Welcome back to In the Loop, new twice-weekly newsletter about AI. If you're reading this in your browser, why not subscribe to have the next one delivered straight to your inbox? When you think about the top echelon of the world's tech elites, Larry Ellison probably doesn't spring to mind. But on Wednesday, the 81-year-old chairman of Oracle briefly became the richest person in the world with a net worth of almost $400 billion, overtaking Elon Musk. Ellison's $100-billion jump was the biggest single-day gain ever, and the result of a promising Oracle growth forecast in which they advertised hundreds of billions of dollars in inbound revenue from AI companies using Oracle's cloud computing capabilities.

Things to Do in L.A. Tap to enable a layout that focuses on the article. 'Breaking Bad' actor accused of spraying woman with water in L.A. parking dispute Raymond Cruz attends the premiere of Season 2 of "Breaking Bad" at ArcLight Cinemas in Hollywood on Feb. 26, 2009. Voice comes from the use of AI. Please report any issues or inconsistencies here . Actor Raymond Cruz was held in custody for five hours on Monday after a sudsy spat with three women in his Los Angeles neighborhood.

Low-mass stars and brown dwarfs -- spectral types (SpTs) M0 and later -- play a significant role in studying stellar and substellar processes and demographics, reaching down to planetary-mass objects. Currently, the classification of these sources remains heavily reliant on visual inspection of spectral features, equivalent width measurements, or narrow-/wide-band spectral indices. Recent advances in machine learning (ML) methods offer automated approaches for spectral typing, which are becoming increasingly important as large spectroscopic surveys such as Gaia, SDSS, and SPHEREx generate datasets containing millions of spectra. We investigate the application of ML in spectral type classification on low-resolution (R $\sim$ 120) near-infrared spectra of M0--T9 dwarfs obtained with the SpeX instrument on the NASA Infrared Telescope Facility. We specifically aim to classify the gravity- and metallicity-dependent subclasses for late-type dwarfs. We used binned fluxes as input features and compared the efficacy of spectral type estimators built using Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) models. We tested the influence of different normalizations and analyzed the relative importance of different spectral regions for surface gravity and metallicity subclass classification. Our best-performing model (using KNN) classifies 95.5 $\pm$ 0.6% of sources to within $\pm$1 SpT, and assigns surface gravity and metallicity subclasses with 89.5 $\pm$ 0.9% accuracy. We test the dependence of signal-to-noise ratio on classification accuracy and find sources with SNR $\gtrsim$ 60 have $\gtrsim$ 95% accuracy. We also find that zy-band plays the most prominent role in the RF model, with FeH and TiO having the highest feature importance.

The moratorium could also have killed laws that have already been adopted around the country, including a Colorado law that targets algorithmic discrimination, laws in Utah and California aimed at making AI-generated content more identifiable, and other legislation focused on preserving data privacy and keeping children safe online. Proponents of the moratorium, such OpenAI and Senator Ted Cruz, have said that a "patchwork" of state-level regulations would place an undue burden on technology companies and stymie innovation. Federal regulation, they argue, is a better approach--but there is currently no federal AI regulation in place. Wiener and other state lawmakers can now get back to work writing and passing AI policy, at least for the time being--with the tailwind of a major moral victory at their backs. The movement to defeat the moratorium was impressively bipartisan: 40 state attorneys general signed a letter to Congress opposing the measure, as did a group of over 250 Republican and Democratic state lawmakers.

A deal that had been reached between Sens. Marsha Blackburn, R-Tenn., and Ted Cruz, R-Texas, over how states can regulate artificial intelligence has been pulled from President Donald Trump's "big, beautiful" bill. The collapsed agreement would have required states seeking to access hundreds of millions of dollars in AI infrastructure funding in the "big, beautiful" bill to refrain from adopting new regulations on the technology for five years, a compromise down from the original 10 years. It also included carveouts to regulate child sexual abuse material, unauthorized use of a person's likeness and other deceptive practices. Blackburn announced Monday night that she is withdrawing her support for the agreement. A deal between Sens. Marsha Blackburn and Ted Cruz over how states can regulate AI has been pulled from the "big, beautiful" bill.

