'Ridiculous amount of games' - has Haaland played too much football? The robot is malfunctioning and in need of a reset. Erling Haaland made a blistering start to the season but that prolific run of form has suffered a glitch. Though the Manchester City and Norway striker has scored a remarkable 39 goals in just 36 games for club and country this season, he has hit a sticky patch of form with only one goal in his past eight games. This has coincided with Pep Guardiola's men falling off the pace in the Premier League title race and suffering a monumental shock at Bodo/Glimt in the Champions League.

'He lives for the goals' - robot Haaland returns from malfunction Is Erling Haaland a big fan of Peter Crouch - or is he actually programmed like a robot? That may - or not be - a question posed after Manchester City's impressive Premier League victory over in-form Bournemouth on Sunday. The Norway striker malfunctioned for only the second time this season when he failed to score in last weekend's loss at Aston Villa, but he was back to being a goal machine with a ruthlessly efficient first-half double against the Cherries. If he is hiding any nuts and bolts under those blonde locks of his, Haaland did prove he was still human by missing a couple of chances to complete his hat-trick. But his scary statistics this season have left many in awe of the 25-year-old's prowess in front of goal, prompting a robot dance to mark his opener in the win that took his side up to second place.

While several high profile video games have served as testbeds for Deep Reinforcement Learning (DRL), this technique has rarely been employed by the game industry for crafting authentic AI behaviors. Previous research focuses on training super-human agents with large models, which is impractical for game studios with limited resources aiming for human-like agents. This paper proposes a sample-efficient DRL method tailored for training and fine-tuning agents in industrial settings such as the video game industry. Our method improves sample efficiency of value-based DRL by leveraging pre-collected data and increasing network plasticity. We evaluate our method training a goalkeeper agent in EA SPORTS FC 25, one of the best-selling football simulations today. Our agent outperforms the game's built-in AI by 10% in ball saving rate. Ablation studies show that our method trains agents 50% faster compared to standard DRL methods. Finally, qualitative evaluation from domain experts indicates that our approach creates more human-like gameplay compared to hand-crafted agents. As a testament to the impact of the approach, the method has been adopted for use in the most recent release of the series.

This paper demonstrates the first application of feedback linearization to replicator dynamics, driving the evolution of non-convergent evolutionary games to systems with guaranteed global asymptotic stability. Replicator dynamics, while a cornerstone of evolutionary game theory, possess neutral stability at Nash equilibria [2], which causes the evolutionary process to oscillate without converging to an optimal strategy. We build a control-theoretic framework that cancels the nonlinear components in replica-tor dynamics, and then apply a linear feedback component to force a strategy change at the Nash equilibrium. Through Lyapunov analysis, we show global convergence from any initial conditions in the probability simplex. We illustrate this approach with a numerical example of a penalty shootout game, where we illustrate that our method guides strategies quickly to mixed Nash equilibria, while the uncontrolled dynamics oscillate. Our work serves as one of the first known connections between nonlinear control theory and evolutionary game dynamics with applications in multi-agent systems, algorithmic trading, and strategic optimization.

Recent research has demonstrated that state-of-the-art LLMs and defenses remain susceptible to multi-turn jailbreak attacks. These attacks require only closed-box model access and are often easy to perform manually, posing a significant threat to the safe and secure deployment of LLM-based systems. We study the effectiveness of the Crescendo multi-turn jailbreak at the level of intermediate model representations and find that safety-aligned LMs often represent Crescendo responses as more benign than harmful, especially as the number of conversation turns increases. Our analysis indicates that at each turn, Crescendo prompts tend to keep model outputs in a "benign" region of representation space, effectively tricking the model into fulfilling harmful requests. Further, our results help explain why single-turn jailbreak defenses like circuit breakers are generally ineffective against multi-turn attacks, motivating the development of mitigations that address this generalization gap.

In the largest gathering of 2026 gubernatorial candidates to date, seven Democrats vying to lead California courted labor leaders on Monday, vowing to support pro-union agreements on housing and infrastructure projects, regulation of artificial intelligence, and government funding for university research. Throughout most of the hourlong event, the hundreds of union members inside the Sacramento hotel ballroom embraced the pro-labor pledges and speeches that dominated the candidates' remarks, though some boos rose from the crowd when former Los Angeles Mayor Antonio Villaraigosa strayed from the other Democrats on stage. Villaraigosa was the only candidate to raise objections when asked if he would support providing state unemployment benefits to striking workers, saying it would depend on the nature and length of the labor action. Gov. Gavin Newsom in 2023 vetoed a bill that would have provided that coverage, saying it would make the state's unemployment trust fund "vulnerable to insolvency." The Monday night event was part of a legislative conference held by the California Federation of Labor Unions and the State Building and Construction Trades Council of California, two of the most influential labor organizations in the state capital.

Despite the vast number of publications, most research has focused on assessing player performance based on isolated metrics such as distance covered, accelerations, or high-intensity runs (HI) (Bradley et al. 2013; Altmann et al. 2021; Ingebrigtsen et al. 2015). In addition, the tactical context tends to be widely simplified and often ignored. For sports scientists and soccer practitioners, the idea that the integration of tactical and qualitative information can be very beneficial to develop a much more in-depth analysis of physical demands does not go unnoticed. However, the lack of spatiotemporal data that allows analyzing individual effort within the collective context has been an enormous barrier for developing this integration between the physical and the tactical. Far on the horizon remains the old question: is it about running more or running better?

The idea behind this project to make a hardware and Big brain AI work together, to implement the real world scenario to help and understanding the machine logic. This idea came from Alpha-Go game but in Alpha-Go the ai work from inside (software) but here we are dealing with hardware to make it real like human player. I am presenting this project idea to make this project real with help of your company so for that I need your support and sponsorship as I'm an undergrad student and it's my opportunity to see the real future by working with you on this project and many more. Carrom is a tabletop game with resemblances to snooker or billiards, and also the more modern games of air hockey. Carrom is played by propelling discs with the fingers with the aim of potting them into one of four corner pockets on a wooden playing board-Game.

Omega Force now has a track record of innovation when given the license to various brands. "Hyrule Warriors" and its 2020 follow-up, "Age of Calamity," saw Omega Force adopting the collect-a-thon mechanics of "Breath of the Wild" into its game. Its "Attack on Titan" series also uses the same Musou premise, but instead of spamming the same combos on the ground, you fling yourself across the skyline just like in the anime. Sony's "Spider-Man" games get plenty of deserved love for their swinging mechanics, but Omega Force's "Attack on Titan" games are too often overlooked for capturing the feel of a swashbuckling and speedy anime episode.

We learned about linear regression in the last post, and now we move to logistic regression. As we cannot use linear regression for a classification task, we use logistic, which is an extension to linear regression for a classification task. Logistic regression is a good approach to problems that require probability as output. Suppose we create a model to predict a striker's probability when he is playing an away match. If a model predicts a p(score Away_Match) of 0.05, then overall away matches, the striker will score approximately 1 goal.