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OpenAI cofounder and CEO Sam Altman sat down for a wide-ranging interview with us late last week, answering questions about some of his most ambitious personal investments, as well as about the future of OpenAI.

This second clip is focused exclusively on artificial intelligence, including how much of what OpenAI is developing Altman thinks should be regulated, whether he’s worried about the commodification of AI, his thoughts about Alphabet’s reluctance to release its own powerful AI, and worst-and best-case scenarios as we move toward a future where AI is ever-more central to our lives.

Continue reading “StrictlyVC in conversation with Sam Altman, part two (OpenAI)” »

Queensland researchers train artificial intelligence to trawl recordings and help confirm presence of elusive eastern bristlebird.

Comprehensive database of AIs available for every task.

This shapeshifting controls the biological processes of living things—for example, opening the protein tunnels dotted along neurons or driving cancerous growth. But it also makes understanding protein behavior and developing drugs that interact with proteins a challenge.

While recent AI breakthroughs in the prediction (and even generation) of protein structures are a huge advance 50 years in the making, they still only offer snapshots of proteins. To capture whole biological processes—and identify which lead to diseases—we need predictions of protein structures in multiple “poses” and, more importantly, how each of these poses changes a cell’s inner functions. And if we’re to rely on AI to solve the challenge, we need more data.

Thanks to a new protein atlas published this month in Nature, we now have a great start.

We’ve all seen Cyborgs in Hollywood blockbusters. But it turns out these fictional beings aren’t so far-fetched. In fact, this program features a true-to-life cyborg, who at four months of age, was the youngest American to be outfitted with a myoelectric hand. And at one ground-breaking engineering.
facility, engineers are developing biotechnologies that can even further enhance high-tech like this by giving mechanical prosthetics something incredible: the physical sensation of touch!

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Continue reading “Prosthetics, Telepathy & Robo-Implants: The Cyborg Revolution | Futurism & Robots Documentary (Ep 1)” »

In a recent article published in Nature Materials, researchers reported a conductance-based organic electrochemical neuron (c-OECN) that mimicked biological signaling in neurons, especially activation/inactivation of their sodium and potassium channels.

Compilation of the top interviews, articles, and news in the last year.

During the past several decades, silicon has undeniably been the crown jewel of the semiconductor industry’s transformation. But with the plateauing of Moore’s Law, the increasing complexity of circuits, and the explosive growth of data-intensive applications, companies need even more innovative ways to compute, store, and move data faster. As a result, scale, speed, and power have become underlying forces to handle both advanced intelligence and computing needs.

Silicon photonics has already earned a stronghold for its impressive performance, power efficiency, and reliability compared to conventional electronic integrated circuits. Overall speed requirements have become fast enough, benefiting the technology’s strengths to transfer data efficiently over ever-shortening distances. Meanwhile, artificial intelligence (AI) is pushing computing to a point where electronic components need to communicate over distances to combine and integrate multiple XPUs (application-specific processing units).

Annaka Harris is the author of Conscious: A Brief Guide to the Fundamental Mystery of the Mind. Please support this podcast by checking out our sponsors:
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Continue reading “Annaka Harris: Free Will, Consciousness, and the Nature of Reality | Lex Fridman Podcast #326” »

Artificial neural networks that are inspired by natural nerve circuits in the human body give primates faster, more accurate control of brain-controlled prosthetic hands and fingers, researchers at the University of Michigan have shown. The finding could lead to more natural control over advanced prostheses for those dealing with the loss of a limb or paralysis.

The team of engineers and doctors found that a feed-forward neural network improved peak finger velocity by 45% during control of robotic fingers when compared to traditional algorithms not using neural networks. This overturned an assumption that more complex neural networks, like those used in other fields of machine learning, would be needed to achieve this level of performance improvement.

Continue reading “Simple neural networks outperform more complex systems for controlling robotic prosthetics” »