Category Archives: essays

LLMs and Tool Use

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Last March, just two weeks after GPT-4 was released, researchers at Microsoft quietly announced a plan to compile millions of APIs—tools that can do everything from ordering a pizza to solving physics equations to controlling the TV in your living room—into a compendium that would be made accessible to large language models (LLMs). This was just one milestone in the race across industry and academia to find the best ways to teach LLMs how to manipulate tools, which would supercharge the potential of AI more than any of the impressive advancements we’ve seen to date…

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December’s Reimagining Democracy Workshop

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Imagine that we’ve all—all of us, all of society—landed on some alien planet, and we have to form a government: clean slate. We don’t have any legacy systems from the US or any other country. We don’t have any special or unique interests to perturb our thinking.

How would we govern ourselves?

It’s unlikely that we would use the systems we have today. The modern representative democracy was the best form of government that mid-eighteenth-century technology could conceive of. The twenty-first century is a different place scientifically, technically and socially…

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Political Milestones for AI

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ChatGPT was released just nine months ago, and we are still learning how it will affect our daily lives, our careers, and even our systems of self-governance.

But when it comes to how AI may threaten our democracy, much of the public conversation lacks imagination. People talk about the danger of campaigns that attack opponents with fake images (or fake audio or video) because we already have decades of experience dealing with doctored images. We’re on the lookout for foreign governments that spread misinformation because we were traumatized by the 2016 US presidential election. And we worry that AI-generated opinions will swamp the political preferences of real people because we’ve seen political “astroturfing”—the use of fake online accounts to give the illusion of support for a policy—grow for decades…

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The Need for Trustworthy AI

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If you ask Alexa, Amazon’s voice assistant AI system, whether Amazon is a monopoly, it responds by saying it doesn’t know. It doesn’t take much to make it lambaste the other tech giants, but it’s silent about its own corporate parent’s misdeeds.

When Alexa responds in this way, it’s obvious that it is putting its developer’s interests ahead of yours. Usually, though, it’s not so obvious whom an AI system is serving. To avoid being exploited by these systems, people will need to learn to approach AI skeptically. That means deliberately constructing the input you give it and thinking critically about its output…

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AI and Microdirectives

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Imagine a future in which AIs automatically interpret—and enforce—laws.

All day and every day, you constantly receive highly personalized instructions for how to comply with the law, sent directly by your government and law enforcement. You’re told how to cross the street, how fast to drive on the way to work, and what you’re allowed to say or do online—if you’re in any situation that might have legal implications, you’re told exactly what to do, in real time.

Imagine that the computer system formulating these personal legal directives at mass scale is so complex that no one can explain how it reasons or works. But if you ignore a directive, the system will know, and it’ll be used as evidence in the prosecution that’s sure to follow…

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The AI Dividend

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For four decades, Alaskans have opened their mailboxes to find checks waiting for them, their cut of the black gold beneath their feet. This is Alaska’s Permanent Fund, funded by the state’s oil revenues and paid to every Alaskan each year. We’re now in a different sort of resource rush, with companies peddling bits instead of oil: generative AI.

Everyone is talking about these new AI technologies—like ChatGPT—and AI companies are touting their awesome power. But they aren’t talking about how that power comes from all of us. Without all of our writings and photos that AI companies are using to train their models, they would have nothing to sell. Big Tech companies are currently taking the work of the American people, without our knowledge and consent, without licensing it, and are pocketing the proceeds…

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On the Need for an AI Public Option

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Artificial intelligence will bring great benefits to all of humanity. But do we really want to entrust this revolutionary technology solely to a small group of US tech companies?

Silicon Valley has produced no small number of moral disappointments. Google retired its “don’t be evil” pledge before firing its star ethicist. Self-proclaimed “free speech absolutist” Elon Musk bought Twitter in order to censor political speech, retaliate against journalists, and ease access to the platform for Russian and Chinese propagandists. Facebook lied about how it enabled Russian interference in the 2016 US presidential election and …

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Snowden Ten Years Later

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In 2013 and 2014, I wrote extensively about new revelations regarding NSA surveillance based on the documents provided by Edward Snowden. But I had a more personal involvement as well.

I wrote the essay below in September 2013. The New Yorker agreed to publish it, but the Guardian asked me not to. It was scared of UK law enforcement, and worried that this essay would reflect badly on it. And given that the UK police would raid its offices in July 2014, it had legitimate cause to be worried.

Now, ten years later, I offer this as a time capsule of what those early months of Snowden were like…

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Open-Source LLMs

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In February, Meta released its large language model: LLaMA. Unlike OpenAI and its ChatGPT, Meta didn’t just give the world a chat window to play with. Instead, it released the code into the open-source community, and shortly thereafter the model itself was leaked. Researchers and programmers immediately started modifying it, improving it, and getting it to do things no one else anticipated. And their results have been immediate, innovative, and an indication of how the future of this technology is going to play out. Training speeds have hugely increased, and the size of the models themselves has shrunk to the point that you can create and run them on a laptop. The world of AI research has dramatically changed…

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Ted Chiang on the Risks of AI

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Ted Chiang has an excellent essay in the New Yorker: “Will A.I. Become the New McKinsey?”

The question we should be asking is: as A.I. becomes more powerful and flexible, is there any way to keep it from being another version of McKinsey? The question is worth considering across different meanings of the term “A.I.” If you think of A.I. as a broad set of technologies being marketed to companies to help them cut their costs, the question becomes: how do we keep those technologies from working as “capital’s willing executioners”? Alternatively, if you imagine A.I. as a semi-autonomous software program that solves problems that humans ask it to solve, the question is then: how do we prevent that software from assisting corporations in ways that make people’s lives worse? Suppose you’ve built a semi-autonomous A.I. that’s entirely obedient to humans­—one that repeatedly checks to make sure it hasn’t misinterpreted the instructions it has received. This is the dream of many A.I. researchers. Yet such software could easily still cause as much harm as McKinsey has…

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