Large Language Models (LLMs) have demonstrated impressive capabilities in automated code generation but frequently produce code that fails formal verification, an essential requirement for hardware and safety-critical domains. To overcome this fundamental limitation, we previously proposed PREFACE, a model-agnostic framework based on reinforcement learning (RL) that iteratively repairs the prompts provided to frozen LLMs, systematically steering them toward generating formally verifiable Dafny code without costly fine-tuning. This work presents Proof2Silicon, a novel end-to-end synthesis framework that embeds the previously proposed PREFACE flow to enable the generation of correctness-by-construction hardware directly from natural language specifications. Proof2Silicon operates by: (1) leveraging PREFACE's verifier-driven RL agent to optimize prompt generation iteratively, ensuring Dafny code correctness; (2) automatically translating verified Dafny programs into synthesizable high-level C using Dafny's Python backend and PyLog; and (3) employing Vivado HLS to produce RTL implementations. Evaluated rigorously on a challenging 100-task benchmark, PREFACE's RL-guided prompt optimization consistently improved Dafny verification success rates across diverse LLMs by up to 21%. Crucially, Proof2Silicon achieved an end-to-end hardware synthesis success rate of up to 72%, generating RTL designs through Vivado HLS synthesis flows. These results demonstrate a robust, scalable, and automated pipeline for LLM-driven, formally verified hardware synthesis, bridging natural-language specification and silicon realization.

NeRFs have enabled highly realistic synthesis of human faces including complex appearance and reflectance effects of hair and skin. These methods typically require a large number of multi-view input images, making the process hardware intensive and cumbersome, limiting applicability to unconstrained settings. We propose a novel volumetric human face prior that enables the synthesis of ultra high-resolution novel views of subjects that are not part of the prior's training distribution. This prior model consists of an identity-conditioned NeRF, trained on a dataset of low-resolution multi-view images of diverse humans with known camera calibration. A simple sparse landmark-based 3D alignment of the training dataset allows our model to learn a smooth latent space of geometry and appearance despite a limited number of training identities. A high-quality volumetric representation of a novel subject can be obtained by model fitting to 2 or 3 camera views of arbitrary resolution. Importantly, our method requires as few as two views of casually captured images as input at inference time.

Anomalous diffusion, as it has come to be called, extends the concept of Brownian motion and is connected to disordered systems, non-equilibrium phenomena, flows of energy and information, and transport in living systems[3]. Anomalous diffusion is "non-universal" in the sense that physically very different systems share the same power-law form of the mean squared displacement x

The firm was working on the controversial Project Maven program - an artificial intelligence (AI) project that analyses imagery and could be used to enhance the efficiency of drone strikes. Google pledges to not work on weapons after Project Maven backlash Google'ditches contract with US military' after employee revolt Google collected personal data about iPhone users, High Court hears Google quietly removes'don't be evil' preface from code of conduct Google'ditches contract with US military' after employee revolt Google quietly removes'don't be evil' preface from code of conduct This week the tech giant's chief executive Sundar Pichai responded by unveiling his company's "concrete standards" surrounding AI. However, some have suggested that the AI Principles, appear more porous than Mr Pichai's language would seem to suggest. Mr Pichai begins by prefacing the seven-point list of "objectives for AI applications" by saying it is by no means fixed or solid. "We acknowledge that this area is dynamic and evolving," he says, adding that whatever principles are included are subject to change due to the company's "willingness to adapt" its approach.

The most famous phrase in Google's corporate philosophy, "Don't be evil," has been almost entirely removed from the technology giant's code of conduct. Google, which is now a subsidiary of Alphabet after a corporate restructuring in 2015, previously included the phrase "Don't be evil" at the very start of its code, and another two times within the first two paragraphs. The simple language was replaced by vague and less specific wording such as "ethical business conduct". "The Google Code of Conduct is one of the ways we put Google's values into practice," the updated guidelines begins. "It's built around the recognition that everything we do in connection with our work at Google will be, and should be, measured against the highest possible standards of ethical business conduct."

This volume contains the papers presented at the Thirty-First International Florida Artificial Intelligence Research Society Conference (FLAIRS-31) held May 21–23, 2018, in Melbourne, Florida, USA. The call for papers attracted 150 paper submissions (61 to the general conference and 89 to the special tracks), and 27 poster abstracts. Special tracks are a vital part of the FLAIRS conferences, with 13 advertised, 12 with accepted papers. All papers were reviewed by at least three reviewers, and were coordinated by the program committees of the general conference and the special tracks.

The book might also supply points of interest, although not always dependable instruction, to social scientists, philosophers, and psychologists. Thornton describes his book as a research memorandum "in keeping with the technicolour spirit of our times" and also owns to "importing various devices from the pop-science genre" (Preface, pp. His pop-science offerings include "light relief through a concoction of dialogues, anecdotes, and other forms of non-scientific material" (Preface, p. 2) of which the more historical chapters make the best reading. Here, departures from strict accuracy are offset by the liveliness of Thornton's accounts of Kepler's work (chapter 3) and Turing's part in breaking the German wartime Enigma code (chapter 6). He is less successful with Hume's demonstration of the fallibility of the inductive process. The old philosophical concern was that theories inductively inferred from sampled facts cannot be guaranteed true for every single future fact that might be ...

This book is not an introductory text. Anyone interested in a particular topic should consult the preface of the text to find out what it is about. The negative reviewers were not fair to the authors on that score. Had they read the preface they would have found out (1) how the authors define data mining, (2) that they see it as a subject with an important mix of statistical methodology and computer science and (3) that it is intended as an advanced undergraduate or first year graduate text on the topic. They also provide a very well organized structure for the text that is well described in the preface.

A new computational paradigm has emerged in computer both Renault and Toyota have deployed online configuration science over the past few decades, which is exemplified by systems based on knowledge compilation). QBF solvers the use of SAT solvers to tackle problems in the complexity have been used in model checking, verification, debugging, class NP. Finally, function problem solvers have and engineering investment is made towards developing been used in model-based diagnosis, design debugging, highly efficient solvers for a prototypical problem CAD and bioinformatics. The cost of this investment is then on a variety of topics, including algorithms; descriptions amortized as these solvers are applied to a broader class of of implementations and/or evaluations of beyond NP problems via reductions (in contrast to developing dedicated solvers; their applications (including encodings); the complexity algorithms for each encountered problem). SAT solvers, classes they reach; and their connections to one for example, are now routinely used to solve problems in another.

This is an exciting time to be an artificial intelligence researcher. AI technologies and applications have truly entered our everyday lives, with AI systems in use throughout society. Against this backdrop of AI’s remarkable success, the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI-2015), to be held in Buenos Aires, Argentina between 25 and 31 July 2015, is poised to break several records. This is the first time the flagship international AI conference has been held in South America, and the number of submissions to the technical program has reached an historical high. These proceedings collect some of the most exciting research taking place in AI today and offer a window into the future. The theme of this year’s conference is Artificial Intelligence and Arts. Being held in Argentina, the home of Tango, the conference will feature invited talks, performances, demos and a technical track dedicated to the exploration and celebration of AI’s growing role in the Arts, both in enriching and producing Arts and in injecting art into AI to make it an elegant and more accessible scientific discipline.