Stanford HAI is actively seeking engagement with companies that share our mission to advance AI research, education, policy, and practice to improve the human condition. Such engagement complements our collaboration with other stakeholders, including academia, policymakers, public sector entities, and civil society.
Environmental, social, and governance risks pose a threat to economies and human well-being around the world. However, we have the power to build a sustainable planet. Recent developments in AI are helping us see issues that were hard to identify before. As machine vision helps us see our world, we are able to detect issues, track them, and create targeted interventions. In this brief, we examine innovations by Stanford researchers that use AI and ML techniques to shift our world from one that depletes resources to one that preserves them for the future. For example, we can now track methane emissions across our energy and food systems, opening an avenue for policy formation and enforcement through near real-time tracing. AI enables knowledge-to-action and will play a key role in measuring and effectively achieving environmental, social, and governance goals.
Environmental, social, and governance risks pose a threat to economies and human well-being around the world. However, we have the power to build a sustainable planet. Recent developments in AI are helping us see issues that were hard to identify before. As machine vision helps us see our world, we are able to detect issues, track them, and create targeted interventions. In this brief, we examine innovations by Stanford researchers that use AI and ML techniques to shift our world from one that depletes resources to one that preserves them for the future. For example, we can now track methane emissions across our energy and food systems, opening an avenue for policy formation and enforcement through near real-time tracing. AI enables knowledge-to-action and will play a key role in measuring and effectively achieving environmental, social, and governance goals.
Robots are becoming a core building block in engineering and healthcare applications, altering the way many industries operate, and improving quality of life for everyone. With AI, robots are further given the ability to learn and adapt so that they can work collaboratively alongside humans and other robots in real-world environments. This industry brief provides a cross-section of key research – at HAI and across Stanford – that leverages AI methods into new algorithms for human robot interaction and robot navigation. Discover how researchers are designing intelligent robots that learn and adapt to human demonstration, and how they could be used to disrupt and create markets in a wide range of industries including manufacturing, healthcare, autonomous vehicles, and many more.
Robots are becoming a core building block in engineering and healthcare applications, altering the way many industries operate, and improving quality of life for everyone. With AI, robots are further given the ability to learn and adapt so that they can work collaboratively alongside humans and other robots in real-world environments. This industry brief provides a cross-section of key research – at HAI and across Stanford – that leverages AI methods into new algorithms for human robot interaction and robot navigation. Discover how researchers are designing intelligent robots that learn and adapt to human demonstration, and how they could be used to disrupt and create markets in a wide range of industries including manufacturing, healthcare, autonomous vehicles, and many more.
The development of digital learning infrastructure and platforms was driven by innovative AI technologies and accelerated by pandemic-imposed needs. This industry brief provides a cross-section of key AI research – at HAI and across Stanford – that are reshaping how we learn. Discover how researchers are facilitating high-quality personalized learning at scale, creating novel systems augmenting teaching and assessments, designing intentional and inclusive learning environments, and more.
The development of digital learning infrastructure and platforms was driven by innovative AI technologies and accelerated by pandemic-imposed needs. This industry brief provides a cross-section of key AI research – at HAI and across Stanford – that are reshaping how we learn. Discover how researchers are facilitating high-quality personalized learning at scale, creating novel systems augmenting teaching and assessments, designing intentional and inclusive learning environments, and more.
The spike in demand since the onset of COVID-19 for digital services from grocery delivery to banking has catalyzed a reimagining of the digital infrastructure we will need to power our post-pandemic world. In this brief you will learn where researchers lead the charge in identifying opportunities and pitfalls in deploying AI in financial services. Their work sheds light on how integrating AI can make financial services and their delivery more inclusive, accessible, and effective.
The spike in demand since the onset of COVID-19 for digital services from grocery delivery to banking has catalyzed a reimagining of the digital infrastructure we will need to power our post-pandemic world. In this brief you will learn where researchers lead the charge in identifying opportunities and pitfalls in deploying AI in financial services. Their work sheds light on how integrating AI can make financial services and their delivery more inclusive, accessible, and effective.
AI will have a profound impact on work, business, and the economy. In this industry brief, we provide a sampling of key research both at HAI and more broadly at Stanford that can help inform the future of work. You will find researchers studying how AI can be used to help teams collaborate, improve workplace culture, promote employee well-being, assist humans in dangerous environments, and more.
AI will have a profound impact on work, business, and the economy. In this industry brief, we provide a sampling of key research both at HAI and more broadly at Stanford that can help inform the future of work. You will find researchers studying how AI can be used to help teams collaborate, improve workplace culture, promote employee well-being, assist humans in dangerous environments, and more.
This industry brief focuses on AI research in healthcare and life sciences, with particular attention to its implications in a post COVID-19 world. Stanford HAI synthesize the latest from Stanford faculty across drug discovery, telehealth, ambient intelligence, operational excellence, medical imaging, augmented intelligence, and data and privacy. Read to learn more about how the adoption of AI may transform these applications.
