“The first email I read, every morning, is News Items.” — Rick Cordella, NBC Sports.
1. Electrical cables usually excite only audiophiles, but physicists are buzzing about IBM’s announcement today that it has connected two copies of its latest quantum computing chip using aluminum cables nearly 1 meter long. The connections are key to IBM’s ambition to scale up its machines from roughly 100 quantum bits, or qubits, to the thousands needed to make a quantum computer far superior to today’s supercomputers at solving certain problems. “It’s a milestone,” says Andrew Cleland, a physicist at the University of Chicago who was not involved in the work. “They’ve shown for the first time I know that they can take two processors and link them with meter-length cables.” However, some researchers caution the interconnect issue is not yet completely solved. The research paper is here. (Sources: science.org, ibm.com, arxiv.org)
2. Cade Metz, The New York Times:
As A.I. systems have grown increasingly powerful over the past several years, decoding the inner workings of these technologies has only become harder. In 2014, Google's Inception technology spanned several million “neurons,” the fundamental mathematical calculations that drive an A.I. system. The latest systems span trillions…
When Nick Cammarata, an AI researcher, started an “interpretability” project at OpenAI three years ago, he estimated that less than 2 percent of the neurons inside an A.I. system could be properly understood. Now that he has a new way of looking at the problem — which involves a phenomenon he calls “recursive generalization” — he thinks that he can understand the behavior of more than 90 percent of neurons.
He argues that as A.I. systems get larger and larger, their neurons start to line up with particular concepts, like shapes, syllables and sounds. He believes that he and other researchers can ultimately crack the interpretability problem under his methods.
But other researchers argue that his essay raises more questions than it answers. (Sources: arxiv.org, nytimes.com, already-clean.goodfire.com)
3. OpenAI, the maker of ChatGPT, claims its AI models have disproven the exponential time hypothesis – a fundamental assumption that underpins computer science. “This came as a complete surprise, a shock,” says Fedor Fomin at the University of Bergen in Norway. “Nobody I know expected it.” Proposed nearly 30 years ago, the exponential time hypothesis (ETH) tries to put a limit on how quickly computers can solve certain logical puzzles. The simplest way would be to try every possible combination of true or false for each part of the puzzle. ETH proposes that, although it may be possible to do this quicker with shortcuts, the amount of work needed has to grow exponentially as the puzzle gets bigger. OpenAI, however, says its models have devised an algorithm that can beat the previously assumed speed limit for solving logical puzzles, apparently proving ETH false. “Assuming that it is confirmed, this is an enormous breakthrough,” says Leslie Goldberg at the University of Oxford. (Sources: newscientist.com, uib.no/en, cs.ox.ac.uk)
