Elon Musk's brain implant company, Neuralink, has filed applications with the United States Patent and Trademark Office (USPTO) to exclusively own the names Telepathy, Telekinesis, and others for future products. Neuralink, which Musk cofounded in 2016, is developing technology known as a brain-computer interface, a system that decodes brain activity to control an output device. Musk has said that the company's first product will be called Telepathy and will allow people with paralysis the ability to control a computer or phone just by thinking. But the Neuralink trademark application suggests that the company has ambitions of its technology enabling telepathic communication not just with electronic devices, but between human beings. Neuralink's interface involves a brain implant that collects neural signals and software that translates those signals into cursor movements on a computer screen.

Once regarded as a futuristic technology that would be exploited by robots to take over the world, artificial intelligence is rapidly growing in scope and capabilities. Automakers, for one, are putting it to use to create advanced concepts and production vehicles. And Italian supercar builder Maserati is harnessing the technology to set world records. This month, an AI-controlled Maserati MC20 reached 197.7 miles per hour at Kennedy Space Center. The Maserati obliterated the previous record set by an Indy Autonomous Challenge AV-21 racecar set in 2022 by nearly five full seconds, an impressive feat for a robot-driven car.

Paolo Ardoino, CEO of the cryptocurrency company Tether, was flying over Switzerland last week as he contemplated the changing regulatory landscape. Tether used to be at war with the establishment. Now it is the establishment. The crypto giant – tether is the most traded cryptocurrency in the world – has had a strange trip. Four years ago, banks were dropping Tether as a client, and regulators in New York had the company against the wall over questions about commingled client and corporate funds.

For 35 years, amateur and professional cryptographers have tried to crack the code on Kryptos, a majestic sculpture that sits behind CIA headquarters in Langley, Virginia. In the 1990s, the CIA, NSA, and a Rand Corporation computer scientist independently came up with translations for three of the sculpture's four panels of scrambled letters. But the final segment, known as K4, was encoded with knottier techniques and remains unsolved. This failure has only deepened the obsession of thousands of would-be cryptanalysts. When one of them thinks they have an answer, they write to Jim Sanborn for confirmation.

EXCLUSIVE: As romance scams are on the rise, a bipartisan group of lawmakers is introducing new legislation aimed at holding accountable those who seek to defraud retirees and steal their hard-earned savings. U.S. Sens. Marsha Blackburn, R-Tenn., and John Hickenlooper, D-Colo., and Rep. David Valadao, R-Calif., introduced the Romance Scam Prevention Act, which would require dating apps and services to issue fraud ban notifications to users who have interacted with a person removed from the app. The move came as Americans are more than ever connected thanks to social media and dating apps that allow us to stay in touch with old friends all over the world and to develop new relationships online. As Americans increasingly go online in search of relationships, scammers are following suit. According to the Federal Trade Commission (FTC), in 2022 almost 70,000 people reported being victims of a romance scam.

Rights advocates raised the alarm, including over free speech concerns, on Thursday after it was reported that the U.S. State Department will use artificial intelligence to revoke the visas of foreign students who it perceives as supporters of Palestinian Hamas militants. The U.S. Constitution's First Amendment protects freedom of speech and assembly. Free speech advocates like the Foundation for Individual Rights and Expression (FIRE) and pro-Palestinian groups said AI should not be relied upon for assessments related to the decades-old and nuance-filled Israeli-Palestinian conflict. Axios cited senior State Department officials to report that an AI-fueled "Catch and Revoke" effort will include AI-assisted reviews of tens of thousands of student visa holders' social media accounts.

Shares of Super Mario maker Nintendo plunged the most in seven months as investors abandoned Japan's outperforming video game stocks on worries that U.S. President Donald Trump's tariffs will drive up console prices in the United States. Nintendo sank 9.2% in Tokyo, its biggest intraday drop since the stock market rout on Aug. 5. The shares had traded at an all-time high last month and jumped 23% this year before Friday's plunge. Under Trump's new levies, game consoles, including the upcoming Switch 2, "could have higher selling prices in the U.S. -- the world's biggest market for game consoles -- due to heftier import costs, as most are either manufactured in China or rely on suppliers in the country for parts," wrote Nathan Naidu, an analyst at Bloomberg Intelligence, in a note. Trump raised duties on Chinese imports to 20% from 10% on March 4.

