Fujitsu's physical AI system, which allows robots to move autonomously and in coordination with one another, is showcased at the CES tech show in Las Vegas on Wednesday. The robots are made by external manufacturers. LAS VEGAS - Fujitsu showed off a foundational technology that uses artificial intelligence to develop programs for software defined vehicles (SDV), at the CES tech show in Las Vegas on Wednesday. The Japanese company's technology helps automakers and other customers design software for SDVs, which can upgrade features by updating programs but require a lot of costs and labor to develop. Fujitsu also showcased its digital twin technology, reproducing the city of Las Vegas in virtual space and showing how traffic congestion there can be eased by analyzing data on the movements of people and vehicles.

Las Vegas might not have invented the buffet, but it did perfect it. Instead, it revealed it is adding food (and calorie) tracking to its Connect app . It combines AI image recognition with a rich food database, so you can monitor your calorie and nutrient intake without leaving the app -- and even log some food through its watches. The food tracking works similarly to existing apps like MyFitnessPal, Noom, LifeSum and many others. Still, Garmin hopes to make its companion app the best place for tracking nutrition without having to leave its app - and tying it into your other fitness goals. This makes sense in a few ways.

Nvidia unveils'reasoning' AI technology for self-driving cars Nvidia boss Jensen Huang on Monday announced Alpamayo, a tech platform the company says will help self-driving cars think like humans. Alpamayo brings reasoning to autonomous vehicles, allowing them to think through rare scenarios, drive safely in complex environments, and explain their driving decisions, Huang said on stage at the annual CES technology conference in Las Vegas. Huang also said Nvidia has begun producing a driverless car powered by its technology, the Mercedes-Benz CLA, in partnership with the German automaker. The vehicle will be released in the US in the coming months before being rolled out in Europe and Asia. Wearing his trademark black leather jacket, Huang told an audience of hundreds that the project has taught Nvidia an enormous amount about how to help partners build robotic systems. Analysts say the announcement reinforces Nvidia's leadership in integrating AI hardware and software, deepening its push into physical AI.

Jensen Huang speaks at CES in Las Vegas on Monday. Jensen Huang speaks at CES in Las Vegas on Monday. Next generation of chips in'full production' and will arrive later this year, Jensen Huang says at CES in Las Vegas Nvidia CEO Jensen Huang said on Monday that the company's next generation of chips is in "full production" saying they can deliver five times the artificial-intelligence computing of the company's previous chips when serving up chatbots and other AI apps. In a speech at the Consumer Electronics Show in Las Vegas, the leader of the world's most valuable company revealed new details about its chips, which will arrive later this year and which Nvidia executives are in the company's labs being tested by AI firms, as Nvidia faces increasing competition from rivals as well as its own customers. The Vera Rubin platform, made up of six separate Nvidia chips, is expected to debut later this year, with the flagship server containing 72 of the company's graphics units and 36 of its new central processors.

At CES 2026, Everything Is AI. Integrated chatbots and built-in machine intelligence are no longer standout features in consumer tech. If companies want to win in the AI era, they've got to hone the user experience. The New Year's Eve champagne isn't even warm yet, and CES week is already upon us. The giant annual celebration of consumer tech kicks off the first full week of January as companies across the world convene in Las Vegas to hawk their latest innovations.

Amazon's Zoox robotaxi service offers free driverless rides to Las Vegas Strip locations including Resorts World, AREA15 and Topgolf as part of its public launch.

Demands for software-defined vehicles (SDV) are rising and electric vehicles (EVs) are increasingly being equipped with powerful computers. This enables onboard AI systems to optimize charge-aware path optimization customized to reflect vehicle's current condition and environment. We present VEGA, a charge-aware EV navigation agent that plans over a charger-annotated road graph using Proximal Policy Optimization (PPO) with budgeted A* teacher-student guidance under state-of-charge (SoC) feasibility. VEGA consists of two modules. First, a physics-informed neural operator (PINO), trained on real vehicle speed and battery-power logs, uses recent vehicle speed logs to estimate aerodynamic drag, rolling resistance, mass, motor and regenerative-braking efficiencies, and auxiliary load by learning a vehicle-custom dynamics. Second, a Reinforcement Learning (RL) agent uses these dynamics to optimize a path with optimal charging stops and dwell times under SoC constraints. VEGA requires no additional sensors and uses only vehicle speed signals. It may serve as a virtual sensor for power and efficiency to potentially reduce EV cost. In evaluation on long routes like San Francisco to New York, VEGA's stops, dwell times, SoC management, and total travel time closely track Tesla Trip Planner while being slightly more conservative, presumably due to real vehicle conditions such as vehicle parameter drift due to deterioration. Although trained only in U.S. regions, VEGA was able to compute optimal charge-aware paths in France and Japan, demonstrating generalizability. It achieves practical integration of physics-informed learning and RL for EV eco-routing.

Breakthroughs, discoveries, and DIY tips sent every weekday. As any diligent stargazer knows, mid-summer means one thing: the Perseids! This meteor shower hits its peak on August 12 this year, and while that date is inconveniently close to that of this month's full moon, there should still be plenty of meteors on show for those who choose their time and location with care. As another long summer day has finally receded into another summer night, look east. If the sky is clear, you might well spy the Summer Triangle.

Uber will invest 300m in electric vehicle maker Lucid in a robotaxi deal that aims to start with one major US city late next year. The two companies announced the new partnership on Thursday. Over six years starting in 2026, Uber will acquire and deploy over 20,000 Lucid Gravity SUVs that will be equipped with autonomous vehicle (AV) technology from startup Nuro, the three companies said in a statement. The agreement illustrates the renewed plans and push for financing for self-driving cabs, years after a first wave of autonomous driving investment produced only a limited number of vehicles. Tesla has recently launched a robotaxi trial in Austin, and Alphabet's driverless taxi unit, Waymo, is speeding up its expansion.

In todays landscape of increasing electronic crime, network forensics plays a pivotal role in digital investigations. It aids in understanding which systems to analyse and as a supplement to support evidence found through more traditional computer based investigations. However, the nature and functionality of the existing Network Forensic Analysis Tools (NFATs) fall short compared to File System Forensic Analysis Tools (FS FATs) in providing usable data. The analysis tends to focus upon IP addresses, which are not synonymous with user identities, a point of significant interest to investigators. This paper presents several experiments designed to create a novel NFAT approach that can identify users and understand how they are using network based applications whilst the traffic remains encrypted. The experiments build upon the prior art and investigate how effective this approach is in classifying users and their actions. Utilising an in-house dataset composed of 50 million packers, the experiments are formed of three incremental developments that assist in improving performance. Building upon the successful experiments, a proposed NFAT interface is presented to illustrate the ease at which investigators would be able to ask relevant questions of user interactions. The experiments profiled across 27 users, has yielded an average 93.3% True Positive Identification Rate (TPIR), with 41% of users experiencing 100% TPIR. Skype, Wikipedia and Hotmail services achieved a notably high level of recognition performance. The study has developed and evaluated an approach to analyse encrypted network traffic more effectively through the modelling of network traffic and to subsequently visualise these interactions through a novel network forensic analysis tool.