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In the latest episode of Microsoft Research Forum, researchers explored the importance of globally inclusive and equitable AI, shared updates on AutoGen and MatterGen, presented novel use cases for AI, including industrial applications and the potential of multimodal models to improve assistive technologies.
Below is a brief recap of the event, including select quotes from the presentations. Full replays of each session and presentation will be available soon.
Keynote: Building Globally Equitable AI
Jacki O’Neill, Lab Director, Microsoft Research Africa, Nairobi
Jacki O’Neill discussed the importance of creating globally equitable generative AI. She addressed the technical and sociotechnical challenges that must be tackled to positively transform the future of work worldwide.
“We’re at the very early stage of generative AI and the impacts it will have on work. This is a fast-moving field, and there’s an immense opportunity to take control of the agenda and build truly globally equitable AI systems. This requires ensuring that diverse contexts and applications, with their diverse datasets, drive the development of generative AI.”
Panel discussion: Generative AI for Global Impact: Challenges and Opportunities
Jacki O’Neill, Lab Director, Microsoft Research Africa, Nairobi (host)
Sunayana Sitaram, Principal Researcher, Microsoft Research India
Daniela Massiceti, Senior Researcher, Microsoft Research Cambridge
Tanuja Ganu, Principal Research SDE Manager, Microsoft Research India
Microsoft researchers discussed the challenges and opportunities of making AI more inclusive and impactful for everyone—from data that represents a broader range of communities and cultures to novel use cases for AI that are globally relevant.
“How can we take this power of generative AI and empower every individual, every individual across the globe—the people who are coming from different nationalities, different ethnicities, cultures, as well as with varied technology access and financial affordability?”
—Tanuja Ganu, Principal Research SDE Manager, Microsoft Research India
“One of the solutions that we’ve been using is to actually design with ‘human in the loop’ in mind because we know that these technologies are not perfect. And so, we really want to figure out ways in which humans and AI systems can work together in order to create the most effective outcome.”
—Sunayana Sitaram, Principal Researcher, Microsoft Research India
“We really need multidisciplinary research that goes beyond anything that we’ve done before, involving researchers and practitioners and community members. And it’s important to remember that machine learning engineers and researchers on their own can’t solve the problem of building globally equitable generative AI. This is something that we really need to do in a large scale.”
—Jacki O’Neill, Lab Director, Microsoft Research Africa, Nairobi
“An estimated 1.3 billion people—around 16 percent of the global population—live with some level of disability today. So, I think it’s really exciting to see these generative AI applications coming online for these communities.”
“As we look to this next decade of generative AI solutions, I really hope to see that we’re going to see more personalized AI models and solutions come through much more strongly, solutions where you as the user have much more control, much more agency, around how your model works for you.”
—Daniela Massiceti, Senior Researcher, Microsoft Research Cambridge
Lightning talk: Insights into the Challenges and Opportunities of Large Multi-Modal Models for Blind and Low Vision Users: A Case Study on CLIP
Daniela Massiceti, Senior Researcher, Microsoft Research Cambridge
Daniela Massiceti explored the transformative potential of multimodal models such as CLIP for assistive technologies. Specifically focusing on the blind/low-vision community, the talk explored the current distance from realizing this potential and the advancements needed to bridge this gap.
“Today’s AI models hold incredible potential for assisting the Blind community—from text recognition to object identification to question answering. Apps like Seeing AI are already deploying some of these AI features. But there is potential for much more.”
Lightning talk: Driving Industry Evolution: Exploring the Impact of Generative AI on Sector Transformation
Jiang Bian, Senior Principal Research Manager, Microsoft Research Asia
Jiang Bian discussed how generative AI transforms industries by bridging gaps between AI capabilities and industrial needs.
“In our dialogues with strategic partners, we have identified crucial gaps in current generative AI capabilities versus the specific needs of industry applications. These include a too-narrow focus on human-like AI but not critical industry applications, limitations in processing complex and noisy data, and concerns about reliability in complex decision-making scenarios. Our research is crucial in addressing these limitations and amplifying the underappreciated potential of generative AI in high-value sectors.”
Lightning talk: MatterGen: A Generative Model for Materials Design
Tian Xie, Principal Research Manager, Microsoft Research
Tian Xie described MatterGen, a generative model that enables the design of new inorganic materials based on a broad range of property conditions required by the application, aiming to shift the traditional paradigm of materials design with generative AI.
“Traditionally, materials design is conducted by search-based methods. We search through a list of candidates and gradually filter them using a list of design criteria for the application. Like for batteries, we need the materials to contain lithium, to be stable, to have a high lithium-ion conductivity, and each filtering step can be conducted using simulation-based methods or AI emulators. At the end, we get five to 10 candidates that we’re sending to the lab for experimental synthesis.”
“In MatterGen, we hope to rethink this process with generative AI. We’re aiming to directly generate materials given the design requirements for the target application, bypassing the process of searching through candidates. You can think of it as using text-to-image generative models like DALL-E to generate the images given a prompt rather than needing to search through the entire internet for images via a search engine.”
Lightning talk: AutoGen Update: Complex Tasks and Agents
Adam Fourney, Principal Researcher, Microsoft Research AI Frontiers
Adam Fourney discussed the effectiveness of using multiple agents, working together, to complete complex multi-step tasks. He showcased their capability to outperform previous single-agent solutions on benchmarks like GAIA, utilizing customizable arrangements of agents that collaborate, reason, and utilize tools to achieve complex outcomes.
“We’re starting to tackle increasingly more complex benchmarks and real-world scenarios with this configuration. And we’re really excited about opportunities to introduce new agents that, for example, learn and self-improve with experience; that understand images and screenshots a little better for maybe more effective web surfing or use of interfaces; and that are maybe a bit more systematic about exploring that solution space. So rather than just updating that ledger and then restarting when they get stuck, they can be a bit more pragmatic about the strategies that they’re employing.”
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