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Most Americans encounter the Federal Trade Commission only if they’ve been scammed: It handles identity theft, fraud, and stolen data. During the Biden administration, the agency went after AI companies for scamming customers with deceptive advertising or harming people by selling irresponsible technologies. With yesterday’s announcement of President Trump’s AI Action Plan, that era may now be over.  In the final months of the Biden administration under chair Lina Khan, the FTC levied a series of high-profile fines and actions against AI companies for overhyping their technology and bending the truth—or in some cases making claims that were entirely false. It found that the security giant Evolv lied about the accuracy of its AI-powered security checkpoints, which are used in stadiums and schools but failed to catch a seven-inch knife that was ultimately used to stab a student. It went after the facial recognition company Intellivision, saying the company made unfounded claims that its tools operated without gender or racial bias. It fined startups promising bogus “AI lawyer” services and one that sold fake product reviews generated with AI. These actions did not result in fines that crippled the companies, but they did stop them from making false statements and offered customers ways to recover their money or get out of contracts. In each case, the FTC found, everyday people had been harmed by AI companies that let their technologies run amok.
The plan released by the Trump administration yesterday suggests it believes these actions went too far. In a section about removing “red tape and onerous regulation,” the White House says it will review all FTC actions taken under the Biden administration “to ensure that they do not advance theories of liability that unduly burden AI innovation.” In the same section, the White House says it will withhold AI-related federal funding from states with “burdensome” regulations. This move by the Trump administration is the latest in its evolving attack on the agency, which provides a significant route of redress for people harmed by AI in the US. It’s likely to result in faster deployment of AI with fewer checks on accuracy, fairness, or consumer harm.
Under Khan, a Biden appointee, the FTC found fans in unexpected places. Progressives called for it to break up monopolistic behavior in Big Tech, but some in Trump’s orbit, including Vice President JD Vance, also supported Khan in her fights against tech elites, albeit for the different goal of ending their supposed censorship of conservative speech.  But in January, with Khan out and Trump back in the White House, this dynamic all but collapsed. Trump released an executive order in February promising to “rein in” independent agencies like the FTC that wage influence without consulting the president. The next month, he started taking that vow to—and past—its legal limits. In March, he fired the only two Democratic commissioners at the FTC. On July 17 a federal court ruled that one of those firings, of commissioner Rebecca Slaughter, was illegal given the independence of the agency, which restored Slaughter to her position (the other fired commissioner, Alvaro Bedoya, opted to resign rather than battle the dismissal in court, so his case was dismissed). Slaughter now serves as the sole Democrat. In naming the FTC in its action plan, the White House now goes a step further, painting the agency’s actions as a major obstacle to US victory in the “arms race” to develop better AI more quickly than China. It promises not just to change the agency’s tack moving forward, but to review and perhaps even repeal AI-related sanctions it has imposed in the past four years. How might this play out? Leah Frazier, who worked at the FTC for 17 years before leaving in May and served as an advisor to Khan, says it’s helpful to think about the agency’s actions against AI companies as falling into two areas, each with very different levels of support across political lines.  The first is about cases of deception, where AI companies mislead consumers. Consider the case of Evolv, or a recent case announced in April where the FTC alleges that a company called Workado, which offers a tool to detect whether something was written with AI, doesn’t have the evidence to back up its claims. Deception cases enjoyed fairly bipartisan support during her tenure, Frazier says. “Then there are cases about responsible use of AI, and those did not seem to enjoy too much popular support,” adds Frazier, who now directs the Digital Justice Initiative at the Lawyers’ Committee for Civil Rights Under Law. These cases don’t allege deception; rather, they charge that companies have deployed AI in a way that harms people. The most serious of these, which resulted in perhaps the most significant AI-related action ever taken by the FTC and was investigated by Frazier, was announced in 2023. The FTC banned Rite Aid from using AI facial recognition in its stores after it found the technology falsely flagged people, particularly women and people of color, as shoplifters. “Acting on false positive alerts,” the FTC wrote, Rite Aid’s employees “followed consumers around its stores, searched them, ordered them to leave, [and] called the police to confront or remove consumers.”

The FTC found that Rite Aid failed to protect people from these mistakes, did not monitor or test the technology, and did not properly train employees on how to use it. The company was banned from using facial recognition for five years.  This was a big deal. This action went beyond fact-checking the deceptive promises made by AI companies to make Rite Aid liable for how its AI technology harmed consumers. These types of responsible-AI cases are the ones Frazier imagines might disappear in the new FTC, particularly if they involve testing AI models for bias. “There will be fewer, if any, enforcement actions about how companies are deploying AI,” she says. The White House’s broader philosophy toward AI, referred to in the plan, is a “try first” approach that attempts to propel faster AI adoption everywhere from the Pentagon to doctor’s offices. The lack of FTC enforcement that is likely to ensue, Frazier says, “is dangerous for the public.”

