"Your face is your ticket," goes the motto of A.I. startup Wicket. "Your face is your credential," says Alcatraz AI, another vendor. Both these companies sell facial recognition technology to sports stadiums across the country. Citi Field, home of the Mets, contracted with Wicket in 2022 to add facial recognition ticket kiosks to all stadium gates. BMO Stadium, home of the Los Angeles Football Club, began using Alcatraz AI technology the year before.

Nathan E. Sanders is a data scientist and an affiliate with the Berkman Klein Center at Harvard University. Bruce Schneier is a security technologist and a fellow and lecturer at the Harvard Kennedy School. Lobbying has long been part of the give-and-take among policymakers and advocates working to balance their competing interests, but some corporate entities are adept at using legal-but-sneaky strategies for tilting the rules in their favor. AI tools could make these kinds of sneaky strategies more widespread and effective. A natural opening for this technology comes in the form of microlegislation, a term for small pieces of proposed law that cater to narrow interests.

A'robot' that was set to make history for advising the first defendant in court with artificial intelligence (AI) has now been accused of operating without a law degree. The AI-powered app DoNotPay faces allegations that it is'masquerading as a licensed practitioner' in a class action case filed by US law firm Edelson. The chatbot-style tool is centred around making legal information and'self-help' accessible to support consumers fighting against large corporations. But Chicago-based law firm Edelson has claimed the service is'unlawful' and the company itself has'substandard' legal documents. In a file published by the Superior Court of the State of California for the County of San Francisco, the complainant said: 'Unfortunately for its customers, DoNotPay is not actually a robot, a lawyer, nor a law firm.

As AI technology like ChatGPT evolves, so do the strategies and tactics used by cybercriminals. Steve Flynn, Sales and Marketing Director at ESET Southern Africa, says ongoing awareness is crucial in understanding how to manage potential cybersecurity challenges posed by these developing tools. As artificial intelligence (AI) technology becomes a new reality for individuals and businesses, its potential impact on cybersecurity cannot be ignored. OpenAI and its language model, ChatGPT, are no exception and while these tools offer significant benefits to almost every industry, they also present new challenges for digital security. ChatGPT raises concerns due to its natural language processing capabilities, which could be used to create highly personalised and sophisticated cyberattacks.

TAICHUNG, Taiwan – Taiwan's top military research unit has unveiled a series of locally made attack and surveillance drones -- including a loitering munition similar in appearance to the U.S.-made AeroVironment Switchblade 300 drone deployed by Ukraine -- as the self-ruled island focuses on asymmetric capabilities to defend against the much larger Chinese military. The move by the state-owned National Chung-Shan Institute of Science and Technology is part of Taipei's efforts to speed up research, development and production of military drones as it draws lessons from Ukraine's use of these relatively low-cost assets in its fight against Russia. To achieve this, NCSIST has been teaming up with private companies that provide key components and sensitive technologies for the unmanned systems. This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites.

Every so often, I like to look at some of the more weird conspiracy theories and then try to debunk them. I consider it a media literacy exercise, but there has been one theory that I've come across that is impressively hard to debunk: the "Dead Internet" theory. I think that the best conspiracy theories are the ones that are hardest to debunk, and this one is increasingly getting more difficult to debunk. The core idea is that the Internet itself is actually dead, no human authorship of any content exists. Any actual human content that is created is isolated into its own little heavenbanned bubble. Mainstream platforms, news outlets, social media sites, Internet forums, chatrooms, everything filled with bot generated content to the point that it's impossible to find another human. To be clear, this theory as literally written is absolute nonsense and probably not worth taking too seriously.

The advent of AlphaGo and its successors marked the beginning of a new paradigm in playing games using artificial intelligence. This was achieved by combining Monte Carlo tree search, a planning procedure, and deep learning. While the impact on the domain of games has been undeniable, it is less clear how useful similar approaches are in applications beyond games and how they need to be adapted from the original methodology. We review 129 peer-reviewed articles detailing the application of neural Monte Carlo tree search methods in domains other than games. Our goal is to systematically assess how such methods are structured in practice and if their success can be extended to other domains. We find applications in a variety of domains, many distinct ways of guiding the tree search using learned policy and value functions, and various training methods. Our review maps the current landscape of algorithms in the family of neural monte carlo tree search as they are applied to practical problems, which is a first step towards a more principled way of designing such algorithms for specific problems and their requirements.

We view the landscape of large language models (LLMs) through the lens of the recently released BLOOM model to understand the performance of BLOOM and other decoder-only LLMs compared to BERT-style encoder-only models. We achieve this by evaluating the smaller BLOOM model variants (\textit{350m/560m} and \textit{1b3/1b7}) on several NLP benchmark datasets and popular leaderboards. We make the following observations: (1) BLOOM performance does not scale with parameter size, unlike other LLMs like GPT and BERT. Experiments fine-tuning BLOOM models show that the 560m variant performs similarly to or better than the 1b7 variant, (2) Zero-shot cross-lingual and multi-lingual fine-tuning experiments show that BLOOM is at par or worse than monolingual GPT-2 models, and (3) Toxicity analysis of prompt-based text generation using the RealToxicityPrompts dataset shows that the text generated by BLOOM is at least 17\% less toxic than GPT-2 and GPT-3 models.

Performance optimization is an increasingly challenging but often repetitive task. While each platform has its quirks, the underlying code transformations rely on data movement and computational characteristics that recur across applications. This paper proposes to leverage those similarities by constructing an embedding space for subprograms. The continuous space captures both static and dynamic properties of loop nests via symbolic code analysis and performance profiling, respectively. Performance embeddings enable direct knowledge transfer of performance tuning between applications, which can result from autotuning or tailored improvements. We demonstrate this transfer tuning approach on case studies in deep neural networks, dense and sparse linear algebra compositions, and numerical weather prediction stencils. Transfer tuning reduces the search complexity by up to four orders of magnitude and outperforms the MKL library in sparse-dense matrix multiplication. The results exhibit clear correspondences between program characteristics and optimizations, outperforming prior specialized state-of-the-art approaches and generalizing beyond their capabilities.

Predicting how distributions over discrete variables vary over time is a common task in time series forecasting. But whereas most approaches focus on merely predicting the distribution at subsequent time steps, a crucial piece of information in many settings is to determine how this probability mass flows between the different elements over time. We propose a new approach to predicting such mass flow over time using optimal transport. Specifically, we propose a generic approach to predicting transport matrices in end-to-end deep learning systems, replacing the standard softmax operation with Sinkhorn iterations. We apply our approach to the task of predicting how communities will evolve over time in social network settings, and show that the approach improves substantially over alternative prediction methods. We specifically highlight results on the task of predicting faction evolution in Ukrainian parliamentary voting.