Large language models (LLMs) are becoming increasingly more impressive at creating human-like text and answering questions, but whether they can understand the meaning of the words they generate is a hotly debated issue. A big challenge is that LLMs are black boxes; they can make predictions and decisions on the order of words, but they cannot communicate the reasons for doing so.

Ellie Pavlick at Brown University is building models that could help understand how LLMs process language compared with humans. In this episode of The Joy of Why, Pavlick discusses what we know and don’t know about LLM language processing, how their processes differ from humans, and how understanding LLMs better could also help us better appreciate our own capacity for knowledge and creativity.

Quantum gravity is one of the biggest unresolved and challenging problems in physics, as it seeks to reconcile quantum mechanics, which governs the microscopic world, and general relativity, which describes the macroscopic world of gravity and space-time.

Efforts to understand quantum gravity have been focused almost entirely at the theoretical level, but Monika Schleier-Smith at Stanford University has been exploring a novel experimental approach — trying to create quantum gravity from scratch. Using laser-cooled clouds of atoms, she is testing the idea that gravity might be an emergent phenomenon arising from quantum entanglement.

In this episode of the Joy of Why podcast, Schleier-Smith discusses the thinking behind what she admits is a high-risk, high-reward approach, and how her experiments could provide important insights about entanglement and quantum mechanical systems even if the end goal of simulating quantum gravity is never achieved.

Quantum computing promises unprecedented speed, but in practice, it’s proven remarkably difficult to find important questions that quantum machines can solve faster than classical ones. One of the most notable demonstrations of this came from Ewin Tang, who rose to prominence in the field as a teenager. When quantum algorithms had in principle cracked the so-called recommendation problem, Tang designed classical algorithms that could match them.

So began the approach of “dequantizing,” in which computer scientists look at quantum algorithms and try to achieve the same speeds with classical counterparts. To understand the ongoing contest between classical and quantum computing, co-host Janna Levin spoke to Tang on The Joy of Why podcast. The wide-ranging conversation covered what it was like for Tang to challenge the prevailing wisdom at such a young age, the role of failure in scientific progress, and whether quantum computing will ultimately fulfill its grand ambitions.