thank you, Prof. Fabio Del Frate, we really appreciated. It has been a clear & interesting snapshot of Artificial Intelligence features and relevant applications.

Quiz question 1/8

True or false?

There are only two different machine learning algorithms.




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True or false?

Neural networks algorithms can be deep or shallow.




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True or false?

Support-vector machines and decision trees are algorithms for supervised machine learning..




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True or false?

Self-organising maps, means clustering and principal component analysis are types of supervised machine learning.




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True or false?

The main difference between classification and regression is in the type of output.




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True or false?

Output in classification is a label/class.




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True or false?

Regression estimates a real number.




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True or false?

We can’t transfer the regression problem into a classification one.




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AIAE » E-LEARNING » INTERVIEW 6

AI and societal challenges

The following interview snippets were given by Simon Delakorda and cover the social impact of AI with daily life examples, the societal challenges of machine learning and the roles of institutions in the field of AI regulations.

transcript

Hello everyone. My name is Simon Delakorda. I’m director at the Institute for Electronic Participation from Ljubljana, Slovenia. The Institute for Electronic Participation is a nongovernment organization which is active in the field of information society, development and digitalization. We are also relating dealing with the issues of artificial intelligence. For example, last year we implemented a citizen dialogue which was also covering actual relevant issues with the development of the artificial intelligence. How this technology is relating to the everyday life of the citizens.

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The Interviewee, Simon Delakorda, is the director of the Institute for Electronic Participation in Ljubljana, which is a non-profit organisation that deals with information society development. At the Institute they also work in the field of digitalisation and they deal with the problem of artificial intelligence.


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So starting with my first question what is artificial intelligence and how it relates to society? Usually artificial intelligence is described as a technique that enables computational system to mimic any type of intelligence. Maybe this is sounds a little bit technical but basically it means that a machine is capable of solving a specific problem. So some refer to artificial intelligence as a technical approach, others define it as the combination of software, hardware and data. But in any case, artificial intelligence compromises various tools and methods. For this reason is also very hard to identify the artificial intelligence. So at the moment we have these broadly accepted definitions because frankly speaking we still don’t know even what actually human intelligence is and how to properly define it. So you can imagine that we also have this problem with the artificial intelligence. But to go to the straight to our experience without artificial intelligence applications everyday lives we can see already today that there is many number of different applications which are offering huge support and also help to make our life easier. For example, most typical examples of usage of artificial intelligence in society are for example speech recognition. Many of you are probably familiar with the theory by Apple or Alexa by Amazon which includes intelligent systems and they’re actually artificial intelligence systems to recognize speech inputs and based on this speech inputs those systems then delivering different services or comments to implement different tasks. So the next such example of using artificial intelligence in society which is quite common today is personalisation. For example when we are browsing online social media platforms or online services or for example Netflix or Amazon, those web services are using artificial intelligence systems in background which is personalizing the content and presenting this content to our web browsing habits or needs. For example if we are using if you’re searching for particular content or a product then based on your history or behaviour of searching these applications then give you the search matching for the content that you are most likely which is fitting your interest and it’s recommended to you. Then the third such example of using artificial intelligence is everyday life which is also very useful for me is for example email filtering. We all know that there is many spam emails coming into our main mailing box and we can use artificial intelligence programs that can distinguish between the real email and the relevant emails and spam emails in your email folders. And also for example one often artificial intelligence application which is used today is also in healthcare, for example clinical diagnostic. In medicine artificial intelligence is more and more used to support the work of doctors in their diagnosis. But what we are talking about in these cases is that we are talking about a narrow artificial intelligence because these are the cases which are able to solve very well defined and somehow narrowed problems, practical problems. But we also when we talk about artificial intelligence. We can also speak in this general artificial intelligence which refers to the system that can perform any intelligent tasks that human will be able to perform. Of course, this is still not a reality. There are some predictions for the future that maybe also artificial intelligence will be able to solve more complex and more bigger tasks that are now at the moment related only to the human type of the intelligence. So to conclude this initial introduction about the artificial intelligence in society, we can see that there are many applications of artificial intelligence. Some of them are incredibly positive, having impact on individual society and individuals and society and are also bringing great opportunities. However, we also have to be aware that artificial intelligence can also discriminate and they can disadvantage certain group of people. That’s why for this reason it’s very important that we understand the relationship between the artificial intelligence and society, especially in terms of accountability, biased transparency, data quality and other ethical problems arising from using artificial intelligence.

Quiz question 1/8

1) What can a machine with AI do? Solve a specific problem.
2) Do we know the exact definition of AI? No, we don’t.
3) What type of results do we get with the help of AI when using a browser? Personalised.
4) What does AI do with relevant and spam emails in online mail services? It filters them.
5) What types of problems does narrow AI solve? Very well-defined problems.
6) What type of task can a general AI perform? Human intelligence task.
7) Could AI in the future solve more complex problems than a human can? Yes.
8) What can be the negative consequence of AI in society? Discrimination in cases where human bias are transferred into the algorithms of an AI system.


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So now the next view or issue relating to the artificial intelligence I would like to focus on is the social challenges of the machine learning. First of all, what is machine learning? Machine learning refers to algorithms and techniques that learn from by themselves, who are confronted with data, observations and interactions with the surrounding world. Basically, machine learning is about algorithms. It’s based on the algorithms and algorithms are simply set instructions how to solve tasks. And now when relating algorithms to the machine learning means that a particular type of algorithm can be learned, but not by the human programmers, but by themselves. Meaning that machine learning is learning by themselves by using statistical approach. And usually there are two phases of machine learning. The first phase is that the machine learning algorithms are being trained by using data, by using the training process. And the result of this process is machine learning model. And in the second phase, this machine learning model is applied to the desired area of application. The idea is that this machine learning model is then solving a problem that we would like to address. For example, machine learning models for prediction if a patient has cancer, tumour or machine learning model, or for prediction how the stock markets is going to behave in the future. Of course, there are some issues issues with machine learning because machine learning can have a serious impact on people’s lives and especially when there can be errors in machine learning which can happen during data collection, data preparation or during the training process. And also we can have issues when we misinterpret the results of the machine learning process. Looking at the social level, a great danger is that the training data used for the training process for machine learning is considered perfect. But we know that perfect data sets would require all possible factors that constitute or influence the situation that we are addressing. To mean that it’s very hard to train algorithm, that they are using all the data from the complex society. And as a result of this imperfect data which is then applied to the machine learning algorithm, it can happen that the results can be too simplified and a situation they are addressing only has a limited number of factors taken into account. The results of this is that the machine learning can lead to bias prediction, especially for the disadvantaged groups of society which are not so much present in data or in the society as a whole and are less represented in the data set. For the case of the bias in the machine learning, this means that the bias data will also result in biased prediction. This is for case when it comes to the rare phenomena or social contexts that are hard to quantify. This means that we really have to be very aware of those specifics with the society which are not seen or included into the machine learning results. Because machine learning is actually operating based on the patterns. And if those patterns are lacking occurrences or being of poor quality, we can get very biased and poor results.

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True or false?

Unlike human operation, there are no errors in machine learning.




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True or false?

In machine learning a mistake can be made during data collection and preparation.




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True or false?

There can be no misinterpretation of results in machine learning.




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True or false?

It’s very hard to train an algorithm which uses a huge amount of data from society.




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True or false?

One of the problems with machine learning is the oversimplification of results..




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True or false?

With machine learning we avoid discriminating a part of the population that is not often included in databases.




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True or false?

Biased results are the consequence of truncated patterns in society.




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So now how to avoid or how to address those biases, those ethical issues with machine learning and artificial intelligence on the international level, but also on the level of the national countries, national states. There is huge debate going on how to establish a system which would govern, which would regulate the usage of the artificial intelligence in society and of course for the impact which is having this technology for the society. And the main question here are who is responsible if something happens with the artificial intelligence? Something goes wrong if there are errors. So who’s responsible, accountable for the results of the artificial intelligence use, how to deal when there is an accident, if there is a wrong diagnostic or if someone is being disconnected because of the artificial intelligence. The UNESCO, this is the United Nation Agency is working very actively on policy proposal how to frame the governing of the artificial intelligence. And they provided a set of policy proposals and policy proposals which are dealing with different aspects of the governing the artificial agents. Now I will briefly present those aspects which I think are very important and also can be used as guidelines for understanding what to be a society and as individuals have to be aware when thinking about the artificial intelligence impact on the society. First of all, the first aspect which is important is that the artificial intelligence is promoting diversity and inclusiveness. Meaning that the artificial intelligence have to function in terms to combat of culture and social tester types. And inequalities this is important to these biases when machine learning patterns, in case that they are based on the poor data, they can often discriminate and they can also enforce the resisting stereotypes. This is for example with this facial recognition artificial systems which are already scientifically proven that they are more often biased towards the colourful faces than to the white faces. Then the second important aspect is the impact of the artificial intelligence on the economy, on the employment. There are different interpretations how the artificial intelligence applications will contribute to losing particular jobs. Many there going to be some jobs that are not going to be necessary anymore and this is going to have an impact on the different working groups. And artificial intelligence, especially high driven economy should take into account upskilling and reskilling measures that will enable workers or employees who are going to be affected by the negatively by artificial intelligence that they still remain a part of the labor market. Then the third important thing is addressing social and economic impact of AI. Meaning that artificial intelligence shouldn’t generate monopolies in terms of research, knowledge, data or the market, that not some specific stakeholders or countries or regions are going to benefit from this technology, but the humankind as a whole. Also then a logical view is also impact on culture and on the environment. Meaning that the artificial intelligence is not having a negative effect on the culture, diversity, development heritage and also on the environment. And then is a set of four or five views which are actually all dealing with the artificial Intelligence ethics. The Artificial Intelligence Ethic is very important in terms of creating a value based background on which we developed the Artificial Intelligence. And there are those aspects of ethics which relating to the education and awareness, meaning that artificial intelligence should be teaching in schools and universities so that the students and schoolers can get familiar with the artificial intelligence technology. And what is this relationship between the technological and social domains. And also very important is the view on the ethics of the artificial intelligence research, meaning that there is research, especially in the private sector, which is being funded by the private money that is taking into account the social responsibility. Also the benefits of the research in terms of the humankind, not only the market and for profit interest, then it’s also an important aspect of ethical use, AI. In development, meaning that the artificial development should strive to provide platforms that allow international cooperation on AI development. Meaning that not the AI development is just for the rich countries, but also that those developments are also applied in the poorer countries which are not so rich. And from this very important aspect is that we create a kind of international cooperation on the Artificial Intelligence ethics. For example, UNESCO forum is a very good example of this, when different countries with different stakeholders are discussing about the artificial Intelligence future and how to govern it in terms of ethics and value for the development of the society. And from this it is very important that there is also a kind of government mechanism which oversees the development of the artificial Intelligence on the global level in terms of inclusiveness, transparency, check and balancing. These are very important and also multilateral cooperation. These government mechanisms are referring to the digital ecosystem for the artificial Intelligence, which includes infrastructure, digital technologies, knowledge sharing options and especially cooperation with different stakeholders that they have voice and they can share their considerations and concerns how to develop Artificial Intelligence for the future.

