The "existential" risk of artificial intelligence has been acknowledged by No 10 for the first time, after the prime minister met the heads of the world's leading AI research groups to discuss safety and regulation. Rishi Sunak and Chloe Smith, the secretary of state for science, innovation and technology, met the chief executives of Google DeepMind, OpenAI and Anthropic AI on Wednesday evening and discussed how best to moderate the development of the technology to limit the risks of catastrophe. "They discussed safety measures, voluntary actions that labs are considering to manage the risks, and the possible avenues for international collaboration on AI safety and regulation," the participants said in a joint statement. "The lab leaders agreed to work with the UK government to ensure our approach responds to the speed of innovations in this technology both in the UK and around the globe. "The PM and CEOs discussed the risks of the technology, ranging from disinformation and national security, to existential threats … The PM set out how the approach to AI regulation will need to keep pace with the fast-moving advances in this technology." It is the first time the prime minister has acknowledged the potential "existential" threat of developing a "superintelligent" AI without appropriate safeguards, a risk that contrasts with the UK government's generally positive approach to AI development.

A bipartisan panel of voters weighed in on the future of artificial intelligence and growing concerns surrounding the potential dangers of the emerging technology. As machine learning technology continues to shock the world, popular artificial intelligence tools such as natural language processing may generate unforeseen issues for humanity. For instance, natural language processing can have implicit biases, create a significant carbon footprint, and stoke concerns about AI sentience. Natural language processing is a field in machine learning where a computer processes human language through vast amounts of data to understand, translate, extract, and organize information. However, the language processing tools such as Open AI's Chat GPT and other tools run into some challenges, such as misspellings, speech recognition, and the ability of a computer to understand the nuances of human language.

A bipartisan panel of voters weighed in on the future of artificial intelligence and growing concerns surrounding the potential dangers of the emerging technology. Sen. Rick Scott, R-Fla., is hoping to give parents more control over their kids' access to AI chatbots as Congress starts to wrestle with how to put guardrails around rapidly advancing artificial intelligence systems. Scott introduced the Artificial Intelligence Shield for Kids (ASK) Act, and told Fox News Digital in an interview that he's already winning support for the bill from Senate colleagues as well as American parents. I mean, they're worried about their kids' access to social media sites," Scott said of parental feedback he's received. "And I think that they're going to do everything they can, the parents I talked to, but there's also things that the government can do to make sure that their children are not subjected to things." Sen. Rick Scott spoke to Fox News Digital about why he introduced his Artificial Intelligence Shield for Kids bill. "Part of government's responsibility is to keep people safe.

OpenAI CEO Sam Altman took questions from reporters after his congressional hearing, including defining "scary AI." Artificial intelligence could become so powerful that it replaces professional experts "in most domains" within the next decade, OpenAI CEO Sam Altman warned. Altman, the chief of the AI lab behind popular platforms such as ChatGPT, published a blog post this week with two other OpenAI leaders, Greg Brockman and Ilya Sutskever, warning that "we must mitigate the risks of today's AI technology. "It's conceivable that within the next ten years, AI systems will exceed expert skill level in most domains, and carry out as much productive activity as one of today's largest corporations," reads the post, which was published on OpenAI's website. "In terms of both potential upsides and downsides, superintelligence will be more powerful than other technologies humanity has had to contend with in the past. We can have a dramatically more prosperous future; but we have to manage risk to get there," the post continued. OPENAI CEO SAM ALTMAN REVEALS WHAT HE THINKS IS'SCARY' ABOUT AI Sam Altman, CEO and co-founder of OpenAI, speaks during a Senate Judiciary subcommittee hearing in Washington, D.C., on May 16, 2023. Altman and his fellow OpenAI executives compared artificial intelligence to nuclear energy and synthetic biology, arguing that regulations must be handled with "special treatment and coordination" to be effective. They suggested that a version of the International Atomic Energy Agency will be needed to regulate the "superintelligence" technology. "Any effort above a certain capability (or resources like compute) threshold will need to be subject to an international authority that can inspect systems, require audits, test for compliance with safety standards, place restrictions on degrees of deployment and levels of security, etc," they wrote. Altman appeared before Congress this month to discuss how to regulate artificial intelligence, saying he welcomes U.S. leaders to craft such rules. Following the hearing, Altman provided examples of "scary AI" to Fox News Digital, which included systems that could design "novel biological pathogens." "An AI that could hack into computer systems," he said. "I think these are all scary.

We propose Riemannian Flow Matching (RFM), a simple yet powerful framework for training continuous normalizing flows on manifolds. Existing methods for generative modeling on manifolds either require expensive simulation, are inherently unable to scale to high dimensions, or use approximations for limiting quantities that result in biased training objectives. Riemannian Flow Matching bypasses these limitations and offers several advantages over previous approaches: it is simulation-free on simple geometries, does not require divergence computation, and computes its target vector field in closed-form. The key ingredient behind RFM is the construction of a relatively simple premetric for defining target vector fields, which encompasses the existing Euclidean case. To extend to general geometries, we rely on the use of spectral decompositions to efficiently compute premetrics on the fly. Our method achieves state-of-the-art performance on real-world non-Euclidean datasets, and we demonstrate tractable training on general geometries, including triangular meshes with highly non-trivial curvature and boundaries.

