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Microsoft Research Forum: New series explores bold ideas in technology research in the era of AI
Microsoft Research Forum (opens in new tab) is a new series of conversations that explore recent advances, bold new ideas, and important discussions within the global research community. Leading Microsoft researchers will share insights into their work, followed by live…
Abstracts: January 25, 2024
| Gretchen Huizinga, Jordan Ash, 和 Dipendra Misra
On “Abstracts,” Jordan Ash & Dipendra Misra discuss the parameter reduction method LASER. Tune in to learn how selective removal of stored data alone can boost LLM performance, then sign up for Microsoft Research Forum for more on LASER &…
We’re proud to have 100+ accepted papers At NeurIPS 2023, plus 18 workshops. Several submissions were chosen as oral presentations and spotlight posters, reflecting groundbreaking concepts, methods, or applications. Here’s an overview of those submissions.
ASL Citizen is the first crowdsourced sign language dataset, advancing the state of the art in sign recognition. The web-based project captured input from people in real-world settings, and from a diverse group of experts, including Deaf team members.
| Hanna Wallach 和 Ashley Llorens
Powerful large-scale AI models like GPT-4 are showing dramatic improvements in reasoning, problem-solving, and language capabilities. This marks a phase change for artificial intelligence—and a signal of accelerating progress to come. In this Microsoft Research Podcast series, AI scientist and…
新闻报道 | TIME
Microsoft on the TIME100 AI list
This morning, TIME released its first “TIME100 Artificial Intelligence” list online, which includes Kevin Scott, Jaime Teevan, Kate Crawford, and Kalika Bali.
New evaluation methods and a commitment to continual improvement are musts if we’re to build multimodal AI systems that advance human goals. Learn about cutting-edge research into the responsible development and use of multimodal AI at Microsoft.
In this issue: An illusion of predictability in scientific results; Kathleen Sullivan named to Insider’s 30 under 40 in healthcare list; FiGURe: Simple and Efficient Unsupervised Node Representations with Filter Augmentations.
| Jessica Maghakian, Akanksha Saran, Cheng Tan, 和 Paul Mineiro
In reinforcement learning, handcrafting reward functions is difficult and can yield algorithms that don’t generalize well. IGL-P, an interaction-grounded learning strategy, learns personalized rewards for different people in recommender system scenarios.