Guided learning lets “untrainable” neural networks realize their potential
CSAIL researchers find even “untrainable” neural nets can learn effectively when guided by another network’s built-in biases using their guidance method.
data
A new way to increase the capabilities of large language models
MIT-IBM Watson AI Lab researchers developed an expressive architecture that provides better state tracking and sequential reasoning in LLMs over long texts.
Read MoreThe technique can help scientists in economics, public health, and other fields understand whether to trust the results of their experiments.
Read MoreMIT Sea Grant students explore the intersection of technology and offshore aquaculture in Norway
AquaCulture Shock program, in collaboration with MIT-Scandinavia MISTI, offers international internships for AI and autonomy in aquaculture
Read MoreWhy Your Company Needs a Chief Data, Analytics, and AI Officer
How to define the role, provide a clear mandate, and ensure they’re positioned to deliver value for the company.
Read MoreResearchers discover a shortcoming that makes LLMs less reliable
Large language models can learn to mistakenly link certain sentence patterns with specific topics — and may then repeat these patterns instead of reasoning.
Read MoreHow a Transparent Company Culture Strengthens Cybersecurity and Data Resilience – SPONSOR CONTENT FROM VEEAM
Sponsor content from Veeam.
Read MoreCharting the future of AI, from safer answers to faster thinking
MIT PhD students who interned with the MIT-IBM Watson AI Lab Summer Program are pushing AI tools to be more flexible, efficient, and grounded in truth.
Read More3 Questions: How AI is helping us monitor and support vulnerable ecosystems
MIT PhD student and CSAIL researcher Justin Kay describes his work combining AI and computer vision systems to monitor the ecosystems that support our planet.
Read MoreHow Aon Transformed into a Future-Ready, Data-Driven Enterprise Built for the Age of Intelligence – SPONSOR CONTENT FROM RELTIO
Sponsor content from Reltio.
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