Lattice field theories are fundamental testbeds for computational physics; yet, sampling their Boltzmann distributions remains challenging due to multimodality and long-range correlations. While normalizing flows offer a promising alternative, their application to large lattices is often constrained by prohibitive memory requirements and the challenge of maintaining sufficient model expressivity. We propose sparse triangular transport maps that explicitly exploit the conditional independence structure of the lattice graph under periodic boundary conditions using monotone rectified neural networks (MRNN). We introduce a comprehensive framework for triangular transport maps that navigates the fundamental trade-off between \emph{exact sparsity} (respecting marginal conditional independence in the target distribution) and \emph{approximate sparsity} (computational tractability without fill-ins). Restricting each triangular map component to a local past enables site-wise parallel evaluation and linear time complexity in lattice size $N$, while preserving the expressive, invertible structure. Using $ϕ^4$ in two dimensions as a controlled setting, we analyze how node labelings (orderings) affect the sparsity and performance of triangular maps. We compare against Hybrid Monte Carlo (HMC) and established flow approaches (RealNVP).

Is it possible to articulate a conception of consciousness that is compatible with the exotic characteristics of contemporary, disembodied AI systems, and that can stand up to philosophical scrutiny? How would subjective time and selfhood show up for an entity that conformed to such a conception? Trying to answer these questions, even metaphorically, stretches the language of consciousness to breaking point. Ultimately, the attempt yields something like emptiness, in the Buddhist sense, and helps to undermine our dualistic inclinations towards subjectivity and selfhood.

My paper Talking About Large Language Models has more than once been interpreted as advocating a reductionist stance towards large language models. But the paper was not intended that way, and I do not endorse such positions. This short note situates the paper in the context of a larger philosophical project that is concerned with the (mis)use of words rather than metaphysics, in the spirit of Wittgenstein's later writing. In (Shanahan, 2024b), I wrote "[a] bare-bones LLM does not really know anything because all it does, at a fundamental level, is sequence prediction". Looking at that sentence in isolation, a reader might be forgiven for assuming that I am taking some sort of reductionist stance according to which an LLM-based chatbot, such as ChatGPT, Claude, or Gemini, is just a next token predictor, where the word "just" here carries great metaphysical weight, and that LLM-based systems therefore do not and cannot have beliefs.

Contemporary conversational AI systems based on large language models (LLMs) can engage users on a wide variety of topics, including philosophy, spirituality, and religion. Suitably prompted, LLMs can be coaxed into discussing such existentially significant matters as their own putative consciousness and the role of artificial intelligence in the fate of the Cosmos. Here we examine two lengthy conversations of this type. We trace likely sources, both ancient and modern, for the extensive repertoire of images, myths, metaphors, and conceptual esoterica that the language model draws on during these conversations, and foreground the contemporary communities and cultural movements that deploy related motifs, especially in their online activity. Finally, we consider the larger societal impacts of such engagements with LLMs.

Anti-vaccine activist Robert F. Kennedy Jr.'s presidential campaign hosted an online panel Wednesday on the future of AI moderated, for some reason, by Ian Carroll, a self-styled journalist with a history of antisemitic statements. In the course of the conversation, Kennedy admitted that he "gets manipulated by AI all the time." "Somebody will send me something and I'll go'Holy cow, did you see this?'," he said, describing how he credulously forwards fake content to his children, only for them to have to correct him. RFK Jr. said he regularly "gets manipulated by AI." While Carroll has no particular public profile on AI, his persona tracks with the campaign's focus on tech figures and influencers as it courts a young, male, and extremely online audience.

The advent of conversational agents with increasingly human-like behaviour throws old philosophical questions into new light. Does it, or could it, ever make sense to speak of AI agents built out of generative language models in terms of consciousness, given that they are "mere" simulacra of human behaviour, and that what they do can be seen as "merely" role play? Drawing on the later writings of Wittgenstein, this paper attempts to tackle this question while avoiding the pitfalls of dualistic thinking.

Kennedy initially launched his presidential bid as a Democrat last April, but he later announced an independent run in October. Independent presidential candidate Robert F. Kennedy, Jr. announced Tuesday that attorney and tech entrepreneur Nicole Shanahan will be his vice presidential running mate heading into the November general election. A native of Oakland, California, the 38-year-old Shanahan is a philanthropist with a long history of donating to Democrat and left-leaning causes, including supporting President Biden in his 2020 election bid before switching to Kennedy when he launched his own run for the Democrat nomination last year. Kennedy announced Shanahan by praising her insight into "how Big Tech uses AI to manipulate the public," her athletic ability, and willingness to be a "partner" in a number of policy areas, including on securing the border. Independent presidential candidate Robert F. Kennedy, Jr., left, and entrepreneur Nicole Shanahan, right.

We consider the problem of sampling discrete field configurations $\phi$ from the Boltzmann distribution $[d\phi] Z^{-1} e^{-S[\phi]}$, where $S$ is the lattice-discretization of the continuous Euclidean action $\mathcal S$ of some quantum field theory. Since such densities arise as the approximation of the underlying functional density $[\mathcal D\phi(x)] \mathcal Z^{-1} e^{-\mathcal S[\phi(x)]}$, we frame the task as an instance of operator learning. In particular, we propose to approximate a time-dependent operator $\mathcal V_t$ whose time integral provides a mapping between the functional distributions of the free theory $[\mathcal D\phi(x)] \mathcal Z_0^{-1} e^{-\mathcal S_{0}[\phi(x)]}$ and of the target theory $[\mathcal D\phi(x)]\mathcal Z^{-1}e^{-\mathcal S[\phi(x)]}$. Whenever a particular lattice is chosen, the operator $\mathcal V_t$ can be discretized to a finite dimensional, time-dependent vector field $V_t$ which in turn induces a continuous normalizing flow between finite dimensional distributions over the chosen lattice. This flow can then be trained to be a diffeormorphism between the discretized free and target theories $[d\phi] Z_0^{-1} e^{-S_{0}[\phi]}$, $[d\phi] Z^{-1}e^{-S[\phi]}$. We run experiments on the $\phi^4$-theory to explore to what extent such operator-based flow architectures generalize to lattice sizes they were not trained on and show that pretraining on smaller lattices can lead to speedup over training only a target lattice size.

Fox News Flash top sports headlines are here. Check out what's clicking on Foxnews.com. San Francisco 49ers quarterback Brock Purdy is expected to join the team as they begin their offseason program on Monday, just a little over a month after undergoing surgery to repair a torn UCL in his throwing arm. Purdy, 23, underwent surgery on March 10 to repair the UCL in his right elbow, which he injured during the NFC Championship game against the Philadelphia Eagles. Despite not being expected to start throwing until June, Purdy told The Athletic on Friday that he still intends to participate in the voluntary offseason program.

Computer scientist and entrepreneur Sergey Brin is one of the co-founders of search engine giant Google. He currently has a net worth of $83.6 billion, making him one of the world's wealthiest people. Here are some of the things you should know about the successful billionaire businessman. Sergey Mikhaylovich Brin was born on Aug. 21, 1973, in Moscow, Russia, to Jewish parents Michael and Eugenia. He and his family moved to the United States in 1979.