The European Parliament has approved sweeping legislation to regulate artificial intelligence, nearly three years after the draft rules were first proposed. Officials reached an agreement on AI development in December. On Wednesday, members of the parliament approved the AI Act with 523 votes in favor and 46 against, There were 49 abstentions. The EU says the regulations seek to "protect fundamental rights, democracy, the rule of law and environmental sustainability from high-risk AI, while boosting innovation and establishing Europe as a leader in the field." The act defines obligations for AI applications based on potential risks and impact.

These problems involve Vandermonde matrices which arise naturally in various statistical modeling settings, including classical polynomial fitting problems, additive models and approximations to recently developed randomized techniques for scalable kernel methods. We show that this structure can be exploited to further accelerate the solution of the regression problem, achieving running times that are faster than "input sparsity".

Measuring similarity is crucial to many learning tasks. To this end, metric learning has been the dominant paradigm. However, similarity is a richer and broader notion than what metrics entail. For example, similarity can arise from the process of aggregating the decisions of multiple latent components, where each latent component compares data in its own way by focusing on a different subset of features. In this paper, we propose Similarity Component Analysis (SCA), a probabilistic graphical model that discovers those latent components from data.

AI-generated deepfake pictures, video or audio of existing people, places or events must be labeled as artificially manipulated. European lawmakers voted on the Artificial Intelligence Act which will lead the world in regulating artificial technology.

An Israeli drone strike has killed a member of Hamas and one other person in southwestern Lebanon near a refugee camp housing displaced Palestinians. It comes amid increased cross-border attacks between Israel and Lebanon.

EXCLUSIVE: House GOP Conference Chair Elise Stefanik, R-N.Y., is criticizing former Attorney General Loretta Lynch for reportedly lobbying the Pentagon on behalf of a company known as DJI, which makes Chinese military tech. "It is disgraceful but unsurprising that Barack Obama's former Attorney General Loretta Lynch is now working on behalf of a Communist Chinese drone company that the Department of Defense has identified as a Chinese military company," Stefanik told Fox News Digital. "Former Attorney General Loretta Lynch is lobbying the DOD to request they remove DJI from this list so the Communist Chinese company can operate with impunity in America. U.S. government officials, both past and present, should be working to ban these Communist Chinese spy drones and bolster the domestic drone industry, not advocating on behalf of a Chinese military company and the Chinese Communist Party." House GOP Conference Chair Elise Stefanik is going after ex-Attorney General Loretta Lynch for a report that links her to a Chinese military firm.

The driver in the Chevy Suburban seemed bent on testing the Waymo robotaxi on the streets of downtown L.A. this week. Playing chicken against Silicon Valley's wheeled robot, he sharply swung into the next lane toward the Waymo. The white driverless Jaguar swerved to avoid the bigger car crossing the line and striking it. The human driver sped then ahead of the robotaxi and braked abruptly in front of it. The machine slowed in time to avoid a collision, shifted into the next lane and the Chevy moved on, ending a brief yet anxiety inducing interaction for a Los Angeles Times reporter and photographer riding in the Waymo vehicle.

This work introduces a model that can recognize objects in images even if no training data is available for the object class. The only necessary knowledge about unseen visual categories comes from unsupervised text corpora. Unlike previous zero-shot learning models, which can only differentiate between unseen classes, our model can operate on a mixture of seen and unseen classes, simultaneously obtaining state of the art performance on classes with thousands of training images and reasonable performance on unseen classes. This is achieved by seeing the distributions of words in texts as a semantic space for understanding what objects look like. Our deep learning model does not require any manually defined semantic or visual features for either words or images. Images are mapped to be close to semantic word vectors corresponding to their classes, and the resulting image embeddings can be used to distinguish whether an image is of a seen or unseen class. We then use novelty detection methods to differentiate unseen classes from seen classes. We demonstrate two novelty detection strategies; the first gives high accuracy on unseen classes, while the second is conservative in its prediction of novelty and keeps the seen classes' accuracy high.

Artificial Intelligence words are seen in this illustration taken on March 31, 2023. RISE OF THE MACHINES: The U.S. government has a "clear and urgent need" to act, as swiftly developing artificial intelligence could potentially lead to human extinction through weaponization and loss of control, according to a government-commissioned report. 'SMALL, SMART, CHEAP': The Pentagon will look to develop new artificial intelligence-guided planes, offering two contracts that several private companies have been competing to obtain. The Pentagon is seen from a flight taking off from Ronald Reagan Washington National Airport in Arlington, Virginia. While this technology offers many astonishing benefits, it also poses significant dangers.

Structure learning algorithms for graphical models have focused almost exclusively on stable environments in which the underlying generative process does not change; that is, they assume that the generating model is globally stationary. In real-world environments, however, such changes often occur without warning or signal. Real-world data often come from generating models that are only locally stationary. In this paper, we present LoSST, a novel, heuristic structure learning algorithm that tracks changes in graphical model structure or parameters in a dynamic, real-time manner. We show by simulation that the algorithm performs comparably to batch-mode learning when the generating graphical structure is globally stationary, and significantly better when it is only locally stationary.