- Massachusetts Institute of Technology - MIT News
New AI system uncovers hidden cell subtypes, boosts precision medicine CellLENS reveals hidden patterns in cell behavior within tissues, offering deeper insights into cell heterogeneity — vital for advancing cancer immunotherapy July 11, 2025 Read full story →
- “Periodic table of machine learning” could fuel AI discovery
The new framework sheds light on how scientists could fuse strategies from different methods to improve existing AI models or come up with new ones For instance, the researchers used their framework to combine elements of two different algorithms to create a new image-classification algorithm that performed 8 percent better than current state
- MIT researchers develop an efficient way to train more reliable AI . . .
Reinforcement learning models, which underlie these AI decision-making systems, still often fail when faced with even small variations in the tasks they are trained to perform In the case of traffic, a model might struggle to control a set of intersections with different speed limits, numbers of lanes, or traffic patterns
- Algorithms and AI for a better world - MIT News
A good example of Raghavan’s intention can be found in his exploration of the use AI in hiring Raghavan says, “It’s hard to argue that hiring practices historically have been particularly good or worth preserving, and tools that learn from historical data inherit all of the biases and mistakes that humans have made in the past ”
- Explained: Generative AI’s environmental impact - MIT News
Plus, generative AI models have an especially short shelf-life, driven by rising demand for new AI applications Companies release new models every few weeks, so the energy used to train prior versions goes to waste, Bashir adds New models often consume more energy for training, since they usually have more parameters than their predecessors
- Novel AI model inspired by neural dynamics from the brain
AI often struggles with analyzing complex information that unfolds over long periods of time, such as climate trends, biological signals, or financial data One new type of AI model, called "state-space models," has been designed specifically to understand these sequential patterns more effectively
- Introducing the MIT Generative AI Impact Consortium
The MIT Generative AI Impact Consortium is a collaboration between MIT, founding member companies, and researchers across disciplines who aim to develop open-source generative AI solutions, accelerating innovations in education, research, and industry
- How we really judge AI - MIT News
For instance, AI appreciation is more pronounced for tangible robots than for intangible algorithms Economic context also matters In countries with lower unemployment, AI appreciation is more pronounced “It makes intuitive sense,” Lu says “If you worry about being replaced by AI, you’re less likely to embrace it ”
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