A United Arab Emirates (UAE) academic lab today launched an artificial intelligence world model and agent, two large language models (LLMs) and a new research center in Silicon Valley as it ramps up its investment in the cutting-edge field. The UAE's Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) revealed an AI world model called PAN, which can be used to build physically realistic simulations for testing and honing the performance of AI agents. Eric Xing, President and Professor of MBZUAI and a leading AI researcher, revealed the models and lab at the Computer History Museum in Mountain View, California today. The UAE has made big investments in AI in recent years under the guidance of Sheikh Tahnoun bin Zayed al Nahyan, the nation's tech-savvy national security advisor and younger brother of president Mohamed bin Zayed Al Nahyan. Xing says the UAE's new center in Sunnyvale, California, will help the nation tap into the world's most concentrated source of AI knowledge and talent.

President Xi Jinping's embrace of Chinese tech bosses in a rare public meeting is fueling hope Beijing is shifting its stance to give the private sector a freer hand as it fights a trade war with U.S. President Donald Trump. Four years after launching a regulatory crackdown that plunged the tech sector into turmoil, China's top leader sat down publicly for the first time with Alibaba co-founder Jack Ma, whose firm bore the brunt of that campaign. Also on the guest list Monday were rising stars from robotics start-up Unitree, electric car giant BYD and AI newcomer DeepSeek -- firms rolling out world-beating innovations despite U.S. export controls. While a similar show of support from Xi in 2018 proved fleeting, developing national tech champions is core to Beijing's plan for boosting the economy as it deflates a bubble in the property market that once drove about a quarter of growth. Underscoring the importance of spurring innovation, high-tech industries contributed to 15% of gross domestic product last year and are set to overtake the housing sector in 2026, according to Bloomberg Economics.

The human cell is a miserable thing to study. Tens of trillions of them exist in the body, forming an enormous and intricate network that governs every disease and metabolic process. Each cell in that circuit is itself the product of an equally dense and complex interplay among genes, proteins, and other bits of profoundly small biological machinery. Our understanding of this world is hazy and constantly in flux. As recently as a few years ago, scientists thought there were only a few hundred distinct cell types, but new technologies have revealed thousands (and that's just the start).

Tuberculosis (TB) is a major global health challenge, and is compounded by co-morbidities such as HIV, diabetes, and anemia, which complicate treatment outcomes and contribute to heterogeneous patient responses. Traditional models of TB often overlook this heterogeneity by focusing on broad, pre-defined patient groups, thereby missing the nuanced effects of individual patient contexts. We propose moving beyond coarse subgroup analyses by using contextualized modeling, a multi-task learning approach that encodes patient context into personalized models of treatment effects, revealing patient-specific treatment benefits. Applied to the TB Portals dataset with multi-modal measurements for over 3,000 TB patients, our model reveals structured interactions between co-morbidities, treatments, and patient outcomes, identifying anemia, age of onset, and HIV as influential for treatment efficacy. By enhancing predictive accuracy in heterogeneous populations and providing patient-specific insights, contextualized models promise to enable new approaches to personalized treatment.

This week has been one of Tesla's worst. The company has cut 10 percent of its workforce, from sales advisers to engineers--the biggest round of layoffs in the company's history. Two top executives--vice president of public policy and business development, Rohan Patel; and senior vice president of powertrain and energy, Drew Baglino--also announced they were leaving. This comes against a difficult financial backdrop: Demand is dropping for electric cars in the US and Europe, just as competition in China intensifies and workers revolt in Europe. Investors are worried: In the past six months, Tesla's stock has dropped 35 percent.

President of MBZUAI Eric Xing said current metrics to grade student's intelligence will be very quickly disrupted by artificial intelligence. The president of the world's first artificial intelligence (AI) university said the educational system's current quantifiers of "intelligence" will face disruption from the new technology, allowing students to focus on solving problems rather than simply recalling information. Eric Xing, the president of Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) in Abu Dhabi, said many of the ways the current education system qualifies student intelligence are questionable, wherein it is not necessarily focusing on solving problems but rather how much knowledge one can remember. He said that because AI technologies bring about vast amounts of highly accessible knowledge easily gleaned from a textbook, current "intelligence" metrics, such as the medical or legal bar examinations, will be quickly disrupted. EXPERTS SAY AI COULD RADICALLY CHANGE'BROKEN' US EDUCATION SYSTEM FOR THE BETTER: 'READY TO BE DISRUPTED' Prior to his appointment as President of MBZUAI in Abu Dhabi, Eric Xing was a professor in the School of Computer Science at Carnegie Mellon University.

The proposed Perception Evolution Network (PEN) is a biologically inspired neural network model for unsupervised learning and online incremental learning. It is able to automatically learn suitable prototypes from learning data in an online incremental way, and it does not require the predefined prototype number and similarity threshold. Meanwhile, being more advanced than the existing unsupervised neural network model, PEN permits the emergence of a new dimension of perception in the perception field of the network. When a new dimension of perception is introduced, PEN is able to integrate the new dimensional sensory inputs with the learned prototypes, i.e., the prototypes are mapped to a high-dimensional space, which consists of both the original dimension and the new dimension of the sensory inputs. We call it a Cognition Deepening Process. Artificial data and real-world data are used to test the proposed PEN, and the results show that PEN can work effectively.

The past thirty years of machine learning breakthroughs are intimately entwined with a big idea in computing: parallel distributed processing, where a parts of a program run simultaneously on multiple processors to speed computation. Eric Xing, a Carnegie Mellon professor of machine learning, three years ago founded Petuum, based in Pittsburg, which has received $108 million in funding from Japanese conglomerate SoftBank, along with Advantech Capital, Chinese computing giant Tencent, Northern Light Venture Capital, and Oriza Ventures. The company plans to ship the first version of its AI platform software next summer, an offering Xing hopes will "industrialize" machine learning, thereby making it more reliable and more broadly available. Petuum founder and CEO Eric Xing came up with the idea for the AI software while on sabbatical from Carnegie Mellon at Facebook in 2010. Much of the challenge of AI is a systems engineering challenge, and at the heart of that is a problem of parallelizing the running of algorithms across all kinds of configurations of machines.

At first blush, Scot Barton might not seem like an AI pioneer. He isn't building self-driving cars or teaching computers to thrash humans at computer games. But within his role at Farmers Insurance, he is blazing a trail for the technology. Barton leads a team that analyzes data to answer questions about customer behavior and the design of different policies. His group is now using all sorts of cutting-edge machine-learning techniques, from deep neural networks to decision trees.

At first blush, Scot Barton might not seem like an AI pioneer. He isn't building self-driving cars or teaching computers to thrash humans at computer games. But within his role at Farmers Insurance, he is blazing a trail for the technology. Barton leads a team that analyzes data to answer questions about customer behavior and the design of different policies. His group is now using all sorts of cutting-edge machine-learning techniques, from deep neural networks to decision trees.