AlphaGeometry scores almost as well as the best students on geometry questions from the International Mathematical Olympiad

An AI from Google DeepMind can solve some International Mathematical Olympiad (IMO) questions on geometry almost as well as the best human contestants.

“The results of AlphaGeometry are stunning and breathtaking,” says Gregor Dolinar, the IMO president. “It seems that AI will win the IMO gold medal much sooner than was thought even a few months ago.”

The IMO, aimed at secondary school students, is one of the most difficult maths competitions in the world. Answering questions correctly requires mathematical creativity that AI systems have long struggled with.  – Read More

If OpenAI’s new model can solve grade-school math, it could pave the way for more powerful systems.

Ever since last week’s dramatic events at OpenAI, the rumor mill has been in overdrive about why the company’s chief scientific officer, Ilya Sutskever, and its board decided to oust CEO Sam Altman.

While we still don’t know all the details, there have been reports that researchers at OpenAI had made a “breakthrough” in AI that had alarmed staff members. Reuters and The Information both report that researchers had come up with a new way to make powerful AI systems and had created a new model, called Q* (pronounced Q star), that was able to perform grade-school-level math. According to the people who spoke to Reuters, some at OpenAI believe this could be a milestone in the company’s quest to build artificial general intelligence, a much-hyped concept referring to an AI system that is smarter than humans. The company declined to comment on Q*.  — Read More

AGI, or artificial general intelligence, is one of the hottest topics in tech today. It’s also one of the most controversial. A big part of the problem is that few people agree on what the term even means. Now a team of Google DeepMind researchers has put out a paper that cuts through the cross talk with not just one new definition for AGI but a whole taxonomy of them.

In broad terms, AGI typically means artificial intelligence that matches (or outmatches) humans on a range of tasks. But specifics about what counts as human-like, what tasks, and how many all tend to get waved away: AGI is AI, but better.

To come up with the new definition, the Google DeepMind team started with prominent existing definitions of AGI and drew out what they believe to be their essential common features. 

The team also outlines five ascending levels of AGI: emerging (which in their view includes cutting-edge chatbots like ChatGPT and Bard), competent, expert, virtuoso, and superhuman (performing a wide range of tasks better than all humans, including tasks humans cannot do at all, such as decoding other people’s thoughts, predicting future events, and talking to animals). They note that no level beyond emerging AGI has been achieved. — Read More

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Transformer models, notably large language models (LLMs), have the remarkable ability to perform in-context learning (ICL) — to perform new tasks when prompted with unseen input-output examples without any explicit model training. In this work, we study how effectively transformers can bridge between their pretraining data mixture, comprised of multiple distinct task families, to identify and learn new tasks in-context which are both inside and outside the pretraining distribution. Building on previous work, we investigate this question in a controlled setting, where we study transformer models trained on sequences of (x,f(x)) pairs rather than natural language. Our empirical results show transformers demonstrate near-optimal unsupervised model selection capabilities, in their ability to first in-context identify different task families and in-context learn within them when the task families are well-represented in their pretraining data. However when presented with tasks or functions which are out-of-domain of their pretraining data, we demonstrate various failure modes of transformers and degradation of their generalization for even simple extrapolation tasks. Together our results highlight that the impressive ICL abilities of high-capacity sequence models may be more closely tied to the coverage of their pretraining data mixtures than inductive biases that create fundamental generalization capabilities. — Read More

#human

Human language. Mathematics. Logic. These are all ways to formalize the world. And in our century there’s a new and yet more powerful one: computation.

And for nearly 50 years I’ve had the great privilege of building an ever taller tower of science and technology based on that idea of computation. And today I want to tell you some of what that’s led to.

There’s a lot to talk about—so I’m going to go quickly… sometimes with just a sentence summarizing what I’ve written a whole book about. — Read More

David Chalmers was not expecting the invitation he received in September of last year. As a leading authority on consciousness, Chalmers regularly circles the world delivering talks at universities and academic meetings to rapt audiences of philosophers—the sort of people who might spend hours debating whether the world outside their own heads is real and then go blithely about the rest of their day. This latest request, though, came from a surprising source: the organizers of the Conference on Neural Information Processing Systems (NeurIPS), a yearly gathering of the brightest minds in artificial intelligence. 

Less than six months before the conference, an engineer named Blake Lemoine, then at Google, had gone public with his contention that LaMDA, one of the company’s AI systems, had achieved consciousness. Lemoine’s claims were quickly dismissed in the press, and he was summarily fired, but the genie would not return to the bottle quite so easily—especially after the release of ChatGPT in November 2022. Suddenly it was possible for anyone to carry on a sophisticated conversation with a polite, creative artificial agent.

Chalmers was an eminently sensible choice to speak about AI consciousness. He’d earned his PhD in philosophy at an Indiana University AI lab, where he and his computer scientist colleagues spent their breaks debating whether machines might one day have minds. In his 1996 book, The Conscious Mind, he spent an entire chapter arguing that artificial consciousness was possible.  — Read More

We’re in the midst of a Cambrian explosion in AI. Over the last decade, AI has gone from theory and small tests to enterprise-scale use cases. But the hardware used to run AI systems, although increasingly powerful, was not designed with today’s AI in mind. As AI systems scale, the costs skyrocket. And Moore’s Law, the theory that the density of circuits in processors would double each year, has slowed.

But new research out of IBM Research’s lab in Almaden, California, nearly two decades in the making, has the potential to drastically shift how we can efficiently scale up powerful AI hardware systems. — Read More

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At every moment of every day, our brains meticulously sculpt a wealth of sensory signals into meaningful representations of the world around us. Yet how this continuous process actually works remains poorly understood.

Today, Meta is announcing an important milestone in the pursuit of that fundamental question. Using magnetoencephalography (MEG), a non-invasive neuroimaging technique in which thousands of brain activity measurements are taken per second, we showcase an AI system capable of decoding the unfolding of visual representations in the brain with an unprecedented temporal resolution.

This AI system can be deployed in real time to reconstruct, from brain activity, the images perceived and processed by the brain at each instant. — Read More

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