Even superforecasters are guessing that they'll soon be obsolete. To live in time is to wonder what will happen next. In every human society, there are people who obsess over the world's patterns to predict the future. In antiquity, they told kings which stars would appear at nightfall. Today they build the quantitative models that nudge governments into opening spigots of capital.

In a building in central Berkeley, not far from the university campus, a group of modern-day Cassandras are looking into concerns around the latest AI models. In a building in central Berkeley, not far from the university campus, a group of modern-day Cassandras are looking into concerns around the latest AI models. The office block where AI'doomers' gather to predict the apocalypse On the other side of San Francisco bay from Silicon Valley, where the world's biggest technology companies tear towards superhuman artificial intelligence, looms a tower from which fearful warnings emerge. At 2150 Shattuck Avenue, in the heart of Berkeley, is the home of a group of modern-day Cassandras who rummage under the hood of cutting-edge AI models and predict what calamities may be unleashed on humanity - from AI dictatorships to robot coups. Here you can hear an AI expert express sympathy with an unnerving idea: San Francisco may be the new Wuhan, the Chinese city where Covid originated and wreaked havoc on the world.

The success of AlphaZero (AZ) has demonstrated that neural-network-based Go AIs can surpass human performance by a large margin. Given that the state space of Go is extremely large and a human player can play the game from any legal state, we ask whether adversarial states exist for Go AIs that may lead them to play surprisingly wrong actions.In this paper, we first extend the concept of adversarial examples to the game of Go: we generate perturbed states that are ``semantically'' equivalent to the original state by adding meaningless moves to the game, and an adversarial state is a perturbed state leading to an undoubtedly inferior action that is obvious even for Go beginners. However, searching the adversarial state is challenging due to the large, discrete, and non-differentiable search space. To tackle this challenge, we develop the first adversarial attack on Go AIs that can efficiently search for adversarial states by strategically reducing the search space. This method can also be extended to other board games such as NoGo. Experimentally, we show that the actions taken by both Policy-Value neural network (PV-NN) and Monte Carlo tree search (MCTS) can be misled by adding one or two meaningless stones; for example, on 58\% of the AlphaGo Zero self-play games, our method can make the widely used KataGo agent with 50 simulations of MCTS plays a losing action by adding two meaningless stones. We additionally evaluated the adversarial examples found by our algorithm with amateur human Go players, and 90\% of examples indeed lead the Go agent to play an obviously inferior action.

What will your life look like in 2035? When AIs become consistently more capable than humans, life could change in strange ways. It could happen in the next few years, or a little longer. If and when it comes, our domestic routines - trips to the doctor, farming, work and justice systems - could all look very different. The'AI' doctor will see you now In 2035, AIs are more than co-pilots in medicine, they have become the frontline for much primary care.

As Large Language Models (LLMs) grow in capability, do they develop self-awareness as an emergent behavior? And if so, can we measure it? We introduce the AI Self-Awareness Index (AISAI), a game-theoretic framework for measuring self-awareness through strategic differentiation. Using the "Guess 2/3 of Average" game, we test 28 models (OpenAI, Anthropic, Google) across 4,200 trials with three opponent framings: (A) against humans, (B) against other AI models, and (C) against AI models like you. We operationalize self-awareness as the capacity to differentiate strategic reasoning based on opponent type. Finding 1: Self-awareness emerges with model advancement. The majority of advanced models (21/28, 75%) demonstrate clear self-awareness, while older/smaller models show no differentiation. Finding 2: Self-aware models rank themselves as most rational. Among the 21 models with self-awareness, a consistent rationality hierarchy emerges: Self > Other AIs > Humans, with large AI attribution effects and moderate self-preferencing. These findings reveal that self-awareness is an emergent capability of advanced LLMs, and that self-aware models systematically perceive themselves as more rational than humans. This has implications for AI alignment, human-AI collaboration, and understanding AI beliefs about human capabilities.

'The biggest decision yet': Jared Kaplan on allowing AI to train itself Anthropic's chief scientist says AI autonomy could spark a beneficial'intelligence explosion' - or be the moment humans lose control Humanity will have to decide by 2030 whether to take the "ultimate risk" of letting artificial intelligence systems train themselves to become more powerful, one of the world's leading AI scientists has said. Jared Kaplan, the chief scientist and co-owner of the $180bn (£135bn) US startup Anthropic, said a choice was looming about how much autonomy the systems should be given to evolve. The move could trigger a beneficial "intelligence explosion" - or be the moment humans end up losing control. In an interview about the intensely competitive race to reach artificial general intelligence (AGI) - sometimes called superintelligence - Kaplan urged international governments and society to engage in what he called "the biggest decision". Anthropic is part of a pack of frontier AI companies including OpenAI, Google DeepMind, xAI, Meta and Chinese rivals led by DeepSeek, racing for AI dominance. Its widely used AI assistant, Claude, has become particularly popular among business customers.

In this paper, we propose ELF, an Extensive, Lightweight and Flexible platform for fundamental reinforcement learning research. Using ELF, we implement a highly customizable real-time strategy (RTS) engine with three game environments (Mini-RTS, Capture the Flag and Tower Defense). Mini-RTS, as a miniature version of StarCraft, captures key game dynamics and runs at 40K frame-per-second (FPS) per core on a laptop. When coupled with modern reinforcement learning methods, the system can train a full-game bot against built-in AIs end-to-end in one day with 6 CPUs and 1 GPU. In addition, our platform is flexible in terms of environment-agent communication topologies, choices of RL methods, changes in game parameters, and can host existing C/C++-based game environments like ALE [4]. Using ELF, we thoroughly explore training parameters and show that a network with Leaky ReLU [17] and Batch Normalization [11] coupled with long-horizon training and progressive curriculum beats the rule-based built-in AI more than 70% of the time in the full game of Mini-RTS. Strong performance is also achieved on the other two games. In game replays, we show our agents learn interesting strategies.

AIS is asymptotically optimal, i.e., learning the sampling policy has no cost asymptotically.

Therefore, in this paper, we study the occurring challenges for co-generation with GANs. To address those challenges we develop an annealed importance sampling based Hamiltonian Monte Carlo co-generation algorithm.

AIS is asymptotically optimal, i.e., learning the sampling policy has no cost asymptotically.