Teaching AI agents to ask better questions by playing “Battleship”
MIT researchers use the classic game as a test bed for AI agents, finding a small AI model can outperform the biggest ones at 1 percent of the cost.
MIT News·
IAIFI enters its second phase with increased funding, broader ambitions, and a growing community at the frontier of AI and fundamental physics.
Read full articleMIT researchers use the classic game as a test bed for AI agents, finding a small AI model can outperform the biggest ones at 1 percent of the cost.
The global AI market feels more Orwellian than Dickensian these days, with headlines hitting our feed like AI contributed “basically zero” growth to US GDP (2025). This latest revelation represents a series of increasingly worrisome disappointments: 95% of GenAI pilots fail (MIT), Amazon’s Kiro agent sparks 13-hour outage by deleting [...] The post What leaders learn when AI meets reality appeared first on SAS Blogs.
A quick search through headlines reveals a range of AI-related disappointments. Consider that 95% of GenAI pilots fail, according to MIT. Amazon’s Kiro agent recently sparked a 13-hour outage by deleting a production environment. And we can’t forget that the resource and energy strain from a new wave of AI [...] The post Reviving the promise of AI with RAG, data and agentic appeared first on SAS Blogs.
The new ChartNet training dataset could improve the accuracy of vision-language models that help analyze business trends or interpret scientific figures.
MeMo's innovative approach could revolutionize AI adaptability, reducing costs and enhancing efficiency in multi-domain applications. The post MIT’s MeMo framework boosts LLM performance by 26% without retraining appeared first on Crypto Briefing.
MeMo's innovative approach could revolutionize AI adaptability, reducing costs and enhancing efficiency in multi-domain applications. The post MIT’s MeMo boosts LLM performance by 26% without retraining appeared first on Crypto Briefing.
With $25 million investment from the Commonwealth of Massachusetts, MIT to build a new shared-use facility to serve as a statewide quantum toolbox.
Researchers from NUS, MIT, and A*STAR propose MEMO, a modular framework that encodes corpus knowledge into a separate trainable MEMORY model. The post MEMO: A Modular Framework for Training a Dedicated Memory Model on New Knowledge Without Modifying LLM Parameters appeared first on MarkTechPost.