AI Godfather says New Tech Could be Dangerous

 Well known AI Scientist Geoffrey Hinton,  often mentioned here,  who we followed closely n the 80s, says AI is real, and conceivably threatening humanity.    Equivalent to Invention of Wheel or electricity.  Scary statements from him. 

Artificial intelligence 'godfather' on AI possibly wiping out humanity: ‘It's not inconceivable’

Artificial Intelligence pioneer Geoffrey Hinton said the development of artificial intelligence is happening rapidly

Andrea VacchianoBy Andrea Vacchiano | Fox News

Geoffrey Hinton, a computer scientist who has been called "the godfather of artificial intelligence", says it is "not inconceivable" that AI may develop to the point where it poses a threat to humanity.

The computer scientist sat down with CBS News this week about his predictions for the advancement of AI. He compared the invention of AI to electricity or the wheel.

Hinton, who works at Google and the University of Toronto, said that the development of general purpose AI is progressing sooner than people may imagine. General purpose AI is artificial intelligence with several intended and unintended purposes, including speech recognition, answering questions and translation.

"Until quite recently, I thought it was going to be like 20 to 50 years before we have general purpose AI. And now I think it may be 20 years or less," Hinton predicted. Asked specifically the chances of AI "wiping out humanity," Hinton said, "I think it's not inconceivable. That's all I'll say." 

Geoffrey Hinton, chief scientific adviser at the Vector Institute, speaks during The International Economic Forum of the Americas (IEFA) Toronto Global Forum in Toronto, Ontario, Canada, on Thursday, Sept. 5, 2019. 

Geoffrey Hinton, chief scientific adviser at the Vector Institute, speaks during The International Economic Forum of the Americas (IEFA) Toronto Global Forum in Toronto .. 

Artificial general intelligence refers to the potential ability for an intelligence agent to learn any mental task that a human can do. It has not been developed yet, and computer scientists are still figuring out if it is possible.  Hinton said it was plausible for computers to eventually gain the ability to create ideas to improve themselves. 

"That's an issue, right. We have to think hard about how you control that," Hinton said.   ... ' 

We Will See a Completely New Type of Computer, Says AI Pioneer Hinton

 Hinton predicts: A 'mortal' Neuromorphic Computer?  Ready to sign up and test.   When? 

We Will See a Completely New Type of Computer, Says AI Pioneer Hinton

In ZDNet, Tiernan Ray, December 1, 2022

Artificial intelligence pioneer and 2018 ACM A.M. Turing award recipient Geoffrey Hinton envisions a "mortal" neuromorphic computer combining hardware and software. Speaking at the Neural Information Processing Systems conference, Hinton said mortal computation means "the knowledge that the system has learned and the hardware, are inseparable." Hinton said such computers could be grown, forgoing costly chip fabrication, and he imagines they will be "used for putting something like GPT-3 in your toaster for $1, so running on a few watts, you can have a conversation with your toaster." He suggested a forward-forward neural network model, eliminating the backpropagation common to most neural networks, might suit mortal computation hardware.

Article

Seeing the way we do

New ways of perceptive seeing, now with Texture and Shape

GLOM: Teaching Computers to See the Way(s) We Do  By John Delaney,  Commissioned by CACM Staff   September 14, 2021

At the virtual Collision technology  earlier this year deep learning pioneer Geoffrey Hinton explained how he conceived of a new type of neural network that, he said, would be able to perceive things the way people do.

Hinton, an emeritus distinguished professor in the department of computer science of the Faculty of Arts & Science at Canada's University of Toronto, and also an Engineering Fellow at Google, is responsible for some of the biggest breakthroughs in deep learning and neural networks. He was honored as co-recipient of the 2018 ACM A.M. Turing Award, along with Yoshua Bengio and Yann LeCun, for conceptual and engineering breakthroughs that have made deep neural networks a critical component of modern computing.

In Hinton's Collision talk, he pointed out that the representations used by most neural networks performing object classification are produced by convolutional neural networks, which work well at classifying objects such as images or words, even winning competitions such as the ImageNet Large Scale Visual Recognition Challenge, but they perceive images in a very different way than people do, which can sometimes lead to "crazy errors."

