A short biography of one of the best known engineers of machine learning & AI: Ilya Sutskever

Recommendable!

(831) The Greatest AI Engineer of All Time - YouTube

'Godfather of AI' Geoffrey Hinton warns AI has 'progressed even faster than I thought'

Recommendable! Geoffrey Hinton is only one of three Godfathers of AI.

Er zweifelte lebenslang an seiner Berufswahl, verlor zwei Ehefrauen an Krebs, kaufte sich eine Insel: Geoffrey Hintons Weg zum Nobelpreis

Zum schicksalhaften Leben eines Nobelpreisträgers! Habe mehrfach über Hinton gebloggt.

Sein beruflicher Werdegang erinnert mich etwas an mich selbst. Ich habe erst im hohen alter angefangen machine learning & AI zu studieren als Autodidakt!

Hinweis: Habe den Artikel nicht gelesen.

Pate der künstlichen Intelligenz: Geoffrey Hintons Weg zum Nobelpreis "Als Student war Geoffrey Hinton ziemlich verloren. Er probierte Biologie aus, Kunstgeschichte, Philosophie, bis er schliesslich etwas fand, was ihn mehr als alles andere faszinierte: die mathematischen Grundlagen des Denkens."

Physics Nobel goes to machine-learning pioneers John Hopfield and Geoffrey Hinton

Good news! Well deserved! Machine learning & AI has finally arrived as a serious science!

I am surprised that the other two of the three godfathers  of ML & AI, i.e. Yann LeCun and Yoshua Bengio, were not recognized for their research.

I have blogged several times about Geoffrey Hinton. Some of his political views are naive at best.

"... In 1982, Hopfield, a theoretical biologist with a background in physics, came up with a network that described connections between virtual neurons as physical forces. By storing patterns as a low-energy state of the network, the system could re-create the pattern when prompted with something similar. It became known as associative memory, because the way in which it ‘recalls’ things is similar to the brain trying to remember a word or concept based on related information.

Hinton, a computer scientist, used principles from statistical physics, which collectively describes systems that have too many parts to track individually, to further develop the ‘Hopfield network’. By building probabilities into a layered version of the network, he created a tool that could recognize and classify images, or generate new examples of the type it was trained on. ..."

Physics Nobel scooped by machine-learning pioneers John Hopfield and Geoffrey Hinton pioneered computational methods that enabled the development of neural networks.

One of the three godfathers of AI Geoffrey Hinton (age 76) says universal basic income will be needed

What a naive fool! No clue about economics or history!

Did he consult with GPT-4 (aka ChatGPT) first? He should have! 😊

How many times has it been said before about previous technological revolutions they would eliminate so many jobs! Every time new jobs were created!

Universal basic income is a leftist utopian fantasy! Who is going to pay for that? How much income for everyone?

"The computer scientist regarded as the “godfather of artificial intelligence” says the government will have to establish a universal basic income to deal with the impact of AI on inequality.

Professor Geoffrey Hinton told BBC Newsnight that a benefits reform giving fixed amounts of cash to every citizen would be needed because he was “very worried about AI taking lots of mundane jobs”. ..."

AI 'godfather' says universal basic income will be needed

'Godfather of AI' Geoffrey Hinton (age 76) speaks on threat of tech surpassing humanity. Really!

Sometimes old men worry too much! Pardon my sarcasm!

Or did Nikkei misrepresent his opinion? The article is behind paywall.

So what! With this kind of thinking we would be still living in the Stone Ages! I have full confidence that younger generations will learn how to deal with AI! The potential of benefits far outweigh the risks!

'Godfather of AI' speaks on threat of tech surpassing humanity - Nikkei Asia Geoffrey Hinton believes AI is already having experiences akin to humans'

How existential risk became the biggest meme in AI thanks to two Turing Award winners

Very recommendable! The alarmism and hysteria recently raised by two leading AI researchers, i.e. Geoffrey Hinton and Yoshua Bengio, was irresponsible!

"Yann LeCun, a Turing Award winner, ... why he thinks the idea that a superintelligent AI system will take over the world is “preposterously ridiculous.” 

