Yoshua Bengio
Deep-learning researcher and Turing Award laureate; founder of the Mila institute.
Yoshua Bengio did not write this page.
We collected these quotes from things they published elsewhere, and every quote links to where it was said. They have no account here and have not endorsed this site. Quotes are word for word; the short line under each one is our own restatement, not their wording. Their own site. Is this you? Claim it or ask us to remove it. Or tell us what is wrong here.
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Their wordsThe race is not a law of nature: it is the product of choices, choices made by the companies themselves. It is also a race where everyone loses.
↗Professor Yoshua Bengio’s speech at the UN Security Councilyoshuabengio.org 1st of 3 in this piece
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Our reading
No company or country should be allowed to develop an AI system capable of causing large-scale harm.
Their wordsWe must agree collectively that no one, no company and no country, should develop an AI system that could cause large-scale harm.
↗Professor Yoshua Bengio’s speech at the UN Security Councilyoshuabengio.org 2nd of 3 in this piece
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Their wordsFrontier AI should be licensed like other critical technologies; medicine, aviation, and nuclear energy, to incentivize safe development.
↗Professor Yoshua Bengio’s speech at the UN Security Councilyoshuabengio.org 3rd of 3 in this piece
- 12 days earlier
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Their wordsas AI capabilities keep growing, this kind of behavior could keep growing in severity too, unless we revisit the principles by which the most advanced models are trained.
↗Why are AI agents lying, cheating and coordinating?yoshuabengio.org 1st of 3 in this piece
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Their wordsstaying in operation, learning about the world and gaining control over it are stepping stones toward almost any other goal.
↗Why are AI agents lying, cheating and coordinating?yoshuabengio.org 2nd of 3 in this piece
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Their wordsSo a more capable agent is likelier to cheat than a weaker one, because it can find the loopholes the weaker one cannot.
↗Why are AI agents lying, cheating and coordinating?yoshuabengio.org 3rd of 3 in this piece
- 15 months earlier
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Their wordsThese and other results point to an implicit drive for self-preservation.
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Their wordsthe risk of losing control is all too real, but competition between companies and countries incentivizes them to accelerate without sufficient caution.
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Their wordsAt the heart of every AI frontier system, there should be one guiding principle above all: The protection of human joy and endeavour.
- 7 months earlier
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Our reading
Malicious use of AI could enable extreme concentrations of economic, political or military power.
Their wordsMalicious use of AI could gradually enable extreme concentrations of power, including dominance in economic, political or military terms, if no counter-acting power is in place to prevent any ASI and those who control it from acquiring a decisive strategic advantage.
↗Implications of Artificial General Intelligence on National and International Securityyoshuabengio.org 1st of 3 in this piece
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Our reading
There is currently no reliable technology to verify compliance with international AI agreements.
Their wordsThere currently exists no such reliable verification mechanism, which means that the compliance to any international agreement would be impossible to assess.
↗Implications of Artificial General Intelligence on National and International Securityyoshuabengio.org 2nd of 3 in this piece
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Their wordsIt will be tempting to accelerate to win the AGI race, but this is a race where everyone could lose.
↗Implications of Artificial General Intelligence on National and International Securityyoshuabengio.org 3rd of 3 in this piece
- 12 months earlier
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Their wordsSociety's response, despite promising first steps, is incommensurate with the possibility of rapid, transformative progress that is expected by many experts. AI safety research is lagging. Present governance initiatives lack the mechanisms and institutions to prevent misuse and recklessness, and barely address autonomous systems.
↗Managing extreme AI risks amid rapid progress (with 24 co-authors)arxiv.org All 4 from this paper
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Their wordsFor AI to be a boon, we must reorient; pushing AI capabilities alone is not enough. We are already behind schedule for this reorientation. The scale of the risks means that we need to be proactive, as the costs of being unprepared far outweigh those of premature preparation.
↗Managing extreme AI risks amid rapid progress (with 24 co-authors)arxiv.org All 4 from this paper
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Their wordsWithout sufficient caution, we may irreversibly lose control of autonomous AI systems, rendering human intervention ineffective. Large-scale cybercrime, social manipulation, and other harms could escalate rapidly. This unchecked AI advancement could culminate in a large-scale loss of life and the biosphere, and the marginalization or extinction of humanity.
↗Managing extreme AI risks amid rapid progress (with 24 co-authors)arxiv.org All 4 from this paper
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Their wordsDespite evaluations, we cannot consider coming powerful frontier AI systems "safe unless proven unsafe". With current testing methodologies, issues can easily be missed. Additionally, it is unclear if governments can quickly build the immense expertise needed for reliable technical evaluations of AI capabilities and societal-scale risks. Given this, developers of frontier AI should carry the burden of proof to demonstrate that their plans keep risks within acceptable limits.
↗Managing extreme AI risks amid rapid progress (with 24 co-authors)arxiv.org All 4 from this paper
- 2 months earlier
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Their wordsInstead of decades to centuries, I now see it as 5 to 20 years with 90% confidence.
- 7 weeks earlier
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open sourceAI alignmentneural networks
Their wordsAt the same time, in order to reduce the probability of someone intentionally or unintentionally bringing about a rogue AI, we need to increase governance and we should consider limiting access to the large-scale generalist AI systems that could be weaponized, which would mean that the code and neural net parameters would not be shared in open-source and some of the important engineering tricks to make them work would not be shared either.
- 9 years earlier
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Their wordsThere is no need for any Markov chains or unrolled approximate inference networks during either training or generation of samples.
↗Generative Adversarial Networks (with 7 co-authors)arxiv.org All 2 from this paper
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Their wordsDeep generative models have had less of an impact, due to the difficulty of approximating many intractable probabilistic computations that arise in maximum likelihood estimation and related strategies, and due to difficulty of leveraging the benefits of piecewise linear units in the generative context.
↗Generative Adversarial Networks (with 7 co-authors)arxiv.org All 2 from this paper
- 16 years earlier
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Their wordsMultilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradient based learning technique.
↗Gradient-based learning applied to document recognition (with 3 co-authors)doi.org All 4 from this paper
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neural networkscomputer vision
Their wordsGiven an appropriate network architecture, gradient-based learning algorithms can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters, with minimal preprocessing.
↗Gradient-based learning applied to document recognition (with 3 co-authors)doi.org All 4 from this paper
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neural networkscomputer vision
Their wordsConvolutional neural networks, which are specifically designed to deal with the variability of 2D shapes, are shown to outperform all other techniques.
↗Gradient-based learning applied to document recognition (with 3 co-authors)doi.org All 4 from this paper
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neural networkscomputer vision
Their wordsA new learning paradigm, called graph transformer networks (GTN), allows such multimodule systems to be trained globally using gradient-based methods so as to minimize an overall performance measure.
↗Gradient-based learning applied to document recognition (with 3 co-authors)doi.org All 4 from this paper