"They use lots of texture information, which people are insensitive to," Hinton said, "but they fail to use a lot of shape information, which people are very sensitive to."  .... '

Turing Lecture on Deep Learning

 Quite good, relatively non technical.   Worth a look.

Turing Lecture

Deep Learning for AI

By Yoshua Bengio, Yann Lecun, Geoffrey Hinton    from ACM

Communications of the ACM, July 2021, Vol. 64 No. 7, Pages 58-65   10.1145/3448250

Yoshua Bengio, Yann LeCun, and Geoffrey Hinton are recipients of the 2018 ACM A.M. Turing Award for breakthroughs that have made deep neural networks a critical component of computing.

Research on artificial neural networks was motivated by the observation that human intelligence emerges from highly parallel networks of relatively simple, non-linear neurons that learn by adjusting the strengths of their connections. This observation leads to a central computational question: How is it possible for networks of this general kind to learn the complicated internal representations that are required for difficult tasks such as recognizing objects or understanding language? Deep learning seeks to answer this question by using many layers of activity vectors as representations and learning the connection strengths that give rise to these vectors by following the stochastic gradient of an objective function that measures how well the network is performing. It is very surprising that such a conceptually simple approach has proved to be so effective when applied to large training sets using huge amounts of computation and it appears that a key ingredient is depth: shallow networks simply do not work as well.

 We reviewed the basic concepts and some of the breakthrough achievements of deep learning several years ago.  Here we briefly describe the origins of deep learning, describe a few of the more recent advances, and discuss some of the future challenges. These challenges include learning with little or no external supervision, coping with test examples that come from a different distribution than the training examples, and using the deep learning approach for tasks that humans solve by using a deliberate sequence of steps which we attend to consciously—tasks that Kahneman56 calls system 2 tasks as opposed to system 1 tasks like object recognition or immediate natural language understanding, which generally feel effortless.   ... " 

Deep Learning in AI Lecture

 Of considerable interest, pointers to the future?  

"Deep Learning in AI," the Turing Lecture of Yoshua Bengio, Yann LeCun, and Geoffrey Hinton, describes the origins and recent advances of deep learning, and its future challenges. The 2018 ACM A.M. Turing Award recipients discuss efforts to bridge the gap between machine learning and human intelligence in an original video at https://bit.ly/35Dvwfs.    in CACM.       Reading ...

Whats Next in AI? From Geoffrey Hinton

This sounded interesting, have always read Hinton's pieces.  About to read this. Will give some thoughts about it later, especially how it might change practical use of AI.   Note the article is 44 pages long!  

Geoffrey Hinton Has a Hunch about What's Next for AI   By MIT Technology Review,  April 16, 2021 from CACM.

Back in November, the computer scientist and cognitive psychologist Geoffrey Hinton had a hunch. After a half-century's worth of attempts—some wildly successful—he'd arrived at another promising insight into how the brain works and how to replicate its circuitry in a computer.

"It's my current best bet about how things fit together," Hinton says from his home office in Toronto, where he's been sequestered during the pandemic. If his bet pays off, it might spark the next generation of artificial neural networks—mathematical computing systems, loosely inspired by the brain's neurons and synapses, that are at the core of today's artificial intelligence. His "honest motivation," as he puts it, is curiosity. But the practical motivation—and, ideally, the consequence—is more reliable and more trustworthy AI.

A Google engineering fellow and cofounder of the Vector Institute for Artificial Intelligence, Hinton wrote up his hunch in fits and starts, and at the end of February announced via Twitter that he'd posted a 44-page paper on the arXiv preprint server.   ( See the PDF at the link)   He began with a disclaimer: "This paper does not describe a working system," he wrote. Rather, it presents an "imaginary system." He named it, "GLOM." The term derives from "agglomerate" and the expression "glom together."

From MIT Technology Review

AI and Tacit Knowledge

Useful, mostly non-technical paper on the topic.  We learned much about this when we trained with explicit rules.  How do we effectively leverage the statement:  'we know more than we can tell"?

