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Category: robotics/AI – Page 1,455

A new study claims machine learning is starting to look a lot like human cognition.

In 2019, The MIT Press Reader published a pair of interviews with Noam Chomsky and Steven Pinker, two of the world’s foremost linguistic and cognitive scientists. The conversations, like the men themselves, vary in their framing and treatment of key issues surrounding their areas of expertise. When asked about machine learning and its contributions to cognitive science, however, their opinions gather under the banner of skepticism and something approaching disappointment.

“In just about every relevant respect it is hard to see how [machine learning] makes any kind of contribution to science,” Chomsky laments, “specifically to cognitive science, whatever value it may have for constructing useful devices or for exploring the properties of the computational processes being employed.”

While Pinker adopts a slightly softer tone, he echoes Chomsky’s lack of enthusiasm for how AI has advanced our understanding of the brain:

“Cognitive science itself became overshadowed by neuroscience in the 1990s and artificial intelligence in this decade, but I think those fields will need to overcome their theoretical barrenness and be reintegrated with the study of cognition — mindless neurophysiology and machine learning have each hit walls when it comes to illuminating intelligence.”

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Rather than engineering robotic solutions from scratch, some of our most impressive advances have come from copying what nature has already come up with.

New research shows how we can extend that approach to robot ‘minds’, in this case by getting a robot to learn the best route out of a maze all by itself – even down to keeping a sort-of memory of particular turns.

A team of engineers coded a Lego robot to find its way through a hexagonal labyrinth: by default it turned right at every function, until it hit a point it had previously visited or came to a dead end, at which point it had to start again.

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The new machine-learning system can generate a 3D scene from an image about 15,000 times faster than other methods. Humans are pretty good at looking at a single two-dimensional image and understanding the full three-dimensional scene that it captures. Artificial intelligence agents are not.

The hunt is on for leptoquarks, particles beyond the limits of the standard model of particle physics —the best description we have so far of the physics that governs the forces of the Universe and its particles. These hypothetical particles could prove useful in explaining experimental and theoretical anomalies observed at particle accelerators such as the Large Hadron Collider (LHC) and could help to unify theories of physics beyond the standard model, if researchers could just spot them.

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We’ve fine-tuned GPT-3 to more accurately answer open-ended questions using a text-based web browser. Our prototype copies how humans research answers to questions online – it submits search queries, follows links, and scrolls up and down web pages. It is trained to cite its sources, which makes it easier to give feedback to improve factual accuracy. We’re excited about developing more truthful AI, but challenges remain, such as coping with unfamiliar types of questions.

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Language models like GPT-3 are useful for many different tasks, but have a tendency to “hallucinate” information when performing tasks requiring obscure real-world knowledge. To address this, we taught GPT-3 to use a text-based web-browser. The model is provided with an open-ended question and a summary of the browser state, and must issue commands such as “Search …”, “Find in page: …” or “Quote: …”. In this way, the model collects passages from web pages, and then uses these to compose an answer.

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We’ve all seen children draw quirky, awesome characters, and even heard them talk about their illustrations as if they were real! How cool would it be to actually bring the characters to life?

Researchers at Meta AI have developed a way to do just that. We’re announcing a first-of-its-kind AI-powered animation tool that can automatically animate children’s drawings of human figures within minutes.

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