Well, that didn’t happen, obviously.
I sat down with MIT professor Max Tegmark, the founder and president of FLI, to take stock of what has happened since. Here are highlights of our conversation.
On shifting the Overton window on AI risk: Tegmark told me that in conversations with AI researchers and tech CEOs, it had become clear that there was a huge amount of anxiety about the existential risk AI poses, but nobody felt they could speak about it openly “for fear of being ridiculed as Luddite scaremongerers.” “The key goal of the letter was to mainstream the conversation, to move the Overton window so that people felt safe expressing these concerns,” he says. “Six months later, it’s clear that part was a success.”
But that’s about it: “What’s not great is that all the companies are still going full steam ahead and we still have no meaningful regulation in America. It looks like US policymakers, for all their talk, aren’t going to pass any laws this year that meaningfully rein in the most dangerous stuff.”
Why the government should step in: Tegmark is lobbying for an FDA-style agency that would enforce rules around AI, and for the government to force tech companies to pause AI development. “It’s also clear that [AI leaders like Sam Altman, Demis Hassabis, and Dario Amodei] are very concerned themselves. But they all know they can’t pause alone,” Tegmark says. Pausing alone would be “a disaster for their company, right?” he adds. “They just get outcompeted, and then that CEO will be replaced with someone who doesn’t want to pause. The only way the pause comes about is if the governments of the world step in and put in place safety standards that force everyone to pause.”
So how about Elon … ? Musk signed the letter calling for a pause, only to set up a new AI company called X.AI to build AI systems that would “understand the true nature of the universe.” (Musk is an advisor to the FLI.) “Obviously, he wants a pause just like a lot of other AI leaders. But as long as there isn’t one, he feels he has to also stay in the game.”
Why he thinks tech CEOs have the goodness of humanity in their hearts: “What makes me think that they really want a good future with AI, not a bad one? I’ve known them for many years. I talk with them regularly. And I can tell even in private conversations—I can sense it.”
Response to critics who say focusing on existential risk distracts from current harms: “It’s crucial that those who care a lot about current problems and those who care about imminent upcoming harms work together rather than infighting. I have zero criticism of people who focus on current harms. I think it’s great that they’re doing it. I care about those things very much. If people engage in this kind of infighting, it’s just helping Big Tech divide and conquer all those who want to really rein in Big Tech.”
These robots know when to ask for help
A new training model, dubbed “KnowNo,” aims to address this problem by teaching robots to ask for our help when orders are unclear. At the same time, it ensures they seek clarification only when necessary, minimizing needless back-and-forth. The result is a smart assistant that tries to make sure it understands what you want without bothering you too much.
Andy Zeng, a research scientist at Google DeepMind who helped develop the new technique, says that while robots can be powerful in many specific scenarios, they are often bad at generalized tasks that require common sense.
For example, when asked to bring you a Coke, the robot needs to first understand that it needs to go into the kitchen, look for the refrigerator, and open the fridge door. Conventionally, these smaller substeps had to be manually programmed, because otherwise the robot would not know that people usually keep their drinks in the kitchen.
That’s something large language models (LLMs) could help to fix, because they have a lot of common-sense knowledge baked in, says Zeng.
Now when the robot is asked to bring a Coke, an LLM, which has a generalized understanding of the world, can generate a step-by-step guide for the robot to follow.
The problem with LLMs, though, is that there’s no way to guarantee that their instructions are possible for the robot to execute. Maybe the person doesn’t have a refrigerator in the kitchen, or the fridge door handle is broken. In these situations, robots need to ask humans for help.
KnowNo makes that possible by combining large language models with statistical tools that quantify confidence levels.
When given an ambiguous instruction like “Put the bowl in the microwave,” KnowNo first generates multiple possible next actions using the language model. Then it creates a confidence score predicting the likelihood that each potential choice is the best one.
The Download: inside the first CRISPR treatment, and smarter robots
The news: A new robot training model, dubbed “KnowNo,” aims to teach robots to ask for our help when orders are unclear. At the same time, it ensures they seek clarification only when necessary, minimizing needless back-and-forth. The result is a smart assistant that tries to make sure it understands what you want without bothering you too much.
Why it matters: While robots can be powerful in many specific scenarios, they are often bad at generalized tasks that require common sense. That’s something large language models could help to fix, because they have a lot of common-sense knowledge baked in. Read the full story.
Medical microrobots that travel inside the body are (still) on their way
The human body is a labyrinth of vessels and tubing, full of barriers that are difficult to break through. That poses a serious hurdle for doctors. Illness is often caused by problems that are hard to visualize and difficult to access. But imagine if we could deploy armies of tiny robots into the body to do the job for us. They could break up hard-to-reach clots, deliver drugs to even the most inaccessible tumors, and even help guide embryos toward implantation.
We’ve been hearing about the use of tiny robots in medicine for years, maybe even decades. And they’re still not here. But experts are adamant that medical microbots are finally coming, and that they could be a game changer for a number of serious diseases. Read the full story.
5 things we didn’t put on our 2024 list of 10 Breakthrough Technologies
We haven’t always been right (RIP, Baxter), but we’ve often been early to spot important areas of progress (we put natural-language processing on our very first list in 2001; today this technology underpins large language models and generative AI tools like ChatGPT).
Every year, our reporters and editors nominate technologies that they think deserve a spot, and we spend weeks debating which ones should make the cut. Here are some of the technologies we didn’t pick this time—and why we’ve left them off, for now.
New drugs for Alzheimer’s disease
Alzmeiher’s patients have long lacked treatment options. Several new drugs have now been proved to slow cognitive decline, albeit modestly, by clearing out harmful plaques in the brain. In July, the FDA approved Leqembi by Eisai and Biogen, and Eli Lilly’s donanemab could soon be next. But the drugs come with serious side effects, including brain swelling and bleeding, which can be fatal in some cases. Plus, they’re hard to administer—patients receive doses via an IV and must receive regular MRIs to check for brain swelling. These drawbacks gave us pause.
Sustainable aviation fuel
Alternative jet fuels made from cooking oil, leftover animal fats, or agricultural waste could reduce emissions from flying. They have been in development for years, and scientists are making steady progress, with several recent demonstration flights. But production and use will need to ramp up significantly for these fuels to make a meaningful climate impact. While they do look promising, there wasn’t a key moment or “breakthrough” that merited a spot for sustainable aviation fuels on this year’s list.
One way to counteract global warming could be to release particles into the stratosphere that reflect the sun’s energy and cool the planet. That idea is highly controversial within the scientific community, but a few researchers and companies have begun exploring whether it’s possible by launching a series of small-scale high-flying tests. One such launch prompted Mexico to ban solar geoengineering experiments earlier this year. It’s not really clear where geoengineering will go from here or whether these early efforts will stall out. Amid that uncertainty, we decided to hold off for now.