AI Frontiers: The future of scale with Ahmed Awadallah and Ashley Llorens
| Ahmed Awadallah and 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.
Microsoft at ACM SIGCOMM 2023: Innovating the future of networking
| Ranveer Chandra, Eashan Gupta, Ilias Marinos, Anuj Kalia, Francis Y. Yan, Rohan Gandhi, Venkat Padmanabhan, Sreangsu Acharyya, Siva Kesava Reddy Kakarla, and Dave Maltz
Editor’s note, Sept. 19, 2023 – The number of papers accepted at SIGCOMM was updated to correct a factual error. Modern applications heavily rely on robust network infrastructure, requiring continuous innovation. In this evolving landscape, Microsoft is at the forefront,…
In the news | TechCrunch
Microsoft open sources EvoDiff, a novel protein-generating AI
This week, Microsoft introduced a general-purpose framework, EvoDiff, that the company claims can generate “high-fidelity,” “diverse” proteins given a protein sequence. Different from other protein-generating frameworks, EvoDiff doesn’t require any structural information about the target protein, cutting out what’s typically…
Research Focus: Week of September 11, 2023
In this issue: Efficient polyglot analytics on semantic data aids query performance; generative retrieval for conversational question answering improves dialogue-based interfaces; a new tool uses ML to address capacity degradation in lithium-ion batteries.
Abstracts: September 13, 2023
| Gretchen Huizinga, Ava Amini, and Kevin Kaichuang Yang
The new #MSRPodcast series “Abstracts” is your source for cutting-edge research in brief. In the first episode, join researchers Ava Amini and Kevin K. Yang to learn about their new paper on using evolutionary-scale protein data to improve protein design.
In the news | VentureBeat
Meet Phi-1.5, the new language model that could make training AI radically cheaper and faster
By generating curated, high quality, synthetic data using existing LLMs (in this case, OpenAI’s ChatGPT) and training a new model on this, the researchers are able to achieve results comparable to leading LLMs at a fraction of the cost and…
FP2: Fully In-Place Functional Programming provides memory reuse for pure functional programs
| Anton Lorenzen, Daan Leijen, and Wouter Swierstra
This research paper was presented at the 28th ACM SIGPLAN International Conference on Functional Programming (opens in new tab) (ICFP), a premier forum for discussing design, implementations, principles, and uses of functional programming. Functional programming languages offer a host of…
大模型时代，如何评估人工智能与人类智能？
编者按：大模型时代的到来，使得人工智能成为了真正影响人类社会的重要技术之一，如何打造“负责任的人工智能”的问题正变得愈发迫切和关键。一直以来，微软始终致力于依照以人为本的伦理原则推进人工智能的发展，微软亚洲研究院也将“社会责任人工智能（Societal AI）”作为一个重要的研究方向。 2023年，微软亚洲研究院特别组织了“社会责任人工智能（Societal AI）”系列研讨会，为计算机领域的科研...
Understanding social biases through the text-to-image generation lens
| Ranjita Naik and Besmira Nushi
Gender, race, and age disparities in AI-generated images persist. This AIES 2023 study on text-to-image models shows that even basic prompts can lead to underrepresentation, calling for responsible bias mitigation strategies.