HHS Is Using AI Tools From Palantir to Target'DEI' and'Gender Ideology' in Grants Since March of 2025, the Trump Administration has used tools from Palantir and the startup Credal AI to weed out "DEI" and "gender ideology from child welfare programs. A view of the Palantir building is seen during the World Economic Forum Annual Meeting 2026 in Davos, Switzerland. Since last March, the Department of Health and Human Services has been using AI tools from Palantir to screen and audit grants, grant applications, and job descriptions for noncompliance with President Donald Trump's executive orders targeting "gender ideology" and anything related to diversity, equity, inclusion (DEI), according to a recently published inventory of all use cases HHS had for AI in 2025. Neither Palantir nor HHS has publicly announced that the company's software was being used for these purposes. During the first year of Trump's second term, Palantir earned more than $35 million in payments and obligations ...

My position feels more precarious than ever. It's a question that I sometimes toss out in the company of friends who--like me, and maybe like you--have a complicated relationship to their job. I've worked at WIRED as a writer for eight years, and with much success. Eight years is also an eternity in news media, and especially if you are Black. All industries suffer from unique growing pains. Ours just so happens to have laughably high turnover rates, a distaste for racial and gender diversity, and the dubious distinction of being perpetually on the verge of extinction. So on nights when friends and I gather, trading war stories of workplace microaggressions and corporate mismanagement under damp bar lighting, we wonder how we've lasted as long as we have. The only reason I've survived, I joke, is because I'm Black. It's a silly thing to say, particularly because I have no actual proof of it other than the occasional feeling. What I do know is that I've been The Only One in more spaces than I care to remember, and rarely by choice.

There's Never Been a Worse Time to Be Authentic at Work Workers have been told to bring themselves to work, only to be disappointed time and time again, argues author Jodi-Ann Burey in her new book. Jodi-Ann Burey was only two weeks into her new role as an inclusion marketing manager for an outdoor retail company when she was accused of having a "race agenda." Burey, who is Black, was no stranger to workplace hypocrisy; as she sees it, the office is a petri dish where the knotty dynamics of society are concentrated. At the time of the accusation in February 2020, however, all she could do was laugh. "I was like, you knew who I was before you poached me. This is exactly what you wanted me to do," she says over Zoom.

The optimization of expensive black-box simulators arises in a myriad of modern scientific and engineering applications. Bayesian optimization provides an appealing solution, by leveraging a fitted surrogate model to guide the selection of subsequent simulator evaluations. In practice, however, the objective is often not to obtain a single good solution, but rather a ''basket'' of good solutions from which users can choose for downstream decision-making. This need arises in our motivating application for real-time control of internal combustion engines for flight propulsion, where a diverse set of control strategies is essential for stable flight control. There has been little work on this front for Bayesian optimization. We thus propose a new Diverse Expected Improvement (DEI) method that searches for diverse ''$\epsilon$-optimal'' solutions: locally-optimal solutions within a tolerance level $\epsilon > 0$ from a global optimum. We show that DEI yields a closed-form acquisition function under a Gaussian process surrogate model, which facilitates efficient sequential queries via automatic differentiation. This closed form further reveals a novel exploration-exploitation-diversity trade-off, which incorporates the desired diversity property within the well-known exploration-exploitation trade-off. We demonstrate the improvement of DEI over existing methods in a suite of numerical experiments, then explore the DEI in two applications on rover trajectory optimization and engine control for flight propulsion.

Large language model (LLM) agents have shown great potential in solving realworld software engineering (SWE) problems. The most advanced open-source SWE agent can resolve over 27% of real GitHub issues in SWE-Bench Lite. To fully harness the diversity of these agents, we propose DEI (Diversity Empowered Intelligence), a framework that leverages their unique expertise. DEI functions as a meta-module atop existing SWE agent frameworks, managing agent collectives for enhanced problemsolving. Experimental results show that a DEI-guided committee of agents is able to surpass the best individual agent's performance by a large margin. For instance, a group of open-source SWE agents, with a maximum individual resolve rate of 27.3% on SWE-Bench Lite, can achieve a 34.3% resolve rate with DEI, making a 25% improvement and beating most closed-source solutions. Our findings contribute to the growing body of research on collaborative AI systems and their potential to solve complex software engineering challenges. Recent advancements in large language models (LLMs) have transformed software engineering (SWE) and other domains.

In late June 2022, Sanas, a Silicon Valley startup, raised $32 million in Series A funding. Sanas is a real-time accent translation technology. For years, call centers in India or the Philippines have worked to neutralize accents by training their employees before they get to answering the phones. This new AI obviates that training – it alters accents in real time, making employees sound more American. From Sanas' perspective, they are working for the good of all concerned. The communication barriers between people with different accents and across multiple continents have lifted.

Words matter - they help frame our understanding of the world and shape our thoughts, actions and interactions. Inclusion, equity and diversity are all words that are increasingly used but have varying meanings for different issues and groups of people. To compound the issue, they are also often used interchangeably. Treated separately or understood differently, they address only part of our human experience. So the word that resonates with me the most is that of'belonging,' as it focuses on the whole.

Even massive events like the 54th edition of Consumer Electronics Show (CES) have gone virtual due to the current pandemic. Since 1967, the Consumer Technology Association (CTA), which is the North American trade association for the consumer technology industry, has been organising the fair, and this year was not going to be any different--well, except they had to take the almost 300,000m${} 2$ from CES 2020 to the cloud. In this post, I mainly put the focus on current and future hardware/robotics trends presented at CES 2021 (because we all love to make predictions, even during uncertain times). "Innovation accelerates and bunches up during economic downturns only to be unleashed as the economy begins to recover, ushering in powerful waves of technological change"--Christopher Freeman, British Economist. With this quote, I start the first session on'my show' of CES 2021, 'Tech trends to watch' by CTA (see their slides here).

The need to pursue racial justice is more urgent than ever, especially in the technology industry. The far-reaching scope and power of machine learning (ML) and artificial intelligence (AI) means that any gender and racial bias at the source is multiplied to the nth power in businesses and out in the world. The impact those technology biases have on society as a whole can't be underestimated. When decision-makers in tech companies simply don't reflect the diversity of the general population, it profoundly affects how AI/ML products are conceived, developed, and implemented. Evolve, presented by VentureBeat on December 8th, is a 90-minute event exploring bias, racism, and the lack of diversity across AI product development and management, and why these issues can't be ignored.

The potential held by the gargantuan volumes of data being generated across networks worldwide has been truly unlocked by machine learning techniques and more recently Deep Learning. The advantages offered by the latter have seen it rapidly becoming a framework of choice for various applications. However, the centralization of computational resources and the need for data aggregation have long been limiting factors in the democratization of Deep Learning applications. Edge Computing is an emerging paradigm that aims to utilize the hitherto untapped processing resources available at the network periphery. Edge Intelligence (EI) has quickly emerged as a powerful alternative to enable learning using the concepts of Edge Computing. Deep Learning-based Edge Intelligence or Deep Edge Intelligence (DEI) lies in this rapidly evolving domain. In this article, we provide an overview of the major constraints in operationalizing DEI. The major research avenues in DEI have been consolidated under Federated Learning, Distributed Computation, Compression Schemes and Conditional Computation. We also present some of the prevalent challenges and highlight prospective research avenues.