Google DeepMind has unveiled new artificial-intelligence software that could help historians recover the meaning and context behind ancient Latin engravings.  Aeneas can analyze words written in long-weathered stone to say when and where they were originally inscribed. It follows Google’s previous archaeological tool Ithaca, which also used deep learning to reconstruct and contextualize ancient text, in its case Greek. But while Ithaca and Aeneas use some similar systems, Aeneas also promises to give researchers jumping-off points for further analysis. To do this, Aeneas takes in partial transcriptions of an inscription alongside a scanned image of it. Using these, it gives possible dates and places of origins for the engraving, along with potential fill-ins for any missing text. For example, a slab damaged at the start and continuing with … us populusque Romanus would likely prompt Aeneas to guess that Senat comes before us to create the phrase Senatus populusque Romanus, “The Senate and the people of Rome.”  This is similar to how Ithaca works. But Aeneas also cross-references the text with a stored database of almost 150,000 inscriptions, which originated everywhere from modern-day Britain to modern-day Iraq, to give possible parallels—other catalogued Latin engravings that feature similar words, phrases, and analogies. 
This database, alongside a few thousand images of inscriptions, makes up the training set for Aeneas’s deep neural network. While it may seem like a good number of samples, it pales in comparison to the billions of documents used to train general-purpose large language models like Google’s Gemini. There simply aren’t enough high-quality scans of inscriptions to train a language model to learn this kind of task. That’s why specialized solutions like Aeneas are needed.  The Aeneas team believes it could help researchers “connect the past,” said Yannis Assael, a researcher at Google DeepMind who worked on the project. Rather than seeking to automate epigraphy—the research field dealing with deciphering and understanding inscriptions—he and his colleagues are interested in “crafting a tool that will integrate with the workflow of a historian,” Assael said in a press briefing. 
Their goal is to give researchers trying to analyze a specific inscription many hypotheses to work from, saving them the effort of sifting through records by hand. To validate the system, the team presented 23 historians with inscriptions that had been previously dated and tested their workflows both with and without Aeneas. The findings, which were published today in Nature, showed that Aeneas helped spur research ideas among the historians for 90% of inscriptions and that it led to more accurate determinations of where and when the inscriptions originated. In addition to this study, the researchers tested Aeneas on the Monumentum Ancyranum, a famous inscription carved into the walls of a temple in Ankara, Turkey. Here, Aeneas managed to give estimates and parallels that reflected existing historical analysis of the work, and in its attention to detail, the paper claims, it closely matched how a trained historian would approach the problem. “That was jaw-dropping,” Thea Sommerschield, an epigrapher at the University of Nottingham who also worked on Aeneas, said in the press briefing.  However, much remains to be seen about Aeneas’s capabilities in the real world. It doesn’t guess the meaning of texts, so it can’t interpret newly found engravings on its own, and it’s not clear yet how useful it will be to historians’ workflows in the long term, according to Kathleen Coleman, a professor of classics at Harvard. The Monumentum Ancyranum is considered to be one of the best-known and most well-studied inscriptions in epigraphy, raising the question of how Aeneas will fare on more obscure samples.  Google DeepMind has now made Aeneas open-source, and the interface for the system is freely available for teachers, students, museum workers, and academics. The group is working with schools in Belgium to integrate Aeneas into their secondary history education.  “To have Aeneas at your side while you’re in the museum or at the archaeological site where a new inscription has just been found—that is our sort of dream scenario,” Sommerschield said.

We’re launching Web Guide, a Search Labs experiment that uses AI to intelligently organize the search results page, making it easier to find information and web pages.Web Guide groups web links in helpful ways — like pages related to specific aspects of your query. Under the hood, Web Guide uses a custom version of Gemini to better understand both a search query and content on the web, creating more powerful search capabilities that better surface web pages you may not have previously discovered. Similar to AI Mode, Web Guide uses a query fan-out technique, concurrently issuing multiple related searches to identify the most relevant results.For example, try it for open-ended searches like “how to solo travel in Japan.” Or try detailed queries in multiple sentences like, “My family is spread across multiple time zones. What are the best tools for staying connected and maintaining close relationships despite the distance?”