Original article was published by Sai Prakash on Artificial Intelligence on Medium
The Birth of A.I. — Men behind our future
Problems posed by the computer are really no different than the problems we have with other products of technology. It’s gonna take a great deal of wisdom on our part to manage them. But if we do, we’re going to make a much better world.
Artificial intelligence or AI has the potential to revolutionize our world the way we do things and how we live and you can say that it’s already starting to do that AI will be one of those big tools that propels us into a new future like computers and the Internet did decades ago.
Recently we’ve seen many examples of neural nets in particular from speeding up video game production , making graphics more realistic and solving age-old physics problems like the three-body orbit problem so that’s all well and interesting. But we have to ask a question on how did AI come into existence? Who were the people that first dreamed their computers could think for themselves? Who are the pioneers of AI?
Here is a small talk show conversation about AI in 1980’s :
Host : What really worries me today is what’s going to happen to us if machines can think?
Guest : If you could ask me that question just a few years ago I’d have said it was very far-fetched and today I just have to admit I don’t really know, I suspect if you come back in four or five years, I’ll say sure they really do think.
As soon as computers came into existence scientists began fantasizing about how they could revolutionize our world. Even in the 1960s they theorized that one day computers would be able to think for themselves. There are many pioneers that laid the foundation of AI even as far back as Aristotle introducing associationism in 300 BC and this would start our attempt to understand the human brain. But in this article we’re going to focus more on the more recent notable contributions the so called fathers of AI.
First attempt :
The beginning of AI all starts with psychologist Frank Rosenblatt in 1957 in that time he developed what was called Perceptron. A perceptron was a digital neural network that was designed to mimic a few brain neurons.
Frank’s first task for the network was to classify images into two categories. He scanned in images of men and woman and hypothesized that over time the network would learn the differences between men and women or at least see the patterns that made men look like men and women like women. Just a year later the media caught onto the idea and the hype was strong.
Unfortunately for Frank despite the higher his neural network system didn’t work very well at all because he only used a single layer of artificial neurons making it extremely limited in what it could do and even worse there wasn’t much that could be done about it at the time computers of that day could only handle this simple setup. These problems were never solved and by 1969 the computer science community had abandoned the idea and with that AI was dead everyone may have given up on the idea.
Second attempt :
Decades later a keen computer scientist by the name of Geoffrey Hinton thought that everyone else was thinking in wrong way. He proposed that the human brain was indeed a neural network and the human brain evidently made for an incredibly powerful system. Hinton saw the genius in the idea that everyone else missed it seems, to him there’s no other way the brain could work. Original implementation has relatively simple processing elements that are very loosely organised models of neurons, each connection has a weight on it and what a neuron does is, it take the activities on the connections, add weights to them and then decides whether to send an output or not.
If it gets a big enough(positive) it sends an output, if the sum is negative it doesn’t send anything and all we need to do is to wire up a gazillion of them and figure out how to change the weights then it’ll do anything. It’s just a question of how you change the weights. Geoffrey Hinton is the superstar in the AI world having authored 200 peer review publications Hinton was instrumental in the fundamental research that brought about the AI revolution after studying psychology.
Multi-layered approach :
After developing multi-layered neural networks Geoffrey and his team quickly realized that the problem with Frank Rosenblatt single-layer approach was that more layers were needed in the network to allow for much greater capabilities. Modern computers were powerful enough to handle this multi-layer approach and solved the problem that Frank Rosenblatt had with the neural networks. This multi-layered approach of a deep neural network in 1985 Hinton co-authored a paper which introduced the Boltzmann machine. They are the fundamental building blocks of early deep neural networks. The concept is to have groups of layers of neurons communicate in such a way where each artificial neuron learns a very basic feature from any data. For example each neuron can represent a pixel in an image that the network is trying to learn.
Practical Implementations :
In the 90s a man by the name of Yann Lecun built a program which recognized handwritten digits.