This industry brief focuses on AI research in healthcare and life sciences, with particular attention to its implications in a post COVID-19 world. Stanford HAI synthesize the latest from Stanford faculty across drug discovery, telehealth, ambient intelligence, operational excellence, medical imaging, augmented intelligence, and data and privacy. Read to learn more about how the adoption of AI may transform these applications.
As the HAI Industry Program has matured over the last couple of years, we find increased opportunities for our researchers to engage with faculty and students to advance areas critical to the future of AI.
Google recognizes that HAI's interdisciplinary perspective is key to shaping an inclusive AI future.
The insights and value we derive from the HAI Industry Program have helped us tune our new to market offerings and reach a better product market fit.
An opportunity to open up Silicon Valley… to all of our clients.
Insights that will help shape the future of artificial intelligence.
Transformative for SCBX, catalyzing a new era of AI integration in our operations.
Instrumental to achieving Accenture’s AI ambition.
Financial support and real-world insights from industry members help ensure that the institute has the resources needed to pursue innovative, interdisciplinary research to address the most critical questions about the future of AI and its development. Learn more about the programs available below.
Learn more about Stanford HAI faculty affiliates’ cutting-edge research spanning industry verticals.
Join Stanford’s only community of AI-focused Startups and Founders.
Your support plays a crucial role in fostering research, education, policy, and collaboration across diverse fields. Whether you are an individual, a corporation, a foundation, or a family office, together we can shape the future of AI to ensure it serves humanity’s best interests.
For more information on Stanford HAI’s Industry Programs or other corporate engagement, please contact: Marc Gough or David Levi.
Gifts from Industry Founding Members are used to underwrite and advance the mission of HAI—propelling our agenda and priorities supporting faculty and fellows; graduate students and postdocs; fundamental and applied research; data sets and compute resources; reports and position papers; and events that convene stakeholders from across sectors.
As a multidisciplinary institute focused on responsibly guiding the future of artificial intelligence, Stanford HAI deeply values engagement with companies interested in AI research, policy and practice. The HAI Industrial Affiliates Program provides them the opportunity to interact with Stanford faculty and students, as well as other industry members, coordinated by HAI’s dedicated industry team. Relationships between companies, faculty, and graduate students provide all constituents with valuable insights on opportunities, problems, and solutions at the intersections of AI research, policy and education with industry.
Benefits
$550K HAI Wallet which can be used towards Research Tokens. Members may choose to use Research Tokens to support a particular area of program research, or the program research of a named faculty member or lab. All research results arising from the use of the Research Tokens will be shared with all program members and the general public.
Membership in the Stanford Digital Economy Lab (S-DEL) affiliate program
Opportunity to send Visiting Scholars to Stanford
Focus area engagement, which brings together multiple faculty members who are interested in related research topics. For the Financial Services Program Area, indicative topics and faculty leads are listed below.
Access to opportunities to support student recruitment and inclusive and engaging activities, and to enhance your visibility across campus
Plus additional, cross-cutting benefits including but not limited to:
Executive Breakfast Series
HAI Startup Series
Early access to and engagement with the HAI Industry Briefs
Presentations of research by HAI-affiliated faculty and students
Conferences, seminars, and workshops
Acknowledgement at HAI’s major public events
SEAMS: Self-improving, Efficient and Accelerated Models and Systems
Foundation models, trained on broad data at immense scale, have revolutionized AI and computing, in the last few years. They exhibit a qualitative leap in capabilities: They can generate full documents and images, they can engage in compelling dialogue, and they can solve a wide range of tasks by simply prompting with natural language instructions. They represent a paradigm shift in how AI systems are built, enabling a new form of rapid prototyping, and they will transform every sector. However, we are still in the early innings of this revolution - similar to the Internet in 1993. Today's systems are still vastly inefficient, functioning only as an existence proof that certain AI capabilities are possible.
Our fundamental thesis is that we can obtain many orders of magnitude of improvement by developing self-improving systems, that is, using AI to optimize AI. An AI system consists of three layers: the systems layer (including both hardware and software), the modeling layer (e.g., the model architecture, training procedure), and the data layer (e.g., how to supervise the system). Currently, all three layers require human experts to manually make decisions (e.g., designing custom kernels, manual hyperparameter tuning, manual weighting of data). However, as AlphaGo and AlphaFold have clearly demonstrated, if we have a clear objective function, automation can optimize it far better than a human can. This enables the human expert to focus on higher-level, strategic planning. Finally, automatic optimization requires simulation, which can be expensive. We will develop a cascade of efficient (possibly approximate) simulators of all three levels of the stack to enable efficient optimization.
Learn more about the SEAMS Industrial Affiliate Program