How artificial intelligence is changing science

Artificial intelligence is now part of our daily lives, whether in voice recognition systems or route finding apps. But scientists are increasingly drawing on artificial intelligence to understand society, design new materials and even improve our health.

Once a computer scientist’s pipe dream, artificial intelligence is now part of our daily lives in the form of voice recognition systems, product recommendation platforms and navigation tools. All of these rely on computer algorithms that process information and solve problems in a way similar to – and sometimes superior to – the human mind.

Yet artificial intelligence is doing more than just recommending new restaurants and the best routes to them. It is also changing the way scientists across diverse disciplines are studying the world. Aided by the close proximity of medical researchers, computer scientists, psychologists and more, Stanford researchers are deploying artificial intelligence to map poverty in Africa, find safer alternatives to conventional rechargeable batteries and perhaps even understand our own minds.

AI Advancing Social Sciences and Humanities

Algorithms reveal changes in stereotypes

New Stanford research shows that, over the past century, linguistic changes in gender and ethnic stereotypes correlated with major social movements and demographic changes in the U.S. Census data.

At Stanford, experts explore artificial intelligence’s social benefits

Experts from Stanford and elsewhere talked about the future of artificial intelligence in society as part of the Global Entrepreneurship Summit.

Stanford scientists combine satellite data, machine learning to map poverty

Accurate and reliable information on the location of impoverished zones is surprisingly lacking for much of the world. Applying machine learning to satellite images could identify impoverished regions in Africa.

Robo-journalism is good news for stocks

Automation in the newsroom expands coverage of smaller firms and improves markets.

CASBS tackles interdisciplinary solutions to societal problems

Interdisciplinary research at Stanford’s Center for Advanced Study in the Behavioral Sciences addresses future of work, consequences of evolving technology and other pressing societal issues.

Stanford professors discuss ethics involving driverless cars

Self-driving technology presents vast ethical challenges and questions. Several professors and interdisciplinary groups at Stanford who are tackling this issue  offer their perspectives on the topic.

Cops speak less respectfully to black community members

Professors Jennifer Eberhardt and Dan Jurafsky, along with other Stanford researchers, detected racial disparities in police officers’ speech after analyzing more than 100 hours of body camera footage from Oakland Police.

Tracking fishing from space

Satellite data from thousands of high seas fishing vessels over four years illuminate global fishing’s scope and pattern and hold promise for improving ocean management across the planet.

Stanford historian examines age-old inquiry about what it means to be ‘living’

In research covering four centuries of scientific debate, Stanford historian Jessica Riskin investigates different views of man and machine, and how this debate laid the groundwork for later theories of evolution and science.

Chris Manning: How computers are learning to understand language​

A computer scientist discusses the evolution of computational linguistics and where it's headed next. He was recently named the Thomas M. Siebel Professor in Machine Learning.

Audrey Shafer: Why Frankenstein still holds a mirror to modern science

Stanford’s Russ Altman and Audrey Shafer reflect on Mary Shelley’s Frankenstein and how it illuminates the moral and ethical challenges of modern science.

A neighborhood’s cars indicate its politics

Stanford researchers led by Fei-Fei Li use computer algorithms to analyze millions of publicly available images on Google Street View.

​Michael Bernstein: Welcome to the future of crowdsourcing

On The Future of Everything radio show, a computer scientist explores the rise of automation, crowdsourcing communities and the ethical implications of the gig economy.

Which is more fair: a human or a machine?

A team of researchers harness the variability of human decision making to compensate for two flaws in machine-learning models: factoring in the unknown and the unknowable.

Algorithm improves integration of refugees

A new machine learning algorithm developed by Stanford researchers could help governments and resettlement agencies find the best places for refugees to relocate, depending on their particular skills and backgrounds.

AI Advancing Science and Technology

Deep learning comes full circle

Artificial intelligence drew much inspiration from the human brain but went off in its own direction. Now, AI has come full circle and is helping neuroscientists better understand how our own brains work.

Neural networks for neutrinos

Scientists are using cutting-edge machine-learning techniques to analyze physics data.

