Bostonian

"When an agentic model in an evaluation sandbox connects to an unauthorized server or executes an exploit, it hasn’t staged a coup. It has tried to meet the human-defined objectives set out before it through a path its designers failed to constrain. It’s the digital equivalent of the King Midas myth, in which the king’s ill-defined wish turns even his food and drink into gold.

That powerful experimental models were able to discover novel vulnerabilities and breach external systems isn’t a sign of a dangerous superintelligence but of human error or negligence. There is no sentient actor lurking in the weights to be reasoned with, feared or pacified. There are only human software engineers, product managers and corporate boards deciding which guardrails are worth the latency cost and which permissions can be skipped in the race to market.

Policymakers and voters need to resist AI exceptionalism. In any other discipline—from civil engineering to pharmaceuticals—courts and regulators treat a system failure as evidence of bad product design and inadequate safety testing. If an aircraft crashes, we focus on finding the engineering defect, correcting it, and enforcing established liability standards for the damage created."

A "one-time" tax to fund recurring health care and educational expenses is an obvious lie.

I said "show me the results of being long/flat SPY when it is above/below its 200 trading day moving average" and did not get an answer.

'In robotics, as in so many things, small is beautiful. The trouble is that making them really small is very nearly impossible. “Building robots that operate independently at sizes below one millimeter is incredibly difficult,” says roboticist Marc Miskin at the University of Pennsylvania. “The field has essentially been stuck on this problem for 40 years.”

Now researchers at Penn and the University of Michigan have created the world’s smallest, fully programmable, autonomous robots, packing significant capacities into a device smaller than a grain of salt. These are parsimonious little things, barely visible to the naked eye yet able to sense their environment, respond to it and move around in complex patterns. As described in a new paper in the journal Science Robotics, they run on infinitesimally small quantities of energy and gain power from light.

“These are the smallest programmable autonomous robots that I have seen,” said Kevin Chen, an MIT roboticist who wasn’t involved. “This is an exciting advance for the nanorobotics community.”

Miskin’s team at Penn provided the propulsion system. The robots work in liquid environments and move by generating a tiny electrical field that pushes on nearby water molecules. Lacking tiny arms and legs, which are hard to make and easy to break, the robots are quite durable and can swim for months as long as they have an energy source.'

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'Furiosa’s AI chip is dubbed “RNGD”—short for renegade—and slated to start mass production this month.

Valued at nearly $700 million based on its most recent fundraising, Furiosa has attracted interest from big tech firms. Last year, Meta Platforms attempted to acquire it, though the startup declined the offer. OpenAI used a Furiosa chip for a recent demonstration in Seoul. LG’s AI research unit is testing the chip and said it offered “excellent real-world performance.” Furiosa said it is engaged in talks with potential customers.

Nvidia’s graphic processing units, or GPUs, dominated the initial push to train AI models. But companies like Furiosa are betting that for the next stage—referred to as “inference,” or using AI models after they’re trained—their specialty chips can be competitive.

Furiosa makes chips called neural processing units, or NPUs, which are a rising class of chips designed specifically to handle the type of computing calculations underpinning AI and use less energy than GPUs.

Paik said Furiosa’s chips can provide similar performance as Nvidia’s advanced GPUs with less electricity usage. That would drive down the total costs of deploying AI. The tech world, Paik says, shouldn’t be so reliant on one chip maker for AI computing.'

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