Quiz question 1/8

Yes
No
 

Is the establishment of principles to regulate the use of AI one of the proposed solutions to avoid the possible negative consequences of AI?


Is it true that in the case of an AI error, the main problem is defining who is responsible for it?


Is UNESCO responsible for governing artificial intelligence?


Would the integration of the principles of diversity and inclusiveness in AI, further increase discrimination and inequality within society?


By following UNESCO’s proposal for diversity and inclusiveness in AI, do we avoid mistakes caused by poor data?


Does the UNESCO’s principle of economics mainly refer to employment issues as a consequence of AI?


Can AI have a negative impact on culture, diversity, development, heritage and the environment?


Should the concepts of AI be taught in schools and universities in order to raise awareness of the relationship between AI and society?


Do international cooperation platforms bring the possibility of AI development closer to poorer countries?


Could governance mechanisms regulate the development of AI, meaning inclusiveness, transparency, multilateral cooperation at the global level?



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So and my last view on the artificial intelligence is relating to that I would like to share today’s relating to the role of the civil society organizations, especially the role of non-governmental organizations. I think that the role of non-government mentalization in the application of artificial intelligence is very important from the viewpoint of explaining citizens, explaining people, users, everyday people how this technology is affecting their lives, especially in terms of trust into the artificial intelligence. Also in terms of helping them to understand the applications and the impact of the everyday life of the citizens, because there are different interpretations and fears and maybe unfounded negative, their relationship towards the artificial intelligences, which are needed to be discussed, learned educated in trustworthy environments and non-governmental organizations with their members, with their voluntary activities are a link often between the high tech research and everyday people because they are having trust because people having trust in non-governmental organizations. And because of their information, their education activities, and can help citizens to understand what the negative and positive effects of artificial intelligence are. This is one role of the NGOs. And the second role of the NGOs is that they act as watch dogs. That they act as a not exactly supervising body, but someone who is paying attention to the possible discrimination, breach of human rights or negative usage of the artificial intelligence, which can lead to discrimination, which can lead to worse conditions, for immigrant groups, for specific social groups like immigrants or women or Socially vulnerable groups which are not as represented in the society as other social groups. And the NGOs must take the role of safeguarding their rights and also battle combat for human rights when they see that the artificial intelligence is about to bridge those standards. I think these are two main rules of the NGOs relating to the artificial intelligence. There are two minor roles. One is also NGOs as a usual user artificial intelligence, especially those NGOs who are generating large data on this society, and then can then use this machine learning, tools to provide some conclusions, to solve problems, to create visualization on different aspects of society. And the second this role is also important is that the stakeholders are acting as a policy actor, meaning that they are taking part in the forums, coalitions, discussions with other stakeholders on the economy, from the politics, from the research. And so that and those forums, those discussions, they are pointing out the issues of accountability, responsibility, privacy, trustworthiness of the artificial system, basically, meaning that they work as advocates of the society and the humans and strive for the human oriented use of the artificial intelligence. So this would be my, short but not really short viewpoints on the artificial intelligence and the society.

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Why are non-governmental organisations (NGO) important for AI?





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In the case of AI what are non-governmental organisations (NGO) paying attention to?





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How do non-governmental organisations (NGO) use AI?





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In what way do non-governmental organisations (NGOs) act as policy actors?





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AIAE » E-LEARNING » INTERVIEW 5

Machine learning and human machine interaction

 

The following interview snippets were given by Jurij Krpan and cover the importance of AI and in particular the relevant aspects, opportunities and risks of machine learning as well as the challenges and threats of human machine interaction.

transcript

My name is Jurij Krpan. I am a curator at Galerija Kapelica, which is a part of the Kersnikova Institute,  which is an art platform. Its purpose is to give artists access to laboratories, where they can carry out  their art projects. Galerija Kapelica has been in existence for 26 years and specialises in the field of  contemporary art research, where artists thematise, a society permeated by high technology, not only  electronics, but also biotechnology, telecommunication, robotics, and so on.

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The interviewee, Jurij Krpan, works within the Kersnikova Institute, which is an art platform. Its purpose is to give artists access to laboratories, where they can carry out their projects. The institute specialises in the field of contemporary research art, where artists thematise the relationship between society, technology, telecommunications and robotics.


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What is machine learning and how does it work? Machine learning is very simply said a written  algorithm that can process a huge amount of data. We must enable the accessibility of the data, then  there are different ways to extract the materials from this entry in the database. The material is later  changed into more or less meaningful sentences or when working with images, more or less  meaningful insights, which are a result of machine learning. Machine learning is called artificial  intelligence in a more affectionate way, since it’s not really about some intelligence but because the  machines are really capable, and their ability fascinates us in the same way as the ability of highly  intelligent people who are capable of quickly processing huge amounts of data and then communicate  it. Here I could list different types of machine learning but taking into consideration that it is in a big  swing, many new concepts are also being developed so I don’t think it makes any particular sense to  list the forms of the machine learning.

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What is machine learning?




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What is the main difference between machine learning and ordinary programming? There are not  many differences, but at the same time they are important. With ordinary programming, we already  have a fairly accurate idea of what is programmed and how things will look. With machine learning we  have a thesis that offers itself as a possible answer through machine learning. Some of these possible  answers are more accurate, they are more elaborated, and some are more like guessing. In fact, it is  all about more or less accurate guessing.

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True or false?

Machine learning and classic programming are basically the same thing.




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True or false?

With classic programming we pretty much know what the end result will look like.




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True or false?

The result of classic programming is either an accurate result or a speculation.




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True or false?

Machine learning proposes different outputs during data processing.




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Can you present some concrete examples of machine learning to make it easier to imagine it? For  example, with the purpose of making it easier for people to imagine what we are dealing with, we have  used one form of machine learning, which is based on the algorithm GPT-2. It is an open AI, the second  generation, which we have included in the design of our newsletter. It is published once per month at  our institute because we have a huge variety of activities. We publish what we are going to be doing  this month, for example some exhibition, workshops, performances, where we are hosting, we also  publish similar events run by our partners around the world. In short, there is quite a lot of data and  at the beginning of this newsletter I wrote an introduction which should have motivated the recipient  to go through our quite long newsletter. I was constantly late with these introductions and our PR  constantly reminded me about it. Together we came up with the idea of algorithm writing the  introduction. Now all the published texts are put through the algorithm, which later transforms it into  a short text. It creates a short metaphor for what was put into the algorithm. The database is rather  small, since the basis is our newsletter, but it manages to summarise the texts more or less  appropriately. Some words are used in a strange way, especially because the algorithm is meant for  English and not Slovene language. Then it searches the web in all the Slavic languages for language and  letter specifics. Texts are usually formed from Polish, Czech, Russian. Every time you press the submit  button, the second suggestion appears. It takes a little bit of effort and pressing the submit button.  Later, human intelligence and judgment take place. The person who creates the newsletter chooses  the appropriate introduction of the newsletter by himself. It is now more efficient. We have delegated  one function to the algorithm. Usually more witty introductions are chosen, by which we point to the  capacity on the ability of this machine learning. Recipients of our newsletter can get a little sense of  what it means to create an introduction with the help of the algorithm.

Quiz question 1/8

For what purpose was machine learning used at the Kersnikova Institute?





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What did the creators of the magazine at the Kersnikova Institute avoid by using machine learning?





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What is the sequence of processes of applying machine learning at the Kersnikova Institute?





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What can be problematic about algorithm-generated texts?





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It is about the cooperation with artificial intelligence and human interaction with a machine. Can you  tell us more about the human-machine interaction? Yes, I’m increasingly convinced that artificial  intelligence is not about some kind of intelligence that is in any way similar to human intelligence, but  that these algorithms are really so capable that they become our dialogue partners. As if when I am  sitting around the table with my colleagues we debate sometimes about the program, sometimes  finance, sometimes about politics and so on, we have another chair where this machine is sitting. It  offers us an echo of our thoughts. We have not yet in such a way implemented the artificial  intelligence, but we intend to, because we are developing an interface which will help the innovators  innovate. We would like to see that one of the innovators is a kind of AI. Perhaps it can contribute an  inspiration in a brainstorming session or a talk with humour or uncanny way of folding things together.  Especially if there is a group of experts around the table who are more egocentric and emotionally  exhausting. On the other hand we have a machine, which creates approximation and suggestions that  release the tension in these conversations. At the beginning I have mention that this machine iterates  what is said or put in by the humans. In artistic circles we are dealing with the idea in which these  machines can emancipate through conversation among themselves, machine to the machine. They  develop some lexicon, some level of communication which is incomprehensible to us humans. But can  be meaningful and through this communication we can better understand the way the algorithms  work. We have art projects where data is taken from sensors which are attached to plants or some  environments in which the animals move and act which are equipped with sensors. Then suddenly you  don’t get any more human-human dialogue (excluding that a person is already included, since he has  written the algorithm), but communication between extracted human in algorithm, the animals, or  plants. Art projects that we do encourage interactivity that is not just responsiveness, for example you  press a button, and something jumps, but true interaction, meaning you press, something happens,  and it influences you back that you press again. It creates a communication loop. We have a situation  of intercognition between more forms of liveliness, machine, and plants. Through this communication  which can be very different from that of a human – plant, we are exploring our own prejudices as to  what the plant is and what the machine is. It acts as a bioindicator and so on. The artists are using  these tools in very innovative ways, so we can better understand these tools on one hand and to better  understand the nature or the environment in which we live on the other hand. The amount of data is  so big that sometimes it is bigger than the average human brain can process and because of that it  makes sense to develop algorithms.

