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Imagine a future where machines permeate every aspect of our lives. | :00:00. | :00:07. | |
That reality is frankly already upon us. | :00:08. | :00:10. | |
Artificial intelligence is in our homes, in our cars, | :00:11. | :00:14. | |
But, how will artificial intelligence or AI changed the way | :00:15. | :00:20. | |
we do business in Asia bastion Mark and what challenges will it bring? | :00:21. | :00:25. | |
All of that and more on this edition of Talking Business. | :00:26. | :00:51. | |
There is so much talk about artificial intelligence these | :00:52. | :00:57. | |
And deep learning, another phrase that is often thrown about. | :00:58. | :01:03. | |
We'll be speaking with a panel of experts in just a moment | :01:04. | :01:06. | |
Artificial intelligence is a blanket term for any kind of technology | :01:07. | :01:14. | |
or software that enables machines to perform tasks on their own. | :01:15. | :01:20. | |
It is very much a part of our everyday lives. | :01:21. | :01:23. | |
From something as simple as automated factories | :01:24. | :01:27. | |
to intelligent personal assistance like Google Assistant, | :01:28. | :01:29. | |
But AI is slowly evolving into something much more complex. | :01:30. | :01:39. | |
Deep learning is one branch of machine learning that teaches | :01:40. | :01:47. | |
computers to process information on several different | :01:48. | :01:50. | |
layers at the same time, much like neural networks | :01:51. | :01:54. | |
Part of that is for machines to learn for themselves | :01:55. | :01:59. | |
by the accumulating and extracting patterns from huge amounts | :02:00. | :02:02. | |
But the eventual goal is for machines to incorporate | :02:03. | :02:14. | |
aspects of human intuition and emotional intelligence | :02:15. | :02:16. | |
As we teach machines to be more independent of human beings, | :02:17. | :02:21. | |
we are also asking questions about what could potentially go | :02:22. | :02:24. | |
wrong, and more importantly, what all who the smart machines | :02:25. | :02:30. | |
of the future will be held accountable to. | :02:31. | :02:36. | |
Artificial intelligence or AI is already being used | :02:37. | :02:46. | |
in many industry and business processing today. | :02:47. | :02:49. | |
Deep learning within AI is really the next frontier. | :02:50. | :02:51. | |
But what kinds of possibilities does this open up for businesses? | :02:52. | :02:56. | |
With us we have Modar Alaoui the founder and CEO of deep learning | :02:57. | :03:00. | |
It makes, amongst other things, Eyeris is a deep emotion recognition | :03:01. | :03:10. | |
software that reads facial micro-expressions in real-time. | :03:11. | :03:12. | |
Vasilios Vonikakis, a research scientist | :03:13. | :03:18. | |
with the Advances Digital Sciences Center. | :03:19. | :03:31. | |
And the chief technology officer of Coin Tech, dealing with deep | :03:32. | :03:34. | |
learning and automation in the transport and | :03:35. | :03:36. | |
Thank you, gentlemen, for joining us today, | :03:37. | :03:38. | |
to talk about artificial intelligence and deep learning. | :03:39. | :03:41. | |
I'd like to start with you, Modar. What is deep learning? | :03:42. | :03:44. | |
How would you describe it to our audience? | :03:45. | :03:46. | |
In image recognition, deep learning enables algorithms | :03:47. | :03:48. | |
to recognise the same face from different angles, | :03:49. | :03:54. | |
and in text recognition, it would try to identify the text | :03:55. | :03:59. | |
In, for example, speech recognition, it would try to identify | :04:00. | :04:06. | |
the same keyword or phrases from different accents. | :04:07. | :04:08. | |
So, it needs a lot of data to be able to do this. | :04:09. | :04:12. | |
Typically, a lot of data but a lot of quality data as well | :04:13. | :04:15. | |
so not just quantity, but also the quality of the data. | :04:16. | :04:17. | |
Yuris, in your business, can you explain how you use deep | :04:18. | :04:21. | |
So, we do a couple of things, basically. | :04:22. | :04:27. | |
One of the things we do is we do recoup addition of passing trains | :04:28. | :04:32. | |
and different kinds of things about it, what is normal, | :04:33. | :04:40. | |
what is not, starting from very simple things like numbers, | :04:41. | :04:42. | |
which was initially how we started it. | :04:43. | :04:45. | |
And, then, moving on to identifying different kinds of issues. | :04:46. | :04:53. | |
Lately, we also use this to identify things which, from video feeds, | :04:54. | :04:56. | |
what is happening in the city, like how many pedestrians, cyclists, | :04:57. | :04:59. | |
cyclists with helmets, cyclists without helmets, | :05:00. | :05:00. | |
