The Pentagon's AI office has rolled out several new artificial intelligence tools. The Pentagon's AI office has rolled out several new artificial intelligence tools. Cameron Stanley talks up'revolutionary' AI capabilities and says Washington working with Nato and Five Eyes partners The Pentagon's top artificial intelligence official said on Tuesday that America's closest allies lack the resources to keep pace with the US military's adoption of AI, and that Washington is working to steer them away from mistakes it made along the way. "They don't have the resources that we do, they don't have the experience that we do, they don't have the scale that we do," Cameron Stanley, chief digital and artificial intelligence officer at the Department of War, said at an event at the Billington Cybersecurity summit. Of Nato and the Five Eyes, an intelligence-sharing partnership of the US, UK, Canada, Australia and New Zealand, Stanley said Washington was "actively working with a number of our partners" so "they don't make the same mistakes that we make".

Technology AI Homemade chess board moves its own pieces. Maker Joshua Stanley Robotics used magnets and an open source chess platform to build this unique board. Breakthroughs, discoveries, and DIY tips sent six days a week. It's been nearly 30 years since chess champion Garry Kasparov lost to IBM's Deep Blue, marking the first time a reigning world champion was defeated by a computer in a match. Chess engines have since improved so dramatically that even a simple smartphone app can now make top grandmasters sweat .

Withaback-offstrategyin the SGD step and an elitist strategy in the evolution step, it guarantees that the best fitness in the population will never degrade.

As a motivating example, consider deploying a robot agent at scale in a varietyofhomeenvironments. Therobotshouldgeneralize byperforming robustlynotonlyintest homes, butinanyenduser'shome.

In reinforcement learning (RL), it is common to learn a single policy that fits an environment.

Three core tenets of reinforcement learning (RL)--concerning the definition of agency, the objective of learning, and the scope of the reward hypothesis--have been highlighted as key targets for conceptual revision, with major implications for theory and application. We propose a framework, inspired by open-ended evolutionary theory, to reconsider these three "dogmas." We revisit each assumption and address related concerns raised alongside them. To make our arguments relevant to RL as a model of biological learning, we first establish that evolutionary dynamics can plausibly operate within living brains over an individual's lifetime, and are not confined to cross-generational processes. We begin by revisiting the second dogma, drawing on evolutionary insights to enrich the "adaptation-rather-than-search" view of learning. We then address the third dogma regarding the limits of the reward hypothesis, using analogies from evolutionary fitness to illuminate the scalar reward vs. multi-objective debate. After discussing practical implications for exploration in RL, we turn to the first--and arguably most fundamental--issue: the absence of a formal account of agency. We argue that unlike the other two problems, the evolutionary paradigm alone cannot resolve the agency question, though it gestures in a productive direction. We advocate integrating ideas from origins-of-life theory, where the thermodynamics of sustenance and replication offer promising foundations for understanding agency and resource-constrained reinforcement learning in biological systems.

Neo-fascism is a political and societal ideology that has been having remarkable growth in the last decade in the United States of America (USA), as well as in other Western societies. It poses a grave danger to democracy and the minorities it targets, and it requires active actions against it to avoid escalation. This work presents the first-of-its-kind neo-fascist coding scheme for digital discourse in the USA societal context, overseen by political science researchers. Our work bridges the gap between Natural Language Processing (NLP) and political science against this phenomena. Furthermore, to test the coding scheme, we collect a tremendous amount of activity on the internet from notable neo-fascist groups (the forums of Iron March and Stormfront.org), and the guidelines are applied to a subset of the collected posts. Through crowdsourcing, we annotate a total of a thousand posts that are labeled as neo-fascist or non-neo-fascist. With this labeled data set, we fine-tune and test both Small Language Models (SLMs) and Large Language Models (LLMs), obtaining the very first classification models for neo-fascist discourse. We find that the prevalence of neo-fascist rhetoric in this kind of forum is ever-present, making them a good target for future research. The societal context is a key consideration for neo-fascist speech when conducting NLP research. Finally, the work against this kind of political movement must be pressed upon and continued for the well-being of a democratic society. Disclaimer: This study focuses on detecting neo-fascist content in text, similar to other hate speech analyses, without labeling individuals or organizations.

Much of the excitement in modern AI is driven by the observation that scaling up existing systems leads to better performance. But does better performance necessarily imply better internal representations? While the representational optimist assumes it must, this position paper challenges that view. We compare neural networks evolved through an open-ended search process to networks trained via conventional stochastic gradient descent (SGD) on the simple task of generating a single image. This minimal setup offers a unique advantage: each hidden neuron's full functional behavior can be easily visualized as an image, thus revealing how the network's output behavior is internally constructed neuron by neuron. The result is striking: while both networks produce the same output behavior, their internal representations differ dramatically. The SGD-trained networks exhibit a form of disorganization that we term fractured entangled representation (FER). Interestingly, the evolved networks largely lack FER, even approaching a unified factored representation (UFR). In large models, FER may be degrading core model capacities like generalization, creativity, and (continual) learning. Therefore, understanding and mitigating FER could be critical to the future of representation learning.

"The whole vision behind Track in the first place," says Veritone CEO Ryan Steelberg, was "if we're not allowed to track people's faces, how do we assist in trying to potentially identify criminals or malicious behavior or activity?" In addition to tracking individuals where facial recognition isn't legally allowed, Steelberg says, it allows for tracking when faces are obscured or not visible. The product has drawn criticism from the American Civil Liberties Union, which--after learning of the tool through MIT Technology Review--said it was the first instance they'd seen of a nonbiometric tracking system used at scale in the US. They warned that it raises many of the same privacy concerns as facial recognition but also introduces new ones at a time when the Trump administration is pushing federal agencies to ramp up monitoring of protesters, immigrants, and students. Veritone gave us a demonstration of Track in which it analyzed people in footage from different environments, ranging from the January 6 riots to subway stations.