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True or false?

AI can produce new ideas and is helpful when brainstorming.




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True or false?

One of the purposes of algorithms in AI is a better understanding of technology and the environment in which we live.




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True or false?

We do not need human intervention to operate artificial intelligence systems.




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True or false?

Artificial intelligence can be a good source for objective communication.




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True or false?

In the expert’s opinion human intelligence can be equated with artificial intelligence.




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True or false?

Artificial intelligence can be an interactive mediator for the communication circle with non- human beings, e.g. animals, plants.




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What are the threats and challenges when working with AI? Challenges are the same as in every  technology we use in our lives. A user without knowledge and sensibility risks that the machine will  use him and not the opposite. We see this already in very simple consumer situations. Our phones  have taken a large part of our attention. We do see that technology enters our intimate lives, as well  it affects the society. Sometimes, say at the beginning of mobile telephony, it was easier for the few  who had the phones and they had a certain advantage in the organization of the work. Today it is so  that if you do not have a mobile phone and if you are not always accessible it is already a disadvantage  and it is already a certain psychological pressure on the people. You must know how and when to  unplug yourself, and also protect your right for privacy. Here I see the need for people to develop their  digital literacy and computer literacy. Simply, as every man develops a certain financial literacy, e.g.  when he goes to the shop he knows that he will not pay a thousand euros for the bread. Similarly, we  have to develop digital and computer literacy.

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Yes
No
 

According to the expert is it true that human self-control and setting boundaries does not affect the optimal use of technology?


Is digital literacy important for a better control of technology?


Is it true that technology does not affect society as a whole?


According to the expert does AI work well in treating contagious diseases.


Can AI provide better access to a patient’s data at the international and national level?


Can the quality of treating patients be improved by using AI?



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In the expert’s opinion it is important to understand and perceive AI through works of art, because artists use algorithms to show the position of a man in today’s society and thus allow us to look at ourselves from an external perspective. Interactive works of art provide a deeper understanding and the identification of myths about artificial intelligence. A proper understanding and usage of technology can lead to an improved quality of life and a better understanding of the environment in which we live.


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AIAE » E-LEARNING » INTERVIEW 4

Perception of AI – AI and societal challenges

The following interview snippets were given by Bambos Papacharalambous and cover the perception of AI and societal challenges and in particular give an approach about algorithms and bias in AI.

transcript

Well, my name is Bambos Papacharalambous. For most of my career, let’s say almost 30 years, I was mostly involved with the telecom part of the IT business. I’m involved with a wide range of projects that are related to IT in general. I’m the CEO and founder of Novum. Novum is a company that does software development and ICT consulting.

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The interviewee, Bambos Papacharalambous, has been mostly involved in a wide range of projects related to information and communications technologies, the so called ICT. He is the founder and CEO of Novun, which is a small company in software development and ICT consulting.


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So could you tell us more about the basics of artificial intelligence, algorithms, and cognitive bias? Yeah, okay. AI.AI is becoming another buzzword of the times. So if we if we try to see what AI really means and what it stands for, a for artificial, I for intelligence. Artificial meaning a machine, something that is nota human or any other living animal. Intelligence, meaning the thinking process that allows us to understand language, to understand speech, to understand the environment around us, and to make decisions. Now, one of the basic ideas of intelligence is that we consider something to be intelligent if it has the ability to acquire knowledge, meaning learn something, and then be able to apply what he learned in order to make decisions academically. There might be different definitions of what intelligence is, but let’s assume that what we define as intelligence is the ability to learn something and then apply that learning into some form of a decision. The intelligence in, let’s say, in academia world, it’s better understood and researched by psychologists or sociologists or other disciplines in academia. But artificial intelligence is better understood by computer scientists, data analysts, and mathematicians, at least today. The reason is that we have vast science disciplines to try to build machines that somehow apply this basic meaning of intelligence. So the same way we humans learn by reading or by processing our past experiences, an AI system should be able to learn by analysing existing sets of data and try to perform intelligence tasks. Now, you have to be aware that AI algorithms, like any other, of course, are developed by humans and therefore carry could carry the same biases and limitations that human thinking carries, right? So the same way people thinking might be skewed by cognitive biases, the AI alpha, this might also reach to the same bias decisions. So if we take a few examples of company biases that are currently identified by the psychology, let’s say, academia world, and let’s take, for example, a football game, our team will definitely win tonight, right? We’ve been beating our tonight’s opponent for the past 20years, so there is no way we lose tonight’s game. Now, this is a cognitive bias that can happen during a logical thinking of a human. But the same cognitive bias might also happen with an AI algorithm. Just because of the fact that if the only information we feed our AI algorithms is the information that our opponent lost to us during the past 20 years, the chances are that the AI algorithm, we reach to the same conclusion that the human reached. So whatever information we give to the AIA algorithm, that algorithm will try to come up with some answer and based, but only based on the information that we buy. So if we try to teach our AI algorithm now to identify, let’s say, smart people by analysing their facial characteristics, all right, and we feed this algorithm images of young white males, and we tell this algorithm, okay, now go study these images. And then when we ask you, when we give you another new image, you tell us if this person is smart or not. Could be smart or not. Well, chances are that if we only fed the algorithm with images of young white males, chances are that if we give this algorithm a mature black woman as an image, chances are that this AI algorithm will have the same bias and will pretty much tell us that this woman cannot be smart. So it’s important to get an understanding of this. And it doesn’t matter how magically we think that the algorithm will perform. It is basically reaching to an answer based on the size and the quality of the data we give. So this is my, let’s say, pretty much the answer to the question of how conclusive biases can be can affect the AI, as well as to come up with skewed results.

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What does artificial exactly mean in the term AI?





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What does intelligence exactly mean in the term AI?





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So far, AI has been most extensively researched by:





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And could you now expand on ethical use of artificial intelligence? Are there some artificial intelligence applications with ethical implications? Okay. As more machines are performing more and more complex tasks nowadays, this immediately directs people’s minds to the use of machines in replacement of human labour. That’s the first thing that comes to people’s minds, and it’s a valid concern. It’s the same concerns that were raised during the Industrial Revolution, and today we see them again as relevant. Is it difficult to allow machines to harvest wheat instead of relying on people’s hands? Is it difficult to allow robots to display human workers in the car assembly lines? So, these basic same questions we see them again coming up, but now in a more, let’s say, complex environment, as machines now have developed, are performing more complex tasks. So, if people had to adjust to the new environment created by machines in the workforce, they would need to go through another readjustment process really soon. For instance, where would the truck drivers find work if the trucks now are driving themselves? Right. Where would the bus drivers find employment if the buses are driven by AI software? Where would you employ an airplane pilot if the planes fly on their own? So, the more complex now tasks that are being developed and are being run by machines, the more these types of jobs are being threatened. And it’s not just hard labour anymore. I mean, why would anyone become a doctor if a robot can perform the same operation better than any human, right? So, these concerns are valid. And of course, there are other examples that raise questions of ethics in the use of AI in our lives as well. It’s not only the labour force. Shouldn’t we be concerned with the use of machines that are used for our security? First, can we be sure that the AI software powering up a security camera in an airport has made the correct decision in identifying the terrorists? Can we be sure that the security system in a mall is following the real thief to his car and not some other innocent bystander? So, these issues are valid. There are a lot of ethical concerns within the AI space, not only through areas like employment or security, but also in the use of military weapons as well. Shouldn’t we be concerned, for instance, by the fact that the armed drones are today doing our fighting for us? Are we sure that the autonomous robot soldier tomorrow would be able to distinguish between the enemy and the friend? So, these are very valid customers. And whenever something big happens, a change happens within the industries in general. Whenever something magical happens, then there is also the chance of negative implications of its use. So the industry needs to eventually find a way to use AI for the good of the society we live in.

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True or false?

Machine learning aims at finding data-driven automation solutions when the automation goal cannot be reached explicitly.




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True or false?

Since AI Algorithms are developed by humans, they could carry the same biases and limitations.




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True or false?

It is not important to get an understanding about cognitive bias.




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True or false?

The biases of AI Algorithms depend on the amount, type and quality of the data we feed into the system.