Polanyi's Revenge and AI's New Romance with Tacit Knowledge  By Subbarao Kambhampati    Communications of the ACM, February 2021, Vol. 64 No. 2, Pages 31-32  10.1145/3446369

In his 2019 Turing Award Lecture, Geoff Hinton talks about two approaches to make computers intelligent. One he dubs—tongue firmly in cheek—"Intelligent Design" (or giving task-specific knowledge to the computers) and the other, his favored one, "Learning" where we only provide examples to the computers and let them learn. Hinton's not-so-subtle message is that the "deep learning revolution" shows the only true way is the second.

Hinton is of course reinforcing the AI Zeitgeist, if only in a doctrinal form. Artificial intelligence technology has captured popular imagination of late, thanks in large part to the impressive feats in perceptual intelligence—including learning to recognize images, voice, and rudimentary language—and bringing fruits of those advances to everyone via their smartphones and personal digital accessories. Most of these advances did indeed come from "learning" approaches, but it is important to understand the advances have come in spheres of knowledge that are "tacit"—although we can recognize faces and objects, we have no way of articulating this knowledge explicitly. The "intelligent design" approach fails for these tasks because we really do not have conscious theories for such tacit knowledge tasks. But, what of tasks and domains—especially those we designed—for which we do have explicit knowledge? Is it forbidden to give that knowledge to AI systems?

(Robot Image)  "Human, grant me the serenity to accept the things I cannot learn, data to learn the things I can, and wisdom to know the difference."

The polymath Polanyi bemoaned the paradoxical fact that human civilization focuses on acquiring and codifying "explicit" knowledge, even though a significant part of human knowledge is "tacit" and cannot be exchanged through explicit verbal instructions. His "we can know more than we can tell" dictum has often been seen as a pithy summary of the main stumbling block for early AI efforts especially in perception.

Polanyi's paradox explains to a certain extent why AI systems wound up developing in a direction that is almost the reverse of the way human babies do. Babies demonstrate aspects of perceptual intelligence (recognizing faces, voices and words), physical manipulation (of putting everything into their mouths), emotional intelligence, and social intelligence, long before they show signs of expertise in cognitive tasks requiring reasoning skills. In contrast, AI systems have demonstrated reasoning abilities—be they expert systems or chess—long before they were able to show any competence in the other tacit facets of intelligence including perception.

In a sense, AI went from getting computers to do tasks for which we (humans) have explicit knowledge, to getting computers to learn to do tasks for which we only have tacit knowledge. The recent revolution in perceptual intelligence happened only after labeled data (such as cats, faces, voices, text corpora, and so forth) became plentiful, thanks to the Internet and the World Wide Web, allowing machines to look for patterns when humans are not quite able to give them explicit know-how. .... '

Deep Learning will do Everything?

But there is still much to learn, to apply,  to understand.  The history is examined, the future is considered. 

AI godfather Geoff Hinton: “Deep learning is going to be able to do everything”

Nearly 30 years ago, Hinton’s belief in neural networks was contrarian. Now it’s hard to find anyone who disagrees, he says.  by Karen Haoarchive  in Technology Review

Geoffrey Hinton (talks AI:

On the AI field’s gaps: "There’s going to have to be quite a few conceptual breakthroughs...we also need a massive increase in scale."

On neural networks’ weaknesses: "Neural nets are surprisingly good at dealing with a rather small amount of data, with a huge numbers of parameters, but people are even better."

On how our brains work: "What’s inside the brain is these big vectors of neural activity."

The modern AI revolution began during an obscure research contest. It was 2012, the third year of the annual ImageNet competition, which challenged teams to build computer vision systems that would recognize 1,000 objects, from animals to landscapes to people.

In the first two years, the best teams had failed to reach even 75% accuracy. But in the third, a band of three researchers—a professor and his students—suddenly blew past this ceiling. They won the competition by a staggering 10.8 percentage points. That professor was Geoffrey Hinton, and the technique they used was called deep learning.

Hinton had actually been working with deep learning since the 1980s, but its effectiveness had been limited by a lack of data and computational power. His steadfast belief in the technique ultimately paid massive dividends. The fourth year of the ImageNet competition, nearly every team was using deep learning and achieving miraculous accuracy gains. Soon enough deep learning was being applied to tasks beyond image recognition, and within a broad range of industries as well.

