"Deep neural networks will be in the 21st century what electricity was in the 20th century"

Says Dr. Eli David from Bar Ilan University and a serial entrepreneur in the field of artificial intelligence. 

machine learning. Illustration: shutterstock
machine learning. Illustration: shutterstock

"The deep neural networks will be in the 21st century what electricity was in the 20th century." Says Dr. Eli David from Bar Ilan University and a serial entrepreneur of start-up companies in the field of artificial intelligence. Dr. David will be one of the lecturers at the Silicon Club meeting that will be held on 18/9 in the Nofim Hall at the Eretz Israel Museum in Ramat Aviv, starting at 18:30 p.m.

Dr. Eli David is a leading expert in the field of artificial intelligence specializing in deep learning (neural networks) and evolutionary computation. He has published over 40 articles in leading magazines in the field of artificial intelligence and in conferences, and focuses mainly on applications of deep learning and genetic algorithms in various fields in the real world. He is the initiator of Falcon, an advanced chess game software based on genetic algorithms and learning Deep. The program came in second place in the world speed chess championship. Dr. David is the founder of Dist Instinct, which applies deep learning to the cyber field, and is an AI consultant to large companies, venture capital funds, and more.

"In the last two or three years, we have seen the rapid development of the field of deep artificial neural networks - or deep learning. Until a few years ago, this field was considered completely negligible and many of the experts looked at it as far-fetched. The field of deep learning managed overnight to reach a state where it affects all fields: computer vision, Word processing, cyber security, games and more, and gave a knock out to all other methods of artificial intelligence."

"Deep learning is itself a subfield within machine learning. There are many methods to teach the machine. One of them is artificial neural networks, also known as deep learning. The significant difference is that in all the classical methods of machine learning we require two steps. Let's take the face recognition problem for example. In all the classical methods we cannot take the raw data in the image and insert it directly into a machine learning module, whether it is pixels in the image, letters in the text, and more, but we were required to perform A stage called feature extraction in which we use a human expert who builds us a set of features that can help recognize a face - the distance between the pupils, or the distance between the nose and the mouth, and so on - dozens of such features."

"In the second step, these features are entered into a deep learning module, which compares the dozens of features and thus it can recognize the image. The feature extraction stage is a bad stage. By taking the rich raw data - an image consisting of millions of pixels and turning it into a list of several Dozens or hundreds of features, however good they may be, we still lost most of the raw data."

Dr. Eli David, Bar Ilan University. Photo courtesy of him.
Dr. Eli David, Bar Ilan University. Photo courtesy of him.

The big problem has more to do with us as humans. Even the experts have a hard time looking at complicated problems and translating them into an orderly list of features. example. Each of us, if we see a picture of a dog or a cat, will tell within milliseconds whether it is a dog or a cat one hundred percent. Try to explain what exactly is the difference between a dog and a cat? I do this in an exercise with my students at the beginning of each course.

"The roots of the field of deep neural networks have their roots in the 4s and 2s. Even then there were neural networks when scientists tried to imitate the brain but achieved few and unimpressive successes. The improvement of the algorithm and the improvement of the hardware, mainly thanks to the Nvidia graphics processors (GPU) we were able to get from flat networks of 20 -XNUMX layers to turn them into deep networks of XNUMX and thirty layers, and instead of having only a few thousand Synapses (a synapse connects two neurons, just like in the brain), there are billions of synapses in the large neuronal networks today.
This is what allowed us to skip the feature extraction step for the first time. If we take facial recognition as an example, we put the pixels into a deep neural network without adding any human knowledge. The deep web learns by itself. In all areas where deep neural networks were used the improvement was unimaginable. In some cases, such as computer vision, we have even surpassed the capabilities of humans.
In the coming years I foresee a continuation of the accelerated improvement we have seen in recent years. We see no sign of slowing down in the pace of improvement in this area. Every day we see dramatic results and in many areas are approaching or even surpassing the capabilities of humans. My expectation for the coming decades is that we will see the continued exponential improvement in results and I am guessing that in most areas the artificial modules will be no less good than humans."

Will computers surpass humans in everything?
This comparison is currently out of place because the largest deep learning module we can train on the most powerful computer, which simulates billions of synapses, is 100 times smaller than the computational capacity of the brain. For comparison, just in the area of ​​the outer cortex of the brain, which is the area of ​​interest from the cognitive aspect, there are 150 trillion synapses. On the other hand, today's computers are already able to run neural networks a million times stronger than 30 years ago. If in the next thirty years we see a million-fold improvement and we overcome the hardware problem, we will have artificial networks that will surpass biological networks, and it will be possible to say that computers will be smarter than humans. There will even be a stage where the cognitive gap between computers and humans will be like the gap between humans and chimpanzees. What will happen then? This is a subject in which many and disturbing discussions are held. At one extreme there is the fear that the computers will take over us and even destroy us, expressed by people like Bill Gates, Elon Musk and Stephen Hawking, peace be upon him. At the other extreme, there are those who claim that it doesn't matter how smart the computers are, they will always serve us."

Will computers replace humans in specific tasks?

"In the coming years, we will see a wave of replacing humans with machines. In ten or at most twenty years, a profession like a taxi driver will be irrelevant and will no longer exist. The profession of a radiologist who interprets X-rays, MRIs, etc., will not exist in the way we know it. Already today Machine learning outperforms humans in many aspects of interpreting radiological images."
However, Dr. David is tenfold optimistic about the job opportunities that artificial intelligence will create. "At the beginning of the twentieth century, there were many workers in every city in the Western world, whose job it was to turn on the street lights one by one every evening. Once electricity came in there was a wave of unemployment, all these were unnecessary. But let's look at the huge amount of jobs created thanks to electricity. I strongly believe in this, that artificial intelligence will do for the 21st century what electricity did for the 20th century. Against the wave of unemployment there will be a wave of professions where a person will still have an advantage. Total replacement is not yet imminent."

Will we see an interface between artificial intelligence and the human mind?

"The rate of development of artificial neural networks is very dizzying and the developments accordingly. On the other hand, the rate of improvement in our understanding of the human brain is still progressing at an extremely slow pace. We have not seen a significant and major breakthrough that helped to better understand our brain. Still the most advanced and expensive machines in the world are very far away From getting to the point of measuring the action of individual neurons and synapses in the brain during the thinking process. I think, again this is my personal opinion that much more We will quickly reach a situation where we have an artificial intelligence as good as us and superior to us than a situation where we can connect to our brains, put chips in it and expand the memory."

to the Silicon Club site

More of the topic in Hayadan: (Beresheet is the Hebrew name for the book of Genesis)

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