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And so you mentioned already the impact on artificial intelligence, on employment, on security, on welfare, in the military service, and they also hold on new laws of robotics in other fields than this street field you mentioned. Yeah, this issue of the rules and the laws for robotics. It’s not an easy issue to solve. We are in unatched territory when it comes to a widely accepted set of rules that should define the ethical framework of how robots should work, even in areas outside the military, but also in the space of security, in the space of the welfare of the elderly, let’s say the space of the personal security. At home, we will see robots that will start taking decisions for us. As I said earlier, this is a very new area, and even though there are groups both in academia, in the industry, and in governments also, that try to touch upon these rules, this is at the very, very early stage right now. Right now, the discussion is being done on what the robot should or shouldn’t shouldn’t do. For instance, a robot shouldn’t harm another human. That could be like a primary, let’s say, directive for a machine. But at the same time, a robot should obey its owner and its creator. So that’s another directive, primary directive for a robot. But what if the robot’s owner is giving instructions to the robot that is outside its main directive? So what if the robot is asked to harm another human? Right, but should we limit the scope of harming something to a human? What if the robot is asked to harm an animal? Or what if the robot is asked to harm another robot? What if the robot needs to harm a human in order to protect another human? So these are ethical questions that are not easy to answer. And they’re not easy even if they are answered. They are not that easy to be put into an algorithm. So don’t forget that eventually, all these rules of the framework, they need to be translated into a mathematical, let’s say, equation. Right? So this is a pretty tough thing to do. Even though it would be better for mankind to define these rules before and not after the fact, I don’t believe that this is an easy task. Companies are fighting for product dominance on a global level. Countries are fighting for military dominance on a global level again. So it would be hard to make rules that could be agreed by anyone and then be enforced by everyone because of just how society and mankind works. A more likely scenario would probably be that this set of acceptable rules are to be defined and enforced at least after the fact. I mean, after we saw that chemical warfare during the First World War was unethical code and code, we designed the framework around it. Right after we dropped the nuclear bomb, we saw that we needed to control it. So we might need to experience the first, let’s say, disaster in order to be forced to define the rules that would govern the robot’s behaviour. We just hope that this experience of the first disaster is not it’s not a really bad move. But personally, I see that even though there are tries of setting these rules up from the beginning, I believe that this is a very impractical task to solve.

transcript

If we, if we take a step back and see what the last an Algorithm is, alright? And try to see, to distinguish between what an algorithm can do for people, but also what people can do with the use of algorithms. So with a regular alchemist’s, these two, let’s say, tasks are pretty much distinguished. So what the algorithm can do for people, it’s one thing. What people can do with this algorithm, it’s a separate thing. So for a computer science to write an algorithm, he will ask you of the rules that you want that algorithm to obey too, right? So let’s say you want an algorithm that when given the volume in leaders, it will give you back the volume in gallons. Okay? So the software developer will ask for the rule that governs this relationship between leaders and guns. And he will use this rule to write this algorithm so everyone would know the outcome, what the outcome would be, provided that the algorithm complies to those rules and it works correctly. Okay? Now this is what I would call a regular algorithm. If now the use of this algorithm can get people in trouble, then it’s not the algorithm that isn’t fault, but it’s the use of the algorithm that is at fault. So let’s say, for instance, that this piece of software that we wrote is used in an airplane and there is a switch in the airplane that says, okay, when you turn the switch on, I can display you the fuel in gallons. When you switch down, I can display the fuel that the plane has in its tank in litres. And suppose now that the switch is in the wrong position and the PILER asks for gallons instead of litres and takes off. Well, chances are that he will have to make an emergency landing somewhere else before his original destination. So what do you do? In this case? You have an algorithm that is functioned correctly, but it’s use made him a function. So in this case, the regulator step in, the industry steps in and you train the pilots correctly. You make sure that the after checks if the plane has enough fuel before you take off. You put regulations to force the ground fueling service to make sure that the plane has enough fuel to reach so we know how to handle, let’s say, all the regulatory issues that we have to handle in a regular code unquote alchemist. Now, what happens with the AI algorithm? Well, let’s take for example, a machine learning algorithm. In this case, you design the algorithm with some basic parameters and you tell the algorithm what data you have. You just fit the algorithm bunch of data and then you run it. The difference with this type of algorithm is that the system changed itself based on the parameters and the data that you gave. So you cannot really predict the outcome of the algorithms. If you had enough time for a human to go through the data that you gave it to and go through the parameters and run the calculations line by line on a piece of paper, then yes, we would predict, let’s say we would make the same predictions that the algorithm did. But it’s impossible for a human to run through that large amount of data with the parameters that you gave the algorithm to work on. So at the end of the day, the result that comes out of an AI algorithm could be considered unpredictable. We could have a result that we didn’t expect. And that’s where the ethics within the outcomes need to come. Because imagine now that you’re the Ministry of Health of a country and you ask an AI company to come up with an outcome that looks at the demographics of the population and it looks at the number of available organ donors. And we want the algorithm to suggest who will get a heart transplant, for instance, and who will not get a heart transplant. So you put in things like age, things like probability of having, let’s say, high probability of accepting this trust plan, and you put in a bunch of parameters, and then you give all the countries demographic data to this algorithm and you let it run. So the ethical questions now are required to be answered by the people who wrote the algorithm and not by the people who just use it. Whereas in a regular algorithm, we can give the responsibility of making the decision to the people that use the software. We roll in the AI version of the machine learning version of the algorithm. Well, basically, the algorithm is making the decision for us. So that is why we need to start thinking about putting ethics rules embedded in the algorithms themselves. And as we said earlier, this is an easy thing to do, because how do you decode all these ethical answers into a mathematical equation? And this algorithmic term and space is a new space. It’s being researched now at an academy, at an academic level. It’s reaching, touching a little bit the industry, but it’s definitely not there yet. So he still needs time. Yeah. Thank you very much for this input about the relevance of algorithmics.

transcript

Now we’ll move to specific questions about artificial intelligence and social challenges. So could you tell us more about assisted, augmented and autonomous intelligence? Yes. Okay, well, as we said earlier, if we take a robot, for instance, that does a specific job right now, the robot that works in the car assembly line is not really considered as a robot with an AI algorithm in it. We’ve seen these robots working in the industry for many years now before even this new wave of AI methods were becoming popular. So what happens in that space? Well, that is a task, that is a repeatable task, that is a task that is easily recreatible and that is accepted by the industry as a harmless piece of equipment. Now, the advantages for this having such a machine is that, well, we remove this high intensive labour activity from humans and we let machines do the boring and the unhealthy tasks and we allow humans to work in more productive and safer environments. Now, the disadvantage is that, of course, OK, this robot that we have in that assembly line has basically taken over the prominent opportunity of the people that we are able and willing to do these tasks. So things are, let’s say, accepted as far as the use of a machine doing a repetitive task of manual labour within a specific industry. Now, the problem becomes more complicated now when you have a robot or a machine that autonomously makes decisions and functions on a more, let’s say, code and code intelligence manner. So imagine now that you don’t have an assembly line and you have a robot which has the intelligence to perform the tasks that the regular robot performs today, but also decides that the design of the actual car needs to change. So let’s say that the robot now has the ability to design the car in a more aerodynamic way, let’s say, and it decides on its own, autonomously, that the shape of the car needs to be changed. And of course the robot will have the ability now to make the design change and at the same time build the car. Okay, the advantage would be that perhaps they come up with a better design and offer better fuel efficiency or whatever. But the disadvantage is that first of all, we displaced some more jobs now from the workforce. We perhaps took out the designer now, but at the same time, we’re not sure if these changes that this new robot now suggests will actually be safe. So yes, we might have a better efficiency, fuel efficiency, but will it be safer or safe or as safe for the driver and the passengers? So, again, everything needs to have a balance. There are advantages, there are disadvantages and that’s why there are risks and threats and challenges. But at the same time, there are opportunities.

transcript

So you already mentioned some opportunities and some risks and threats. In your opinion, what are the most relevant social concerns? I think personally, what I’m really worried about is things that come to it affects the security of people. So things like the job market and employment. I think eventually mankind will find its way and if we are careful enough, we can find answers to these problems because technology will always be there. We’ve been through this again and I’m hopeful that we’ll find answer to those questions one way or another. When it comes to things like safety, human people’s safety is one of the hardest problems to solve. And that’s where I think we should be paying more attention to. For instance, what happens if we have a virus in a car and that car now drives my mother every day to the grocery store? What happens if that piece of software is taken by a terrorist organization? What happens if the software is at floor by design, it hasn’t been tested properly? And then something happens during this trip of my mother’s trip to the grocery store and then we have a crash. The same type of problem that affects the safety and security of people. Of course, it’s multiplied by 1000 when we get into the use of machines in warfare. So right now we have drones that are driven by a pile of joystick that is sitting on the other side of the world. And this drone now is being flown by this pilot. And the pilot makes the decision of where to drop its bonds. What happens now when this drone is given the okay to decide on its own? How do you pass these ethical questions to the drone’s software? It’s the same problems that warfare was challenged every time a new web phone becomes available to the masses. So these types of decisions that affect the security and the welfare of people, I think these are the major areas that need to be looked at. And it’s probably the hottest areas that we can finance to.

transcript

And you already presented some examples in different areas. Could you give us maybe a few more examples related to facial recognition, justice and social network challenges? Yeah, the facial recognition, it’s a problem that’s been found, found and raised repeatedly the past few years. You could say that it started directly as they need to identify solutions in the security space, but it turns out that now it’s all over the different industries, around the technology. Let’s say industry. Let’s say, for example, that we train a system to use a camera that is sitting at the mall’s entrance, and we want to see if the person that is getting into the mall has a COVID or not, right? So COVID 19 is a very current issue. And let’s say that we have some kind of system that looks at the face and if we found the algorithms that I can identify along with other sensors and data can identify if this person might have coffee. So let’s say that we do identify a person that has coffee. A good next step would be, okay, let’s see, let’s use AI facial recognition and let’s find who this person is. Let’s go to Facebook, let’s go to whatever other social platform, let’s identify this person and then let’s see the pictures that this person has posted in the different social media platforms and see who this person’s friends are. And perhaps we need to reach them in order to see if they got together and they could have. They are suspects now of a COVID-19 positive test result. So where do we stop this? Right? Where do we put an end to this? If we have rules that have to do with personal data and security of data, well, perhaps these rules need to be pushed aside because of a pandemic. So do we let the algorithms do all that work for us without any control, offer help? But the fact that it’s algorithms can make decisions on its own creates this uncertainty for us. And that’s why people rightfully are sceptical about it.

transcript

Could you now tell us a bit more about the status and perspective of some regulations? For example, OECD or EU UNESCO GPAI. Yeah, like I said, I mean there are initiatives and people, smart people, are spending time on placing a framework around the use of AI. There’s also research being done right now for areas of ethics that are not being tackled by government agencies. But as we said earlier, they are at a very early stage and I have a feeling that the industry will be once again ahead of the regulators. If you have a look at the research that is currently happening with companies in the areas of robotics, you will reach the same wow reaction when you see how robots are performing today. And I have a feeling that the industry is already ahead of the regulatory framework and it’s most likely going to be another scenario being repeated where something bad will need to happen in order for everybody to react positively. The way things are moving, I don’t see why should AI be treated in any different manner. I think the industry will be moving along at the incredibly fast rates and the regulators and governments will be a step behind. This is, I believe, is the current trend right now and I don’t see any major factor changing. Thank you very much. Is there anything you would like to add regarding this issue? No, I think pretty much the question of ethics within AI. One could say that it is a valid question. It is a question that is being raised by societies throughout the world. I also believe that the industry will go ahead and design and implement whatever it thinks it’s best for their creators. I don’t think there will be anyway of stopping that from happening. I just hope that at the end of the day, the results of these AI initiatives will be beneficial to mankind and not create all these issues that we are afraid of creating. Eventually mankind kind and humanity will find its way, but I think it’s going to be a bumpy road.

