Froggett to support accelerating growth and continued international expansion. Froggett was formerly Head of Global Infrastructure Defense, CISO Cybersecurity Services at Citi. In his previous role, Carl was responsible for delivering integrated risk reduction capabilities and services aligned to the architectural, business, and CISO priorities across Citi's devices and networks in 100 countries. Since 1998, he has held various regional and global roles for Citi, covering all aspects of architecture, engineering, global operations, as well as running critical enterprise cyber services for Citi's cybersecurity functions. "Carl has a proven track record in building teams, systems architecture, large scale enterprise software implementation, as well as aligning processes and tools with business requirements and I believe he will play a key role in helping our company grow and scale," said Guy Caspi, CEO of Deep Instinct.

The IJCAI distinguished paper awards recognise some of the best papers presented at the conference each year. This year, three articles were named as distinguished papers. The winners were selected by the associate programme committee chairs, the programme and general chairs, and the president of EurAI. Abstract: The metric distortion framework posits that n voters and m candidates are jointly embedded in a metric space such that voters rank candidates that are closer to them higher. A voting rule's purpose is to pick a candidate with minimum total distance to the voters, given only the rankings, but not the actual distances.

UT Austin will be undertaking research on how robots can build competency models of their perception algorithms.

Disturbing footage emerged this week of a chess-playing robot breaking the finger of a seven-year-old child during a tournament in Russia. Public commentary on this event highlights some concern in the community about the increasing use of robots in our society. Some people joked on social media that the robot was a "sore loser" and had a "bad temper". Of course, robots cannot actually express real human characteristics such as anger (at least, not yet). But these comments do demonstrate increasing concern in the community about the "humanisation" of robots.

Federal agencies and their Federal Systems Integrator (FSI) partners are considering how to tap into artificial intelligence (AI) to advance their missions. The National Security Commission on Artificial Intelligence is calling on Federal leaders to double research and development spending on AI, to $32 billion by Fiscal Year 2026. As with any new technology, there is uncertainty on the best way to move from pilot projects in the lab to fully implemented production solutions. MeriTalk recently sat down with Bob Venero, CEO of Future Tech Enterprise, Inc., to talk about how agencies and FSI's can overcome barriers to get AI programs up and running quickly, and contribute to mission success. MeriTalk: We know Federal agencies are dipping their toes into using emerging technology including AI and machine learning (ML) to support a wide range of projects.

Machine learning (ML) is increasingly being used to support high-stakes decisions, a trend owed in part to its promise of superior predictive power relative to human assessment. However, there is frequently a gap between decision objectives and what is captured in the observed outcomes used as labels to train ML models. As a result, machine learning models may fail to capture important dimensions of decision criteria, hampering their utility for decision support. In this work, we explore the use of historical expert decisions as a rich -- yet imperfect -- source of information that is commonly available in organizational information systems, and show that it can be leveraged to bridge the gap between decision objectives and algorithm objectives. We consider the problem of estimating expert consistency indirectly when each case in the data is assessed by a single expert, and propose influence function-based methodology as a solution to this problem. We then incorporate the estimated expert consistency into a predictive model through a training-time label amalgamation approach. This approach allows ML models to learn from experts when there is inferred expert consistency, and from observed labels otherwise. We also propose alternative ways of leveraging inferred consistency via hybrid and deferral models. In our empirical evaluation, focused on the context of child maltreatment hotline screenings, we show that (1) there are high-risk cases whose risk is considered by the experts but not wholly captured in the target labels used to train a deployed model, and (2) the proposed approach significantly improves precision for these cases.

Work continues onboard the orbiting laboratory as it does every day. On Tuesday, the day Russia announced it was leaving the station, Russia's Oleg Artemyev, the current space station commander, was working on a cardiac research study designed to help doctors learn how to keep astronauts safe on long-duration space missions. Along with fellow cosmonaut Denis Matveev, he helped put away the tools used in a spacewalk last week while the third cosmonaut on the station, Sergey Korsakov, was checking the robotic arm on the Russian side of the station.

There is great interest in how the growth of artificial intelligence and machine learning may affect global GHG emissions. However, such emissions impacts remain uncertain, owing in part to the diverse mechanisms through which they occur, posing difficulties for measurement and forecasting. Here we introduce a systematic framework for describing the effects of machine learning (ML) on GHG emissions, encompassing three categories: computing-related impacts, immediate impacts of applying ML and system-level impacts. Using this framework, we identify priorities for impact assessment and scenario analysis, and suggest policy levers for better understanding and shaping the effects of ML on climate change mitigation. The rapid growth of artificial intelligence (AI) is reshaping our society in many ways, and climate change is no exception. This Perspective presents a framework to assess how AI affects GHG emissions and proposes approaches to align the technology with climate change mitigation.

Artificial Intelligence (AI) has the potential to transform many aspects of life and the UK government has recognized that it is important to review IP laws to ensure that they evolve and promote innovation in this fast-paced area of technology. That was the motivation behind a recent UKIPO consultation which reported earlier this week. With regards to patent protection for AI-devised inventions, the report concluded that no changes are required to UK patent law, at least for the time being. At present, despite claims of certain parties and the international court case relating to the DABUS system which its promotors sought to name as the inventor on patent applications in a number of countries, there is no evidence of AI currently having the capacity to invent. Rather, the general consensus from respondents was that AI technology cannot, at least at present, invent without human assistance.

People have been wondering for years – when and even sometimes IF artificial intelligence will live up to its incredible potential. The technology is finally beginning to change industries and lives. Now implemented across everything from smartphone cameras and self-driving vehicles to manufacturing facilities, AI has racked up numerous high-profile success stories: People now rely on AI to silently optimize photos, perfect their parallel parking, and discover product defects. AI can either be cool or creepy, but it's currently on the right side of that line. At the same time, however, the public is becoming increasingly aware of AI ethics, as researchers and journalists question the sources of data powering AI innovations, and spotlight ways AI data is being misused by tech giants.