Computer science

Computer scientist Melanie Mitchell discusses why artificial intelligence doesn’t “think” or “reason” like humans, and how we can create better methods for measuring machine cognition.

No one yet knows which technology will power the quantum computers of the future, but the race to create them has already produced some of science’s most intricate machinery.

The idea that artificial intelligence can “reason” is more intuitive than ever. But intuitions can be wrong, and the science is far from settled.

Shayan Oveis Gharan has won the Abacus Medal for using tools from across mathematics to boost the power of algorithms.

Today’s computers need safeguards against random energy fluctuations. Thermodynamic computers would put those fluctuations to use.

When checking that solutions to certain problems are correct, it turns out, you can’t get around the inherent complexity of the quantum world.

Our genetic heritage is not a blueprint or an algorithm, as many biologists have imagined, but something else entirely.

With automated proof-checkers, a problem can be broken up into small chunks, solved bit-by-bit, then reassembled with confidence that every piece is correct. For some, this heralds a new area in mathematical research.

The podcast returns with 12 all-new episodes that explore the biggest questions in basic science and mathematics.