Second, visual signals usually havehigh information densities, which may contain distractions and redundancies for policylearning.

Thank you for joining us on the Enterprise AI hub. In this episode of the Infosys Knowledge Institute Podcast, Dylan Cosper speaks with Prasad Banala, director of software engineering at a large US-based retail organization, about operationalizing agentic AI across the software development lifecycle. Prasad explains how his team applies AI to validate requirements, generate and analyze test cases, and accelerate issue resolution, while maintaining strict governance, human-in-the-loop review, and measurable quality outcomes. A "QuitGPT" campaign is urging people to cancel their ChatGPT subscriptions Michelle Kim Here are our picks for the advances to watch in the years ahead--and why we think they matter right now. A "QuitGPT" campaign is urging people to cancel their ChatGPT subscriptions Backlash against ICE is fueling a broader movement against AI companies' ties to President Trump. The viral social network for bots reveals more about our own current mania for AI as it does about the future of agents.

Thank you for joining us on the Enterprise AI hub. In this episode of the Infosys Knowledge Institute Podcast, Dylan Cosper speaks with Sriram Kalyan, head of applications and data at Nutrien Ag Solutions, Australia, about turning a high-risk post-acquisition IT landscape into a scalable digital foundation. Sriram shares how the merger of two major Australian agricultural companies created duplicated systems, fragile integrations, and operational risk, compounded by the sudden loss of key platform experts and partners. He explains how leadership alignment, disciplined platform consolidation, and a clear focus on business outcomes transformed integration from an invisible liability into a strategic enabler, positioning Nutrien Ag Solutions for future growth, cloud transformation, and enterprise scale. A "QuitGPT" campaign is urging people to cancel their ChatGPT subscriptions Michelle Kim Here are our picks for the advances to watch in the years ahead--and why we think they matter right now. A "QuitGPT" campaign is urging people to cancel their ChatGPT subscriptions Backlash against ICE is fueling a broader movement against AI companies' ties to President Trump.

Game Theory: Is legal doping in sport a good idea? As the Winter Games celebrate the Olympic motto, Faster, Higher, Stronger -- Together, a new competition is openly allowing the use of performance-enhancing drugs. Samantha Johnson looks at the Enhanced Games and how doping, once sport's red line, is now being marketed as innovation. AFCON: To walk or not to walk?

With the disentangled representations, we synthesize the counterfactual unbiased training samples to further decorrelate causal and bias variables.

Inthiswork,we1 propose thefirstfinite-time system identification algorithm forpartiallyobservable linear dynamical systems (LDS)2 inadaptive and closed-loop settings. Prior estimation methods only work when the actions/controls are iidrandom3 noise and do not allow for any exploitation or strategic exploration. Our proposed estimation5 algorithm allows the data collection with an adaptive controller and the design of fully adaptive RL methods. We6 believe this contribution alone has a great interest in both RL and control communities. Note that prior works in this area, such as [6,12,37,40-46] have been published in recent machine learning21 conferences(NeurIPS,ICML...).

Current value-based multi-agent reinforcement learning methods optimize individual Q values to guide individuals' behaviours via centralized training with decentralized execution (CTDE). However, such expected, i.e., risk-neutral, Q value is not sufficient even with CTDE due to the randomness of rewards and the uncertainty in environments, which causes the failure of these methods to train coordinating agents incomplexenvironments. Toaddress these issues, we propose RMIX, anovelcooperativeMARL method with theConditional Value at Risk (CVaR) measure over the learned distributions of individuals' Q values. Specifically, we first learn the return distributions of individuals to analytically calculate CVaRfordecentralized execution. Then,tohandle thetemporal nature of the stochastic outcomes during executions, we propose a dynamic risk level predictorforriskleveltuning.