Kunle Olukotun: How to Make AI More Democratic
A chip designer talks about how advances in hardware will be needed to make the much-hyped artificially intelligent future a reality.
Electrical engineer Kunle Olukotun has built a career out of building computer chips for the world.
These days his attention is focused on new-age chips that will broaden the reach of artificial intelligence to new uses and new audiences — making AI more democratic.
The future will be dominated by AI, he says, and one key to that change rests in the hardware that makes it all possible — faster, smaller, more powerful computer chips. He imagines a world filled with highly efficient, specialized chips built for specific purposes, versus the relatively inefficient but broadly applicable chips of today.
Making that vision a reality will require hardware that focuses less on computation and more on streamlining the movement of data back and forth, a function that now claims 90% of computing power, as Olukotun tells host Russ Altman, associate director of the Stanford Institute for Human-Centered AI, on this episode of Stanford Engineering’s The Future of Everything podcast. Watch below and subscribe here.
Stanford HAI's mission is to advance AI research, education, policy and practice to improve the human condition. Learn more.
Related News
Dashboards created by Stanford HAI’s Digital Economy Lab measure how AI adoption is altering employment trends and job growth, along with broader economic indicators.
Dashboards created by Stanford HAI’s Digital Economy Lab measure how AI adoption is altering employment trends and job growth, along with broader economic indicators.
Over three years, we’ve built progressively harder, more realistic benchmarks for medical AI. Here’s what we learned.
Over three years, we’ve built progressively harder, more realistic benchmarks for medical AI. Here’s what we learned.
It's one of AI policy's hardest unsolved problems. Expert Deb Raji joins Stanford HAI to find answers that move beyond principles and into practice.
It's one of AI policy's hardest unsolved problems. Expert Deb Raji joins Stanford HAI to find answers that move beyond principles and into practice.