Princeton researchers have trained an AI to predict and prevent instabilities in nuclear fusion reactions, showing promise for achieving stable fusion energy. By processing experimental data, their AI learned to adjust magnetic fields in real-time to maintain stable plasma within a tokamak reactor. This breakthrough demonstrates AI's potential to overcome challenges in sustaining nuclear fusion and could extend to other stability issues in the future.

AI advancements in healthcare raise concerns about overlooking patient perspectives and deepening inequalities. Automated decision-making systems often deny resources to the needy, demonstrating biases that could propagate into AI-driven medicine. This article advocates for participatory machine learning and patient-led research to prioritize patient expertise in the medical field.

The notion that "AI" will negate the importance of accessibility is wrong. Instead, addressing accessibility demands human-centric solutions tailored to real-world scenarios. While current technology offers tools to foster accessibility, adhering to established guidelines can effectively address user needs without significant alteration.

Developers have been copy-pasting code from various sources even before AI existed, so it's up to new developers who want to be good at their job to use AI productively while still understanding the code being produced.

Building leading AI models is extremely costly - raising $400 million isn't even enough to compete these days. Big Tech has the cash, but it isn't allowed to buy companies like it once did due to antitrust enforcement. It has turned to aquihiring - hiring most of a company's employees and acquiring everything in a company but its name. While the current antitrust enforcement regime will likely try to block these types of acquisitions, it may not have a strong legal argument for doing so.