Man versus Machine Talking Business

Man versus Machine

Karishma Vaswani talks to experts about the challenges and opportunities of machine learning and artificial intelligence.

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Imagine a future where machines permeate every aspect of our lives.


That reality is frankly already upon us.


Artificial intelligence is in our homes, in our cars,


But, how will artificial intelligence or AI changed the way


we do business in Asia bastion Mark and what challenges will it bring?


All of that and more on this edition of Talking Business.


There is so much talk about artificial intelligence these


And deep learning, another phrase that is often thrown about.


We'll be speaking with a panel of experts in just a moment


Artificial intelligence is a blanket term for any kind of technology


or software that enables machines to perform tasks on their own.


It is very much a part of our everyday lives.


From something as simple as automated factories


to intelligent personal assistance like Google Assistant,


But AI is slowly evolving into something much more complex.


Deep learning is one branch of machine learning that teaches


computers to process information on several different


layers at the same time, much like neural networks


Part of that is for machines to learn for themselves


by the accumulating and extracting patterns from huge amounts


But the eventual goal is for machines to incorporate


aspects of human intuition and emotional intelligence


As we teach machines to be more independent of human beings,


we are also asking questions about what could potentially go


wrong, and more importantly, what all who the smart machines


of the future will be held accountable to.


Artificial intelligence or AI is already being used


in many industry and business processing today.


Deep learning within AI is really the next frontier.


But what kinds of possibilities does this open up for businesses?


With us we have Modar Alaoui the founder and CEO of deep learning


It makes, amongst other things, Eyeris is a deep emotion recognition


software that reads facial micro-expressions in real-time.


Vasilios Vonikakis, a research scientist


with the Advances Digital Sciences Center.


And the chief technology officer of Coin Tech, dealing with deep


learning and automation in the transport and


Thank you, gentlemen, for joining us today,


to talk about artificial intelligence and deep learning.


I'd like to start with you, Modar. What is deep learning?


How would you describe it to our audience?


In image recognition, deep learning enables algorithms


to recognise the same face from different angles,


and in text recognition, it would try to identify the text


In, for example, speech recognition, it would try to identify


the same keyword or phrases from different accents.


So, it needs a lot of data to be able to do this.


Typically, a lot of data but a lot of quality data as well


so not just quantity, but also the quality of the data.


Yuris, in your business, can you explain how you use deep


So, we do a couple of things, basically.


One of the things we do is we do recoup addition of passing trains


and different kinds of things about it, what is normal,


what is not, starting from very simple things like numbers,


which was initially how we started it.


And, then, moving on to identifying different kinds of issues.


Lately, we also use this to identify things which, from video feeds,


what is happening in the city, like how many pedestrians, cyclists,


cyclists with helmets, cyclists without helmets,


with child seats, without trial seats in the back, transport


all the information you can get from any video stream,


transferring this into actually valuable real-life data.


Vasilios, how do you see deep learning being useful in Asia?


Because you are working here at the advanced science


How far along has the research and the technology become advanced?


Basically, there is a lot of research in this field


going on in the local universities and in many different research


I have seen a lot of start-ups, actually, utilising this type


of technology, start-ups that have to do with observer recognition,


in order to help shoppers find similar looking items from a big


database and going directly and buying this item online.


It has also been used in autonomous vehicles, a lot.


This is a very ongoing research in this direction.


Modar, where do you see the most advanced usage


Image recognition still holds a number of challenges,


simply because we cannot map the entire world in a very


short period of time, nor can we do it that efficiently.


So, I do believe that is an area that is also ripe for research.


Not in Asia, but in the world in general.


What I'm interested in is the deep learning that is trying to break.


Because I understand having developed from machine learning,


as a result of the limitations of machine learning,


would it be fair to say that there are limitations


I would say that deep learning is just the next


step, not even close to full artificial intelligence.


The limitations are set there are regarding the ideas


about what are the tasks that we want deep learning


Do we want to recognise images? Do we want to recognise speech?


What kind of images are we searching for?


So, it is not about making decisions.


It is actually about doing specific tasks at a certain level,


so precision is also important, to understand what is relevant


and what is an acceptable level of precision, for example.


So, to follow up on that, how is it being used in real life?


If you can talk specifically to some industries, what kinds of functions


or what kind of people is deep learning replacing?


Yes, some examples of the things we are doing.


