UK Prime Minister Keir Starmer has signed a 100-year partnership agreement with Ukraine to provide support across various sectors, including healthcare and military technology, while pledging to provide security guarantees if an end to Russia's war comes. During Starmer's first visit to Kyiv since becoming prime minister, the British leader told a news conference on Thursday that the United Kingdom would examine "the practical ways to get a just and lasting peace … that guarantees your security, your independence and your right to choose your own future". "We will work with you and all of our allies on steps that would be robust enough to guarantee Ukraine's security," Starmer said. "Those conversations will continue for many months ahead." While Starmer was speaking with Ukrainian President Volodymyr Zelenskyy at the presidential palace, loud blasts and air raid sirens were heard over Kyiv as air defence systems took aim at a Russian drone attack. The British leader said the Russian attack served as a reminder of the situation on the ground.

OpenAI's new model, called GPT-4b micro, was trained to suggest ways to re-engineer the protein factors to increase their function. According to OpenAI, researchers used the model's suggestions to change two of the Yamanaka factors to be more than 50 times as effective--at least according to some preliminary measures. "Just across the board, the proteins seem better than what the scientists were able to produce by themselves," says John Hallman, an OpenAI researcher. Hallman and OpenAI's Aaron Jaech, as well as Rico Meinl from Retro, were the model's lead developers. Outside scientists won't be able to tell if the results are real until they're published, something the companies say they are planning.

Tasks is a new beta feature for the paid-for versions of ChatGPT. This feature allows you to schedule a prompt to run at a certain time. In this article, I'll explain that feature. Then I'll take you through the incredibly frustrating process of trying to get ChatGPT to do what you want it to do using Tasks. I hesitate to anthropomorphize the AI, but in this round of testing, ChatGPT has been singularly uncooperative.

A groundbreaking chatbot created in the 1960s has been painstakingly reconstructed from archived records and run for the first time in over half a century, as part of an effort to preserve one of the earliest examples of artificial intelligence. ELIZA was written by computer scientist Joseph Weizenbaum at MIT in just 420 lines of code. The AI model is extremely rudimentary in comparison to today's large language models (LLMs) like ChatGPT but wowed researchers at the time with…

The Association for the Advancement of Artificial Intelligence's 2024 Fall Symposium Series was held at Westin Arlington Gateway, Arlington, Virginia, November 7-9, 2024. There were seven symposia in the fall program: AI Trustworthiness and Risk Assessment for Challenging Contexts (ATRACC), Artificial Intelligence for Aging in Place, Integrated Approaches to Computational Scientific Discovery, Large Language Models for Knowledge Graph and Ontology Engineering (LLMs for KG and OE), Machine Intelligence for Equitable Global Health (MI4EGH), Unifying Representations for Robot Application Development, Using AI to Build Secure and Resilient Agricultural Systems: Leveraging AI to mitigate Cyber, Climatic and Economic Threats in Food, Agricultural, and Water (FAW) Systems. This report contains summaries of the workshops, which were submitted by some, but not all, of the workshop chairs. The rapid embrace of AI-based critical systems introduces new dimensions of errors that induce increased levels of risk, limiting trustworthiness. Thus, AI-based critical systems must be assessed across many dimensions by different parties (researchers, developers, regulators, customers, insurance companies, end-users, etc.) for different reasons. Assessment of trustworthiness should be made at both the full system level and at the level of individual AI components. The focus of this symposium was on AI trustworthiness broadly and methods that help provide bounds for fairness, reproducibility, reliability, and accountability in the context of quantifying AI-system risk, spanning the entire AI lifecycle from theoretical research formulations all the way to system implementation, deployment, and operation. This first AAAI symposium on AI Trustworthiness and Risk Assessment in Challenging Contexts was triggered by two initiatives on responsible and trustworthy AI that came together thanks to encouragement given by AAAI: an international community (mostly European and Asia-South Pacific) around AI trustworthiness assessment for critical systems, already gathered at the AITA SSS Symposium in 2023; and a US-based community around University of West Florida, gathered about the question of AI risk assessment in challenging contexts e.g., for security or defense applications.

We present the first provably sublinear time hashing algorithm for approximate \emph{Maximum Inner Product Search} (MIPS). Searching with (un-normalized) inner product as the underlying similarity measure is a known difficult problem and finding hashing schemes for MIPS was considered hard. While the existing Locality Sensitive Hashing (LSH) framework is insufficient for solving MIPS, in this paper we extend the LSH framework to allow asymmetric hashing schemes. Our proposal is based on a key observation that the problem of finding maximum inner products, after independent asymmetric transformations, can be converted into the problem of approximate near neighbor search in classical settings. This key observation makes efficient sublinear hashing scheme for MIPS possible.

Sparsely gated Mixture-of-Expert (MoE) has demonstrated its effectiveness in scaling up deep neural networks to an extreme scale. Despite that numerous efforts have been made to improve the performance of MoE from the model design or system optimization perspective, existing MoE dispatch patterns are still not able to fully exploit the underlying heterogeneous network environments. In this paper, we propose TA-MoE, a topology-aware routing strategy for large-scale MoE trainging, from a model-system co-design perspective, which can dynamically adjust the MoE dispatch pattern according to the network topology. Based on communication modeling, we abstract the dispatch problem into an optimization objective and obtain the approximate dispatch pattern under different topologies. On top of that, we design a topology-aware auxiliary loss, which can adaptively route the data to fit in the underlying topology without sacrificing the model accuracy.

We study an algorithmic equivalence technique between non-convex gradient descent and convex mirror descent. We start by looking at a harder problem of regret minimization in online non-convex optimization. We show that under certain geometric and smoothness conditions, online gradient descent applied to non-convex functions is an approximation of online mirror descent applied to convex functions under reparameterization. In continuous time, the gradient flow with this reparameterization was shown to be \emph{exactly} equivalent to continuous-time mirror descent by Amid and Warmuth, but theory for the analogous discrete time algorithms is left as an open problem. We prove an O(T {\frac{2}{3}}) regret bound for non-convex online gradient descent in this setting, answering this open problem.

Performance metrics for binary classification are designed to capture tradeoffs between four fundamental population quantities: true positives, false positives, true negatives and false negatives. Despite significant interest from theoretical and applied communities, little is known about either optimal classifiers or consistent algorithms for optimizing binary classification performance metrics beyond a few special cases. We consider a fairly large family of performance metrics given by ratios of linear combinations of the four fundamental population quantities. This family includes many well known binary classification metrics such as classification accuracy, AM measure, F-measure and the Jaccard similarity coefficient as special cases. Our analysis identifies the optimal classifiers as the sign of the thresholded conditional probability of the positive class, with a performance metric-dependent threshold.

The problem of drawing samples from a discrete distribution can be converted into a discrete optimization problem. In this work, we show how sampling from a continuous distribution can be converted into an optimization problem over continuous space. Central to the method is a stochastic process recently described in mathematical statistics that we call the Gumbel process. We present a new construction of the Gumbel process and A* sampling, a practical generic sampling algorithm that searches for the maximum of a Gumbel process using A* search. We analyze the correctness and convergence time of A* sampling and demonstrate empirically that it makes more efficient use of bound and likelihood evaluations than the most closely related adaptive rejection sampling-based algorithms.