Of course, that was what he said in September. With top talent now jumping ship, OpenAI’s future is far less certain than it was.
What next for Microsoft?
The tech giant, and its CEO Satya Nadella, seem to have emerged from the crisis as the winners. With Altman, Brockman, and likely many more top people from OpenAI joining its ranks—or even the majority of the company, if today’s open letter from 500 OpenAI employees is to be believed—Microsoft has managed to concentrate its power in AI further. The company has the most to gain from embedding generative AI into its less sexy but very profitable productivity and developer tools.
The big question remains how necessary Microsoft will deem its expensive partnership with OpenAI to create cutting-edge tech in the first place. In a post on X announcing how “extremely excited” he was to have hired Altman and Brockman, Nadella said his company remains “committed” to OpenAI and its product road map.
But let’s be real. In an exclusive interview with MIT Technology Review, Nadella called the two companies “codependent.” “They depend on us to build the best systems; we depend on them to build the best models, and we go to market together,” Nadella told our editor in chief, Mat Honan, last week. If OpenAI’s leadership roulette and talent exodus slows down its product pipeline, or leads to AI models less impressive than those it can build itself, Microsoft will have zero problems ditching the startup.
What next for AI?
Nobody outside the inner circle of Sutskever and the OpenAI board saw this coming—not Microsoft, not other investors, not the tech community as a whole. It has rocked the industry, says Amir Ghavi, a lawyer at the firm Fried Frank, which represents a number of generative AI companies, including Stability AI: “As a friend in the industry said, ‘I definitely didn’t have this on my bingo card.’”
It remains to be seen whether Altman and Brockman make something new at Microsoft or leave to start a new company themselves down the line. The pair are two of the best-connected people in VC funding circles, and Altman, especially, is seen by many as one of the best CEOs in the industry. They will have big names with deep pockets lining up to support whatever they want to do next. Who the money comes from could shape the future of AI. Ghavi suggests that potential backers could be anyone from Mohammed bin Salman to Jeff Bezos.
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.