Like, when you talk about on the railroad,


passing over the train or the wagon between two companies or two


motocross and points or something like that,


we actually have a bunch of people running around doing


different kinds of checks, visually checking it,


doing this stuff 24 hours a day, seven days a week at all the time.


So, what we do, we actually managed to replace them,


to take the necessity of way, and this is a very simple


and but there are still thousands of people who are actually


So, Vassilios, currently, deep learning isn't taking over


the entire supply chain of the labour force employed


Do you see that changing in the future?


Do you see more businesses employing deep learning and replacing people


I think that I can see in the future a lot of jobs being displaced by AI.


So, many people are talking about new


Yes, for sure, but I think the rate at which this happens will not be


as great as the rate with which the jobs become obsolete.


So, for example, if you take autonomous vehicles,


actually went this bull happen, you can say that a lot of jobs


will be displaced there, like truck drivers, taxi


Yes, I can add on top of this as well, a lot


You talk about security guards in the malls and everything else,


that is another industry where there are a lot


of people working for it, they are considerably not


the highest level of jobs, they are not that technology


intensive, and these are very likely to be either replaced or optimised


as part of applying more and more technology solutions.


Modar, do you see this becoming a sort of long-running industry


trend for the future or will deep learning, as we have been


discussing, hit a barrier and another breakthrough has


As in everything, technology will definitely be a barrier,


Just like what we have seen with machine learning and other


But I do have to say that deep learning is still in its infancy,


there is still a long way for it to mature, and to disrupt a number


of industries and lives before we can see it being potentially


So, as it is now, deep learning requires a lot of data and a lot


of good quality data, as you mentioned.


So, as it is now, deep learning requires a lot of data and a lot


of good quality data, as you mentioned.


So, many people are working towards making this technology able


to generalise better with less number of data,


How can you train in the neural network with fewer examples in order


to generalise as well as it will be with a large area of data assets?


Another direction is how to make these deep learning networks learn


by themselves unsupervised without having to tell them exactly.


But on that point of unsupervised learning, we have seen incidents


already just recently of when unsupervised learning


or an attempt to make this kind of technology more independent,


One example that comes to mind, for example, the hate tweets.


That is an example of trying to educate a chat bot, if you will,


to do a certain thing, has ended up having the adverse


Supervised is here to stay they because at least we know that,


you know, ground truth data is being labelled basically by human


beings, therefore we are stirring up the level of accuracy there.


Unsupervised is still going to have a certain number of question


marks around it at least for the foreseeable future.


There is a lot of effort being conducted there.


One thing I wanted to add to this also is that it is not just that


deep learning technology which has to advance, we also have to add arms


The ability to interact with people, the ability to see the world,


so we talk about all sorts of vision development which include also


sound, image, video, whatever you are processing that


So, it has to be hand-in-hand to become better, so it is not just


Hold that thought, gentlemen, we are going to be talking


about deep learning in just a few minutes again but before that,


let's take a tongue in cheek look at deep learning


Deep learning, it is the latest phrase that I'm going to impress


But it will have applications that might actually surprise you.


I've come to the seat of learning here at Trinity College Dublin,


to find out about one of the surprising ways in which deep


Deep learning, it is a great phrase, isn't it?


It combines two wonderful words, learning, what


You take away key learnings from the meeting, use it


at the foot of the master, learning lots of wisdom and deep,


Deep Thought was the famous computer in the Hitchhiker's


Deep Blue was the first computer to beta human at chess,


so combine the two of them, it has got to be good, right?


It certainly sounds better than careful, now.


Let's meet Dr Ahmed Salim at the Connect Research Institute


He's developed, along with Dr Mohammed Elgary,


a portrait painting technique which is based on deep learning.


This is how Frank Gough would paint me.


What we have here enables you to photograph and the van Gogh


painting, the model by itself, produces these results.


Look at me. A masterpiece.


We are using these models to do feature extraction.


Then we upload these features into our mathematical model.


It will generate the painting that we are looking for.


Professor Linda Doyle has a view on the bigger picture.


Actually, it is true, the eyes do follow you around the room.


Artificial intelligence is already actually embedded


in the world around us, and people don't realise that.


So, when they think of artificial intelligence, they tend to think


about robots and science fiction and things they have seen on TV,


whereas in fact in everything that you do, in Google,


in Facebook, and in all the applications that


you use, there is some kind of artificial intelligence there.


