Personal messages to the AI community
As AI increasingly automates coding, it frees up developers to spend time on high-level software development tasks traditionally reserved for senior engineers, like deciding on technical architecture and participating in scoping product requirements.
Universities in the U.S. are rapidly rolling out undergraduate majors, minors, and specializations in artificial intelligence to meet the growing demand for AI expertise.
The phrase recursive self-improvement erupted on social media following an Anthropic report that tracked AI-driven gains in the company’s internal software-engineering productivity.
AI agents seem to be increasingly capable of performing economically valuable tasks, but current benchmarks measure this capability only narrowly.
Half of workers in the United States used AI at work at least a few times last year, a sign of steadily rising AI adoption in U.S. workplaces.
Typically, large language models are trained to act as helpful, harmless, honest assistants. However, during long or emotionally charged conversations, traits can emerge that are less beneficial. Researchers devised a way to steady the assistant personas of LLMs.
I’ve been hearing from people at all levels of seniority about a feeling of job insecurity.
A high-profile acquisition could enable Facebook, Instagram, and WhatsApp to offer built-in agents that do users’ bidding.
A little more than three years ago, OpenAI released ChatGPT, and education changed forever. For students, the ability to generate fluent, credible text on demand in seconds is an incredible new tool.
Leading AI companies fought a ferocious war for talent, luring top talent from competitors with levels of compensation more commonly associated with pro sports.
Another year of rapid AI advances has created more opportunities than ever for anyone — including those just entering the field — to build software.
I recently received an email titled “An 18-year-old’s dilemma: Too late to contribute to AI?” Its author, who gave me permission to share this, is preparing for college.
OpenAI completed its transition from nonprofit to for-profit in a feat of legal engineering that took an army of lawyers, investment bankers, and two state attorneys general 18 months to negotiate.
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The approach known as LoRA streamlines fine-tuning by training a small adapter that modifies a pretrained model’s weights at inference. Researchers built a model that generates such adapters directly.
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