with child seats, without trial seats in the back, transport | :05:01. | :05:03. | |
all the information you can get from any video stream, | :05:04. | :05:05. | |
transferring this into actually valuable real-life data. | :05:06. | :05:07. | |
Vasilios, how do you see deep learning being useful in Asia? | :05:08. | :05:11. | |
Because you are working here at the advanced science | :05:12. | :05:14. | |
How far along has the research and the technology become advanced? | :05:15. | :05:29. | |
Basically, there is a lot of research in this field | :05:30. | :05:31. | |
going on in the local universities and in many different research | :05:32. | :05:34. | |
I have seen a lot of start-ups, actually, utilising this type | :05:35. | :05:37. | |
of technology, start-ups that have to do with observer recognition, | :05:38. | :05:40. | |
in order to help shoppers find similar looking items from a big | :05:41. | :05:45. | |
database and going directly and buying this item online. | :05:46. | :05:51. | |
It has also been used in autonomous vehicles, a lot. | :05:52. | :05:57. | |
This is a very ongoing research in this direction. | :05:58. | :06:02. | |
Modar, where do you see the most advanced usage | :06:03. | :06:07. | |
Image recognition still holds a number of challenges, | :06:08. | :06:15. | |
simply because we cannot map the entire world in a very | :06:16. | :06:22. | |
short period of time, nor can we do it that efficiently. | :06:23. | :06:27. | |
So, I do believe that is an area that is also ripe for research. | :06:28. | :06:30. | |
Not in Asia, but in the world in general. | :06:31. | :06:33. | |
What I'm interested in is the deep learning that is trying to break. | :06:34. | :06:44. | |
Because I understand having developed from machine learning, | :06:45. | :06:53. | |
as a result of the limitations of machine learning, | :06:54. | :06:55. | |
would it be fair to say that there are limitations | :06:56. | :06:58. | |
I would say that deep learning is just the next | :06:59. | :07:01. | |
step, not even close to full artificial intelligence. | :07:02. | :07:03. | |
The limitations are set there are regarding the ideas | :07:04. | :07:13. | |
about what are the tasks that we want deep learning | :07:14. | :07:15. | |
Do we want to recognise images? Do we want to recognise speech? | :07:16. | :07:20. | |
What kind of images are we searching for? | :07:21. | :07:22. | |
So, it is not about making decisions. | :07:23. | :07:24. | |
It is actually about doing specific tasks at a certain level, | :07:25. | :07:27. | |
so precision is also important, to understand what is relevant | :07:28. | :07:29. | |
and what is an acceptable level of precision, for example. | :07:30. | :07:31. | |
So, to follow up on that, how is it being used in real life? | :07:32. | :07:35. | |
If you can talk specifically to some industries, what kinds of functions | :07:36. | :07:38. | |
or what kind of people is deep learning replacing? | :07:39. | :07:40. | |
Yes, some examples of the things we are doing. | :07:41. | :07:44. | |
Like, when you talk about on the railroad, | :07:45. | :07:48. | |
passing over the train or the wagon between two companies or two | :07:49. | :07:51. | |
motocross and points or something like that, | :07:52. | :07:56. | |
we actually have a bunch of people running around doing | :07:57. | :07:59. | |
different kinds of checks, visually checking it, | :08:00. | :08:09. | |
doing this stuff 24 hours a day, seven days a week at all the time. | :08:10. | :08:21. | |
So, what we do, we actually managed to replace them, | :08:22. | :08:24. | |
to take the necessity of way, and this is a very simple | :08:25. | :08:27. | |
and but there are still thousands of people who are actually | :08:28. | :08:29. | |
So, Vassilios, currently, deep learning isn't taking over | :08:30. | :08:32. | |
the entire supply chain of the labour force employed | :08:33. | :08:34. | |
Do you see that changing in the future? | :08:35. | :08:46. | |
Do you see more businesses employing deep learning and replacing people | :08:47. | :08:49. | |
I think that I can see in the future a lot of jobs being displaced by AI. | :08:50. | :08:56. | |
So, many people are talking about new | :08:57. | :08:58. | |
Yes, for sure, but I think the rate at which this happens will not be | :08:59. | :09:04. | |
as great as the rate with which the jobs become obsolete. | :09:05. | :09:07. | |
So, for example, if you take autonomous vehicles, | :09:08. | :09:10. | |
actually went this bull happen, you can say that a lot of jobs | :09:11. | :09:15. | |
will be displaced there, like truck drivers, taxi | :09:16. | :09:23. | |
Yes, I can add on top of this as well, a lot | :09:24. | :09:30. | |
