India's outsourcing industry is worth $300bn. Indian technology stocks have seen an unprecedented rout over the past few weeks over fears of artificial intelligence upending the traditional outsourcing model that powers the country's $300bn (£223bn) back-office industry. The sell-off - part of a global correction in traditional software and IT stocks - preceded the market nervousness caused by recent geopolitical uncertainty, and is particularly significant for India. Over the past three-and-a-half decades, India's software industry has created millions of white-collar jobs, spawning a new middle class driven by high ambition and strong purchasing power. This, in turn, has fuelled demand for apartments, cars and restaurants across top-tier cities such as Bengaluru, Hyderabad and Gurugram over the past 30 years.

We study one-sided and $α$-correct sequential hypothesis testing for data generated by an ergodic Markov chain. The null hypothesis is that the unknown transition matrix belongs to a prescribed set $P$ of stochastic matrices, and the alternative corresponds to a disjoint set $Q$. We establish a tight non-asymptotic instance-dependent lower bound on the expected stopping time of any valid sequential test under the alternative. Our novel analysis improves the existing lower bounds, which are either asymptotic or provably sub-optimal in this setting. Our lower bound incorporates both the stationary distribution and the transition structure induced by the unknown Markov chain. We further propose an optimal test whose expected stopping time matches this lower bound asymptotically as $α\to 0$. We illustrate the usefulness of our framework through applications to sequential detection of model misspecification in Markov Chain Monte Carlo and to testing structural properties, such as the linearity of transition dynamics, in Markov decision processes. Our findings yield a sharp and general characterization of optimal sequential testing procedures under Markovian dependence.

Emerging ethical approaches have attempted to filter pretraining material, but such approaches have been ad hoc and failed to take context into account. We offer an approach to filtering grounded in law, which has directly addressed the tradeoffs in filtering material.

In this setting, the arms arrive in a stream, and the number of arms that can be storedinthememory atanytime,isbounded.

As inthe classical problem, weights are fixed by an adversary and elements appear in random order. In contrast to previous variants of predictions, our algorithm only has access toamuch weakerpiece ofinformation: anadditive gapc.

In image classification, it is common to utilize a pretrained model to extract meaningful features of the input images, and then to train a classifier on top of it to make predictions for any downstream task.