Microsoft Research Forum is a continuous exchange of ideas about science and technology research in the era of general AI. In the latest episode (opens in new tab), researchers discussed the latest multimodal AI models, advanced benchmarks for AI evaluation and model self-improvement, and an entirely new kind of computer for AI inference and hard optimization. Researchers at Microsoft are working to explore breakthrough technology that can help advance everything from weather prediction to materials design.
Below is a brief recap of the event, including select quotes from the presentations. Register to join future Research Forum episodes and view previous sessions. Transcripts and additional resources can be found in the Research Forum briefing book.
Keynote
Phi-3-Vision: A highly capable and “small” language vision model (opens in new tab)
Jianfeng Gao introduced Phi-3-Vision, an advanced and economical open-source multimodal model. As a member of the Phi-3 model family, Phi-3-Vision enhances language models by integrating multisensory skills, seamlessly combining language and vision capabilities.
“Phi-3-Vision is the first multimodal model in the Phi small model family. It matches and sometimes exceeds some of the capabilities of much larger models … at a much lower cost. And to help everyone build more affordable and accessible AI systems, we have released the model weights into the open-source community.”
— Jianfeng Gao, Distinguished Scientist and Vice President, Microsoft Research Redmond
Panel Discussion
Beyond language: The future of multimodal models in healthcare, gaming, and AI (opens in new tab)
This discussion examined the transformative potential and core challenges of multimodal models across various domains, including precision health, game intelligence, and foundation models. Microsoft researchers John Langford, Hoifung Poon, Katja Hofmann, and Jianwei Yang shared their thoughts on future directions, bridging gaps, and fostering synergies within the field.
“One of the really cutting-edge treatments for cancer these days is immunotherapy. That works by mobilizing the immune system to fight the cancer. And then one of the blockbuster drugs is a KEYTRUDA, that really can work miracles for some of the late- stage cancers … Unfortunately, only 20 to 30 percent of the patients actually respond. So that’s … a marquee example of what are the growth opportunity in precision health.”
— Hoifung Poon, General Manager, Microsoft Research Health Futures
“We experience the world through vision, touch, and all our other senses before we start to make sense of any of the language that is spoken around us. So, it’s really, really interesting to think through the implications of that, and potentially, as we start to understand more about the different modalities that we can model and the different ways in which we combine them.”
— Katja Hofmann, Senior Principal Researcher, Microsoft Research
“To really have a capable multimodal model, we need to encode different information from different modalities, for example, from vision, from language, from even audio, speech, etc. We need to develop a very capable encoder for each of these domains and then … tokenize each of these raw data.”
— Jianwei Yang, Principal Researcher, Microsoft Research Redmond
Lightning Talks
Analog optical computing for sustainable AI and beyond (opens in new tab)
This talk presented a new kind of computer—an analog optical computer—that has the potential to accelerate AI inference and hard optimization workloads by 100x, leveraging hardware-software co-design to improve the efficiency and sustainability of real-world applications.
“Most likely, you or your loved ones have been inside an MRI scan — not really a great place to be in. Imagine if you can reduce that amount of time from 20 to 40 minutes to less than five minutes.”
— Francesca Parmigiani, Principal Researcher, Microsoft Research Cambridge
“I’m really excited to share that we have just completed the second generation of [this] computer. It is much smaller in physical size, and this is a world first in that exactly the same computer is simultaneously solving hard optimization problems and accelerating machine learning inference. Looking ahead, we estimate that at scale, this computer can achieve around 450 tera operations per second per watt, which is a 100-times improvement as compared to state-of-the-art GPUs.”
— Jiaqi Chu, Principal Researcher, Microsoft Research Cambridge
Direct Nash Optimization: Teaching language models to self-improve with general preferences (opens in new tab)
This talk explored teaching language models to self-improve using AI preference feedback, challenging the model to play against itself and a powerful teacher until it arrives at a Nash equilibrium, resulting in state-of-the-art win rates against GPT-4 Turbo on benchmarks such as AlpacaEval and MT-Bench.
“The traditional way to fine-tune an LLM for post-training … basically tells the model to emulate good behaviors, but it does not target or correct any mistakes or bad behaviors that it makes explicitly. … Self-improving post-training explicitly identifies and tries to correct bad behaviors or mistakes that the model makes.”
— Corby Rosset, Senior Researcher, Microsoft Research AI Frontiers
Project Aurora: The first large-scale foundation model of the atmosphere (opens in new tab)
This talk presented Aurora, a cutting-edge foundation model that offers a new approach to weather forecasting that could transform our ability to predict and mitigate the impacts of extreme events, air pollution, and the changing climate.
“If we look at Aurora’s ability to predict pollutants such as nitrogen dioxide that are strongly related to emissions from human activity, we can see that the model has learned to make these predictions with no emissions data provided. It’s learned the implicit patterns that cause the gas concentrations, which is very impressive.”
— Megan Stanley, Senior Researcher, Microsoft Research AI for Science
A generative model of biology for in-silico experimentation and discovery (opens in new tab)
This talk explored how deep learning enables generation of novel and useful biomolecules, allowing researchers and practitioners to better understand biology. This includes EvoDiff, a general-purpose diffusion framework that combines evolutionary-scale data with the distinct conditioning capabilities of diffusion models to generate new proteins, given a protein sequence.
“Often, protein engineers want proteins that perform a similar function to a natural protein, or they want to produce a protein that performs the same function but has other desirable properties, such as stability. By conditioning EvoDiff with a family of related sequences, we can generate new proteins that are very different in sequence space to the natural proteins but are predicted to fold into similar three-dimensional structures. These may be good starting points for finding new functions or for discovering versions of a protein with desirable properties.”
— Kevin Yang, Senior Researcher, Microsoft Research New England
Fostering appropriate reliance on AI (opens in new tab)
Since AI systems are probabilistic, they can make mistakes. One of the main challenges in human-AI interaction is to avoid overreliance on AI and empower people to determine when to accept or not accept an AI system’s recommendation. This talk explores Microsoft’s work in this area.
“This is where I think it is our responsibility as people working in UX disciplines—as people researching UX and human-computer interaction—to really, really step up to the front and see how it is our moment to shine and to address this problem.”
— Mihaela Vorvoreanu, Director UX Research and Responsible AI Education, Microsoft AI Ethics and Effects in Engineering and Research (Aether)