However the idea of AI being used was short-lived. The field was restrained by slow and inadequate competing power and lack of data. The world had finally caught up with computer processing speed and grown significantly since the 90s. Moore’s law observed by Intel’s co-founder Gordon Moore stated that the number of transistors per square inch doubles about every two years. This meant that computers were growing and processing power exponentially. Meanwhile thanks the Internet because large amount of data had been acquired and this solved the data problem.
The Birth :
The birth of the modern AI movement can be traced back to a single date September 30th 2012. On this day Geoffrey and his team created the first artificial deep neural network to be used on a widely known benchmark image recognition test called ImageNet. This program is called AlexNet and when it was unleashed on this state it had performance like no one had ever seen. AlexNet destroyed the competition scoring an over 75% success rate and performed 41% better than the best previous attempt. This one event showed the world that artificial neural networks were indeed something special.
This sent an earthquake through the science community a wave of neural net innovations began and soon the world took notice after this point everyone began using neural networks in the image benchmark challenge and the accuracy of identifying objects improved from 75% to 97% in just seven years. For now 97% accuracy is surpassing the human ability to recognize objects. Computers recognizing objects better than humans has never happened in history, Soon the floodgates of research and the general interest in neural nets would change the world.
Machines are now almost as good as humans at object recognition, and the turning point occurred in 2012 — says Computer Scientists.
Today we see AI everywhere, Tesla among many companies has created a sophisticated self-driving AI which is already sharing the road with humans. It is predicted their self-driving cars will reduce accidents by up to 90% while smart traffic lights would reduce travel time by 26%. Even Netflix and YouTube uses AI to learn what shows you watch and recommend new ones. Uber uses machine learning AI to determine surge pricing, your rides estimated time of arrival and how to optimize the services to avoid detours.
AI is everywhere it’s in our daily lives even if we’re not aware of it of course there’s many examples of AI being used but perhaps the most interesting uses will come after we reach singularity. It is the concept of AI surpassing human intelligence. After this point what happens is a bit of an open-ended question. In this state computers would be able to reinvent better versions of themselves, they could progress in fields such as medicine and science without human intervention. AlphaGo Zero is a graphic illustration of the possible rate of this progress. In 2016 experts thought that it would take an AI around 12 years to beat a human at the ancient game GO, a game with virtually infinite possibilities and a game that relies on human intuition to master. But the experts were very wrong that the 12-year prediction in reality was actually zero. AI did in fact beat the grandmaster of go in that very same 2016 year, the next version of the AlphaGo Zero learned to play the game from scratch and beat the previous version with a hundred games to zero in just three days.
It was so good that it was able to be applied to other things that it wasn’t trained for like lowering the power usage on Google’s data centers, the new breeds of AI could even begin to invent new tools that humans would never be able to. University of Alberta says that singularity is widely estimated to happen around 2040. By 2030 we should have the hardware capability to achieve this allowing for another decade for people to make the code that achieves singularity.
Here is a small conversation from Geoffrey Hinton’s Interview:
Host : How many years away do you think we are from a neural network being able to do anything that a brain can do?
Geoffrey : I don’t think it’ll happen in the next five years beyond that it’s all a kind of fog so I’d be very cautious about making a prediction.
Host : Is there anything about this that makes you nervous in the very long run?
Geoffrey : Yes, I mean obviously having other super intelligent beings or more intelligent than us is something to be nervous about it’s not gonna happen for a long time but it is something to be nervous about in the long run. Also the movies always portray it as an individual intelligence. I think it may be that it goes in a different direction where we sort of developed jointly with these things so the things aren’t fully autonomous they’re developed to help us, they’re like personal assistance and we’ll develop with them and it’ll be more of a symbiosis than a rivalry so seen the future.
Without the work of these pioneers who refuse to give up our future may be very different May be we don’t fully understand the potential of AI but nonetheless it should be obvious that their work has created a significant point in human history much like the invention of Fire, Wheel, Electricity Computers and the Internet artificial intelligence will be one of humanity’s greatest tools.
I hope I’ve given you a brief idea about the inception of AI. Give it a 💚, if you like this post for extra motivation. I am always open to your suggestions and queries .