This article is part of our special report on AI, “The Great AI Reckoning.”
If you've seen IEEE Spectrum's October 2021 special issue on artificial intelligence, you may have noticed provocative quotes scattered throughout the magazine's pages. These quotes were drawn from the many Q&As and articles we've done over the years with top thinkers in AI. Here are the quotes, with links to the full articles.
“In terms of how much progress we've made in this work over the last two decades: I don't think we're anywhere close today to the level of intelligence of a two-year-old child. But maybe we have algorithms that are equivalent to lower animals for perception.”
—YOSHUA BENGIO, founder and scientific director of Mila–Quebec AI Institute. From “Yoshua Bengio, Revered Architect of AI, Has Some Ideas About What to Build Next" (December 2019).
“Dig into every industry, and you'll find AI changing the nature of work.”
—DANIELA RUS, director of MIT's Computer Science and Artificial Intelligence Laboratory. From “AI and the Future of Work: The Economic Impacts of Artificial Intelligence" (November 2019).
“We need to think about how to steer AI. How do we transform today's buggy and hackable AI systems into systems we can really trust and make sure they really do what we intend them to do? How do we teach machines to understand our goals, adopt our goals, and retain our goals?”
—MAX TEGMARK, professor at MIT and cofounder of the Future of Life Institute. From “Interview: Max Tegmark on Superintelligent AI, Cosmic Apocalypse, and Life 3.0" (September 2017).
“The policy goal is to get the regulators to enforce their laws in the age of algorithms. You can't give up on enforcing antidiscrimination laws because you don't understand the technology.”
—CATHY O'NEIL, founder and CEO of O'Neil Risk Consulting and Algorithmic Auditing. From “Racial Bias Found in Algorithms That Determine Health Care for Millions of Patients" (October 2019).
“General intelligence doesn't mean you have to understand humans. There will be a lot of intelligent machines that don't need to know that stuff. [Take the] example of robotic construction workers on Mars: I don't think they need to understand human emotions and human desires to be able to construct things.”
—JEFF HAWKINS, cofounder and chief scientist of Numenta. From “Deep Learning Isn't Deep Enough Unless It Copies From the Brain" (March 2021).
“AI will take many single-task, single-domain jobs away. You can argue that humans have abilities that AI does not: We can conceptualize, strategize, create. Whereas today's AI is just a really smart pattern recognizer that can take in data [and] optimize. But how many jobs in the world are simple repetitions of tasks that can be optimized?”
—KAI-FU LEE, chairman and CEO of Sinovation Ventures. From “Former Head of Google China Foresees an AI Crisis—and Proposes a Solution" (September 2018).
“AI is fundamentally an applied technology that's going to serve our society. Humanistic AI not only raises the awareness of the importance of the technology. It's a really important way to attract diverse students, technologists, and innovators to participate.”
—FEI-FEI LI, codirector of the Stanford Institute for Human-Centered Artificial Intelligence. From “Computer Vision Leader Fei-Fei Li on Why AI Needs Diversity" (October 2016).
“Those of us in machine learning are really good at doing well on a test set, but unfortunately deploying a system takes more than doing well on a test set. All of AI…has a proof-of-concept-to-production gap.”
—ANDREW NG, CEO and cofounder of Landing AI. From “Andrew Ng X-Rays the AI Hype" (May 2021).
“While the science-fiction discussions about AI and superintelligence are fun, they are a distraction. There's not been enough focus on the real problem, which is building planetary-scale machine learning–based systems that actually work, deliver value to humans, and do not amplify inequities.”
—MICHAEL JORDAN, professor at University of California, Berkeley. From “Stop Calling Everything AI, Machine Learning Pioneer Says" (March 2021).
“What's missing is a principle that would allow our machine to learn how the world works by observation and by interaction with the world. A learning predictive world model is what we're missing today, and in my opinion is the biggest obstacle to significant progress in AI.”
—YANN LECUN, professor at New York University and chief AI scientist at Facebook. From “Will the Future of AI Learning Depend More on Nature or Nurture?" (October 2017).
“The beautiful thing about AI and robotics is that you're never done.”
—MANUELA VELOSO, head of AI research at J.P. Morgan. From “Manuela Veloso: Robocup's Champion" (February 2015).
“Those who argue that the risk from AI is negligible have failed to explain why superintelligent AI systems will necessarily remain under human control; and they have not even tried to explain why superintelligent AI systems will never be developed.”
—STUART RUSSELL, professor at University of California, Berkeley. From “Many Experts Say We Shouldn't Worry About Superintelligent AI. They're Wrong" (October 2019).
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Eliza Strickland is a senior editor at IEEE Spectrum, where she covers AI, biomedical engineering, and other topics. She holds a master’s degree in journalism from Columbia University.