Last year, for his foundational contributions to the field, Hinton was awarded the Turing Award, alongside other AI pioneers Yann LeCun and Yoshua Bengio. On October 20, I spoke with him at MIT Technology Review’s annual EmTech MIT conference about the state of the field and where he thinks it should be headed  .... " 

Interview with Turing Award recipients on Neural Nets

We read some of the earliest work of Hinton and were inspired by the direction.   Also a lesson about hype and the lessons from the fringe of typical research. But this is still not complete enough to create strong AI.    Lots more to do.  So this interview is interesting, just not long or detailed enough.

Reaching New Heights with Artificial Neural Networks
By Leah Hoffmann 

Communications of the ACM, June 2019, Vol. 62 No. 6, Pages 96-ff
10.1145/3324011

2018 Turing Award recipients Yoshua Bengio, Geoffrey Hinton, and Yann LeCun

Once treated by the field with skepticism (if not outright derision), the artificial neural networks that 2018 ACM A.M. Turing Award recipients Geoffrey Hinton, Yann LeCun, and Yoshua Bengio spent their careers developing are today an integral component of everything from search to content filtering. So what of the now-red-hot field of deep learning and artificial intelligence (AI)? Here, the three researchers share what they find exciting, and which challenges remain.

There's so much more noise now about artificial intelligence than there was when you began your careers—some of it well-informed, some not. What do you wish people would stop asking you?

GEOFFREY HINTON: "Is this just a bubble?" In the old days, people in AI made grand claims, and they sometimes turned out to be just a bubble. But neural nets go way beyond promises. The technology actually works. Furthermore, it scales. It automatically gets better when you give it more data and a faster computer, without anybody having to write more lines of code.  ... " 

Conversation on Explainable AI

Ajit Joakar makes some good points...  in DSC.  Yes, explain-ability is often useful, but depending on context is not always a requirement.  One way its useful is it helps you build yet further intelligence.

Why I agree with Geoff Hinton: I believe that Explainable AI is over-hyped by media  Posted by ajit jaokar

Geoffrey Hinton dismissed the need for explainable AI. A range of experts have explained why he is wrong.

I actually tend to agree with Geoff.

Explainable AI is overrated and hyped by the media.
And I am glad someone of his stature is calling it out

To clarify, I am not saying that interpretability, transparency, and explainability are not important (and nor is Geoff Hinton for that matter)  .... " 

Hinton and Capsule Networks

We followed neural networks from their earliest days, but where they failed they did not have the data to consistently work under changing context, and at high enough speed, say for analyzing real-time video.  Geoffrey Hinton's work brought a new approach forward.  Now he addresses some of the lingering problems with these methods:  speed, error rates and the need for huge amounts of data. Tech papers pointed to in the link below.

Google's AI Wizard Takes a New Twist on Neural Networks in Wired, by Tom Simonite.

" ... But Hinton now belittles the technology he helped bring to the world. “I think the way we’re doing computer vision is just wrong,” he says. “It works better than anything else at present but that doesn’t mean it’s right.”

In its place, Hinton has unveiled another “old” idea that might transform how computers see—and reshape AI. That’s important because computer vision is crucial to ideas such as self-driving cars, and having software that plays doctor.

Late last week, Hinton released two research papers that he says prove out an idea he’s been mulling for almost 40 years. “It’s made a lot of intuitive sense to me for a very long time, it just hasn’t worked well,” Hinton says. “We’ve finally got something that works well.”

Hinton’s new approach, known as capsule networks, is a twist on neural networks intended to make machines better able to understand the world through images or video. In one of the papers posted last week, Hinton’s capsule networks matched the accuracy of the best previous techniques on a standard test of how well software can learn to recognize handwritten digits.

In the second, capsule networks almost halved the best previous error rate on a test that challenges software to recognize toys such as trucks and cars from different angles. Hinton has been working on his new technique with colleagues Sara Sabour and Nicholas Frosst at Google’s Toronto office.

