AI Frontiers

Much of the discussion around AI safety is motivated by concerns around existential risk: the idea that autonomous systems will grow smarter than humans and go on to eradicate our species, either deliberately or as an unintended consequence.

The founders of the AI safety movement took these possibilities seriously when many people still brushed them off as science fiction. Nick Bostrom's 2014 book Superintelligence, for example, explored risks and opportunities humanity might face after developing AI systems with cognitive capabilities drastically more powerful than our own.

His work built on even earlier scholarship from Stephen Omohundro, Stuart Russell, Eliezer Yudkowsky, and others whose foundational ideas were published during an era where the most advanced machine learning algorithms did things like rank search results.

These classical arguments still underlie many of the conversations in AI risk.

As forward-thinking as they were, many important details of these arguments are now behind [...]

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Outline:

(01:45) The Classic Arguments for Existential AI Risk

(04:37) Flaws in the Classic Arguments

(08:42) New Foundations of AI Existential Risk

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US lawmakers propose a new system to track where chips end up.

US export controls are meant to keep advanced AI chips out of rival hands — but tens of thousands slip through each year. A new bill aims to change that by checking where these chips end up.

US Senator Tom Cotton (R-AK) introduced the Chip Security Act on May 9, which, if enacted, will require “a location verification mechanism on export-controlled advanced chips.” A bipartisan House companion bill was introduced on May 15 by Representatives Bill Huizenga (R-MI), Bill Foster (D-IL), John Moolenaar (R-MI), and Raja Krishnamoorthi (D-IL). These bills were proposed as the Trump administration works toward replacing the Biden-era diffusion rule, which set up a tiered system governing countries’ access to AI chips exported from the US. Office of Science and Technology Policy (OSTP) Director Michael Kratsios recently called for “strict and simple” export controls, while White House AI and Crypto Czar David Sacks said that a [...]

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Outline:

(01:52) How Smugglers Evade Export Controls

(03:42) Delay-based Location Verification

(04:54) Privacy and Security

(07:40) Feasibility of Location Verification

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Despite years of effort, mechanistic interpretability has failed to provide insight into AI behavior — the result of a flawed foundational assumption.

In March this year, Google DeepMind announced it was deprioritizing its work on mechanistic interpretability. The following month, Anthropic CEO Dario Amodei published an essay advocating for greater focus on “mechanistic interpretability” and expounding his optimism about achieving “MRI for AI” in the next 5-10 years. While policymakers and the public tend to assume interpretability would be a good thing, there has recently been intensified debate among experts about the value of research in this field.

‍Mechanistic interpretability aims to reverse-engineer AI systems. Mechanistic interpretability research, which has been going on for over a decade, aims to uncover the specific neurons and circuits in a model that are responsible for given tasks. In so doing, it hopes to trace the model's reasoning process and offer a “nuts-and-bolts” explanation of its behavior. This is an understandable impulse: knowledge is power; to name is to know, and to know is to [...]

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Outline:

(01:50) AI and Complex Systems

(07:01) High Investment, No Returns

(11:30) Bottom-Up vs Top-Down

(14:09) Conclusion

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Dynamism vs. stasis is a clearer lens for criticizing controversial AI safety prescriptions.

This post was cross-published on the author's Substack, Rising Tide.

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Historically, the way we’ve dealt well with rapidly evolving uncertain processes is classical liberalism.
-Dwarkesh Patel, X

I wasn’t expecting a book from 1998 to explain the 2023-2024 AI safety wars, but Virginia Postrel's The Future and Its Enemies — which I picked up at the recommendation of libertarian AI policy wonk Adam Thierer — does a surprisingly good job.

Postrel's book helped reframe something that had been bothering me. I think there's plenty to critique about AI safety ideas and the AI safety community, but when they come under fire — as has happened a lot over the past couple of years — the critiques often miss the mark. One common theme is portraying AI safety advocates as anti-technology, which is totally out of step with the nerdy, early-adopter, industrial-revolution-enthusiast crowd I know. Another recurring criticism [...]

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Outline:

(02:54) Stasism: Finding the One Best Way

(08:13) Advanced AI as a threat to a dynamic future

(10:40) Dynamism: An open-ended future

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Securing AI weights from foreign adversaries would require a level of security never seen before.

In 1943, a new town appeared in the mountains of northern New Mexico. It didn’t show up on any maps. Families arrived by train under code names. Scientists were issued ration books and cover stories. Children went to school behind fences, while their parents worked on a secret project that would change the world. The town was Los Alamos. It was the home of the Manhattan Project.

Built almost overnight, Los Alamos became the center of America's effort to develop a technology with unprecedented power: the atomic bomb. Its existence demanded total secrecy, centralized control, and infrastructure that had never existed before. Protecting the work happening inside required surveillance. Working groups were separated. Scientists and their families lived in government-run housing. All communication with the outside world was tightly monitored.

Today, calls for a “Manhattan Project for AI” echo from Washington, D.C., to Silicon Valley. The comparison isn’t just [...]

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Outline:

(02:21) A Level of Security Never Seen Before

(04:37) How It's Built

(06:35) Who Builds It

(10:18) How It's Deployed

(11:28) The Fork in the Road

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Making the most of “adaptation buffers” is a more realistic and less authoritarian strategy.