Technology 

Quanta Magazine

As computer scientists tackle a greater range of problems, their work has grown increasingly interdisciplinary. This year, many of the most significant computer science results also involved other scientists and mathematicians. Perhaps the most practical involved the cryptographic questions underlying the security of the internet, which tend to be complicated mathematical problems. One such problem — the product of two elliptic curves and their relation to an abelian surface — ended up bringing down a promising new cryptography scheme that was thought to be strong enough to withstand an attack from a quantum computer. And a different set of mathematical relationships, in the form of one-way functions, will tell cryptographers if truly secure codes are even possible.

Computer science, and quantum computing in particular, also heavily overlaps with physics. In one of the biggest developments in theoretical computer science this year, researchers posted a proof of the NLTS conjecture, which (among other things) states that a ghostly connection between particles known as quantum entanglement is not as delicate as physicists once imagined. This has implications not just for our understanding of the physical world, but also for the myriad cryptographic possibilities that entanglement makes possible. 

And artificial intelligence has always flirted with biology — indeed, the field takes inspiration from the human brain as perhaps the ultimate computer. While understanding how the brain works and creating brainlike AI has long seemed like a pipe dream to computer scientists and neuroscientists, a new type of neural network known as a transformer seems to process information similarly to brains. As we learn more about how they both work, each tells us something about the other. Perhaps that’s why transformers excel at problems as varied as language processing and image classification. AI has even become better at helping us make better AI, with new “hypernetworks” helping researchers train neural networks faster and at a lower cost. So now the field is not only helping other scientists with their work, but also helping its own researchers achieve their goals.

 

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