This work presents a physics-based machine learning framework to predict and analyze oxides of nitrogen (NOx) emissions from compression-ignition engine-powered vehicles using on-board diagnostics (OBD) data as input. Accurate NOx prediction from OBD datasets is difficult because NOx formation inside an engine combustion chamber is governed by complex processes occurring on timescales much shorter than the data collection rate. Thus, emissions generally cannot be predicted accurately using simple empirically derived physics models. Black box models like genetic algorithms or neural networks can be more accurate, but have poor interpretability. The transparent model presented in this paper has both high accuracy and can explain potential sources of high emissions. The proposed framework consists of two major steps: a physics-based NOx prediction model combined with a novel Divergent Window Co-occurrence (DWC) Pattern detection algorithm to analyze operating conditions that are not adequately addressed by the physics-based model. The proposed framework is validated for generalizability with a second vehicle OBD dataset, a sensitivity analysis is performed, and model predictions are compared with that from a deep neural network. The results show that NOx emissions predictions using the proposed model has around 55% better root mean square error, and around 60% higher mean absolute error compared to the baseline NOx prediction model from previously published work. The DWC Pattern Detection Algorithm identified low engine power conditions to have high statistical significance, indicating an operating regime where the model can be improved. This work shows that the physics-based machine learning framework is a viable method for predicting NOx emissions from engines that do not incorporate NOx sensing.

Large Language Model~(LLM) based agents have been increasingly popular in solving complex and dynamic tasks, which requires proper evaluation systems to assess their capabilities. Nevertheless, existing benchmarks usually either focus on single-objective tasks or use overly broad assessing metrics, failing to provide a comprehensive inspection of the actual capabilities of LLM-based agents in complicated decision-making tasks. To address these issues, we introduce DSGBench, a more rigorous evaluation platform for strategic decision-making. Firstly, it incorporates six complex strategic games which serve as ideal testbeds due to their long-term and multi-dimensional decision-making demands and flexibility in customizing tasks of various difficulty levels or multiple targets. Secondly, DSGBench employs a fine-grained evaluation scoring system which examines the decision-making capabilities by looking into the performance in five specific dimensions and offering a comprehensive assessment in a well-designed way. Furthermore, DSGBench also incorporates an automated decision-tracking mechanism which enables in-depth analysis of agent behaviour patterns and the changes in their strategies. We demonstrate the advances of DSGBench by applying it to multiple popular LLM-based agents and our results suggest that DSGBench provides valuable insights in choosing LLM-based agents as well as improving their future development. DSGBench is available at https://github.com/DeciBrain-Group/DSGBench.

Differential-algebraic equations (DAEs) integrate ordinary differential equations (ODEs) with algebraic constraints, providing a fundamental framework for developing models of dynamical systems characterized by timescale separation, conservation laws, and physical constraints. While sparse optimization has revolutionized model development by allowing data-driven discovery of parsimonious models from a library of possible equations, existing approaches for dynamical systems assume DAEs can be reduced to ODEs by eliminating variables before model discovery. This assumption limits the applicability of such methods to DAE systems with unknown constraints and time scales. We introduce Sparse Optimization for Differential-Algebraic Systems (SODAs), a data-driven method for the identification of DAEs in their explicit form. By discovering the algebraic and dynamic components sequentially without prior identification of the algebraic variables, this approach leads to a sequence of convex optimization problems and has the advantage of discovering interpretable models that preserve the structure of the underlying physical system. To this end, SODAs improves numerical stability when handling high correlations between library terms -- caused by near-perfect algebraic relationships -- by iteratively refining the conditioning of the candidate library. We demonstrate the performance of our method on biological, mechanical, and electrical systems, showcasing its robustness to noise in both simulated time series and real-time experimental data.