What we still don't know about weight-loss drugs Weight-loss drugs have been back in the news this week. First, we heard that Eli Lilly, the company behind Mounjaro and Zepbound, became the first healthcare company in the world to achieve a trillion-dollar valuation. But we also learned that, disappointingly, GLP-1 drugs don't seem to help people with Alzheimer's disease. And that people who stop taking the drugs when they become pregnant can experience potentially dangerous levels of weight gain. On top of that, some researchers worry that people are using the drugs postpartum to lose pregnancy weight without understanding potential risks. All of this news should serve as a reminder that there's a lot we still don't know about these drugs.

The economist Bryan Caplan was sure the artificial intelligence baked into ChatGPT wasn't as smart as it was cracked up to be. Caplan, of George Mason University in Virginia, seemed in a good position to judge. He has made a name for himself by placing bets on a range of newsworthy topics, from Donald Trump's electoral chances in 2016 to future US college attendance rates. And he nearly always wins, often by betting against predictions he views as hyperbolic. That was the case with wild claims about ChatGPT, the AI chatbot that's become a worldwide phenomenon.

So say Carnegie Mellon University's Tom Mitchell and MIT's Erik Brynjolfsson in a Policy Forum commentary to be published in the Dec. 22 edition of the journal Science. Mitchell, who founded the world's first Machine Learning Department at CMU, and Brynjolfsson, director of the MIT Initiative on the Digital Economy in the Sloan School of Management, describe 21 criteria to evaluate whether a task or a job is amenable to machine learning (ML). "Although the economic effects of ML are relatively limited today, and we are not facing the imminent'end of work' as is sometimes proclaimed, the implications for the economy and the workforce going forward are profound," they write. The skills people choose to develop and the investments businesses make will determine who thrives and who falters once ML is ingrained in everyday life, they argue. ML is one element of what is known as artificial intelligence.

ML systems are very strong at learning empirical associations in data but are less effective when the task requires long chains of reasoning or complex planning that rely on common sense or background knowledge unknown to the computer. Ng's "one-second rule" (4) suggests that ML will do well on video games that require quick reaction and provide instantaneous feedback but less well on games where choosing the optimal action depends on remembering previous events distant in time and on unknown background knowledge about the world (e.g., knowing where in the room a newly introduced item is likely to be found) (12). Exceptions to this are games such as Go and chess, because these nonphysical games can be rapidly simulated with perfect accuracy, so that millions of perfectly self-labeled training examples can be automatically collected. However, in most real-world domains, we lack such perfect simulations.

According to Carnegie Mellon University scientists machine learning computer systems will lead to a transformation of modern economies. The analogy used is with steam engines and electricity overhauling economies of the past. The key message is where machine learning and artificial intelligence can outperform a person, then the job that this task relates to will most likely disappear. The more promising news for those who fear machine-dominated society is that not all jobs are expected to be bettered by a'thinking' computer. Machine learning, as one aspect of artificial intelligence, has advanced considerably in the past two years: facial recognition, natural language understanding and computer vision as three examples.

The idea that computers are people has a long and storied history. It goes back to the very origins of computers, and even from before. There's always been a question about whether a program is something alive or not since it intrinsically has some kind of autonomy at the very least, or it wouldn't be a program. There has been a domineering subculture--that's been the most wealthy, prolific, and influential subculture in the technical world--that for a long time has not only promoted the idea that there's an equivalence between algorithms and life, and certain algorithms and people, but a historical determinism that we're inevitably making computers that will be smarter and better than us and will take over from us. You'll have a figure say, "The computers will take over the Earth, but that's a good thing, because people had their chance and now we should give it to the machines."

Editor's Note: Staff from the Council of Economic Advisers, the Domestic Policy Council, the National Economic Council, the Office of Management and Budget, the Office of Science and Technology Policy contributed to this post. Today, in order to ready the United States for a future in which artificial intelligence (AI) plays a growing role, the White House released a report on Artificial Intelligence, Automation, and the Economy. This report follows up on the Administration's previous report, Preparing for the Future of Artificial Intelligence, which was released in October 2016, and which recommended that the White House publish a report on the economic impacts of artificial intelligence by the end of 2016. Accelerating AI capabilities will enable automation of some tasks that have long required human labor. These transformations will open up new opportunities for individuals, the economy, and society, but they will also disrupt the current livelihoods of millions of Americans.

Today, in order to ready the United States for a future in which artificial intelligence (AI) plays a growing role, the White House released a report on Artificial Intelligence, Automation, and the Economy. This report follows up on the Administration's previous report, Preparing for the Future of Artificial Intelligence, which was released in October 2016, and which recommended that the White House publish a report on the economic impacts of artificial intelligence by the end of 2016. Accelerating AI capabilities will enable automation of some tasks that have long required human labor. These transformations will open up new opportunities for individuals, the economy, and society, but they will also disrupt the current livelihoods of millions of Americans. The new report examines the expected impact of AI-driven automation on the economy, and describes broad strategies that could increase the benefits of AI and mitigate its costs.

SINCE THE EARLY 1800s, several waves of technological change have transformed how we work and live. Each new technological marvel -- the steam engine, railroad, ocean steamship, telegraph, harvester, automobile, radio, airplane, TV, computer, satellite, mobile phone, and now the Internet -- has changed our home lives, communities, workplaces, schools, and leisure time. For two centuries we've asked whether ever-more-powerful machines would free us from drudgery or would instead enslave us. The question is becoming urgent. IBM's Deep Blue and other chess-playing computers now routinely beat the world's chess champions.