AI Frontiers
新闻与深度文章
In this issue: Generative kaleidoscopic networks; Text diffusion with reinforced conditioning; PRISE – Learning temporal action abstractions as a sequence compression problem.
| Arindam Mitra, Hamed Khanpour, Corby Rosset, 和 Ahmed Awadallah
Microsoft’s Orca-Math, a specialized small language model, outperforms much larger models in solving math problems that require multi-step reasoning and shows the potential of using feedback to improve language models. Learn more.
新闻报道 | Microsoft News Center
3 big AI trends to watch in 2024
2023 was a major year for generative AI, as it went from research labs to real life with millions of people using it through popular tools like ChatGPT and Microsoft Copilot. This year, AI is expected to become more accessible,…
Research Focus: New Research Forum series explores bold ideas in the era of AI; LASER improves reasoning in language models; Cache-Efficient Top-k Aggregation over High Cardinality Large Datasets; Six Microsoft researchers named 2023 ACM Fellows.
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…
新闻报道 | The Verge
Microsoft LASERs away LLM inaccuracies
During the January Microsoft Research Forum, Dipendra Misra, a senior researcher at Microsoft Research Lab NYC and AI Frontiers, explained how Layer-Selective Rank Reduction (or LASER) can make large language models more accurate.
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 &…
| Xin Wang 和 Neel Joshi
HoloAssist is a new multimodal dataset consisting of 166 hours of interactive task executions with 222 participants. Discover how it offers invaluable data to advance the capabilities of next-gen AI copilots for real-world tasks.
| Ahmed Awadallah 和 Ashley Llorens
What’s the driving force behind AI’s recent, rapid progress? Research manager Ahmed Awadallah shares his insights on this, the two-stage approach to training large-scale models, and the need for better model evaluation in this episode of the #MSRPodcast.