The Trump Administration Is Coming for Nonprofits. As the Trump administration threatens them, liberal nonprofits have been quietly preparing to do everything from surrendering 501(c)(3) status to relocating outside the US. President Donald Trump listens as White House deputy chief of staff for policy Stephen Miller speaks on April 29, 2025, in Warren, Michigan. Within hours of the murder of conservative podcaster and activist Charlie Kirk--and in the absence of a suspect--high-profile figures on the right, from vice president JD Vance to deputy White House chief of staff for policy Stephen Miller, already had a different culprit in mind: nonprofit organizations. On September 11, a day after Kirk's murder, US representative Chip Roy, a Republican of Texas, sent a letter to request the formation of a select committee on "the money, influence, and power behind the radical left's assault on America and the rule of law."

As artificial intelligence replaces entry-level jobs, California's universities and community colleges are offering a glimmer of hope for students: free AI training that will help them master the new technology. "You're seeing in certain coding spaces significant declines in hiring for obvious reasons," Gov. Gavin Newsom said in early August from the seventh floor of Google's San Francisco office. Flanked by leadership from California's higher education systems, he called attention to the recent layoffs at Microsoft, Google's parent company, Alphabet, and at nearby Salesforce Tower, home to the tech company that is still the city's largest private employer. Now, some of those companies -- including Google and Microsoft -- will offer a suite of AI resources free to California schools and universities. In return, the companies could gain access to millions of new users.

Spatial data is ubiquitous, encompassing a wide range of applications from environmental observations and biological measurements to more recent fields like computer vision. A critical challenge in the analysis of spatial data is spatial prediction, which involves estimating unobserved values based on nearby observations under the assumption of certain correlations. Among parametric algorithms, Kriging is particularly notable ((Matheron (1963))). Described as the best linear unbiased estimator (BLUE), Kriging employs a weighted average of nearby observations, with weights determined by a covariance function typically presumed to be stationary. However, this assumption does not hold in many real-world scenarios, such as data from satellites, monitoring stations, and urban streets, which tend to exhibit nonstationarity (Katzfuss (2013)).

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Rob Reich wears many hats: political philosopher, director of the McCoy Family Center for Ethics in Society, and associate director of the Stanford Institute for Human-Centered Artificial Intelligence. In recent years, Reich has delved deeply into the ethical and political issues posed by revolutionary technological advances in artificial intelligence (AI). His work is not always easy for technologists to hear. In his book, System Error: Where Big Tech Went Wrong and How We Can Reboot, Reich and his co-authors (computer scientist Mehran Sahami and social scientist Jeremy M. Weinstein) argued that tech companies and developers are so fixated on "optimization" that they often trample on human values.

In the world of computer science and artificial intelligence, few topics are generating as much interest as the rise of so-called "foundation models." These models can be thought of as meta-AI--but not Meta-AI, if you see what I mean--systems that incorporate vast neural networks with even bigger datasets. They are able to process a lot but, more importantly, they are easily adaptable across information domain areas, shortening and simplifying what has previously been a laborious process of training AI systems. If foundation models fulfill their promise, it could bring AI into much broader commercial use. To give a sense of the scale of these algorithms, GPT-3, a foundation model for natural language processing released two years ago, contains upwards of 170 billion parameters, the variables that guide functions within a model.

On August 27, 2020, Amazon introduced its Amazon Halo: a technology comprised of AI software and a wristband that monitors body indicators including voice to detect problems, suggests a behavioral change, or other actions to potentially improve our health.a One day later, Elon Musk and his team presented their Neuralink technology--AI software and a skull chip implant that receives and sends signals to our brain to compensate for brain malfunctioning, aiming to solve various brain-related health problems. These announcements seem like great news amid the health crisis that engulfs many of us, with technology coming to our rescue to confront some of the most critical diseases of humankind. Yet risks remain, and once the genie is out of the bottle, they are often difficult to manage and contain--they range from unintended consequences and side effects to threats to privacy and loss or misdirection of control. Endless devices surrounding us include processors that compute and monitor our abundant but wasteful lifestyle, with generations of products getting faster, cheaper, and "better."

In the fall of 2015, Rob Reich, a philosopher and a political scientist at Stanford, was chatting with a freshman during office hours. "I asked him what he planned to study," Reich recalled recently. "He said, 'Definitely computer science. I have some ideas for startups.' " In the spirit of small talk, Reich asked, What kind? "He looked at me with total earnestness and said, 'To tell you that, I'd have to ask you to sign a nondisclosure agreement.'

Earlier this spring, a paper studying covid forecasting appeared on the medRxiv preprint server with an authors' list running 256 names long. At the end of the list was Nicholas Reich, a biostatistician and infectious-disease researcher at the University of Massachusetts, Amherst. The paper reported results of a massive modeling project that Reich has co-led, with his colleague Evan Ray, since the early days of the pandemic. The project began with their attempts to compare various models online making short-term forecasts about covid-19 trajectories, looking one to four weeks ahead, for infection rates, hospitalizations, and deaths. All used varying data sources and methods and produced vastly divergent forecasts.

Machine Learning (ML) in its literal terms implies, writing algorithms to help Machines learn better than human. ML is an aspect of Artificial Intelligence (AI) that deals with the development of a mathematical model which is fed with training data to identify patterns in that data and produce an output. In other words, ML is concerned with the utilization of data to train machines (e.g., computer) to learn things (e.g., identifying different dog breeds) and produce an accurate output when fed with a test data (e.g., this dog is a terrier). The machine gets better (in terms of accuracy of the output) with time and/or with more data. The goal of ML is to make machines that act like humans, to make man's tasks easier.

Inside an abandoned warehouse on the San Francisco docks, as the damp air floods through the holes in its rusted tin roof, Sunny Tang is playing her cello while recovering from the flu. She is 45 percent sad and 0.01 percent disgusted. That, at least, is the read from the AI that's tracking her expressions, gestures, and body language from the other side of the warehouse, flashing these stats on the movie screen behind her. The audience--several hundred people huddled between her and the AI, dressed in scarfs, hats, and overcoats--lets out a collective laugh. Tang is playing alongside the rest of the Kronos Quartet, the iconic San Francisco string ensemble known for its unorthodox experimentation, and the AI is obeying orders from Trevor Paglen, the American artist who poses big questions about technology and surveillance through nearly any medium he can get his hands on. It's all part of Sight Machine, a Paglen-orchestrated performance that explores the rise of computer vision.