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Standardizing the Brain-Machine Interface

Every neural-prosthetics lab has its own brain-decoding algorithm, but could one size fit all?

3 min read

Earlier this year in a lab at Duke University, in Durham, N.C., a clever, raisin-gobbling ­monkey named Idoya made a robot move in Japan—just by thinking. And she wasn’t alone. She joined ranks with, among others, a ­paraplegic man who recently used his brain to move a cursor around a computer screen.

Researchers have endowed subjects with seemingly telekinetic powers by extracting the patterns of brain ­activity that occur when we move parts of our bodies. However those patterns are tapped electronically, algorithms are needed to interpret them and discern their salient ­features so that the appropriate ­signals can be sent to external devices. Groups working on brain-machine interfaces have designed brain decoders differently, depending on the type of neural data they collect and the purposes of their research. As a result, most algorithms have to be written from the ground up. But some in the field say it’s time to develop a generic algorithm that will incorporate the best work of the last decade and serve as a foundation for all labs ­working on neural prosthetics.

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An IBM Quantum Computer Will Soon Pass the 1,000-Qubit Mark

The Condor processor is just one quantum-computing advance slated for 2023

4 min read
This photo shows a woman working on a piece of apparatus that is suspended from the ceiling of the laboratory.

A researcher at IBM’s Thomas J. Watson Research Center examines some of the quantum hardware being constructed there.

Connie Zhou/IBM

IBM’s Condor, the world’s first universal quantum computer with more than 1,000 qubits, is set to debut in 2023. The year is also expected to see IBM launch Heron, the first of a new flock of modular quantum processors that the company says may help it produce quantum computers with more than 4,000 qubits by 2025.

This article is part of our special report Top Tech 2023.

While quantum computers can, in theory, quickly find answers to problems that classical computers would take eons to solve, today’s quantum hardware is still short on qubits, limiting its usefulness. Entanglement and other quantum states necessary for quantum computation are infamously fragile, being susceptible to heat and other disturbances, which makes scaling up the number of qubits a huge technical challenge.

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