Accuracy and interpretability are two dominant features of successful predictive models. Typically, a choice must be made in favor of complex black box models such as recurrent neural networks (RNN) for accuracy versus less accurate but more interpretable traditional models such as logistic regression. This tradeoff poses challenges in medicine where both accuracy and interpretability are important. We addressed this challenge by developing the REverse Time AttentIoN model (RETAIN) for application to Electronic Health Records (EHR) data. RETAIN achieves high accuracy while remaining clinically interpretable and is based on a two-level neural attention model that detects influential past visits and significant clinical variables within those visits (e.g.

Many practical perception systems exist within larger processes which often include interactions with users or additional components that are capable of evaluating the quality of predicted solutions. In these contexts, it is beneficial to provide these oracle mechanisms with multiple highly likely hypotheses rather than a single prediction. In this work, we pose the task of producing multiple outputs as a learning problem over an ensemble of deep networks -- introducing a novel stochastic gradient descent based approach to minimize the loss with respect to an oracle. Our method is simple to implement, agnostic to both architecture and loss function, and parameter-free. Our approach achieves lower oracle error compared to existing methods on a wide range of tasks and deep architectures. We also show qualitatively that solutions produced from our approach often provide interpretable representations of task ambiguity.

A fundamental problem in cognitive neuroscience is how humans make decisions, act, and behave in relation to other humans. Here we adopt the hypothesis that when we are in an interactive social setting, our brains perform Bayesian inference of the intentions and cooperativeness of others using probabilistic representations. We employ the framework of partially observable Markov decision processes (POMDPs) to model human decision making in a social context, focusing specifically on the volunteer's dilemma in a version of the classic Public Goods Game. We show that the POMDP model explains both the behavior of subjects as well as neural activity recorded using fMRI during the game. The decisions of subjects can be modeled across all trials using two interpretable parameters. Furthermore, the expected reward predicted by the model for each subject was correlated with the activation of brain areas related to reward expectation in social interactions. Our results suggest a probabilistic basis for human social decision making within the framework of expected reward maximization.

A long-term goal of machine learning research is to build an intelligent dialog agent. Most research in natural language understanding has focused on learning from fixed training sets of labeled data, with supervision either at the word level (tagging, parsing tasks) or sentence level (question answering, machine translation). This kind of supervision is not realistic of how humans learn, where language is both learned by, and used for, communication. In this work, we study dialog-based language learning, where supervision is given naturally and implicitly in the response of the dialog partner during the conversation. We study this setup in two domains: the bAbI dataset of (Weston et al., 2015) and large-scale question answering from (Dodge et al., 2015). We evaluate a set of baseline learning strategies on these tasks, and show that a novel model incorporating predictive lookahead is a promising approach for learning from a teacher's response. In particular, a surprising result is that it can learn to answer questions correctly without any reward-based supervision at all.

Estimating patient's clinical state from multiple concurrent physiological streams plays an important role in determining if a therapeutic intervention is necessary and for triaging patients in the hospital. In this paper we construct a non-parametric learning algorithm to estimate the clinical state of a patient. The algorithm addresses several known challenges with clinical state estimation such as eliminating bias introduced by therapeutic intervention censoring, increasing the timeliness of state estimation while ensuring a sufficient accuracy, and the ability to detect anomalous clinical states. These benefits are obtained by combining the tools of non-parametric Bayesian inference, permutation testing, and generalizations of the empirical Bernstein inequality. The algorithm is validated using real-world data from a cancer ward in a large academic hospital.

In this paper, we introduce the public-private framework of data summarization motivated by privacy concerns in personalized recommender systems and online social services. Such systems have usually access to massive data generated by a large pool of users. A major fraction of the data is public and is visible to (and can be used for) all users. However, each user can also contribute some private data that should not be shared with other users to ensure her privacy. The goal is to provide a succinct summary of massive dataset, ideally as small as possible, from which customized summaries can be built for each user, i.e. it can contain elements from the public data (for diversity) and users' private data (for personalization). To formalize the above challenge, we assume that the scoring function according to which a user evaluates the utility of her summary satisfies submodularity, a widely used notion in data summarization applications.

In quantum computers, the information is contained in qubits that can work through vast numbers of different outcomes, which is not possible with classical computers. In quantum computers, the information is contained in qubits that can work through vast numbers of different outcomes, which is not possible with classical computers. The UK will not let quantum computing talent slip through its fingers and must learn lessons from US dominance of the AI race, the technology secretary has said, as the government announced a £1bn quantum funding pledge. Liz Kendall said the government hoped to retain homegrown quantum startups, engineers and researchers rather than lose them to competing countries, with the US stealing a march on its western rivals in AI. "I do look at what's happened on AI," said Kendall. "I do think we need to learn the lessons and make sure we give our brilliant scientists, spinouts and startups the ability to stay here and make it happen. And that requires a government that is bold and ambitious and confident in these technologies of the future."

Graves being prepared for the victims of an airstrike on a school in Minab in southern Iran, 2 March 2026. Graves being prepared for the victims of an airstrike on a school in Minab in southern Iran, 2 March 2026. A photo of Iran's bombed schoolgirl graveyard went around the world. T he graves, freshly dug, lie in neat rows of 20 across. More than 60 have already been carved out of the earth, with a few clusters of people standing gathered around them.

In 2025, when Elon Musk joined the government as the de facto head of something called the "department of government efficiency", he declared that governments were poorly configured "big dumb machines". To the senator Ted Cruz, he explained that "the only way to reconcile the databases and get rid of waste and fraud is to actually look at the computers". Muskism came to Washington soaked in memes, adolescent boasts and sadistic victory dances over mass firings. Leading a team of teenage coders and mid-level managers drawn from his suite of companies, Musk aimed to enter the codebase and rewrite regulations and budget lines from within. He would drag the paper-pushing bureaucracy kicking and screaming into the digital 21st century, scanning the contents of cavernous rooms of filing cabinets and feeding the data into a single interoperable system. The undertaking combined features of private equity-led restructuring with startup management, shot through with the sensibility of gaming and rightwing culture war. To succeed, he would need "God mode", an overview of the whole. If the mandate of Doge was to "[modernise] federal technology and software to maximise governmental efficiency and productivity", in the words of the executive order that launched the initiative on 20 January 2025, the reality was a strengthening of the state's surveillance capacities. Over time, Musk had become convinced that the real bugs in the code were people, especially the non-white illegal immigrants whom he saw as pawns in a liberal scheme to corrupt democracy and beneficiaries of what he called "suicidal empathy". He understood empathy itself in coding terms.

Tennessee minors sue Musk's xAI, alleging Grok generated sexual images of them Governments and regulators around the world have launched probes into xAI, imposed bans and demanded safeguards in a growing push to curb illegal and offensive material. Three Tennessee plaintiffs, including two minors, sued Elon Musk's xAI on Monday, alleging that it knowingly designed its Grok image generator to let people create sexually explicit content by using real photos of others. The lawsuit, filed in the San Jose, California federal court, is seeking class-action status for people in the United States who were reasonably identifiable in sexualized images or videos generated by Grok based on real images of themselves. The artificial intelligence company did not immediately respond to a request for comment. After an outcry over sexually explicit content generated by the chatbot, xAI said in January that it had blocked all users from editing images of real people in revealing clothing and from generating images of people in revealing clothing in jurisdictions where it's illegal. Governments and regulators around the world have also since launched probes, imposed bans and demanded safeguards in a growing push to curb illegal and offensive material.