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MIT Technology Review’s What’s Next series looks across industries, trends, and technologies to give you a first look at the...
In this article, you will learn how prompt caching and fine-tuning differ as strategies for reducing cost and latency in...
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in...
Meta has released Muse Glimmer, a 30-billion-parameter multimodal model distilled from Muse Spark. It is tuned for always-on local agent...
This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox...
In this article, you will learn how prompt caching and fine-tuning differ as strategies for reducing cost and latency in...
Long-running agents accumulate state that no transcript captures. A coding agent at step 10 holds edited files, a running dev...
In this tutorial, we develop an end-to-end sentiment analysis workflow using the Stanford NLP IMDb Large Movie Review Dataset and...
LLM applications fail in ways traditional software does not. The same prompt can produce different outputs. A retrieval step can...
When Annotators Agree but Labels Disagree: The Projection Problem in Stance Detection
When and How Long Did Therapy Happen? Soft-Supervising Temporal Localization Using Audio-Language Models
When AI designs a drug, who gets the credit?
When AI Agents Collude Online: Financial Fraud Risks by Collaborative LLM Agents on Social Platforms
What’s next for IVF
What’s next for EV batteries in 2026
What’s next for Chinese open-source AI
What’s in a name? Moderna’s “vaccine” vs. “therapy” dilemma
What your tools miss at 2:13 AM: How gen AI attack chains exploit telemetry lag – Part 1