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AIAE » E-LEARNING » INTERVIEW 2

AI and machine learning

The following interview snippets were given by Dr. Sebastian Lapuschkin and cover the topic of AI and machine learning, gives an approach about automation and mentions further examples of machine learning.

transcript

My name is Dr. Sabasana. I’m the head of experimental AI at home in Berlin, and my task is to conduct research towards the expendability of artificial intelligence.

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The interviewee, Dr. Sebastian Lapuschkin, is the Head of Explainable Artificial Intelligence Group at Fraunhofer HHI in Berlin. He is in charge of conducting research towards the explainability of artificial intelligence.


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What is machine learning and how does it work? So machine learning is essentially a way to find automation solutions, data driven automation solutions when this automation goal cannot be reached explicitly for example by using or programming down algorithms. Specifically, the idea behind machine learning is to use data which represents the problem set or describes the problem set and then use machine learning algorithms to let them figure out a solution to those problems. This approach is called data driven. And could you tell us how machine learning is related to artificial intelligence and to big data? Yes, of course. Artificial intelligence at first more or less is only a marketing term describing machine learning. Or one could also say that artificial intelligence is a current subfield of machine learning. To understand this one needs to know that everything which is going on inartificial intentions is using machine learning. And with regards to this marketing term thing, this has happened or that the term artificial intentions has reappeared in the early 2000 and tens with the immersion of the deep learning hype. The three emergency is of the deep learning height at this time and it was first coined actually in the1960s or 50s with the emergence of the first machine learning big data describes the approach to the state of London to collect and organize a lot of data. Right? And in order to conduct machine learning efficiently you need data which sufficiently describes your problem and representatively describes your problem. That being said, however, if you have a lot of data it doesn’t mean that your data is good, that it describes a problem. You might also introduce some confounding features which means information which correlates with your intended targets but causes your model in the end to sort of another target because it cannot figure out what you want from the data specifically. Right? I think this is a bit convoluted to describe it. The problem is you use machine learning to solve a problem which you only can describe via data and if your data doesn’t describe the solution you want to obtain the machine learning algorithm will probably not find the solution you want but some other solution which also works. But this might be helpful.

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True or false?

Machine learning aims at finding data-driven automation solutions when the automation goal cannot be reached explicitly.




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True or false?

Machine learning aims at finding manual solutions to human problems based on data.




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True or false?

Machine learning is a data-driven approach in order to find human solutions to problems caused by machines.




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True or false?

Big Data describes the approach to collect and organise larger and complex data from various sources.




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True or false?

In order to conduct machine learning efficiently, you need representative data that describes your problem sufficiently.




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True or false?

Having a lot of data means the data is automatically good and can describe your problem.




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And could you maybe tell us more about deep learning and what is the difference between machine learning and deep learning? Yes, deep learning again is a subtitle of machine learning and describes the use and training of machine learning algorithms which have a deep representation of information. And this usually describes deep neural networks. The depth in this deep learning is in the deep neural networks is that one usually stacks multiple layers of possible representation of data. You can imagine it as just layers of mathematical operations which are then learned in training. So you give the shape of the network and the function of the network is learned in an Iterative training process by providing an example data and the term deep and learning. The approach comes from the depth of the network. And what is the difference between machine learning, deep learning and traditional programming? Okay, as I said, deep learning is part of machine learning. The difference between machine learning and traditional programming is the following so consider you have some data and you know the rules how to process this data, right? Then you can implement your solutions. And this is a typical programming approach. You have your data, you know how to process it, you implement your programs and outcomes and answer. The approach to machine learning is thus you have a lot of data and you know the answers to this data, but you have no idea how to come up. Basically, you don’t have the rules, right? The task of machine learning is to train your machine to learn the rules which allow you to connect the data and produce the expected answers. And once you have this, you have a trained machine learning model which can receive new data, the data it has never seen before because it has learned the rules and should not have learned the data by heart and it can then produce answers. So in machine learning speed, we say the model should generalize, which means it should have learned general rules on how to handle this data you put in to provide the correct answers. Once you have such a model, you can plug it in as a set of rules in your programming task. For example, if the set of rules would be so complex that you never would explicitly be able to COVID it by writing the code manually.

transcript

And can you provide one or more examples of popular use of machine learning? I think one example which is used fairly often is optical character recognition which means the machines and the post office which read the target address of your letter you write this is usually not done by humans but it’s just sensory machine. The machine deciphers your handwriting then digitizes the address and feeds all this information into a database and then the letter gets directed to the target. Another approach would be facial recognition, for example. For example in digital video cameras, webcams also valiant systems. So the spectrum of applications in machine learning is quite useful. For example, what we are doing in our lab is we use machine learning for natural disaster prevention for example, where we track where we have, for example climate data or air pollution data of the last years, months and so on. And then we train a model which should be able to predict how temperature arrival and so on behaves. Given a lot of factors which cooker over the last days months could you provide one or few examples of popular use of deep learning? Pretty much everything which is quite complex and was apparently unsolvable about ten years ago we licensed deep learning and that is image recognition, for example uses deep learning because using the step of the deep networks this deep architecture allows the model to learn like cascade of different feature processing steps. Right? As a matter of fact deep neural networks are somewhat motivated by the visual cortex of the human brain which processes information in several steps beginning from just receiving color information to basically neurons triggering to simple shapes like edges and round shapes and so on neural networks actually do quite similar things. And by going from the most atomic to very complex features for example, from edges color gradients to neurons which are there or have learned to recognize the heads of lizards, for example this complex image information can be processed efficiently and quite fast. And this leads to current machine learning models in image recognition and outperforming humans for example especially if you factor in time.

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Machine learning is used in face recognition, for example in digital video cameras, webcams and surveillance systems. The scope of application for machine learning is very large. In interviewee’s labs they use machine learning for natural disaster prevention, in which they track climate data and air pollution data from the previous years and then train a model which should be able to predict weather and climate factors. Deep learning is being used in many complex processes like image recognition.


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And what are the opportunities and the positive aspects of machine learning for society? For one, it’s the potential to reach a state of automation which strips away tasks which are labour-intensive but boring and no one actually should do because it can be automated nicely. This, of course, increases efficiency. It reduces errors because the machine never gets tired. In a medical setting, for example, machines could be used to augment the decisions of trainee. Mr. Pathologist, for example. Mr. Pathologist is an especially interesting domain here because it is known that a historian has its highest value when he’s going into retirement because he has a lifelong basic period of learning behind himself or herself. And those almost retiree his are very much faster than the new guys who need to learn the trade, right? And by faster, I mean they intuitively look at one of those histopathology slides and immediately see what’s going on, why the newbie needs to scan every bit of the slides meticulously and take time and so on and so on. And there’s also a group in class acting from Andreas Holzinger. He’s actually training machine learning methods based on data annotations made by an expert, Mr. Pathologist, with the goal of encapsulating his life experience in his topology into machine learning model. So it can potentially be used as a training companion for starters in this domain.

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And what are the most relevant risks related to ethics, for example, in your opinion? For one, of course, is the intended use case of machine learning. For example, do you want to use it for the general good? Do you want to improve of society? Do you want to improve environment? Or do you want to plug it into a cruise missile? This is the core difference here. And the next thing is these are the extreme ends of the spectrum. And then there’s a plethora of societal issues in between. For example, do you automate your credit worthiness estimation of a person and use machine learning for that? And then there’s a question what data did you use to train this model? And did you maybe model, maybe train unwanted correlations between some features and data and the outcome? For example, we had learned that some ethnicities, for whatever reason, for example, skin color, ethnicity, as I said, should not receive, I don’t know, financial aid because of this reason, right? The question is always what data do you feed in? What data do you want someone to use? There’s this principle of data spacity which means only use the data you need to solve the task because additional data might create confronting behaviour in the model. And this is of course one of the current big issues with the ongoing automation. With machine learning. On the other hand, there’s always a question if you use real data to train the machine learning models and don’t like what the model is doing because the model itself is objective, the trust can learn from the data you provide. The data is the model’s sole reality. Does it mean that you don’t like what the model is doing? Or do you not like reality? Right. And I’m thinking it might not always be the right way to fix and curate your data to get rid of certain behaviours of the model. I would see it as an indicator for change necessary in society producing this.

