Things to Do in L.A. Tap to enable a layout that focuses on the article. L.A.'s defense industry is booming. This is read by an automated voice. Please report any issues or inconsistencies here . L.A. defense-tech startups like Gambit face funding shortfalls as the Small Business Innovation Research program expired in September amid a Capitol Hill dispute.

'Special Report' anchor Bret Baier discusses Democrats' backlash over Elon Musk's effort to rid the government of wasteful spending, the USAID and CIA's alleged connections to the Trump impeachment and the president's plan for Gaza. As the Trump administration's Department of Government Efficiency (DOGE) works to slash government waste, a bipartisan bill in Congress is aiming to bring the federal government's computer systems "out of the Stone Age." The bipartisan Strengthening Agency Management And Oversight Of Software Assets (SAMOSA) Act passed the House in December, and Sen. Joni Ernset, R-Iowa, is leading efforts to get it passed in the upper chamber. Ernst, the chair of the Senate DOGE Caucus, said the SAMOSA Act will "bring Washington out of the Stone Age and into the 21st century." Fox News Digital is told the bill could potentially save 750 million annually for taxpayers by consolidating federal agencies' cloud computing software licenses.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. A member of the House Intelligence Committee warned Americans to stay away from DNA testing services as the information could be used to develop bioweapons targeting specific groups of Americans or even individuals. Rep. Jason Crow, D-Colo., made the comments during an appearance at the Aspen Security Forum in Colorado on Friday, saying many Americans are far too willing to give up their DNA information to private companies. "You can't have a discussion about this without talking about privacy and the protection of commercial data because expectations of privacy have degraded over the last 20 years," Crow said "Young folks actually have very little expectation of privacy, that's what the polling and the data show."

Summary: An update and observations about China's plan to become the world leader in AI. Whether you get your news from Facebook or from the Wall Street Journal you can't help having heard that China is out to displace the US as the world leader in AI. Variously you may have heard that it's already happened or soon inevitably will. The twin questions of when they will succeed (is it inevitable) or whether they will succeed (if ever) is one I get all the time. As a red-white-and-blue American I hope not.

Is China pulling ahead of the US in AI? Not quite, argues Dieter Ernst of CIGI in a recent report entitled "Competing in artificial intelligence chips: China's challenge amid technology war." In addition to the hard engineering, Ernst reveals a social story of a global AI community on the verge of fracture. These new restrictions will likely bring the best out of some Chinese firms, while putting others out to pasture. All the while, basic research is likely to suffer worldwide as ties that bound the Chinese and western academic communities fray.

Rise of frontier technologies like Artificial Intelligence has caused fears of robots taking over blue-collar jobs, but a UN expert says mass unemployment is not expected as humans still have the upper hand given their creative abilities. Ekkehard Ernst, Chief of Macro-economic policies and job unit at the UN International Labour Organization (ILO), says the manufacturing sector does not stand to profit most from AI, at least not in developed countries, and will not suffer the forecast demise. The jobs more likely to be impacted are in service sectors such as construction, health care and business. "It is not so much about losing jobs but about how jobs are being transformed and employees in these sectors will add new tasks to their profile while being supported by computers and robots in others," Ernst said. The type of tasks that are being replaced by AI algorithms are routine, repetitive tasks that take a lot of time and can be more easily and more effectively performed by machines and by robots – leaving people to focus on interpersonal, social, emotional skills.

Fitted Q-iteration (FQI) stands out among reinforcement learning algorithms for its flexibility and ease of use. FQI can be combined with any regression method, and this choice determines the algorithm's statistical and computational properties. The combination of FQI with an ensemble of regression trees gives rise to an algorithm, FQIT, that is computationally efficient, scalable to high dimensional spaces, and robust to noise. Despite its nice properties and good performance in practice, FQIT also has some limitations: the fact that an ensemble of trees must be constructed (or updated) at each iteration confines the algorithm to the batch scenario. This paper aims to address this specific issue. Based on a strategy recently proposed in the literature, called the stochastic-factorization trick, we propose a modification of FQIT that makes it fully incremental, and thus suitable for on-line learning. We call the resulting method tree-based stochastic factorization (TBSF). We derive upper bounds for the difference between the value functions computed by FQIT and TBSF, and also show in which circumstances the approximations coincide. A series of computational experiments is presented to illustrate the properties of TBSF and to show its usefulness in practice, including a medical problem involving the treatment of patients infected with HIV.