You talk about security guards in the malls and everything else, | :09:31. | :09:34. | |
that is another industry where there are a lot | :09:35. | :09:36. | |
of people working for it, they are considerably not | :09:37. | :09:38. | |
the highest level of jobs, they are not that technology | :09:39. | :09:41. | |
intensive, and these are very likely to be either replaced or optimised | :09:42. | :09:44. | |
as part of applying more and more technology solutions. | :09:45. | :09:47. | |
Modar, do you see this becoming a sort of long-running industry | :09:48. | :09:49. | |
trend for the future or will deep learning, as we have been | :09:50. | :09:57. | |
discussing, hit a barrier and another breakthrough has | :09:58. | :10:00. | |
As in everything, technology will definitely be a barrier, | :10:01. | :10:04. | |
Just like what we have seen with machine learning and other | :10:05. | :10:15. | |
But I do have to say that deep learning is still in its infancy, | :10:16. | :10:19. | |
there is still a long way for it to mature, and to disrupt a number | :10:20. | :10:23. | |
of industries and lives before we can see it being potentially | :10:24. | :10:26. | |
So, as it is now, deep learning requires a lot of data and a lot | :10:27. | :10:41. | |
of good quality data, as you mentioned. | :10:42. | :10:43. | |
So, as it is now, deep learning requires a lot of data and a lot | :10:44. | :10:48. | |
of good quality data, as you mentioned. | :10:49. | :10:51. | |
So, many people are working towards making this technology able | :10:52. | :10:54. | |
to generalise better with less number of data, | :10:55. | :10:56. | |
How can you train in the neural network with fewer examples in order | :10:57. | :11:01. | |
to generalise as well as it will be with a large area of data assets? | :11:02. | :11:07. | |
Another direction is how to make these deep learning networks learn | :11:08. | :11:10. | |
by themselves unsupervised without having to tell them exactly. | :11:11. | :11:13. | |
But on that point of unsupervised learning, we have seen incidents | :11:14. | :11:17. | |
already just recently of when unsupervised learning | :11:18. | :11:22. | |
or an attempt to make this kind of technology more independent, | :11:23. | :11:29. | |
One example that comes to mind, for example, the hate tweets. | :11:30. | :11:36. | |
That is an example of trying to educate a chat bot, if you will, | :11:37. | :11:40. | |
to do a certain thing, has ended up having the adverse | :11:41. | :11:47. | |
Supervised is here to stay they because at least we know that, | :11:48. | :11:55. | |
you know, ground truth data is being labelled basically by human | :11:56. | :12:03. | |
beings, therefore we are stirring up the level of accuracy there. | :12:04. | :12:12. | |
Unsupervised is still going to have a certain number of question | :12:13. | :12:15. | |
marks around it at least for the foreseeable future. | :12:16. | :12:17. | |
There is a lot of effort being conducted there. | :12:18. | :12:19. | |
One thing I wanted to add to this also is that it is not just that | :12:20. | :12:24. | |
deep learning technology which has to advance, we also have to add arms | :12:25. | :12:27. | |
The ability to interact with people, the ability to see the world, | :12:28. | :12:41. | |
so we talk about all sorts of vision development which include also | :12:42. | :12:44. | |
sound, image, video, whatever you are processing that | :12:45. | :12:45. | |
So, it has to be hand-in-hand to become better, so it is not just | :12:46. | :12:50. | |
Hold that thought, gentlemen, we are going to be talking | :12:51. | :12:54. | |
about deep learning in just a few minutes again but before that, | :12:55. | :12:57. | |
let's take a tongue in cheek look at deep learning | :12:58. | :13:00. | |
Deep learning, it is the latest phrase that I'm going to impress | :13:01. | :13:07. | |
But it will have applications that might actually surprise you. | :13:08. | :13:15. | |
I've come to the seat of learning here at Trinity College Dublin, | :13:16. | :13:18. | |
to find out about one of the surprising ways in which deep | :13:19. | :13:21. | |
Deep learning, it is a great phrase, isn't it? | :13:22. | :13:28. | |
It combines two wonderful words, learning, what | :13:29. | :13:31. | |
You take away key learnings from the meeting, use it | :13:32. | :13:36. | |
at the foot of the master, learning lots of wisdom and deep, | :13:37. | :13:51. | |
Deep Thought was the famous computer in the Hitchhiker's | :13:52. | :13:53. | |
Deep Blue was the first computer to beta human at chess, | :13:54. | :13:57. | |
so combine the two of them, it has got to be good, right? | :13:58. | :14:00. | |
