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- Massachusetts Institute of Technology - MIT News
Teaching AI models the broad strokes to sketch more like humans do SketchAgent, a drawing system developed by MIT CSAIL researchers, sketches up concepts stroke-by-stroke, teaching language models to visually express concepts on their own and collaborate with humans
- How we really judge AI | MIT News | Massachusetts Institute of Technology
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 ”
- 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
- 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 ”
- MIT researchers introduce generative AI for databases
Researchers from MIT and elsewhere developed an easy-to-use tool that enables someone to perform complicated statistical analyses on tabular data using just a few keystrokes Their method combines probabilistic AI models with the programming language SQL to provide faster and more accurate results than other methods
- 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
- Explained: Generative AI | MIT News | Massachusetts Institute of Technology
Before the generative AI boom of the past few years, when people talked about AI, typically they were talking about machine-learning models that can learn to make a prediction based on data For instance, such models are trained, using millions of examples, to predict whether a certain X-ray shows signs of a tumor or if a particular borrower is
- 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
- “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
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