Modern software for propositional satisfiability problems gives a powerful automated reasoning toolkit, capable of outputting not only a satisfiable/unsatisfiable signal but also a justification of unsatisfiability in the form of resolution proof (or a more expressive proof), which is commonly used for verification purposes. Empirically, modern SAT solvers produce relatively short proofs, however, there are no inherent guarantees that these proofs cannot be significantly reduced. This paper proposes a novel branch-and-bound algorithm for finding the shortest resolution proofs; to this end, we introduce a layer list representation of proofs that groups clauses by their level of indirection. As we show, this representation breaks all permutational symmetries, thereby improving upon the state-of-the-art symmetry-breaking and informing the design of a novel workflow for proof minimization. In addition to that, we design pruning procedures that reason on proof length lower bound, clause subsumption, and dominance. Our experiments suggest that the proofs from state-of-the-art solvers could be shortened by 30-60% on the instances from SAT Competition 2002 and by 25-50% on small synthetic formulas. When treated as an algorithm for finding the shortest proof, our approach solves twice as many instances as the previous work based on SAT solving and reduces the time to optimality by orders of magnitude for the instances solved by both approaches.

Susceptibility to misinformation describes the extent to believe unverifiable claims, which is hidden in people's mental process and infeasible to observe. Existing susceptibility studies heavily rely on the self-reported beliefs, making any downstream applications on susceptability hard to scale. To address these limitations, in this work, we propose a computational model to infer users' susceptibility levels given their activities. Since user's susceptibility is a key indicator for their reposting behavior, we utilize the supervision from the observable sharing behavior to infer the underlying susceptibility tendency. The evaluation shows that our model yields estimations that are highly aligned with human judgment on users' susceptibility level comparisons. Building upon such large-scale susceptibility labeling, we further conduct a comprehensive analysis of how different social factors relate to susceptibility. We find that political leanings and psychological factors are associated with susceptibility in varying degrees.

Autonomous vehicle (AV) algorithms need to be tested extensively in order to make sure the vehicle and the passengers will be safe while using it after the implementation. Testing these algorithms in real world create another important safety critical point. Real world testing is also subjected to limitations such as logistic limitations to carry or drive the vehicle to a certain location. For this purpose, hardware in the loop (HIL) simulations as well as virtual environments such as CARLA and LG SVL are used widely. This paper discusses a method that combines the real vehicle with the virtual world, called vehicle in virtual environment (VVE). This method projects the vehicle location and heading into a virtual world for desired testing, and transfers back the information from sensors in the virtual world to the vehicle. As a result, while vehicle is moving in the real world, it simultaneously moves in the virtual world and obtains the situational awareness via multiple virtual sensors. This would allow testing in a safe environment with the real vehicle while providing some additional benefits on vehicle dynamics fidelity, logistics limitations and passenger experience testing. The paper also demonstrates an example case study where path following and the virtual sensors are utilized to test a radar based stopping algorithm.

The use of artificial intelligence in business is showing signs of acceleration. Nearly three-quarters of companies are now using AI (31%) or are exploring the use of AI (43%), according to IBM's "2021 Global AI Adoption Index." IT professionals responding to the IBM survey cited changing business needs in the wake of the pandemic as a driving factor in the adoption of AI at their companies. Indeed, 43% said their companies have accelerated AI rollouts as a result of the pandemic. Advances in AI tools have made artificial intelligence more accessible for companies, according to survey respondents.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Data is precious – so it's been asserted; it has become the world's most valuable commodity. And when it comes to training artificial intelligence (AI) and machine learning (ML) models, it's absolutely essential. Still, due to various factors, high-quality, real-world data can be hard – sometimes even impossible – to come by. This is where synthetic data becomes so valuable.

There is an increasing demand to improve the procedural generation of game levels. Our approach empowers game designers to author and control level generators, by expressing gameplay-related design constraints. Graph grammars, resulting from these designer-expressed constraints, can generate sequences of desired player actions as well as their associated target content. These action graphs are used to determine layouts and content for game levels.

The use of artificial intelligence in business is showing signs of acceleration. Nearly three-quarters of companies are now using AI (31%) or are exploring the use of AI (43%), according to IBM's "2021 Global AI Adoption Index." IT professionals responding to the IBM survey cited changing business needs in the wake of the pandemic as a driving factor in the adoption of AI at their companies. Indeed, 43% said their companies have accelerated AI rollouts as a result of the pandemic. Advances in AI tools have made artificial intelligence more accessible for companies, according to survey respondents.

Mythical Games is proud of Blankos Block Party, sure. Co-founders John Linden and Rudy Koch are happy it's found a substantial audience, and they're pleased to partner with brands like Burberry and Deadmau5. They were super happy to receive an additional $75 million from investors this month, bringing their funding total to $120 million. But really, Blankos Block Party is more of a side hustle for Mythical Games. What Linden and Koch are actually selling is an ecosystem of NFT-driven gameplay and development.

Images of seven people were passed on by local police for use in a facial recognition system at King's Cross in London in an agreement that was struck in secret, the details of which were made public for the first time today. A police report, published by the deputy London mayor Sophie Linden on Friday, showed that the scheme ran for two years from 2016 without any apparent central oversight from either the Metropolitan police or the office of the mayor, Sadiq Khan. Writing to London assembly members, Linden said she "wanted to pass on the [Metropolitan police service's] apology" for failing to previously disclose that the scheme existed and announced that similar local image sharing agreements were now banned. There had been "no other examples of images having been shared with private companies for facial recognition purposes" by the Met, Linden said, according to "the best of its knowledge and record-keeping". The surveillance scheme – controversial because it involved tracking individuals without their consent – was originally agreed between borough police in Camden and the owner of the 27-hectare King's Cross site in 2016.

No, artificial intelligence can't replace the human brain, and no, we'll never really be able to make AI bias-free. Those are two of the 10 myths IT analyst and consulting firm Gartner tackled in its recent report, "Debunking Myths and Misconceptions About Artificial Intelligence." According to the report, while AI may seem "clever," it's really just a set of software tools and math and logic techniques that can solve specific problems. As an example, image recognition technology "is more accurate than most humans," but the same coding can't also address a math problem. As Research Vice President Alexander Linden, one of the authors, explained, "The rule with AI today is that it solves one task exceedingly well, but if the conditions of the task change only a bit, it fails."