Metrics for rigorously defining a distance between two events have been used to study the properties of the dataspace manifold of particle collider physics. The probability distribution of pairwise distances on this dataspace is unique with probability 1, and so this suggests a method to search for and identify new physics by the deviation of measurement from a null hypothesis prediction. To quantify the deviation statistically, we directly calculate the probability distribution of the number of event pairs that land in the bin a fixed distance apart. This distribution is not generically Gaussian and the ratio of the standard deviation to the mean entries in a bin scales inversely with the square-root of the number of events in the data ensemble. If the dataspace manifold exhibits some enhanced symmetry, the number of entries is Gaussian, and further fluctuations about the mean scale away like the inverse of the number of events. We define a robust measure of the non-Gaussianity of the bin-by-bin statistics of the distance distribution, and demonstrate in simulated data of jets from quantum chromodynamics sensitivity to the parton-to-hadron transition and that the manifold of events enjoys enhanced symmetries as their energy increases.

The problem of interpretability of machine learning architecture in particle physics has no agreed-upon definition, much less any proposed solution. We present a first modest step toward these goals by proposing a definition and corresponding practical method for isolation and identification of relevant physical energy scales exploited by the machine. This is accomplished by smearing or averaging over all input events that lie within a prescribed metric energy distance of one another and correspondingly renders any quantity measured on a finite, discrete dataset continuous over the dataspace. Within this approach, we are able to explicitly demonstrate that (approximate) scaling laws are a consequence of extreme value theory applied to analysis of the distribution of the irreducible minimal distance over which a machine must extrapolate given a finite dataset. As an example, we study quark versus gluon jet identification, construct the smeared likelihood, and show that discrimination power steadily increases as resolution decreases, indicating that the true likelihood for the problem is sensitive to emissions at all scales.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. An artificial intelligence system can't be registered as the inventor of a patent, Britain's Supreme Court ruled Wednesday in a decision that denies machines the same status as humans. The U.K.'s highest court concluded that "an inventor must be a person" to apply for patents under the current law. The decision was the culmination of American technologist Stephen Thaler's long-running British legal battle to get his AI, dubbed DABUS, listed as the inventor of two patents.

AI may or may not take people's jobs in years to come, but in the meantime, there's one thing they cannot obtain: patents. Dr. Stephen Thaler has spent years trying to get patents for two inventions created by his AI "creativity machine" DABUS. Now, the United Kingdom's Supreme Court has rejected his appeal to approve these patents when listing DABUS as the inventor, Reuters reports. The court's rationale stems from a provision in UK patent law that states, "an inventor must be a natural person." The ruling stipulated that the appeal was unconcerned with whether this should change in the future. "The judgment establishes that UK patent law is currently wholly unsuitable for protecting inventions generated autonomously by AI machines," Thaler's lawyers said in a statement.

Artificial intelligence cannot be legally named as an inventor to secure patent rights, the UK supreme court has ruled. In a judgment on Wednesday, Britain's highest court concluded that "an inventor must be a person" in order to apply for patents under the current law. The ruling comes after the technologist Dr Stephen Thaler took his long-running dispute with the Intellectual Property Office (IPO) to the country's top court over its rejection of his attempt to list an AI he created as the inventor for two patents. The US-based developer claims the AI machine named DABUS autonomously created a food or drink container and a light beacon and that he is entitled to rights over its inventions. However, the IPO concluded in December 2019 that the expert was unable to officially register DABUS as the inventor in patent applications because it was not a person.

Allen explained that he "input numerous revisions and text prompts at least 624 times to arrive at the initial version of the image," which created an initial version of the image that Allen edited with Photoshop.

"A Recent Entrance to Paradise" is a pixelated pastoral scene of train tracks running under a moss-flecked bridge. It was, according to its creator's creator, drawn and named in 2012 by an artificial intelligence called DABUS (Device for the Autonomous Bootstrapping of Unified Sentience). Thaler is appealing the decision. Thaler, a Missouri-based inventor and AI researcher, has become something of a serial litigant on behalf of DABUS. Judges have swatted away similar lawsuits in the European Union, the United States, and, eventually, on appeal, in Australia.

The Friday decision follows losses for Thaler on bids for U.S. patents covering inventions he said were created by DABUS, short for Device for the Autonomous Bootstrapping of Unified Sentience.

The USCO agreed that the work was generated by an AI model that Thaler calls the Creativity Machine. He claimed that the USCO's "human authorship" requirement was unconstitutional. However, Howell indicated that Thaler's case wasn't an especially complex one, since he admitted that he wasn't involved in the creation of A Recent Entrance to Paradise. "In the absence of any human involvement in the creation of the work, the clear and straightforward answer is the one given by the [Federal] Register: No," Howell ruled. Thaler plans to appeal the decision.

March 31, 2023 - Artificial Intelligence suddenly seems to be everywhere. ChatGPT is writing human-sounding sermons, news updates, and answers to law school exam questions, while Dall·E is generating images ranging from the lifelike to the surreal in response to virtually any prompt. With much less fanfare, AI has already become ubiquitous in myriad ways. AI curates social media feeds and generates purchasing suggestions to fill internet shopping carts. AI saves lives by identifying potential pharmaceutical compounds and by quickly and accurately interpreting medical scans and images.