Eric J Horvitz

Nov 27, 2024 | microsoft.com | Eric J Horvitz |Harsha Nori |Naoto Usuyama

Groundbreaking advancements in frontier language models are progressing rapidly, paving the way for boosts in accuracy and reliability of generalist models, making them highly effective in specialized domains. As part of our ongoing exploration of foundation model capabilities, we developed Medprompt last year—a novel approach to maximize model performance on specialized domain and tasks without fine-tuning.

Feb 9, 2024 | linkedin.com | Eric J Horvitz

Eric Horvitz Chief Scientific Officer, Microsoft February 9, 2024 No single solution exists to confront the complex challenge of deceptive online content, whether that content is handcrafted or AI-generated. Thwarting motivated adversaries will take ongoing vigilance and a multifaceted approach in technical and sociotechnical realms.

Jan 24, 2024 | blogs.microsoft.com | Eric J Horvitz

We are delighted to announce our support for the National AI Research Resource (NAIRR) pilot, a vital initiative highlighted in the President’s Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence. This initiative aligns with our commitment to broaden AI research and spur innovation by providing greater computing resources to AI researchers and engineers in academia and non-profit sectors.

Dec 12, 2023 | microsoft.com | Eric J Horvitz |Harsha Nori |Yin Tat Lee |Brenda Potts

We’re seeing exciting capabilities of frontier foundation models, including intriguing powers of abstraction, generalization, and composition across numerous areas of knowledge and expertise. Even seasoned AI researchers have been impressed with the ability to steer the models with straightforward, zero-shot prompts. Beyond basic, out-of-the-box prompting, we’ve been exploring new prompting strategies, showcased in our Medprompt work, to evoke the powers of specialists.

Nov 28, 2023 | microsoft.com | Eric J Horvitz |Alyssa Hughes

Today, we published an exploration of the power of prompting strategies that demonstrates how the generalist GPT-4 model can perform as a specialist on medical challenge problem benchmarks. The study shows GPT-4’s ability to outperform a leading model that was fine-tuned specifically for medical applications, on the same benchmarks and by a significant margin.