transcript

One question regarding Extendable artificial intelligence X-A-Y can you explain what it is like? Yes. So the target of Expandable AI is to shadow into the black box of machine learning. So current machine learning is so usually the best performing machine learning models are quite complex, which means the outside observer really even the developer really has an insight on what the model is actually learning. And with accessibility or X AI, we aim to gain back some transparency on what the model is doing. Yeah, this can be done in several ways. What we did in our lab is we developed a modified backdrop method which means if you feed in some data points into the model, the model gets transformed layer by layer to the network or the model. Basically it traverses through the model in the end and the end results in answer of the model. Right. And we can somewhat invert this process by for example, if the model receives a picture and tells me it’s a cat, I can start with the cat output and say yeah, but why? And then I can pick apart the partial decisions of the models layer by layer until I reach the input again and I can then obtain, we call it a heat map correctly. It’s basically been masking in the input space where the more things that information is and you can do this for any potential outcome. For example, if you have a dog output of the model and then you can do the same process with the dog output and then you might receive the answer why the model thinks there is no dog in the image or where the dog information is not right. This is a way to connect the model’s use of information as given by the data points to the model’s output. Yes, expandability is a quite young field.I would say the earliest serious steps and more complex models have been done in the 2010 and since then it has been evolving quite rapidly. So there’s a lot of work going on. We are working towards providing explanations which go beyond simple heat map visualizations which need a lot of interpretation at times, especially if the data is hard to understand and need to acknowledge. But our end goal is to model under the treatment of improved exploitability which you’re currently working on should be more or less self-explanatory right by not saying look at this part of the image there’s information which I think as a model speaks for cat. But the model should then inform the user. For example, that I think there’s a cat because I see this and that and that and that. Cat like features the model has, for example, learned as features to use during prediction making. Thank you very much. And what does explainable AI make possible? What can you achieve through that? For one, you can understand what the model is actually doing and you can gain understanding on a per sample basis per sample basis means in this case for each data point you put into the model you get receive feedback on the model reasoning based on this data. You can then of course use this to verify your model. But in some cases you might also end up with the information that the model is producing the right output for the wrong reasons. For example. And this might point you at faults in your training data where you have introduced some confounding information, some confounding features which the model then connects to the output of cats but which are absolutely not Cadillac just because it’s easier for the model. And then we have the problem again that the training gate of the model is the model’s sole reality. You just give it a couple of thousand images of cats and the model learns how to get from this data source to the cat. And if those images, for example, have been crowd from Flicker and they all have a copyright watermark because they are stock images or something, the model might pick up that stock images are cats. Right? This is the problem one of the problems we can identify with externality and this then to the option to improve the model, improve the data source and so on so we are basically way more informed machine learning developers than before extending.

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AIAE » E-LEARNING » INTERVIEW 3

AI in healthcare

The following interview snippets were given by Adriana Dvorsak and cover the application of AI in healthcare such as in diagnosis, early detection of diseases, treatment, training of medical staff and administration, the challenges of AI in healthcare as well as the norms and regulations of AI in healthcare.

transcript

Well, hello. My name is Adriana. I’m working at the university medical center here in Ljubljana in Slovenia. My role is more or less in the field of informatics. So, I am employed in the department of the informatics and we are responsible for the introduction for the new It technologies into the hospital.

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The Interviewee, Adriana Dvorsak, is working at the University of Medical Centre in Ljubljana in the department of informatics, and they are responsible for the introduction of new IT technologies into the hospital.


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What are the relevant AI applications used in health care nowadays? Well, at the moment we are actually at the beginning of the introduction of AI in the healthcare system here in Slovenia and I believe also in the other small countries or small state, the European Union here we have to know that probably the big states are a little bit ahead of us. So France, Germany, United Kingdom, they are probably a step ahead though the consequences and the benefits from the AI in healthcare are particularly good for the small countries and the patients and citizens who live in the small countries. Perhaps later on I can tell you. Why is it? So actually where can we use artificial intelligence in the hospitals and in the clinical environment? Well, first of all, we are very enthusiastic about improving the diagnostics, which means that the diagnosis can be set at the earlier stage and the diagnosis are more accurate. The next step for us is for example, the treatment of the diseases or the therapeutics. This is like a big field in the introduction of the robot into the surgical field. So we can have autonomous robots that can perform a surgical operation on the patient and this can be done from the distance on the national level and also on the European level. I must stress this. Also on the European level, we are actually looking at the benefits from the AI in healthcare in terms of health management and treating diseases that are not only that are contagious diseases, contagious diseases. It means that can actually be transmitted from one person to another. And we know that in the European Union we have like a free movement of people. That means that we all form like one health nation. So this international component is very important in the artificial intelligence. And the last thing that I would like to stress is like use of artificial intelligence in administration and regulation. This also means that the access to our data, which means like the access to the patient’s data, can be on the national or on the international level when itis needed for the good of the patient. You mentioned already some advantages. Can you maybe talk more about the opportunities of artificial intelligence in healthcare in general or also more specifically? I can tell you that we can see like many advantages in introduction of the artificial intelligence in the clinical environment. From the clinical point of view, this means that people who work in the health hospitals or that actually run the hospitals, we can see like a very good and strong improvement of what we do and how we do it. Though only on the second level the patients and the general public can experience this improved procedures, better diagnostics, better therapeutics, more accurate dosage of the administration of the medicine and soon and so forth. So we are the first ones who know that the quality of treating the patients can be improved by artificial intelligence. Therefore, I also would say that, yes, there is a big opportunity to talk and to talk to the healthcare authorities and to include the hospitals and the healthcare authorities into the stocks of the artificial intelligence in healthcare.

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Yes
No
 

According to the expert are the consequences of AI in health care good?


In the expert’s opinion is AI in healthcare resulting in more accurate diagnoses at earlier stages?


Is robotic surgery a part of AI in the healthcare system?


According to the expert does AI work well in treating contagious diseases.


Can AI provide better access to a patient’s data at the international and national level?


Can the quality of treating patients be improved by using AI?



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What are the challenges or the risks or maybe the threats of artificial intelligence in the field of health care at the moment, as we see, the biggest challenge is the answer to the question does it pay off? The introduction of different systems and new technologies is namely very expensive. It is especially expensive for small countries because you have calculated per capita per number of persons. So for us, new technologies are bloody expensive. On the European level that would be much easier. It would be much easier to COVID the expenses at the European level because then the cost per person or the cost per treatment would be much lower. So this is one of the first challenges. The technology is very expensive. On the other hand, you have to know whenever the hospital managers decide about the introduction of new technology, they make like a short calculation is it better than the existing procedure? And if it is better, does it pay off? Is it better for 20%, for 50%, or only for 0.5%? If the improvement is very low and very small, then this new technology will probably not be introduced into the hospital because there are many costs that are also including the education of the staff that are included in these costs. So for me, the most important challenge is the costs of this new technology. For some people, for example, challenge and risk at the same time is represented by the fact of the nature of the data. Data can be easily and quickly transferred. The owner of the data, which is the patient, the owner of the data doesn’t have a full control of his own data because data manager, which in our case is the hospital, could transfer the patient’s data to other institutions. So or to share it within the European Union. For some people this is like a challenge under risk. But for me it is also a kind of advantage. We have seen in the case of Pandemics that the national healthcare systems should be much closer connected and that the authorities, like the European or national authorities, should have a good insight what is happening with a certain disease in a certain population. So personally, I’m not against the sharing of data in order to preserve the good healthcare of the nation. I don’t see many threats. Some people see threats. But you see, I work in the It department, so we embrace the technology. And again, I must stipulate that some norms and some guidelines that European Union has introduced are very well accepted. The European Union is actually trying to reduce the threats in the use of AI in healthcare. And from my point of view, this is like the requirement that the data managers must tell the patients which algorithms they use. And for patients this will be, from the very beginning kind of difficult to understand. And perhaps we shouldn’t expect from all the patients that they will understand everything that is related to artificial intelligence. But at least I think it would be very good to educate the patients and the Europeans that their data is their data and they are in control. What will be done with their data in the future?

Quiz question 1/8

1) What is the main problem with AI technologies in smaller countries?
Money/expensiveness/expenses

2) On what basis do hospital managers decide whether or not to incorporate new technology?
Improvement

3) Why can making the hospital owner of the patient’s data be useful?
Data transfer

4) What is the EU trying to reduce in the context of AI in the healthcare system?
Threats

5) What can data managers inform patients about in order to educate them about AI?
About algorithms


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And could you now tell us more about the status of artificial intelligence in decision support systems, the so-called DS for healthcare? Right. This is like decision support systems are, again, like a very important novelty in the European health care systems. And I must admit that in small nations and in small health care systems like Slovenia, Cyprus and other small states, actually we haven’t introduced them yet, so we know how they work. We consider that the introduction will be beneficial to the doctors and to the patients. But at this very moment, we haven’t introduced them yet. The reason for this lays in the fact that artificial intelligence actually learns from the data from the hospital where it is introduced. And we haven’t introduced so many Novarties that we could have say, okay, now we have a very good basis and our decision support systems benefits all our patients. Not at this very moment. And still, you know that in terms of healthcare in Europe, I think this is like in all Western Hemisphere, including Japan. The fact is that you must prove that the nobility that you introduce is reliable and better than the previous practice. So as long as we cannot prove that this new technique is better, we will probably not introduce it.

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Decision support systems are a very important innovation in European healthcare systems, although they haven’t been introduced to doctors nor patients yet in smaller countries. The reason for that is that there hasn’t been enough data of novelties in the hospitals, from which AI could learn. That way it can’t be proven that this novelty is reliable and better than the previous practice.