People are worried about AI systems that “are going to be able to recruit all the resources in the world to transform the universe into paper clips,” LeCun said. “That’s just insane.” (He was referring to the “paper clip maximizer problem,” a thought experiment in which an AI asked to make as many paper clips as possible does so in ways that ultimately harms humans, while still fulfilling its main objective.) 

He is in stark opposition to Geoffrey Hinton and Yoshua Bengio, two pioneering AI researchers (and the two other “godfathers of AI”), who shared the Turing prize with LeCun. Both have recently become outspoken about  existential AI risk.

Joelle Pineau, Meta’s vice president of AI research, agrees with LeCun. She calls the conversation ”unhinged.” The extreme focus on future risks does not leave much bandwidth to talk about current AI harms, she says. ...  [The existential-risk crowd] have essentially put an infinite cost on that outcome,” says Pineau. 

“When you put an infinite cost, you can’t have any rational discussions about any other outcomes. And that takes the oxygen out of the room for any other discussion, which I think is too bad.”

While talking about existential risk is a signal that tech people are aware of AI risks, tech doomers have a bigger ulterior motive, LeCun and Pineau say: influencing the laws that govern tech. ..."

How existential risk became the biggest meme in AI

One of the so called three godfathers of AI Geoffrey Hinton warns of dangers as he quits Google

The 75 year old professor from Toronto got a lot of publicity for his resignation!

It appears, he did not release an official statement about his resignation and his motives (I actually inquired him by email, he confirmed)! Quite regrettable if correct! Or is this his official statement as posted on his Twitter account?

I also suspect, Professor Hinton was perhaps not happy that Google decided to respond to the increasing competitive pressure by more aggressive efforts to commercialize AI research. E.g. the CEO of Google has recently consolidated and streamlined multiple, separate AI research units at Google. Until now, the often exceptional AI research originated at Google was more akin to an academic than commercial endeavor!

The rapid development of machine learning & AI is certainly very challenging going forward. However, if history is any guide, humans were quite capable of dealing with previous very momentous technological revolutions that were also perceived as great risks to society by the people at the time and we mastered them!

P.S. In case you wondered who the other two godfathers of AI are: Yann LeCun (Meta/Facebook) and Yoshua Bengio (University of Montreal). All three received the very prestigious ACM A.M. Turing Award in 2018. 

AI 'godfather' Geoffrey Hinton warns of dangers as he quits Google - BBC News

"Godfather of artificial intelligence" Geoffrey Hinton talks impact and potential of new AI

Geoffrey Hinton says the AI & machine learning revolution is comparable to the Industrial revolution, the invention of electricity or even the wheel! Until the wheels come off! 😊 He also expects AI to make accelerated and self improving progress in the next 5 years or so. Get ready!

Here is a link to the full interview with Hinton.

Google AI Blog: Advancing Self-Supervised and Semi-Supervised Learning with SimCLR

Very recommendable! Cutting edge computer vision by Geoffrey Hinton and collaborators at Google! 

This research work contains a number of interesting results e.g.:

1. They analyzed typical image pre-processing/augmentation techniques. "We found that while no single transformation (that we studied) suffices to define a prediction task that yields the best representations, two transformations stand out: random cropping and random color distortion. Although neither cropping nor color distortion leads to high performance on its own, composing these two transformations leads to state-of-the-art results." This helps to prevent or reduce spurious feature learning like color histogram similarity
2. "Scaling up significantly improves performance. We found that (1) processing more examples in the same batch, (2) using bigger networks, and (3) training for longer all lead to significant improvements. While these may seem like somewhat obvious observations, these improvements seem larger for SimCLR than for supervised learning. For example, we observe that the performance of a supervised ResNet peaked between 90 and 300 training epochs (on ImageNet), but SimCLR can continue its improvement even after 800 epochs of training." You have to remember that early stopping of training is a very common method, but it is doubtful that this is really useful!

Google AI Blog: Advancing Self-Supervised and Semi-Supervised Learning with SimCLR: Posted by Ting Chen, Research Scientist, and Geoffrey Hinton, VP & Engineering Fellow, Google Research Recently, natural language proces...