Capsule networks aim to remedy a weakness of today’s machine-learning systems that limits their effectiveness. Image-recognition software in use today by Google and others needs a large number of example photos to learn to reliably recognize objects in all kinds of situations. That’s because the software isn’t very good at generalizing what it learns to new scenarios, for example understanding that an object is the same when seen from a new viewpoint.   ...." 

AI is Riding a One Trick Pony

In technology review.  See also the comments, which include quite a few people, like myself, who participated in both AI 'revolutions'.    What we know now is that this latest one works very well for certain very useful contexts.  So at one level, who cares its not universal intelligence?  But we need to be careful about future forecasts of its use.

 Intelligent Machines

Is AI Riding a One-Trick Pony?   In Technology Review. by James Somers.

Just about every AI advance you’ve heard of depends on a breakthrough that’s three decades old. Keeping up the pace of progress will require confronting AI’s serious limitations.  .... 

I’m standing in what is soon to be the center of the world, or is perhaps just a very large room on the seventh floor of a gleaming tower in downtown Toronto. Showing me around is Jordan Jacobs, who cofounded this place: the nascent Vector Institute, which opens its doors this fall and which is aiming to become the global epicenter of artificial intelligence.

We’re in Toronto because Geoffrey Hinton is in Toronto, and Geoffrey Hinton is the father of “deep learning,” the technique behind the current excitement about AI. “In 30 years we’re going to look back and say Geoff is Einstein—of AI, deep learning, the thing that we’re calling AI,” Jacobs says. Of the AI researchers at the top of the field, Hinton has more citations than the next three combined. His students and postdocs have gone on to run the AI labs at Apple, Facebook, and OpenAI; Hinton himself is a lead scientist on the Google Brain AI team. In fact, nearly every achievement in the last decade of AI—in translation, speech recognition, image recognition, and game playing—traces in some way back to Hinton’s work.

The Vector Institute, this monument to the ascent of ­Hinton’s ideas, is a research center where companies from around the U.S. and Canada—like Google, and Uber, and Nvidia—will sponsor efforts to commercialize AI technologies. Money has poured in faster than Jacobs could ask for it; two of his cofounders surveyed companies in the Toronto area, and the demand for AI experts ended up being 10 times what Canada produces every year. Vector is in a sense ground zero for the now-worldwide attempt to mobilize around deep learning: to cash in on the technique, to teach it, to refine and apply it. Data centers are being built, towers are being filled with startups, a whole generation of students is going into the field.

The impression you get standing on the Vector floor, bare and echoey and about to be filled, is that you’re at the beginning of something. But the peculiar thing about deep learning is just how old its key ideas are. Hinton’s breakthrough paper, with colleagues David Rumelhart and Ronald Williams, was published in 1986. The paper elaborated on a technique called backpropagation, or backprop for short. Backprop, in the words of Jon Cohen, a computational psychologist at Princeton, is “what all of deep learning is based on—literally everything.”

When you boil it down, AI today is deep learning, and deep learning is backprop—which is amazing, considering that backprop is more than 30 years old. It’s worth understanding how that happened—how a technique could lie in wait for so long and then cause such an explosion—because once you understand the story of backprop, you’ll start to understand the current moment in AI, and in particular the fact that maybe we’re not actually at the beginning of a revolution. Maybe we’re at the end of one. .... " 

Geoffrey Hinton on Deep Learning

Thoughtfully done short, non technical interview on the topic.  with Google's AI and Deep Learning 'Godfather' Geoffrey Hinton.    We worked with neural nets in the 80s to learn rudimentary tasks, mostly with supervised learning examples.  These new methods do unsupervised learning that looks for patterns in complex and voluminous data.   Increasingly useful for things like image understanding and natural language translation.    Following, a more technical talk by Hinton on the progress of Neural Nets.  Which talks some of the details of improving learning in networks.

Thought Vectors for Common Sense

Note my recent post on solving the common sense problem,  and Hinton's work on deep learning.  quite an interesting direction, which if solved,  makes a big step towards intelligence.   Implicatons for capturing emotions and related human patterns.  Following.

