Is Google's new AI Mode feature theft? The News/Media Alliance, trade association representing news media organizations in the U.S. and Canada, certainly thinks so. At Google's I/O showcase earlier this week, the tech company announced the public release of AI Mode in Google Search. AI Mode expands AI Overviews in search and signifies a pivot away from Google's traditional search. Users will see a tab at the top of their Google Search page that takes them to a chatbot interface much like, say, ChatGPT, instead of your typical Google Search results.

Breakthroughs, discoveries, and DIY tips sent every weekday. Drones can be a divisive subject, but they do have their uses (beyond causing mass panic). Professional unpiloted aerial vehicles (UAVs) are already instrumental in conservation efforts and engineering projects, but even personal use drones do more than recording cool aerial shots of your vacation. In the case of a recent emergency in Florida, one man's drone helped save a teenager's life. Earlier this month, amateur shark fisherman Andrew Smith was convinced by a friend to come with them to Pensacola Beach after getting off from work.

Politico became one of the first newsrooms last year to win a union contract that included rules on how the media outlet can deploy artificial intelligence. The PEN Guild, which represents Politico and its sister publication, environment and energy site E&E News, is now gearing up for another first. The union's members allege that the AI provisions in their contract have been violated, and they're preparing for a groundbreaking legal dispute with management. The outcome could set a precedent for how much input journalists ultimately have over how AI is used in their newsrooms. Last year, Politico began publishing AI-generated live news summaries during big political events like the Democratic National Convention and the US vice presidential debates.

We propose a new randomized algorithm for solving L2-regularized least-squares problems based on sketching. We consider two of the most popular random embeddings, namely, Gaussian embeddings and the Subsampled Randomized Hadamard Transform (SRHT). While current randomized solvers for least-squares optimization prescribe an embedding dimension at least greater than the data dimension, we show that the embedding dimension can be reduced to the effective dimension of the optimization problem, and still preserve high-probability convergence guarantees. In this regard, we derive sharp matrix deviation inequalities over ellipsoids for both Gaussian and SRHT embeddings. Specifically, we improve on the constant of a classical Gaussian concentration bound whereas, for SRHT embeddings, our deviation inequality involves a novel technical approach. Leveraging these bounds, we are able to design a practical and adaptive algorithm which does not require to know the effective dimension beforehand. Our method starts with an initial embedding dimension equal to 1 and, over iterations, increases the embedding dimension up to the effective one at most. Hence, our algorithm improves the state-of-the-art computational complexity for solving regularized least-squares problems. Further, we show numerically that it outperforms standard iterative solvers such as the conjugate gradient method and its pre-conditioned version on several standard machine learning datasets.

Localized receptive fields--neurons that are selective for certain contiguous spatiotemporal features of their input--populate early sensory regions of the mammalian brain. Unsupervised learning algorithms that optimize explicit sparsity or independence criteria replicate features of these localized receptive fields, but fail to explain directly how localization arises through learning without efficient coding, as occurs in early layers of deep neural networks and might occur in early sensory regions of biological systems. We consider an alternative model in which localized receptive fields emerge without explicit top-down efficiency constraints--a feedforward neural network trained on a data model inspired by the structure of natural images. Previous work identified the importance of non-Gaussian statistics to localization in this setting but left open questions about the mechanisms driving dynamical emergence. We address these questions by deriving the effective learning dynamics for a single nonlinear neuron, making precise how higher-order statistical properties of the input data drive emergent localization, and we demonstrate that the predictions of these effective dynamics extend to the many-neuron setting. Our analysis provides an alternative explanation for the ubiquity of localization as resulting from the nonlinear dynamics of learning in neural circuits.

The Lipschitz constant of a network plays an important role in many applications of deep learning, such as robustness certification and Wasserstein Generative Adversarial Network. We introduce a semidefinite programming hierarchy to estimate the global and local Lipschitz constant of a multiple layer deep neural network. The novelty is to combine a polynomial lifting for ReLU functions derivatives with a weak generalization of Putinar's positivity certificate. This idea could also apply to other, nearly sparse, polynomial optimization problems in machine learning. We empirically demonstrate that our method provides a trade-off with respect to state of the art linear programming approach, and in some cases we obtain better bounds in less time.

In the high desert east of Reno, Nevada, construction crews are flattening the golden foothills of the Virginia Range, laying the foundations of a data center city. Google, Tract, Switch, EdgeCore, Novva, Vantage, and PowerHouse are all operating, building, or expanding huge facilities nearby. Meanwhile, Microsoft has acquired more than 225 acres of undeveloped property, and Apple is expanding its existing data center just across the Truckee River from the industrial park. The corporate race to amass computing resources to train and run artificial intelligence models and store information in the cloud has sparked a data center boom in the desert--and it's just far enough away from Nevada's communities to elude wide notice and, some fear, adequate scrutiny. This story is part of Power Hungry: AI and our energy future--our new series shining a light on the energy demands and carbon costs of the artificial intelligence revolution.

Special cases of problem include the well-known Fermat-Weber problem - or geometric median problem - where z = 1, the mean or centroid where z = 2, and the Minimum Enclosing Ball problem, where z = . We consider these problem in the big data regime. Here, we are interested in sampling as few points as possible such that we can accurately estimate m. More specifically, we consider sublinear algorithms as well as coresets for these problems. Sublinear algorithms have a random query access to the set A and the goal is to minimize the number of queries.

Texas high school student Elliston Berry joins'Fox & Friends' to discuss the House's passage of a new bill that criminalizes the sharing of non-consensual intimate images, including content created with artificial intelligence. The Chicago Sun-Times admitted on Tuesday that it published an AI-generated list of books that don't exist for its summer reading list. On Sunday, the publication released a special 64-page section titled "Heat Index: Your Guide to the Best of Summer" which featured a list of 15 recommended books for summer. However, upon further look, it was found that 10 of the 15 books on the list were not real. One example included a book called "Nightshade Market" by Min Jin Lee, which was described as a "riveting tale set in Seoul's underground economy" and follows "three women whose paths intersect in an illegal night market" exploring "class, gender and the shadow economies beneath prosperous societies."

Training regimes based on Maximum Likelihood Estimation (MLE) suffer from known limitations, often leading to poorly generated text sequences. At the root of these limitations is the mismatch between training and inference, i.e. the so-called exposure bias, exacerbated by considering only the reference texts as correct, while in practice several alternative formulations could be as good. Generative Adversarial Networks (GANs) can mitigate those limitations but the discrete nature of text has hindered their application to language generation: the approaches proposed so far, based on Reinforcement Learning, have been shown to underperform MLE. Departing from previous works, we analyze the exploration step in GANs applied to text generation, and show how classical sampling results in unstable training. We propose to consider alternative exploration strategies in a GAN framework that we name ColdGANs, where we force the sampling to be close to the distribution modes to get smoother learning dynamics. For the first time, to the best of our knowledge, the proposed language GANs compare favorably to MLE, and obtain improvements over the state-of-the-art on three generative tasks, namely unconditional text generation, question generation, and abstractive summarization.