As Congress races to pass President Donald Trump's "Big Beautiful Bill," it's also sprinting to placate the many haters of the bill's "AI moratorium" provision which originally required a 10-year pause on state AI regulations. The provision, which was championed by White House AI czar and venture capitalist David Sacks, has proved remarkably unpopular with a diverse contingent of lawmakers ranging from 40 state attorneys general to the ultra-MAGA Representative Marjorie Taylor Greene. Sunday night, Senator Marsha Blackburn and Senator Ted Cruz announced a new version of the AI moratorium, knocking the pause from a full decade down to five years and adding a variety of carve-outs. But after critics attacked the watered-down version of the bill as a "get-out-of-jail free card" for Big Tech, Blackburn reversed course Monday evening. "While I appreciate Chairman Cruz's efforts to find acceptable language that allows states to protect their citizens from the abuses of AI, the current language is not acceptable to those who need these protections the most," Blackburn said in a statement to WIRED.

On May 16, 2023, Sam Altman appeared before a subcommittee of the Senate Judiciary. The title of the hearing was "Oversight of AI." The session was a lovefest, with both Altman and the senators celebrating what Altman called AI's "printing press moment"--and acknowledging that the US needed strong laws to avoid its pitfalls. "We think that regulatory intervention by governments will be critical to mitigate the risks of increasingly powerful models," he said. The legislators hung on Altman's every word as he gushed about how smart laws could allow AI to flourish--but only within firm guidelines that both lawmakers and AI builders deemed vital at that moment.

High school student Elliston Berry discusses the Take It Down Act, a measure that would force social media companies to remove graphic deepfakes, prevent them from being posted and criminalize the act. Senate lawmakers unanimously passed the bipartisan-led Take It Down Act that would force social media companies to speedily remove sexually explicit deepfakes, prevent them from being posted and criminalize the act. For deepfake pornography victims like 15-year-old Elliston Berry, the measure would be long overdue. The Texas high school student is working with lawmakers to get the bill passed to protect victims like herself. She's inspired by her own story from last year, when she discovered deepfake nude images of herself circulating across social media in a sinister cyber scheme that turned her life upside down.

Recent large language model (LLM) defenses have greatly improved models' ability to refuse harmful queries, even when adversarially attacked. However, LLM defenses are primarily evaluated against automated adversarial attacks in a single turn of conversation, an insufficient threat model for real-world malicious use. We demonstrate that multi-turn human jailbreaks uncover significant vulnerabilities, exceeding 70% attack success rate (ASR) on HarmBench against defenses that report single-digit ASRs with automated single-turn attacks. Human jailbreaks also reveal vulnerabilities in machine unlearning defenses, successfully recovering dual-use biosecurity knowledge from unlearned models. We compile these results into Multi-Turn Human Jailbreaks (MHJ), a dataset of 2,912 prompts across 537 multi-turn jailbreaks. We publicly release MHJ alongside a compendium of jailbreak tactics developed across dozens of commercial red teaming engagements, supporting research towards stronger LLM defenses.

Google has come under fire from conservatives in the United States amid claims that the tech giant is suppressing information about the attempted assassination of Donald Trump in order to influence the presidential election. Trump, who is running for a second term in the White House on the Republican Party ticket, narrowly escaped being killed when a lone gunman opened fire at a campaign rally in Pennsylvania on July 13. The attack, which killed one rally attendee, injured two others, and bloodied the former president's ear, has spawned a number of unsubstantiated claims and conspiracy theories. The latest revolves around Google Search's autocomplete feature, which is designed to help users save time by predicting their search query based on the opening letters or words that are inputted. Over the weekend, some internet users noticed that writing about assassination attempts in the Google search bar did not automatically prompt search queries about the shooting of Trump.