Based on early appearances, you should expect the unexpected when characters from Game of Thrones, Looney Tunes, and other popular Warner Bros. franchises team up and scrap together in WB Interactive Entertainment's upcoming MultiVersus. The "platform fighter" from the developers at Player First Games is built like a gaming sandbox where magical moments of play emerge from happy accidents and inventive players. The wascally wabbit can toss a projectile-blocking safe on the ground, but it's also a physics-based object that can be moved -- which means a punch can knock it into other players. Arya Stark, meanwhile, steps into the battlefield armed with a throwing knife that she can teleport herself over to, even if a teammate -- or, say, a cartoon safe -- is touching it. "Bugs Bunny will knock the safe up in the air and [Arya] will throw the dagger and teleport to the safe...and then re-direct it."

Autonomous vehicle startup Gatik says it will start using its self-driving box trucks in Kansas as it expands to more territories. Governor Laura Kelly last week signed a bill that makes it legal for self-driving vehicles to run on public roads under certain circumstances. Following a similar effort in Arkansas, Gatik says it and its partner Walmart worked with legislators and stakeholders to "develop and propose legislation that prioritizes the safe and structured introduction of autonomous vehicles in the state." Before Gatik's trucks hit Kansas roads, the company says it will provide training to first responders and law enforcement. Gatik claims that, since it started commercial operations three years ago, it has maintained a clean safety record in Arkansas, Texas, Louisiana and Ontario, Canada.

Popularised in the Brad Pitt film Moneyball, groundbreaking analytics almost saw the Oakland A's crowned the kings of baseball back in 2002. General manager Billy Beane's evidence-based, sabermetric approach allowed the small-market franchise to compete against teams with much bigger budgets by finding undervalued players through revolutionary statistical analysis. The concept sparked the adoption of more data-driven principles across a myriad of sports – with teams and coaches all trying to gain a competitive advantage – but the latest innovation may be the biggest game-changer of the lot. Invented by artificial intelligence company Zone7, the new Silicon Valley algorithm is being used by teams in the NBA, NFL and Premier League as a way to detect injury risk and recommend pre-emptive action. One of those clubs, Liverpool FC, has deployed it to great success this season in their hunt for an unprecedented quadruple, cutting the number of days players have lost to injury to 1,008 from more than 1,500 in 2020/21.

Unraveled, chaotic meals could be a thing of the past for burrito lovers thanks to a group of engineering students from Johns Hopkins University and their lunch-saving invention. Dubbed'Tastee Tape', the invention is simply edible sticky tape designed to hold a burrito together while it's being eaten. 'Tastee Tape allows you to put full faith in your tortilla and enjoy your meal, mess-free,' said Tyler Guarino, who led the project. Unraveled, chaotic meals could be a thing of the past for burrito lovers thanks to a group of engineering students from Johns Hopkins University and their lunch-saving invention. Dubbed'Tastee Tape', the invention is simply edible sticky tape designed to hold a burrito together while it's being eaten The team tested a'multitude' of ingredients and combinations before settling on a final recipe.

If you spend too much time on your smartphone, scientists have a list of 10 solutions that can help you cut back on screen time. The small but effective changes can help curb smartphone addiction and mental health issues such as depression, say experts at McGill University in Canada. In experiments, people following the strategies reduced their screen time, felt less addicted to their phone and improved their sleep quality, the experts report. Among the 10 strategies are changing the phone display to'greyscale' so the display appears black and white, and disabling facial recognition as a method of unlocking the screen. A black and white screen makes smartphones'less gratifying' to look at compared to the bright colours offered by app icons such as TikTok and Instagram.

We've now tested every version of Apple's M1 processor, from the first M1 chip in the 13-inch Macbook Pro all the way up to the M1 Ultra in the new Mac Studio, and the chip's ability to scale performance is pretty incredible. The M1 Ultra fuses two M1 Max chips together to get you a processor with 20 CPU cores and 64 GPU cores, along with up to 128GB of RAM, and it's one of the fastest processors we've ever tested. We asked what tests you'd like to see run on the M1 Ultra and assembled quite a list, including Adobe Lightroom and Premiere Pro, Davinci Resolve and Fusion, 3D modeling in Blender, machine learning tests like TensorFlow and Pytorch, and even some gaming. Amazingly, the M1 Ultra really does seem to be around twice as fast as the M1 Max in most applications. Whatever overhead is required to shuffle data around such a large chip, it rarely impacts CPU performance.

Never mind reading generic guides or practicing with friends -- Google is betting that algorithms can get you ready for a job interview. The company has launched an Interview Warmup tool that uses AI to help you prepare for interviews across various roles. The site asks typical questions (such as the classic "tell me a bit about yourself") and analyzes your voiced or typed responses for areas of improvement. You'll know when you overuse certain words, for instance, or if you need to spend more time talking about a given subject. Interview Warmup is aimed at Google Career Certificates users hoping to land work, and most of its role-specific questions reflect this.

Figure 1: Summary of our recommendations for when a practitioner should BC and various imitation learning style methods, and when they should use offline RL approaches. Offline reinforcement learning allows learning policies from previously collected data, which has profound implications for applying RL in domains where running trial-and-error learning is impractical or dangerous, such as safety-critical settings like autonomous driving or medical treatment planning. In such scenarios, online exploration is simply too risky, but offline RL methods can learn effective policies from logged data collected by humans or heuristically designed controllers. Prior learning-based control methods have also approached learning from existing data as imitation learning: if the data is generally "good enough," simply copying the behavior in the data can lead to good results, and if it's not good enough, then filtering or reweighting the data and then copying can work well. Several recent works suggest that this is a viable alternative to modern offline RL methods.

This week, host Karen Han talks to voice actor and performer Erika Ishii, whose very long resume includes video games, animated series, and live action projects. In the interview, Erika explains their process of bringing video game characters to life–characters like Valkyrie in the game Apex Legends. Then Erika discusses diversity among both characters and performers in the video game industry and the ability to say no to projects that aren't the right fit. After the interview, Karen and co-host Isaac Butler talk about diversity in entertainment and the progress that has yet to be made. In the exclusive Slate Plus segment, Erika lists some of the voice actors and performances that have inspired them over the years.

Deep reinforcement learning (DRL) is transitioning from a research field focused on game playing to a technology with real-world applications. Notable examples include DeepMind's work on controlling a nuclear reactor or on improving Youtube video compression, or Tesla attempting to use a method inspired by MuZero for autonomous vehicle behavior planning. But the exciting potential for real world applications of RL should also come with a healthy dose of caution – for example RL policies are well known to be vulnerable to exploitation, and methods for safe and robust policy development are an active area of research. At the same time as the emergence of powerful RL systems in the real world, the public and researchers are expressing an increased appetite for fair, aligned, and safe machine learning systems. The focus of these research efforts to date has been to account for shortcomings of datasets or supervised learning practices that can harm individuals.