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How can AI artificial intelligence help in early detection of diseases? Well, the early detection of diseases is probably one of the most important features of the artificial intelligence. The reason for this is that the monitoring of the development or following the signs of the development of the disease cannot always be done by the doctor or by the certified nurse. So if we have artificial intelligence in place, together with different visual components that can actually follow the patient or the European citizen, then it would be easy to detect the early onset of the disease. Now, why is this important, we can say yes, you will get your diagnosis six months earlier than if you would have been waiting for the doctor. How does it benefit you? Well, if your diagnosis is set early, you can start your treatment earlier and there will be less complications. You will be younger and the younger body debuffs the illness much better than the older body. So if we catch the disease in its early onset and the patient is still relatively young, the prospects for the development are much better than in case when we wait until it is already almost too late. And how can artificial intelligence help in treatment and authority prescriptions? I think this is like one of the best fields where we can expect the benefits, namely treatment and following the therapy might be boring and dull. On the other hand, it is very important that the medicines are prescribed and given to the patient in the exact prescribed manner. So mistakes here can be very costly. And artificial intelligence is actually very good at detecting the mistakes or the processes and actions that are not exactly the average for treatment of a certain disease. So if you have like a fisher intelligent intelligence system that follows the state of the patient and his improval of his disease, and then you can adjust automatically the dosage and the type of the medicine that had been prescribed. Again, it would be very good for the patients to realize that a certain drug is not really good for him or her and that certain adverse effects occurred sometimes. Adverse effects, some people call them undesired effects, they can be very small and cannot be seen by a naked eye. Therefore it is very good to have like this artificial intelligence system that is actually working on the kind of alert systems and it says right now, this and that parameters are not doing well with your patients. Go and adjust the treatment in this and that direction. Then, of course, the doctor goes and sees the proposal from the artificial intelligence and he or she decides whether he or she will follow the advice from the artificial intelligence. From my point of view, this is actually really improving the quality of treatment of the individual patients. How can absolute intelligence help in education and training of staff at all levels? Education and training of staff is really highly organized and very hierarchical, which means that all nation systems have their own ways how they will educate the doctors, how they will give them their certificate or licenses. And of course, for example, in European Union we also have a system of, I think it is called mutual acknowledge of the certificates and licenses. So in this basic sense I am not aware that the artificial intelligence would really help to form a young doctor. However, once the doctors are in their working environment they can learn from certain functionalities from the artificial intelligence and most often that would mean that a strong visual component is included and that the doctors are actually in their certain field they can improve their knowledge in a certain field.

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How does AI help with early detection of disease?





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Why is early detection of disease with AI beneficial?





Quiz question 1/8

How can AI help in treatment and therapy prescriptions?





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How does AI help with educating medical staff?





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And could you tell us more about norms and regulations concerning artificial intelligence in health care? Well, this is like a very good question in general for the hospitals and national authorities. The norms and the regulations from my point of view are very well set. We also have to follow the European norms and regulations. Once again, there is a reason over being small. Now, in small states we rarely formulate our own norms. We cannot set the standards. Usually we follow the standards of the big states or in this case the standards of the European Union. So what we are actually saying we support this development, we also support like making an example of artificial intelligence strategy on the European Union level because then you can follow the same strategy on the national level. So what European Union does at first place, it is later on mimicking or following in the next stage on the national level. So this is like a really good example of a norms and standard setting. Norms and regulations are here to protect the interests of the patient. And sometimes we actually do not explain the patients that all the forms they have to fill in are actually here to protect their rights. It also means that the patients can be somehow dubious if they go to a healthcare practitioner who doesn’t ask anything, who doesn’t give you any consent and who doesn’t pay attention to your personal data. So being aware of norms and regulations is also sound of a quality. And so far how much is artificial intelligence implied in national healthcare systems or in insurance companies? Well, the question of application of artificial intelligence in national Hascar systems is probably very important for the development of healthcare systems in the future. It is my firm belief that the national healthcare systems that will ignore the artificial intelligence will in the long run prove to be less effective. So of course we have like different levels. You can have like interests of the ministries of health that want to deliver good care to the patients. We have the interest of the politicians because it is a very important political question how healthy is your population? Because you can be a very rich and very prosperous politician, but as long as you do not guarantee the good health status of your nation, you will probably not be reflected, at least in the democratic society. Therefore, from my point of view, the superior or good healthcare systems are also in the interests of the politicians. Now, who else could benefit from the implications of the artificial intelligence? Mobile, different research companies, different insurance companies and different laboratories. So all of them are actually like very important stakeholders, institutional stakeholders who will actually benefit from the data. Get it from the artificial intelligence though all these institutions that I have mentioned, for example laboratories and the research institutions, they are not people, they will not improve their healthcare stage. They have to transfer their knowledge and the knowledge acquired with the help of artificial intelligence to the healthcare status of the nation. And this transfer from having the data to delivering better healthcare to the patients, this is a very important stage. Well for the future. Directions it is important to know that at this very moment not all fields of the artificial intelligence are equally developed. And when treating the patients, we must know that the best pay off with the technologies that have a good visual component. Therefore, artificial intelligence at this very moment is primarily introduced into the field of radiology, pathology, ophthalmology and dermatology. We are also aware that, for example, the cardiovascular diseases and so on and so forth are very well covered with the artificial intelligence. And this could also mean that certain types of priorities will probably be introduced in the hospitals. In the sense of using artificial intelligence, itis not useful for all the fields, but for the ones that I just mentioned.

Quiz question 1/8

True or false?

Smaller countries are developing their own norms and guidelines for the use of AI in the health system.




Quiz question 1/8

True or false?

Smaller countries support and follow the European Union's guidelines for the use of AI in healthcare.




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True or false?

AI regulations protect the interest of doctors.




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True or false?

Countries that will neglect the application of AI in the healthcare system will have a less efficient healthcare system.




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True or false?

An important political issue affecting the interest in implementing AI in healthcare systems is how healthy the population is in the country.




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True or false?

Research and insurance companies have no major gains if AI is applied into the healthcare system.




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True or false?

Laboratories and research companies have to transfer the knowledge gained from artificial intelligence to the healthcare status of the nation.




Quiz question 1/8

True or false?

All fields of AI are equally developed.




Quiz question 1/8

True or false?

At the moment AI is mostly used in the fields of pathology, radiology, ophthalmology, dermatology and cardiovascular diseases.




Quiz question 1/8

True or false?

AI is pretty much used in all medical fields.




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AIAE » E-LEARNING » INTERVIEW 1

AI applications, machine learning, human-machine interaction

The following interview snippets were given by Pierre Lison and cover various topics such as the main applications of AI in our society, machine learning and its relations with AI and big data, deep learning, human-machine interaction and its goals as well as the trends and challenges of human-machine interaction.

transcript

So my name is Kelly Zone. I’m a senior researcher at the Norwegian Computing Center, which is a research institute working on AI and statistical modeling and generally computer science. And my field of research is AI, and inparticular, everything that has to do with language, so called language technologies or natural language processing.

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The interviewee, Pierre Lison is a senior researcher at a Norwegian computing centre, which is a research institute. They are working on artificial intelligence (AI), statistical modelling and in general computer sciences. The interviewee’s field of research is AI, language technologies and natural language processing.


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Thank you very much. So what are the main AI technologies and the main AI applications up to now? So, AI is already used in our daily lives. If you think about search engines, Google or computer vision used to recognize objects, or code bars on your cell phone, or speech recognition, anything that has to do with complex problems that require some level of intelligence to be solved. And how about object recognition and voice recognition? Can relate face and speech recognition? Yes. So anything that has to do with recognition of objects on pictures or on video streams is really part of this large field called computer vision. And you can recognize objects, in which case you sort of define the objects that you want to recognize. Like you want to recognize couches or different kind of furniture or Legos or whatever to define the categories that you want to recognize. Typically, you have a system that trains a model to recognize these kinds of objects. And for face recognition, it’s the same, except that instead of physical objects, you work as human faces. And then again, you need a system that you’re training a model that recognizes different kind of persons according to their face. And for speech recognition, it’s essentially the same, except of course, it’s not a visual input, it’s an audio input. But the idea is the same, that you recognize some patterns in the audio, in the audio data that defines different kind of sounds and putting together the sounds, they will make words, they will make sentences, and then you can recognize what is being said and transcribe it. Could you tell us what are the emerging trends of today? AI is a large field already, and it’s growing at a quite fast pace. But some of the biggest trends, of course, is given the widespread availability of large amounts of training data. One of the important trend is really to scale up the technology so that it can process really large amounts of data, everything that you can find on the Web, basically. And that really is both for language technology and so text and computer vision, because you have billions of images on the web. So this scaling up technology is one important trend. On the other hand, an important trend which I find very interesting is really the fact that more and more researchers are looking at the ethical issues behind the technology. Partly because, well, for instance, we’re using basically everything that is found on the Web without tasking human persons that are shown pictures for their consent about their usage for these large models, but also because we realize that AI has really an influence on our daily lives. And then we should think about whether, for instance, AI can become biased or express stereotypes or become hateful, sometimes even because it’s trained on data that is found everywhere on the Web. And we know that the Web is a strange place sometimes. So thinking about how AI what kind of what does the model learn when they scoring the web for data and how we can make sure that they meet our expectations? And, of course, regulations when it comes to non-discrimination. For instance.

Quiz question 1/8

True or false?

In the expert’s opinion AI applications are already broadly used in our society and in our daily life.




Quiz question 1/8

True or false?

Search engines, Google, computer vision, voice/speech recognition, object/face recognition, language technology are examples of AI technologies and AI applications.




Quiz question 1/8

True or false?

Recognition of objects in pictures and video screens is not part of this large field called computer vision.




Quiz question 1/8

True or false?

Computer vision is about training a model to recognise patterns such as physical objects, human voice and faces, visual, audio or video input.




Quiz question 1/8

True or false? According to the expert AI is not growing at a fast pace.




Quiz question 1/8

True or false?

Big Data analytics is one of the emerging technology trends that allows the processing of very large amounts of data.




Quiz question 1/8

True or false?

Researchers don’t consider the ethical issues behind AI technologies as important.




Quiz question 1/8

True or false? In the expert’s opinion AI has no influence in our daily routine, nor in our society.




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transcript

Now we will talk a bit more about machine learning. So could you tell us what is machine learning and how does it work? So, machine learning is a branch of AI, but which has become known as the dominant branch of artificial intelligence. And it’s really about building models fora particular task where things are not preprogramed, but are learned from data. So instead of writing down, specifying detailed rules about what the system should do in every situation, you provide some data, training data, because you’re trying to make the system learn from those. And the system will then automatically identify some patterns that are useful for the task at hand and automatically then use it on new data once it’s learned. Could you tell us how is machine learning related to artificial intelligence and also to big data? Yes. So when it comes to the relation to AI, it really has become the dominant way of solving complex AI tasks. Machine learning is to do that on large training sets, and that’s how it relates to big data, in the sense that you are learning model’s toper form different tasks using large data sets. So it’s also really directly related to big data.

