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Illustration: Sarah Grillo/Axios

A new academic group is sounding a warning about powerful, if poorly understood, AI systems that are increasingly driving the field.

Why it matters: New models like OpenAI's text-generating GPT-3 have proven so impressive that they're serving as the foundation of further AI research, but that risks propagating the biases that may be built into these systems.

What's happening: This morning a group of more than 100 researchers released a new report on the "opportunities and risks" of foundational AI systems as part of the launch of a new group at Stanford's Institute for Human-Centered AI called the Center for Research on Foundation Models (CRFM).

  • The report warns that the very qualities that have made these models so exciting — and potentially so commercially valuable — creates what Percy Liang, a Stanford computer science professor and the director of CRFM, calls "a double-edged sword."
  • "We're building AI infrastructure on a handful of models," he adds, but our inability to fully understand how they work or what they might do "means that these models actually form a shaky foundation."

Background: Liang notes that until recently, AI systems were built for specific purposes — if you needed machine translation, you built a machine translation model.

  • But that began to change in 2019, when Google introduced its BERT natural language processing (NLP) model.
  • BERT now plays a role in most of Google's search functions, while Facebook researchers harnessed BERT as the basis for an even larger NLP model that it uses for AI content moderation.
  • At the same time companies like OpenAI and AI21 Labs have begun allowing developers to build commercial applications off their own massive NLP systems.

How it works: With these systems, "you just grab a ton of data, you build a huge model, and then you go in and discover what it can do," says Liang.

  • As an AI scientist, he adds, the power of these models is "so cool," but they also risk homogenizing the AI field.
  • Any biases in these models — or in the data they're built upon — "risks being amplified in a way that everyone inherits," says Liang.

The bottom line: The good news is this foundation is still being built, so interdisciplinary groups like CRFM can work to study those defects and hopefully correct them.

  • The bad news is that we may be running out of time to do just that.

Editor's note: This story was updated to clarify that the Center for Research on Foundation Models (CRFM) is at Stanford's Institute for Human-Centered AI.

Go deeper

Bryan Walsh, author of Future
Nov 17, 2021 - Technology

An insider's view of "algorithmic warfare"

Illustration: Eniola Odetunde/Axios

Artificial intelligence will "change the nature of war," according to Robert Work, a former deputy secretary of defense.

Why it matters: Success on the battlefield will increasingly come down to the ability to make algorithmically aided decisions faster, and while the U.S. has long maintained a decisive technological advantage in warfare, its lead in AI is much narrower.

Bryan Walsh, author of Future
Nov 18, 2021 - Technology

OpenAI's GPT-3 gets a little bit more open

Illustration: Sarah Grillo/Axios

The artificial intelligence research company OpenAI will eliminate the waiting list for access to the API of its natural language processing program (NLP) GPT-3.

Why it matters: The move will accelerate access to the world's best-known reading and writing AI model, and is a sign that OpenAI believes the program is safe enough — and can be monitored sufficiently — to be disseminated more widely.

Bryan Walsh, author of Future
Nov 17, 2021 - Technology

Taming the wild west of AI-based hiring

Illustration: Aïda Amer/Axios

New laws and regulations are aiming to draw some boundaries for the fast-growing but often black-box approach of using AI to hire employees.

Why it matters: Companies large and small have embraced AI-based tools to screen, assess and select job candidates, but algorithmic approaches have been largely unregulated and risk perpetuating biases on race, gender, disability and more.