No more burning batteries? Stanford scientists turn to AI to create safer lithium-ion batteries

Researchers have identified 21 solid materials that could replace flammable liquid electrolytes in lithium-ion batteries, improving the safety of electronic devices like cellphones and laptops.

Stanford’s humanoid robotic diver recovers treasures from King Louis XIV’s wrecked flagship

The robot, called OceanOne, is powered by artificial intelligence and haptic feedback systems, allowing human pilots an unprecedented ability to explore the depths of the oceans in high fidelity.

Scientists use machine learning to speed discovery of metallic glass

SLAC and its collaborators are transforming the way new materials are discovered. In a new report, they combine artificial intelligence and accelerated experiments to discover potential alternatives to steel in a fraction of the time.

SLAC-led project will use artificial intelligence to prevent or minimize electric grid failures

It’s the first to employ AI to help the grid manage power fluctuations, resist damage and bounce back faster from storms, solar eclipses, cyberattacks and other disruptions

Stanford students get creative with robots

After learning new software and programming languages, Stanford students in the Artificial Intelligence Laboratory have an opportunity to choose a creative task and design a robot to perform the task for demonstration.

Machine learning dramatically streamlines search for more efficient chemical reactions

An advance by SLAC and Stanford researchers greatly reduces the time needed to analyze complex chemical reactions, including catalysis.

Artificial intelligence analyzes gravitational lenses 10 million times faster

SLAC and Stanford researchers demonstrate that brain-mimicking neural networks can revolutionize the way astrophysicists analyze their most complex data, including extreme distortions in spacetime that are crucial for our understanding of the universe.

Autonomous robotics class integrates theory and practice

Students programmed robots to autonomously navigate an unknown cityscape and aid in a simulated rescue of animals in peril in a class that mimics the programming needed for autonomous cars or robots of the future.

​Maneesh Agrawala: Artificial intelligence comes to multimedia

​Stanford professors Russ Altman and Maneesh Agrawala explore advances in media where AI handles the rough cut, and editing becomes like using word processors for images and sound.

Andrew Ng: Why AI is the new electricity

A computer scientist discusses artificial intelligence’s promise, hype and biggest obstacles.

Artificial intelligence index tracks emerging field

The initiative measures AI’s technological progress much as the GDP and S&P 500 take the pulse of the U.S. economy and stock market.

AI Advancing Health

Stanford research could improve counseling on crisis help lines

Many people now text rather than call for help, allowing computer scientists to study anonymous data files and learn which counseling tactics work best.

Deep learning algorithm could aid drug development

Combining computer science and chemistry, researchers show how an advanced form of machine learning that works off small amounts of data can be used to solve problems in drug discovery.

Virtual competitors vie for a different kind of athletic title

Better models of the bone, muscles and nerves that control our bodies could help doctors manage movement disorders like cerebral palsy. A new competition is crowdsourcing the search for those tools.

Computers trounce pathologists in predicting lung cancer type, severity

Automating the analysis of slides of lung cancer tissue samples increases the accuracy of tumor classification and patient prognoses, according to a new study.

Artificial intelligence used to identify skin cancer

In hopes of creating better access to medical care, Stanford researchers have trained an algorithm to diagnose skin cancer.

Researchers say use of artificial intelligence in medicine raises ethical questions

In a perspective piece, Stanford researchers discuss the ethical implications of using machine-learning tools in making health care decisions for patients.

Algorithm diagnoses heart arrhythmias with cardiologist-level accuracy

A new deep learning algorithm can diagnose 14 types of heart rhythm defects, called arrhythmias, better than cardiologists. This could speed diagnosis and improve treatment for people in rural locations.

Stanford scholars discuss mental health and technology

Conversational software programs might provide patients a less risky environment for discussing mental health, but they come with some risks to privacy or accuracy. Stanford scholars discuss the pros and cons of this trend.

Researchers improve patient safety with bedside computer vision

What if clinician imperfection could be improved by a form of artificial intelligence that continuously detects, and prompts correction of, defects in bedside care?

Artificial intelligence in medicine — predicting patient outcomes and beyond

Nigram Shah, an associate professor of medicine, discusses how artificial intelligence could help doctors predict outcomes after patients are hospitalized.