In material research, structural characterization often requires multiple complementary techniques to obtain a holistic morphological view of the synthesized material. Depending on the availability of and accessibility of the different characterization techniques (e.g., scattering, microscopy, spectroscopy), each research facility or academic research lab may have access to high-throughput capability in one technique but face limitations (sample preparation, resolution, access time) with other techniques(s). Furthermore, one type of structural characterization data may be easier to interpret than another (e.g., microscopy images are easier to interpret than small angle scattering profiles). Thus, it is useful to have machine learning models that can be trained on paired structural characterization data from multiple techniques so that the model can generate one set of characterization data from the other. In this paper we demonstrate one such machine learning workflow, PairVAE, that works with data from Small Angle X-Ray Scattering (SAXS) that presents information about bulk morphology and images from Scanning Electron Microscopy (SEM) that presents two-dimensional local structural information of the sample. Using paired SAXS and SEM data of novel block copolymer assembled morphologies [open access data from Doerk G.S., et al. Science Advances. 2023 Jan 13;9(2): eadd3687], we train our PairVAE. After successful training, we demonstrate that the PairVAE can generate SEM images of the block copolymer morphology when it takes as input that sample's corresponding SAXS 2D pattern, and vice versa. This method can be extended to other soft materials morphologies as well and serves as a valuable tool for easy interpretation of 2D SAXS patterns as well as creating a database for other downstream calculations of structure-property relationships.

Algorithms play a crucial role in many technological systems that control or affect various aspects of our lives. As a result, providing explanations for their decisions to address the needs of users and organisations is increasingly expected by laws, regulations, codes of conduct, and the public. However, as laws and regulations do not prescribe how to meet such expectations, organisations are often left to devise their own approaches to explainability, inevitably increasing the cost of compliance and good governance. Hence, we envision Explainability-by-Design, a holistic methodology characterised by proactive measures to include explanation capability in the design of decision-making systems. The methodology consists of three phases: (A) Explanation Requirement Analysis, (B) Explanation Technical Design, and (C) Explanation Validation. This paper describes phase (B), a technical workflow to implement explanation capability from requirements elicited by domain experts for a specific application context. Outputs of this phase are a set of configurations, allowing a reusable explanation service to exploit logs provided by the target application to create provenance traces of the application's decisions. The provenance then can be queried to extract relevant data points, which can be used in explanation plans to construct explanations personalised to their consumers. Following the workflow, organisations can design their decision-making systems to produce explanations that meet the specified requirements. To facilitate the process, we present a software architecture with reusable components to incorporate the resulting explanation capability into an application. Finally, we applied the workflow to two application scenarios and measured the associated development costs. It was shown that the approach is tractable in terms of development time, which can be as low as two hours per sentence.

Quantum Monte Carlo coupled with neural network wavefunctions has shown success in computing ground states of quantum many-body systems. Existing optimization approaches compute the energy by sampling local energy from an explicit probability distribution given by the wavefunction. In this work, we provide a new optimization framework for obtaining properties of quantum many-body ground states using score-based neural networks. Our new framework does not require explicit probability distribution and performs the sampling via Langevin dynamics. Our method is based on the key observation that the local energy is directly related to scores, defined as the gradient of the logarithmic wavefunction. Inspired by the score matching and diffusion Monte Carlo methods, we derive a weighted score matching objective to guide our score-based models to converge correctly to ground states. We first evaluate our approach with experiments on quantum harmonic traps, and results show that it can accurately learn ground states of atomic systems. By implicitly modeling high-dimensional data distributions, our work paves the way toward a more efficient representation of quantum systems.

Can language models transform inputs to protect text classifiers against adversarial attacks? In this work, we present ATINTER, a model that intercepts and learns to rewrite adversarial inputs to make them non-adversarial for a downstream text classifier. Our experiments on four datasets and five attack mechanisms reveal that ATINTER is effective at providing better adversarial robustness than existing defense approaches, without compromising task accuracy. For example, on sentiment classification using the SST-2 dataset, our method improves the adversarial accuracy over the best existing defense approach by more than 4% with a smaller decrease in task accuracy (0.5% vs 2.5%). Moreover, we show that ATINTER generalizes across multiple downstream tasks and classifiers without having to explicitly retrain it for those settings. Specifically, we find that when ATINTER is trained to remove adversarial perturbations for the sentiment classification task on the SST-2 dataset, it even transfers to a semantically different task of news classification (on AGNews) and improves the adversarial robustness by more than 10%.

The collaboration between humans and artificial intelligence (AI) is a significant feature in this digital age. However, humans and AI may have observation, interpretation, and action conflicts when working synchronously. This phenomenon is often masked by faults and, unfortunately, overlooked. This paper systematically introduces the human-AI conflict concept, causes, measurement methods, and risk assessment. The results highlight that there is a potential second decision-maker besides the human, which is the AI; the human-AI conflict is a unique and emerging risk in digitalized process systems; and this is an interdisciplinary field that needs to be distinguished from traditional fault and failure analysis; the conflict risk is significant and cannot be ignored. Keywords: human-AI conflict, risk, digitization, automation. 1. Introduction Automation, digitization, and artificial intelligence (AI) have become the trends in the development of industrial history (Pistikopoulos et al., 2021).