It certainly sounds better than careful, now. | :14:01. | :14:01. | |
Let's meet Dr Ahmed Salim at the Connect Research Institute | :14:02. | :14:04. | |
He's developed, along with Dr Mohammed Elgary, | :14:05. | :14:09. | |
a portrait painting technique which is based on deep learning. | :14:10. | :14:11. | |
This is how Frank Gough would paint me. | :14:12. | :14:19. | |
What we have here enables you to photograph and the van Gogh | :14:20. | :14:25. | |
painting, the model by itself, produces these results. | :14:26. | :14:28. | |
Look at me. A masterpiece. | :14:29. | :14:30. | |
We are using these models to do feature extraction. | :14:31. | :14:33. | |
Then we upload these features into our mathematical model. | :14:34. | :14:36. | |
It will generate the painting that we are looking for. | :14:37. | :14:41. | |
Professor Linda Doyle has a view on the bigger picture. | :14:42. | :14:48. | |
Actually, it is true, the eyes do follow you around the room. | :14:49. | :14:53. | |
Artificial intelligence is already actually embedded | :14:54. | :14:55. | |
in the world around us, and people don't realise that. | :14:56. | :14:58. | |
So, when they think of artificial intelligence, they tend to think | :14:59. | :15:02. | |
about robots and science fiction and things they have seen on TV, | :15:03. | :15:05. | |
whereas in fact in everything that you do, in Google, | :15:06. | :15:08. | |
in Facebook, and in all the applications that | :15:09. | :15:10. | |
you use, there is some kind of artificial intelligence there. | :15:11. | :15:12. | |
If you have an algorithm that shows you how to avoid having an accident, | :15:13. | :15:17. | |
there has to be some decision-making process that says A is more | :15:18. | :15:25. | |
important than B so I'll bump into B rather than A. | :15:26. | :15:31. | |
That have to be put in by humans somewhere. | :15:32. | :15:33. | |
Yes, there is a lot of to think about when it comes to artificial | :15:34. | :15:36. | |
intelligence and this iterative machine learning. | :15:37. | :15:38. | |
So, when you think about artificial intelligence, what you are saying | :15:39. | :15:42. | |
is that the algorithms running in the background that are learning | :15:43. | :15:44. | |
behaviour and making decisions, but they don't do that in a vacuum, | :15:45. | :15:47. | |
If you believe that human beings are a finite number | :15:48. | :15:51. | |
of cells or whatever it is, trillions, eventually, | :15:52. | :15:53. | |
we are going to make artificial intelligence that will be smarter | :15:54. | :15:56. | |
We have to grapple with the ethical issues now because artificial | :15:57. | :16:00. | |
intelligence isn't something of the future, it is | :16:01. | :16:02. | |
Mm, where to immortalise myself on the wall? | :16:03. | :16:09. | |
Of course, when you talk about anything like AI, | :16:10. | :16:15. | |
there are still so many questions that remain because so much | :16:16. | :16:18. | |
One thing I can say, now, though, is a rebuttal to anyone who ever | :16:19. | :16:28. | |
If you like that, you can catch more of those videos | :16:29. | :16:34. | |
Now, back to some serious business with our panel of experts. | :16:35. | :16:43. | |
Today we have with us Modar Alaoui from Eyeris. | :16:44. | :16:45. | |
Vassilios Vonikakis with the Advanced Digital Sciences | :16:46. | :16:48. | |
Before the break, we were discussing the possibilities of deep learning. | :16:49. | :16:58. | |
Now I want to talk about some of the fears that have cropped up, | :16:59. | :17:01. | |
There is always this discussion about how machines | :17:02. | :17:11. | |
Who is to be held responsible with the current technology | :17:12. | :17:17. | |
that we have in place today for example, autonomous cars, | :17:18. | :17:20. | |
if there is an accident when an autonomous car | :17:21. | :17:22. | |
Maybe, Modar, you could pick that up. | :17:23. | :17:26. | |
Yeah, well, there has to be a certain, you know, | :17:27. | :17:29. | |
I do potentially think that there will have to be some sort | :17:30. | :17:36. | |
I also leave that companies and governments and authorities | :17:37. | :17:42. | |
will have at some point to decide how the AI should respond | :17:43. | :17:51. | |
For example, the autonomous vehicle accidents, recently an announcement | :17:52. | :18:00. | |
was made by one of the automotive companies, they made the choice | :18:01. | :18:06. | |
for the AI that is going to be inside the vehicle to protect | :18:07. | :18:11. | |
the driver at any given time, whether that protects the pedestrian | :18:12. | :18:18. | |
or the person that is in front of the vehicle. | :18:19. | :18:25. | |
the driver at any given time, rather that protects the pedestrian | :18:26. | :18:27. | |
