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Blog – Page 2897

High-speed drone racing has just had a shocking “Deep Blue” moment, as an autonomous AI designed by University of Zurich researchers repeatedly forced three world champion-level pilots to eat its dust, showing uncanny precision in dynamic flight.

If you’ve ever watched a high-level drone race from the FPV perspective, you’ll know how much skill, speed, precision and dynamic control it takes. Like watching Formula One from the driver’s perspective, or on-board footage from the Isle of Man TT, it’s hard to imagine how a human brain can make calculations that quickly and respond to changing situations in real time. It’s incredibly impressive.

When Deep Blue stamped silicon’s dominance on the world of chess, and AlphaGo established AI’s dominance in the game of Go, these were strategic situations, in which a computer’s ability to analyze millions of past games and millions of potential moves and strategies gave them the edge.

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Less than a year into the AI boom and startups are already grappling with what may become an industry reckoning.

Take Jasper, a buzzy AI startup that raised $125 million for a valuation of $1.5 billion last year — before laying off staff with a gloomy note from its CEO this summer.

Now, in a provocative new story, the Wall Street Journal fleshes out where the cracks are starting to form. Basically, monetizing AI is hard, user interest is leveling off or declining, and running the hardware behind these products is often very expensive — meaning that while the tech does sometimes offer a substantial “wow” factor, its path to a stable business model is looking rockier than ever.

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The abilities of artificial intelligence (AI) systems are advancing at an astounding rate, nearing or bettering what humans can do in simulations and test environments.

Setting aside the ethical and environmental concerns around AI and those of autonomous drones for a minute, we can marvel at this latest feat: an AI-controlled drone system that beat three professional drone pilots in a series of head-to-head races, winning more often than not.

Swift is the name of the autonomous system, which outmaneuvered the world-champion human pilots in 15 of the 25 races, on a track full of sweeping turns and screeching pivots designed by a professional drone-racing pilot.

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Scientists believe they have found an explanation for an “impossible” blast of energy that hit Earth.

Last year, scientists reported that they had seen evidence that gamma-ray bursts could come out of mergers between neutron stars and another compact object, in the form of a neutron star or black hole. That was previously thought not to be possible.

Scientists had initially thought that the 50-second blast came when a massive star collapsed, but further work looking at the afterglow of the emission showed that it was in fact a “kilonova”, which happens when neutron stars merge with other compact objects. Previously, it was thought that only a supernova could make a long gamma-ray burst of that kind.

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Michael Levin discusses his 2022 paper “Technological Approach to Mind Everywhere: An Experimentally-Grounded Framework for Understanding Diverse Bodies and Minds” and his 2023 paper with Joshua Bongard, “There’s Plenty of Room Right Here: Biological Systems as Evolved, Overloaded, Multi-scale Machines.” Links to papers flagged 🚩below.

Michael Levin is a scientist at Tufts University; his lab studies anatomical and behavioral decision-making at multiple scales of biological, artificial, and hybrid systems. He works at the intersection of developmental biology, artificial life, bioengineering, synthetic morphology, and cognitive science.

❶ Polycomputing (observer-dependent)
1:59 Outlining the discussion.
3:50 My favorite comment from round 1 interview.
5:00 What is polycomputing?
8:50 An ode to Richard Feynman’s “There’s plenty of room at the bottom“
11:10 How/when was this discovered? Reductionism, causal power…
14:40 “It’s a view that steps away from prediction.“
16:20 From abstract: Polycomputing is the ability of the same substrate to simultaneously compute different things *but emphasis on the observer(s)*
17:05 What’s an example of polycomputing?
19:40 They took a different approach and actually did experiments with gene regulatory networks (GRNs)
23:18 Different observers extract different utility from the exact same system.
26:35 Spatial causal emergence graphs (determinism, degeneracy) | Erik Hoel’s micro/macro & effective information.
29:25 Inventiveness of John Conway’s Game of Life.

❷ Technological Approach to Mind Everywhere.

Continue reading “Agency, Attractors, & Observer-Dependent Computation in Biology & Beyond” | >