In packet switches, packets queue at switch inputs and contend for out- puts. The contention arbitration policy directly affects switch perfor- mance. The best policy depends on the current state of the switch and current traffic patterns. This problem is hard because the state space, possible transitions, and set of actions all grow exponentially with the size of the switch. We present a reinforcement learning formulation of the problem that decomposes the value function into many small inde- pendent value functions and enables an efficient action selection.

Just like Napster triggered a global, technological shift in the way music is consumed and distributed, we are now on the precipice of another major revolution certain to disrupt the music industry. Artificial intelligence, or "AI" as it is more commonly referred, has quickly emerged as a game changer across a myriad of industries and music is no exception. AI offers the promise of innovative opportunities and avenues for music creation, publishing, recording, synchronization, distribution, consumption and revenue generation. However, these opportunities also present significant, novel challenges for music rights holders and users alike--and the legal challenges have just begun. AI is not a new concept.

We study gender discrimination of human faces using a combination of psychophysical classification and discrimination experiments together with methods from machine learning. We reduce the dimensionality of a set of face images using principal component analysis, and then train a set of linear classifiers on this reduced representation (linear support vec- tor machines (SVMs), relevance vector machines (RVMs), Fisher linear discriminant (FLD), and prototype (prot) classifiers) using human clas- sification data. Because we combine a linear preprocessor with linear classifiers, the entire system acts as a linear classifier, allowing us to visu- alise the decision-image corresponding to the normal vector of the separ- ating hyperplanes (SH) of each classifier. We predict that the female-to- maleness transition along the normal vector for classifiers closely mim- icking human classification (SVM and RVM [1]) should be faster than the transition along any other direction. A psychophysical discrimina- tion experiment using the decision images as stimuli is consistent with this prediction.

OpenAI on Thursday did not immediately respond to a request for comment sent overnight. In an earlier statement in response to the chatbot's false claims about the law professor, OpenAI spokesperson Niko Felix said: "When users sign up for ChatGPT, we strive to be as transparent as possible that it may not always generate accurate answers. Improving factual accuracy is a significant focus for us, and we are making progress."

An Australian mayor has threatened a defamation suit against OpenAI, alleging that its chatbot, ChatGPT, made some false claims about him, probably the first of its kind action in the generative AI space. Called "Segment Anything", this AI model can detect objects from photos and videos, and allows users to select those objects by clicking them or using text prompts. Italian regulators said last Friday that the firm had no legal basis to engage in massive data collection and questioned the way it was handling the information it had gathered.

DataGrade founder Joe Toscano and MRC Free Speech America VP Dan Schneider said ChatGPT poses various dangers to jobs and information. Artificial intelligence (AI) experts and industry leaders have warned that automation, and reliance on programs like OpenAI's ChatGPT could have a drastic impact on millions of jobs, particularly in industries that can be easily automated. ChatGPT itself, a software which has sparked debate in recent months, said it could "potentially replace jobs that involve written communication," such as translation services and social media managers. When asked what jobs could be replaced by AI broadly, ChatGPT said financial analysts and customer service representatives, among others, could potentially be replaced by AI in the future. Goldman Sachs warned in a report Sunday that around the world, as many as 300 million jobs could be impacted by artificial intelligence automation, CNBC reported.

Dynamic Bayesian networks have been applied widely to reconstruct the structure of regulatory processes from time series data. The standard approach is based on the assumption of a homogeneous Markov chain, which is not valid in many real-world scenarios. Recent research efforts addressing this shortcoming have considered undirected graphs, directed graphs for discretized data, or over-flexible models that lack any information sharing between time series segments. In the present article, we propose a non-stationary dynamic Bayesian network for continuous data, in which parameters are allowed to vary between segments, and in which a common network structure provides essential information sharing across segments. Our model is based on a Bayesian change-point process, and we apply a variant of the allocation sampler of Nobile and Fearnside to infer the number and location of the change-points.

A new methodology is developed for joint analysis of a matrix and accompanying documents, with the documents associated with the matrix rows/columns. The documents are modeled with a focused topic model, inferring latent binary features (topics) for each document. A new matrix decomposition is developed, with latent binary features associated with the rows/columns, and with imposition of a low-rank constraint. The matrix decomposition and topic model are coupled by sharing the latent binary feature vectors associated with each. The model is applied to roll-call data, with the associated documents defined by the legislation.

Russian state media is now mocking former president Donald Trump after he was indicted on 34 felony counts of falsifying business records for charges stemming from an alleged hush money payment to an adult film star. Last Friday, the Russian state show "60 Minutes" displayed an AI-generate image of Trump wearing an orange outfit while panelists discussed what could happen in the United States should the former president be arrested. Meanwhile on Russian state TV, even the biggest fans of the former president are using AI-generated images of Trump in orange. Another picture of Trump wearing similar orange overalls was also used on "Sunday Evening with Vladimir Solovyov." During the weekend broadcast, host Vladimir Solovyov and the show's panelists wondered if Trump has a chance of winning the 2024 election following the indictment.

The AI bot, known for providing restaurant recommendations and answering privacy policy questions, has now managed to concoct a sexual harassment scandal involving a real-life law professor., Technology & Science News, Times Now