or the person that is in front of the vehicle. | :18:28. | :18:30. | |
Who to blame and how to think about how AI would respond | :18:31. | :18:34. | |
But doesn't this bring up a whole host of questions about how this | :18:35. | :18:39. | |
kind of technology is going to be applied in daily life? | :18:40. | :18:42. | |
Given the fact that currently it is on track to replace so many | :18:43. | :18:47. | |
jobs, and we were talking about it before the break, it is going to be | :18:48. | :18:52. | |
something that we are going to see more of in the future, | :18:53. | :18:55. | |
shouldn't we be trying to answer these questions | :18:56. | :18:57. | |
From my perspective, I would like to use a very | :18:58. | :19:02. | |
One of the Ps would be for precision. | :19:03. | :19:11. | |
That will differ from case to case, from application to application. | :19:12. | :19:14. | |
Another one would be personal data protection. | :19:15. | :19:15. | |
The third thing would be predictability which would be | :19:16. | :19:21. | |
actually to do with the ability to predict something. | :19:22. | :19:23. | |
And the fourth one would be the productivity issue. | :19:24. | :19:27. | |
Where we say that machines can be more productive | :19:28. | :19:29. | |
If you look at this perspective, then the question of ethics | :19:30. | :19:33. | |
Because it is not just about autonomous vehicles, | :19:34. | :19:37. | |
it is also about health care, it is also about diagnostics, | :19:38. | :19:39. | |
and so, the way people perceive trust is actually changing | :19:40. | :19:42. | |
And I think that is also one of the changes which isn't | :19:43. | :19:49. | |
going to be about technology, isn't going to be about science, | :19:50. | :19:52. | |
it is going to be one of the changes which will be about the perception | :19:53. | :19:56. | |
of people, the way they see the world, the way they see | :19:57. | :19:58. | |
the tech ologies they trust, and then of course we can talk | :19:59. | :20:01. | |
principles about decision-making. into the deep learning, | :20:02. | :20:11. | |
But the more complex systems get, the more demands there will be. How | :20:12. | :20:19. | |
do we make the technology fair for everyone? Click for example Google | :20:20. | :20:27. | |
along with Amazon and Facebook and Microsoft, if I were to mention | :20:28. | :20:33. | |
these as the four or five horsemen companies, they have the ability to | :20:34. | :20:40. | |
time they recognise that using it time they recognise that using it | :20:41. | :20:47. | |
for the greater good of advancing humanity is of paramount role than | :20:48. | :20:57. | |
to use it otherwise. What happens if or when an entity like this comes an | :20:58. | :21:03. | |
independent freethinking entity? I know we are delving into the realm | :21:04. | :21:08. | |
of science fiction here but, you know, it was far-fetched about five | :21:09. | :21:11. | |
or ten years ago to talk about deep learning, who is to say it is deep | :21:12. | :21:18. | |
-- far-fetched to talk about this? If you look at where we are now, we | :21:19. | :21:23. | |
are still miles, not even Miles, millions of miles away from having | :21:24. | :21:29. | |
an entity like this. Unless there is some hidden research which nobody | :21:30. | :21:33. | |
knows about. Most of the research which is open is still focusing on | :21:34. | :21:37. | |
specific tasks, it is still about being better in a very specific, | :21:38. | :21:46. | |
narrow task. It is about being good about decision-making, being | :21:47. | :21:52. | |
cognitive, making decisions on a larger scale, we are still very far | :21:53. | :21:58. | |
away from this. Do you think we are getting closer to the stage where | :21:59. | :22:01. | |
machines will be able to make decisions that currently humans do? | :22:02. | :22:07. | |
Gradually, yes, but it will take some time. I expect that the things | :22:08. | :22:13. | |
that we already have will become better and better over time. | :22:14. | :22:17. | |
Usually, I think that weekend to overestimate what we can do in the | :22:18. | :22:23. | |
short term. We tend to overestimate what we can do in the long run. I | :22:24. | :22:30. | |
think, generally, AI will become better and better but it will always | :22:31. | :22:35. | |
be applications specific, at least for the near future. Thank you | :22:36. | :22:40. | |
gentlemen for joining us for this discussion on deep learning. That is | :22:41. | :22:44. | |
it for this edition of Talking Business in Singapore. | :22:45. | :23:03. | |
After a great and misty day, a murky night on the way tonight. Be wary | :23:04. | :23:11. | |
and the Woakes, fog would | :23:12. | :23:12. |