Agentic AI is helping organizations completely reimagine core banking processes and operations from the customer perspective, rather than simply making incremental improvements. Despite years of digitization, organizations capture less than one-third of the value expected from digital investments, according to McKinsey research . That's because most big companies begin with technological capabilities and bolt applications onto them, rather than starting with customer needs and working backward to technology solutions. Not prioritizing the customer can create fragmented solutions; disjointed customer experiences; and ultimately, failed transformations. Organizations that achieve outsized results from AI flip the script. They adopt a "customer-back engineering" mindset, putting customers at the heart of technology transformation.

From sorting chicken nuggets to screwing in light bulbs, Eka's robots are eerily lifelike. But do they have real physical smarts? It starts gingerly pawing around the table, as if searching for its glasses on the nightstand. It gently positions the bulb between its two pincers. The claw goes chasing it across the table. After a few nips, the bulb is back in its grasp. In more than a decade of writing about robots, I have never seen one move so naturally.

Smooth functions on graphs have wide applications in manifold and semi-supervised learning. In this work, we study a bandit problem where the payoffs of arms are smooth on a graph. This framework is suitable for solving online learning problems that involve graphs, such as content-based recommendation. In this problem, each item we can recommend is a node of an undirected graph and its expected rating is similar to the one of its neighbors. The goal is to recommend items that have high expected ratings. We aim for the algorithms where the cumulative regret with respect to the optimal policy would not scale poorly with the number of nodes. In particular, we introduce the notion of an effective dimension, which is small in real-world graphs, and propose three algorithms for solving our problem that scale linearly and sublinearly in this dimension. Our experiments on content recommendation problem show that a good estimator of user preferences for thousands of items can be learned from just tens of node evaluations.

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CascadeXML significantly outperforms allexisting approaches with non-trivial gains obtained on benchmark datasets consisting of up to three millionlabels.

We establish an optimal sample complexity of $O(ε^{-2})$ for obtaining an $ε$-optimal global policy using a single-timescale actor-critic (AC) algorithm in infinite-horizon discounted Markov decision processes (MDPs) with finite state-action spaces, improving upon the prior state of the art of $O(ε^{-3})$. Our approach applies STORM (STOchastic Recursive Momentum) to reduce variance in the critic updates. However, because samples are drawn from a nonstationary occupancy measure induced by the evolving policy, variance reduction via STORM alone is insufficient. To address this challenge, we maintain a buffer of small fraction of recent samples and uniformly sample from it for each critic update. Importantly, these mechanisms are compatible with existing deep learning architectures and require only minor modifications, without compromising practical applicability.

Abstract--Bringing AI to vehicles and enabling them as sensing platforms is key to transforming maintenance from reactive to proactive. Now is the time to integrate AI copilots that speak both languages: machine and driver. This article offers a conceptual and technical perspective intended to spark interdisciplinary dialogue and guide future research and development in intelligent vehicle systems, predictive maintenance, and AI-powered user interaction. Vehicle maintenance remains largely reactive to this day, often triggered by the dreaded check engine light, sometimes at the worst possible time: in the middle of a busy week, or right before a road trip. However, today's vehicles are equipped with a dense network of sensors that can monitor nearly every aspect of performance in real time.

At the moment they may exist only in our wildest dreams or in Hollywood science-fiction epics. But humanoid robots that wash dishes, vacuum the carpets, cook and pick up dirty laundry could be available within a decade – and all for the price of a family car. These machines – equipped with hands, arms and legs capable of doing basic household chores – are currently in development around the world. Pulkit Agrawal, associate professor in the department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology (MIT), said: 'Silicon Valley companies are promising this year you can buy a robot, but my guess would be more like five to ten years, at least. 'The technology is progressing, but it's good to be realistic that it will take time to deploy.'

Weaknesses: - My primary concern is insufficient comparison with the existing literature on online LP, like the two works cited [Agrawal '14, Kesselheim et al. '14]: - The paper claims novelty in the sublihear competitive ratios obtained in those works of the form O(1 - \eps(m,n)), so that \eps(m,n) * OPT is the regret. From a glance at the works cited by [Agrawal '14], "Online stochastic packing applied to display ad allocation" [Feldman et al. '10] has an 1/OPT term in this competitive ratio, giving a sublinear regret bound. Some clarifying discussion is necessary here. Some discussion and a clearer comparison is necessary, since this line of work is so well-established. Some clarification about this would be appreciated; in any case, the manuscript should discuss this at greater depth.