When you’re trying to communicate or understand ideas, words don’t always do the trick. Sometimes the more efficient approach is to do a simple sketch of that concept — for example, diagramming a circuit might help make sense of how the system works. But what if artificial intelligence could help us explore these visualizations? While…
Every year, thousands of students take courses that teach them how to deploy artificial intelligence models that can help doctors diagnose disease and determine appropriate treatments. However, many of these courses omit a key element: training students to detect flaws in the training data used to develop the models. Leo Anthony Celi, a senior research…
From large language models (LLMs) to reasoning agents, today’s AI tools bring unprecedented computational demands. Trillion-parameter models, workloads running on-device, and swarms of agents collaborating to complete tasks all require a new paradigm of computing to become truly seamless and ubiquitous. First, technical progress in hardware and silicon design is critical to pushing the boundaries…
Back in April, OpenAIannounced it was rolling back an update to its GPT-4o model that made ChatGPT’s responses to user queries too sycophantic. An AI model that acts in an overly agreeable and flattering way is more than just annoying. It could reinforce users’ incorrect beliefs, mislead people, and spread misinformation that can be dangerous—a…
Join Danielle Belgrave and Ben Lorica for a discussion of AI in healthcare. Danielle is VP of AI and machine learning at GSK (formerly GlaxoSmithKline). She and Ben discuss using AI and machine learning to get better diagnoses that reflect the differences between patients. Listen in to learn about the challenges of working with health…
Sarah Alnegheimish’s research interests reside at the intersection of machine learning and systems engineering. Her objective: to make machine learning systems more accessible, transparent, and trustworthy. Alnegheimish is a PhD student in Principal Research Scientist Kalyan Veeramachaneni’s Data-to-AI group in MIT’s Laboratory for Information and Decision Systems (LIDS). Here, she commits most of her energy…
Scientists at the McGovern Institute for Brain Research at MIT and the Broad Institute of MIT and Harvard have re-engineered a compact RNA-guided enzyme they found in bacteria into an efficient, programmable editor of human DNA. The protein they created, called NovaIscB, can be adapted to make precise changes to the genetic code, modulate the…
Separating AI reality from hyped-up fiction isn’t always easy. That’s why we’ve created the AI Hype Index—a simple, at-a-glance summary of everything you need to know about the state of the industry. Large language models confidently present their responses as accurate and reliable, even when they’re neither of those things. That’s why we’ve recently seen…
While I prefer “AI native” to describe the product development approach centered on AI that we’re trying to encourage at O’Reilly, I’ve sometimes used the term “AI first” in my communications with O’Reilly staff. And so I was alarmed and dismayed to learn that in the press, that term has now come to mean “using…
The rise of artificial intelligence resurfaces a question older than the abacus: If we have a tool to do it for us, why learn to do it ourselves? The answer, argues MIT electrical engineering and computer science (EECS) Professor Devavrat Shah, hasn’t changed: Foundational skills in mathematics remain essential to using tools well, from knowing…