Grounding language in vision is an active field of research seeking to construct cognitively plausible word and sentence representations by incorporating perceptual knowledge from vision into text-based representations. Despite many attempts at language grounding, achieving an optimal equilibrium between textual representations of the language and our embodied experiences remains an open field. Some common concerns are the following. Is visual grounding advantageous for abstract words, or is its effectiveness restricted to concrete words? What is the optimal way of bridging the gap between text and vision? To what extent is perceptual knowledge from images advantageous for acquiring high-quality embeddings? Leveraging the current advances in machine learning and natural language processing, the present study addresses these questions by proposing a simple yet very effective computational grounding model for pre-trained word embeddings. Our model effectively balances the interplay between language and vision by aligning textual embeddings with visual information while simultaneously preserving the distributional statistics that characterize word usage in text corpora. By applying a learned alignment, we are able to indirectly ground unseen words including abstract words. A series of evaluations on a range of behavioural datasets shows that visual grounding is beneficial not only for concrete words but also for abstract words, lending support to the indirect theory of abstract concepts. Moreover, our approach offers advantages for contextualized embeddings, such as those generated by BERT, but only when trained on corpora of modest, cognitively plausible sizes. Code and grounded embeddings for English are available at https://github.com/Hazel1994/Visually_Grounded_Word_Embeddings_2.

NoMoPy is a code for fitting, analyzing, and generating noise modeled as a hidden Markov model (HMM) or, more generally, factorial hidden Markov model (FHMM). This code, written in Python, implements approximate and exact expectation maximization (EM) algorithms for performing the parameter estimation process, model selection procedures via cross-validation, and parameter confidence region estimation. Here, we describe in detail the functionality implemented in NoMoPy and provide examples of its use and performance on example problems.

Bayesian Generalized Linear Models (GLMs) define a flexible probabilistic framework to model categorical, ordinal and continuous data, and are widely used in practice. However, exact inference in GLMs is prohibitively expensive for large datasets, thus requiring approximations in practice. The resulting approximation error adversely impacts the reliability of the model and is not accounted for in the uncertainty of the prediction. In this work, we introduce a family of iterative methods that explicitly model this error. They are uniquely suited to parallel modern computing hardware, efficiently recycle computations, and compress information to reduce both the time and memory requirements for GLMs. As we demonstrate on a realistically large classification problem, our method significantly accelerates training by explicitly trading off reduced computation for increased uncertainty.

Learning continuous-time point processes is essential to many discrete event forecasting tasks. However, integration poses a major challenge, particularly for spatiotemporal point processes (STPPs), as it involves calculating the likelihood through triple integrals over space and time. Existing methods for integrating STPP either assume a parametric form of the intensity function, which lacks flexibility; or approximating the intensity with Monte Carlo sampling, which introduces numerical errors. Recent work by Omi et al. [2019] proposes a dual network approach for efficient integration of flexible intensity function. However, their method only focuses on the 1D temporal point process. In this paper, we introduce a novel paradigm: AutoSTPP (Automatic Integration for Spatiotemporal Neural Point Processes) that extends the dual network approach to 3D STPP. While previous work provides a foundation, its direct extension overly restricts the intensity function and leads to computational challenges. In response, we introduce a decomposable parametrization for the integral network using ProdNet. This approach, leveraging the product of simplified univariate graphs, effectively sidesteps the computational complexities inherent in multivariate computational graphs. We prove the consistency of AutoSTPP and validate it on synthetic data and benchmark real-world datasets. AutoSTPP shows a significant advantage in recovering complex intensity functions from irregular spatiotemporal events, particularly when the intensity is sharply localized. Our code is open-source at https://github.com/Rose-STL-Lab/AutoSTPP.

Earlier today, President Joe Biden signed the most sweeping set of regulatory principles on artificial intelligence in America to date: a lengthy executive order that directs all types of government agencies to make sure America is leading the way in developing the technology while also addressing the many dangers that it poses. The order explicitly pushes agencies to establish rules and guidelines, write reports, and create funding and research initiatives for AI--"the most consequential technology of our time," in the president's own words. The scope of the order is impressive, especially given that the generative-AI boom began just about a year ago. But the document's many parts--and there are many--are at times in tension, revealing a broader confusion over what, exactly, America's primary attitude toward AI should be: Is it a threat to national security, or a just society? Is it a geopolitical weapon?

An executive order on artificial intelligence issued by US president Joe Biden aims to show leadership in regulating AI safety and security – but most of the follow-through still requires action from US lawmakers and the voluntary goodwill of tech companies. Biden's executive order directs a wide array of US government agencies to develop guidelines for testing and using AI systems, including having the National Institute of Standards and Technology set guidelines for "red-team testing" that can probe for potential AI vulnerabilities prior to public release. "The language in this executive order and in the White House's discussion of it suggests an interest in being seen as the most aggressive and proactive in addressing AI regulation," says Sarah Kreps at Cornell University in New York. It is probably "no coincidence" that Biden's executive order came out just before the UK government convened its own AI summit, says Kreps. But she cautioned that the executive order alone will not have much impact unless the US Congress can produce bipartisan legislation and resources to back it up – something that she sees as unlikely during the US 2024 election year.

Global leaders, tech executives and experts – including Elon Musk – are gathering on Wednesday and Thursday at Bletchley Park, the home of second world war codebreakers, for a landmark summit on safety in artificial intelligence. In a speech last week Rishi Sunak said AI – the term for computer systems that can perform tasks typically associated with intelligent beings – brought opportunities but also significant risks, such as making it easier for rogue actors to make chemical or biological weapons. Here we answer your questions about the summit. The AI safety summit will look at frontier AI systems, which the government describes as "highly capable" models that can perform a wide variety of tasks matching or exceeding the performances of the most advanced AI available today. An example of frontier AI, according to a government document released last week, is the "large language model" technology that underpins AI tools such as the ChatGPT chatbot and its Google-made rival, Bard.

This week, UK prime minister Rishi Sunak is hosting a group of 100 representatives from the worlds of business and politics to discuss the potential and pitfalls of artificial intelligence. The AI Safety Summit, held at Bletchley Park, UK, begins on 1 November and aims to come up with a set of global principles with which to develop and deploy "frontier AI models" – the terminology favoured by Sunak and key figures in the AI industry for powerful models that don't yet exist, but may be built very soon. While the Bletchley Park event is the focal point, there is a wider week of fringe events being held in the UK, alongside a raft of UK government announcements on AI. Here are the latest developments. The UK government is testing a large language model chatbot called Gov.uk Chat that can answer questions citizens may have about tax, student loans and benefits, according to The Telegraph.

President Biden today issued an Executive Order on "Safe, Secure, and Trustworthy Artificial Intelligence". A fact sheet from the White House states that the order "establishes new standards for AI safety and security, protects Americans' privacy, advances equity and civil rights, stands up for consumers and workers, promotes innovation and competition, advances American leadership around the world, and more."

The goal of the order, according to the White House, is to improve "AI safety and security." It also includes a requirement that developers share safety test results for new AI models with the US government if the tests show that the technology could pose a risk to national security. This is a surprising move that invokes the Defense Production Act, typically used during times of national emergency. The executive order advances the voluntary requirements for AI policy that the White House set back in August, though it lacks specifics on how the rules will be enforced. Executive orders are also vulnerable to being overturned at any time by a future president, and they lack the legitimacy of congressional legislation on AI, which looks unlikely in the short term.