Your Next Great Read Might Be Certified'Organic' Amid a sea of AI writing slop, a new stamp for "organic literature" will help readers to pick out books authored by real, corn-fed, free-range humans. As scandals involving the use of artificial intelligence by professional authors pile up, a British startup has established a way for publishers to certify their books as "organic"--written by real people, no machines in sight. On Wednesday, Books by People confirmed it had received approval from the UK's Intellectual Property Office to begin distributing a mark that indicates a published book has been authored by a human. The stamp, which looks like a thumbprint, will be displayed on the cover. "If people are going to be writing AI books or seeking it out for their own enjoyment, that's their prerogative," Esme Dennys, cofounder of Books by People, tells WIRED.

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Scientists are expected to read newly published papers in their field to stay current and keep their work relevant. However, when faced with the massive number of publications, it may seem an overwhelming task to read all these papers, even if one were to reduce this to only a fraction related to one's own area of research. As an example, in 2024 alone, IEEE published no less than 46,968 papers on "robotics" or "automation", and IEEE publications represent only a fraction of the total research available online To assess the magnitude of this challenge, as well as to evaluate how much genuine progress is reported in today's publications, we undertook exactly this effort. For the task to be reasonable, we reduced our search to one particular subarea, learning from demonstration (LfD), that is methods whereby robots are taught by human experts. We monitor progress through both quantitative and qualitative metrics, offering a review on current trends and notable contributions.

Could the next great novel be written by AI (and would you even be able to tell)? Can you tell which, if any, were AI generated? "The hotel is in a great location for everything. Lots of places to eat and drink. The hotel itself is always abuzz. The tavern located on the ground floor is definitely a must. Food, service, prices and atmosphere were great." "A good hotel, though the room had the proportions of a well-appointed lift.

The New Movie Is Sparking a Right-Wing Backlash. Emily Wilson's 2017 translation of Homer's epic--the first by a woman--was called a woke "abomination" by online reactionaries. Christopher Nolan's film is facing similar critiques. Who'd have thought Helen of Troy would cause so much trouble? Earlier this year, certain quarters of the internet spun out at news that Kenyan-Mexican Oscar-winning actress Lupita Nyong'o was rumored to appear as the impossibly beautiful Spartan noble Helen--whose face, it was later written, launched a thousand ships--in Christopher Nolan's forthcoming Hollywood Homeric epic, The Odyssey The confirmation of her casting in May kicked off another wave of conniption fits.

We provide the first sharp characterization of the optimal first-order query complexity of agnostic active learning, and propose a new general active learning algorithm which achieves it. Remarkably, the optimal query complexity admits a leading term which is always strictly smaller than the sample complexity of passive supervised learning (by a factor proportional to the best-in-class error rate). This was not previously known to be possible. For comparison, in all previous general analyses, the leading term exhibits an additional factor, such as the disagreement coefficient or related complexity measures, and therefore only provides improvements over passive learning in restricted cases. The present work completely removes such factors from the leading term, implying that every concept class benefits from active learning in the non-realizable case. Whether such benefits are possible has been the driving question underlying the past two decades of research on the theory of agnostic active learning. This work finally settles this fundamental question.

Recent interest in Multi-Agent Systems of Large Language Models (MAS LLMs) has led to an increase in frameworks leveraging multiple LLMs to tackle complex tasks.

Recent work on first-order optimizers for empirical risk minimization (ERM) has suggested that smoothness of ERM loss functions in the training data, rather than in the optimization parameters, can be leveraged to improve the oracle complexity of gradient descent (GD) methods. In this paper, we propose an inexact gradient method, piecewise polynomial interpolation-based gradient descent (PPI-GD), which approximates the full gradient in each iteration by querying the first-order oracle at equidistant points in the data domain to construct polynomial interpolants of the resulting gradient samples over appropriately sized patches of the data domain. We analyze the oracle complexity of PPI-GD for strongly convex and non-convex loss functions when the data space dimension is bounded by a polylogarithmic function of the number of training samples, and find it to outperform several GD variants in key regimes when the loss function is sufficiently smooth. Furthermore, our analysis extends several techniques from the error analysis of bicubic spline interpolants to the setting of $d$-variate tensor product polynomial interpolants which may be of independent interest in interpolation analysis.

AI-driven discovery can greatly reduce design time and enhance new therapeutics' effectiveness. Models using simulators explore broad design spaces but risk violating implicit constraints due to a lack of experimental priors. For example, in a new analysis across diverse models on the GuacaMol benchmark using supervised classifiers, over 60% of molecules proposed had a high probability of being mutagenic. In this work, we introduce Medex, a dataset of priors for design problems extracted from literature describing compounds used in lab settings. It is constructed with LLM pipelines for discovering therapeutic entities in relevant paragraphs and summarizing information in concise fair-use facts. Medex consists of 32.3 million pairs of natural language facts, and appropriate entity representations (i.e.

We propose a flexible framework for modeling the predictive distributions of nonlinear, possibly multivariate time series. Our approach expresses a general predictive distribution in an appropriate generative representation that is based on a folklore result from measure theoretic probability. This representation provides a direct simulation-based approximation to the predictive distribution, enabling straightforward computation of forecasts for the conditional mean and variance, fan charts, value at risk, expected shortfall, joint tail risks, and other quantities of interest. We estimate this generative representation using a version of conditional generative adversarial networks and provide a formal statistical analysis of estimation under weak temporal dependence. Specifically, estimation is expressed as a particular minimax problem and we establish consistency of its approximate solutions in Hausdorff distance. The empirical relevance of the approach is illustrated using applications to equity returns, realized variance, and realized covariances. The proposed method is also computationally manageable, with estimation in our applications taking approximately one minute on a standard laptop.