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Machine learning is:





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Machine learning is about: (2 right answers)






Quiz question 1/8

Machine learning: (2 right answers)





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And what is the difference between machine learning and deep learning? So deep learning is a particular kind of technology for machine learning. It’s not the only one, but it’s become a very popular one for some types of problems. The point there in deep learning is to learn so called neural network networks with many processing layers. So you have a quite complex mathematical model which is comprised of small computing nodes that receive information and then forwarded on to other neurons or other computing nodes that have specific mathematical properties. And even though these small nodes are quite simple in isolation, the fact that you combine them together and connect them in large networks with millions of such notes, a bit like in your brain where neurons are connected to one another, can make systems learn how to perform complicated tasks. For instance, deep neural networks. And deep learning has been used for machine translation. So the automated translation like in Google Translate, we know that it’s a complicated task, machine translation, you have to understand the context of the sentence. You have to understand the linguistic properties of both the input language in the target language. You have to understand how words combine with each other so that if two words are put together, it makes construction that is translated differently. So these are really complex tasks and we know that using traditional machine learning Latif was stored until 2010 that give results that were okay most cases, but with serious mistakes and problems understanding contextual factors, for instance, whereas these deep learning networks were better shown that they could find translation of high quality. This being said, it’s far from an open problem and there are still many problems with machine translation but it has been shown that they were better at understanding how to translate. And what is the difference between machine learning, deep learning and traditional programming? So machine learning is really an umbrella term for everything in AI that is trained from data. So it’s really quite large. It includes deep learning but also with other techniques, some of them dating back to the seventies. While deep learning is really a specific kind of technology based on neural networks which works in many cases, but there are many other techniques that are working well and have other properties that are interesting. Maybe I can say that one of the main shortcomings of neural networks. So first of all, it requires large amounts of data and for some problems you have large volumes of data base far from the case for everything if you translate it. For instance, machine translation for a language for which you don’t have a lot of resource, it’s going to be difficult to use deep learning. Another problem is that these are complete black boxes. That means you don’t understand what the system has learned and sometimes it’s okay. You don’t always need to understand everything. But if, let’s say you’re building a system for deciding whether you should give a loan to a person. And you have a system that may based on the salary of the person and where they live and whether they married or not, et cetera, et cetera. Different kind of demographic factors. And you have a system that may give a good prediction about whether a person is entitled to no, no. But it’s not able to explain the rationale behind their decision. It’s going to be heavily problematic because you need a system that not only gives an answer but explains why they came to that particular answer. And so that’s an important shortcoming of neural networks. And that’s why sometimes machine learning systems may have a slightly lower performance on some respect but are able to explain the steps, the processing steps behind their decision sometimes are much better. So there’s no one single way to evaluate those models. There are different kinds of pros and cons behind different kinds of technologies. Yes, And traditional programming is simply what you do when you don’t learn the model, where you simply pre-program all the rules in advance.

Quiz question 1/8

Yes
No
 

Deep learning is the only machine learning technology.


The goal of deep learning is to train neural networks with many processing layers.


Systems can learn how to perform complicated tasks thanks to these many nodes combined and connected in a large network of such nodes.


Deep learning has been used in machine translation which is automated translation.


In machine translation like Google Translate, you don’t need to understand the context of the sentences, the linguistic properties of both languages or how words can combine with each other.


Deep neural machine translation works better and is of higher quality than traditional machine translation, but still needs to improve a lot.


Machine learning is a very general term for everything in AI that is trained from data.


Machine learning does not include deep learning.


Deep learning is a very specific technology based on neural networks.


Traditional programming is what you do when you pre-program the rules in advance and don’t train the models.



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And so you mentioned already machine translation. Can you provide maybe one or more examples of popular use of machine learning? Yes. So machine learning search, like in Google search, search engines, computer vision, speech recognition. It’s used there basically for any kind of classification or prediction. And most companies nowadays have some kind of system. There automate some of their decision or predictions. In robotics, it’s always first also a field where you have a lot of machine learning models for helping the robots decide what they should do and how they should do it. In the industry, machine learning and deep learning are so heavily used. Before, it was, you know, statistical models and machine learning models for the development of this. You mentioned already some advantages and opportunities for machine learning for society. Can you also mention maybe some more? One of the main advantages is probably that of automation. It’s automating tasks that may be repetitive and interesting yeah, something I forgot maybe to mention. But also, everything that you do with autonomous, semi-autonomous cars. It’s also another example where much of driving is repetitive, is routine. It’s about looking for patterns, basically. And computers are quite good at recognizing patterns and doing that systematically.24/7 while human drivers are notoriously bad at keeping track of what’s happening on the road for hours and hours on end. That’s another case where automating a process that is more or less routine and repetitive in most cases can be heavily beneficial. Of course, it’s a good case in point because much of what’s happening on the road is repetitive. But sometimes you have a complex situation, you have something that is unexpected that happens on the road. And machine learning is based on historical data. It’s learning from what has been seen in the past and trying to generalize from it. But the capacity to generalize is much weaker than what we have as human individuals. So while we can understand quickly a new situation that unfolds, it’s much harder for computer.

1/3: Where is machine learning being used nowadays?

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1/3: Where is machine learning being used nowadays?

In machine translation, search engines like google search, computer vision, speech recognition, automated decision making, classifications and predictions, robotics, autonomous cars.

2/3: What is the main advantage of machine learning for society?

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2/3: What is the main advantage of machine learning for society?

Automation of repetitive and monotonous tasks and processes.

3/3: What is machine learning based on?

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3/3: What is machine learning based on?

Historical data in supervised machine learning, learning from what we’ve seen in the past and generalising from it.

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Now we will move to human mission interaction. What are the goals of human machine interactions, in your opinion? So the goal of human machine interaction is simply to create good interfaces between a human individual that wishes to perform a given task and a machine that supports that task. And just a graphical user interface isa very simple example of this. But of course, anything that has to do with interaction, with technology is part of this. So I’ve been working on talking robots, for instance, and that’s another kind of interaction that is quite different from a window on your screen, but it’s also an interface in the sense that you in that case using spoken language to provide instructions to receive feedback on what’s going on in the execution of the task. And the question is always how we can have a communication that is fluid, where the person understand what’s going on and can easily convey what ought to be done. And how can devices be controlled? It really depends on the technology. I mean, for a computer window it’s rather straight forward as long as you understand the interface that a small cross means that you closing the window. And for more sophisticated tools, it’s of course a bit harder because you need to find something that is intuitive enough, yet also powerful enough to enable the range of possibilities for the particular device that you have. For a robot, for instance, it’s quite large, but the set of possible things that the robot can say and do in the world. So you need some kind of and it’s one of the core tenets of human robot interaction, you need some kind of transparency. That means the system should as much as possible provide very explicit signals as to what the system has understood, what it has not understood, where itis in the execution of the task, so that the user is as much as possible in control, because without information there is no control.

1/3: What is the main goal of human machine interaction?

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1/3: What is the main goal of human machine interaction?

Create good interfaces between a human who wishes to perform a particular task and a machine that can perform this task.

2/3: What examples of human machine interaction are there?

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2/3: What examples of human machine interaction are there?

A graphical user interface, talking robots, interaction with technologies.

3/3: What quality do tools/devices/systems/machines need to be like?

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3/3: What quality do tools/devices/systems/machines need to be like?

intuitive enough and transparent

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What will be the evolution of human mission interaction and what are the emerging trends? Well, one important trend is that early human machine interaction was really about the human adapting themselves to the kind of constraints of the interface they were dealing with. In the 70s you had very complete machines with a lot of buttons and the point was that the human had to learn how to operate the interface and the interface itself had zero adaptation. And nowadays the trend is really as much as possible to have the interface the machine trying to adapt to the human user and trying to talk their language. And what interests me as research in language technology is to have machines that talk a language that are able to interact in the communication medium that is most intuitive for us human beings which is natural spoken language because that’s what we’ve spent most of our life doing talking and listening to each other in spoken language. This is the most powerful one because we can express ideas and thoughts and basically anything in a completely effortless manner in a way that would be impossible with a rigid interface where we had to click on buttons. But of course it’s also hard because natural language is ambiguous, is vague, it’s sometimes contradictory and uncertain. It’s always a lot of interpretation going on in order to understand each other. But it’s all interesting because having a machine that understands spoken language means it’s a machine for which you don’t need any training. You know how to speak your mother tongue and it’s both very useful and user friendly. And in some cases if you think, for instance, that driving a car or cooking when your hands are in the middle of your cooking preparation, it’s also one of the only ways just to have a concrete interaction with the machine because you cannot expect the user in anonymous car to spend the time looking at the screen and touching buttons. So in many cases the future of human robot human machine interaction is really having systems that can interact with us in a way that is familiar to us either through language or through some visual language that is easy to understand. And as the last question so you mentioned already some of the opportunities and some of the challenges. Can you think of more positive effects and also some challenges of human mission interaction for society? Well, there’s always the thinking specifically of language technology. There is a challenge of unequal access and unequal resources and that goes beyond human machine interaction. But an important problem for much of today’s language technologies is, for instance, that languages have not the same degree of support, and it makes a big difference if you’re a speaker of American English or if you’re a speaker of some remote African languages that has no access. Where Google Translate does not work, you have no possibility to use the technology in any way. Sometimes you have not even a keyboard on your cell phone to express yourself in your mother tongue. And that’s, in the future, is an important challenge. Not to speak, of course, access to technology. I mean, if you don’t have a phone to begin with, you have even less of an access. But all of this how to ensure that this technology is actually providing and use as an equalizer instead of existing inequalities in the world, that’s an important challenge.

Quiz question 1/8

One important emerging trend of machine learning is to have machines that can adapt to human users, speak their language. Humans spend most of their time talking and listening to each other in spoken language. The future of human machine interaction is to have systems that can interact with us in a way that is familiar to us, either through language or visual effects. The challenge of language technology is the different access to technologies depending on the country and the language.


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