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- Massachusetts Institute of Technology - MIT News
Researchers present bold ideas for AI at MIT Generative AI Impact Consortium kickoff event Presentations targeted high-impact intersections of AI and other areas, such as health care, business, and education June 20, 2025 Read full story →
- 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 ”
- 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
- “An AI future that honors dignity for everyone” - MIT News
AI will likely evolve through a cycle of inflated expectations, disillusionment, and eventual pragmatic inspiration ” Still, Vinson suggested there were substantial differences between AI and some of our earlier technological leaps — the industrial revolution, the electrical revolution, and the digital revolution, among others
- “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
- 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 ”
- AI tool generates high-quality images faster than state-of-the-art . . .
A hybrid AI approach known as hybrid autoregressive transformer can generate realistic images with the same or better quality than state-of-the-art diffusion models, but that runs about nine times faster and uses fewer computational resources The new tool uses an autoregressive model to quickly capture the big picture and then a small diffusion model to refine the details of the image
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