Quanta Magazine’s computer science coverage in 2023 included progress on new approaches to artificial intelligence, a fundamental advance on a seminal quantum computing algorithm, and emergent behavior in large language models.
00:05 Vector-Driven AI As powerful as AI has become, the artificial neural networks that underpin most modern systems share two flaws: They require tremendous resources to train and operate, and it’s too easy for them to become inscrutable black boxes. Researchers have developed a new approach called hyperdimensional computing which is more versatile, making its computations far more efficient while also giving researchers greater insight into the model’s reasoning.
04:01 Improving the Quantum Standard For decades, Shor’s algorithm has been the paragon of the power of quantum computers. This set of instructions allows a machine that can exploit the quirks of quantum physics to break large numbers into their prime factors much faster than a regular, classical computer — potentially laying waste to much of the internet’s security systems. In August, a computer scientist developed an even faster variation of Shor’s algorithm, the first significant improvement since its invention.
07:14 The Powers of Large Language Models Get enough stuff together, and you might be surprised by what can happen. This year, scientists found so-called “emergent behaviors,” in large language models — AI programs trained on enormous collections of text to produce humanlike writing. After these models reach a certain size, they can suddenly do unexpected things that smaller models can’t, such as solving certain math problems.