If you have an algorithm that shows you how to avoid having an accident,


there has to be some decision-making process that says A is more


important than B so I'll bump into B rather than A.


That have to be put in by humans somewhere.


Yes, there is a lot of to think about when it comes to artificial


intelligence and this iterative machine learning.


So, when you think about artificial intelligence, what you are saying


is that the algorithms running in the background that are learning


behaviour and making decisions, but they don't do that in a vacuum,


If you believe that human beings are a finite number


of cells or whatever it is, trillions, eventually,


we are going to make artificial intelligence that will be smarter


We have to grapple with the ethical issues now because artificial


intelligence isn't something of the future, it is


Mm, where to immortalise myself on the wall?


Of course, when you talk about anything like AI,


there are still so many questions that remain because so much


One thing I can say, now, though, is a rebuttal to anyone who ever


If you like that, you can catch more of those videos


Now, back to some serious business with our panel of experts.


Today we have with us Modar Alaoui from Eyeris.


Vassilios Vonikakis with the Advanced Digital Sciences


Before the break, we were discussing the possibilities of deep learning.


Now I want to talk about some of the fears that have cropped up,


There is always this discussion about how machines


Who is to be held responsible with the current technology


that we have in place today for example, autonomous cars,


if there is an accident when an autonomous car


Maybe, Modar, you could pick that up.


Yeah, well, there has to be a certain, you know,


I do potentially think that there will have to be some sort


I also leave that companies and governments and authorities


will have at some point to decide how the AI should respond


For example, the autonomous vehicle accidents, recently an announcement


was made by one of the automotive companies, they made the choice


for the AI that is going to be inside the vehicle to protect


the driver at any given time, whether that protects the pedestrian


or the person that is in front of the vehicle.


the driver at any given time, rather that protects the pedestrian


or the person that is in front of the vehicle.


Who to blame and how to think about how AI would respond


But doesn't this bring up a whole host of questions about how this


kind of technology is going to be applied in daily life?


Given the fact that currently it is on track to replace so many


jobs, and we were talking about it before the break, it is going to be


something that we are going to see more of in the future,


shouldn't we be trying to answer these questions


From my perspective, I would like to use a very


One of the Ps would be for precision.


That will differ from case to case, from application to application.


Another one would be personal data protection.


The third thing would be predictability which would be


actually to do with the ability to predict something.


And the fourth one would be the productivity issue.


Where we say that machines can be more productive


If you look at this perspective, then the question of ethics


Because it is not just about autonomous vehicles,


it is also about health care, it is also about diagnostics,


and so, the way people perceive trust is actually changing


And I think that is also one of the changes which isn't


going to be about technology, isn't going to be about science,


it is going to be one of the changes which will be about the perception


of people, the way they see the world, the way they see


the tech ologies they trust, and then of course we can talk


principles about decision-making. into the deep learning,


But the more complex systems get, the more demands there will be. How


do we make the technology fair for everyone? Click for example Google


along with Amazon and Facebook and Microsoft, if I were to mention


these as the four or five horsemen companies, they have the ability to


time they recognise that using it time they recognise that using it


for the greater good of advancing humanity is of paramount role than


to use it otherwise. What happens if or when an entity like this comes an


independent freethinking entity? I know we are delving into the realm


of science fiction here but, you know, it was far-fetched about five


or ten years ago to talk about deep learning, who is to say it is deep


-- far-fetched to talk about this? If you look at where we are now, we


are still miles, not even Miles, millions of miles away from having


an entity like this. Unless there is some hidden research which nobody


knows about. Most of the research which is open is still focusing on


specific tasks, it is still about being better in a very specific,


narrow task. It is about being good about decision-making, being


cognitive, making decisions on a larger scale, we are still very far


away from this. Do you think we are getting closer to the stage where


machines will be able to make decisions that currently humans do?


Gradually, yes, but it will take some time. I expect that the things


that we already have will become better and better over time.


Usually, I think that weekend to overestimate what we can do in the


short term. We tend to overestimate what we can do in the long run. I


think, generally, AI will become better and better but it will always


be applications specific, at least for the near future. Thank you


gentlemen for joining us for this discussion on deep learning. That is


it for this edition of Talking Business in Singapore.


After a great and misty day, a murky night on the way tonight. Be wary


and the Woakes, fog would