Google Is Working On A New Type Of Algorithm Called “Thought Vectors”

Professor Geoff Hinton, who was hired by Google two years ago to develop intelligent operating systems, said that the company is on the brink of developing algorithms with the capacity for logic, natural conversation and even flirtation.

The researcher told the Guardian that Google is working on a new type of algorithm designed to encode thoughts as sequences of numbers – something he described as “thought vectors”.

Although the work is at an early stage, he said there is a plausible path from the current software to a more sophisticated version that would have something approaching human-like capacity for reasoning and logic. “Basically, they’ll have common sense.”   .... ' 

More in Extreme Tech.

Learning Machines

Current and future state of machine learning.  Technical, Historical.  " ... We hear the second part of our conversation with with Geoffrey Hinton (Google and University of Toronto), Yoshua Bengio (University of Montreal) and Yann LeCun (Facebook and NYU). They talk with us about this history (and future) of research on neural nets. We explore how to use Determinantal Point Processes. Alex Kulesza  and Ben Taskar (who passed away recently) have done some really exciting work in this area, for more on DPPs check out their paper on the topic.  Also, we take a listener question about machine learning and function approximation (spoiler alert: it is, and then again, it isn’t). .... " 

Deep Learning with Structure and Interpretation

A dozen years ago we were using neural nets to capture aspects of consumer behavior and interpret the results to apply to marketing decisions.  Not what is today called 'Deep Learning', but uses the same some of the same math tools. One of the primary problems was that it was hard to interpret the results to determine their validity.  The problem has not gone away with new applications, but is now being addressed:
 
In KDNuggets: 
A big problem with Deep Learning networks is that their internal representation lacks interpretability. At the upcoming #DeepLearning Summit, Charlie Tang, a student of Geoff Hinton, will present an approach to address this concern - here is a preview ... " 

Google and Neural Nets

A look at what Geoffrey Hinton has been doing at Google with artificial neural nets.  We examined his work in the 80s.  Used it to mimic some things that had been done with statistics before.  Now it has taken off to enhance human efforts in new ways.   Will search evolve into your brain in this way?

Google Hires more Artificial Intelligence

In Wired:  More indication that Google is very interested in the topic.  Hiring Geoff Hinton.  He was a person we watched closely during our long exploration of the use of neural nets for learning.  Was unaware of some of the background provided here.  How will we achieve deep learning? The intent:  " ... Where will this next generation of researchers take the deep learning movement? The big potential lies in deciphering the words we post to the web — the status updates and the tweets and instant messages and the comments — and there’s enough of that to keep companies like Facebook, Google, and Yahoo busy for an awfully long time. The aim to give these services the power to actually understand what their users are saying — without help from other humans. “We want to take AI and CIFAR to wonderful new places,” Hinton says, “where no person, no student, no program has gone before.” ... " 

Google Acquires Nets Research Company

Google acquires Neural net focused company DNNresearch.  We worked with artificial neural nets to replace some statistical techniques for shopping behavior analysis.

" ... Google acquires Toronto University startup focused on neural networks ... Researchers from the startup DNNresearch will at Google apply large-scale machine learning to a variety of domains ...   IDG News Service - Google has acquired a startup from the computer science department of the University of Toronto to get key researchers in the area of deep neural networks.

The Internet company acquired DNNresearch, which was set up last year by Professor Geoffrey Hinton and two of his graduate students, Alex Krizhevsky and Ilya Sutskever, the University of Toronto said on Tuesday. ... " 

Deep Learning and Artificial Intelligence

Mindhacks mentions an article in the NYTimes that I have not yet read, about advances in artificial intelligence.   Recall we did early work in using AI applied to research and manufacturing decision making in areas like blending, maintenance, diagnostics and other related human intelligence areas.   Mentioned is Geoffrey Hinton, a long time icon in the interaction of data and intelligence.   Hinton was particularly known for the linking of neural nets and learning systems.  We used some of his concepts and directions for application to learning systems that could be taught to do what humans achieve in the enterprise.   The brief outline of the work that he did was for integrating pharma research.  We did similar work with R&D concepts to develop new products for the enterprise.

The NYT article about deep learning is here.  Reading.  See Hinton's background at the U of Toronto with some descriptive information about his Pharma work.