This material may not be published, broadcast, rewritten, or redistributed. Quotes displayed in real-time or delayed by at least 15 minutes. Market data provided by Factset . Powered and implemented by FactSet Digital Solutions . Mutual Fund and ETF data provided by Refinitiv Lipper .

Traditional machine learning relies on explicit models and domain assumptions, limiting flexibility and interpretability. We introduce a model-free framework using surprisal (information theoretic uncertainty) to directly analyze and perform inferences from raw data, eliminating distribution modeling, reducing bias, and enabling efficient updates including direct edits and deletion of training data. By quantifying relevance through uncertainty, the approach enables generalizable inference across tasks including generative inference, causal discovery, anomaly detection, and time series forecasting. It emphasizes traceability, interpretability, and data-driven decision making, offering a unified, human-understandable framework for machine learning, and achieves at or near state-of-the-art performance across most common machine learning tasks. The mathematical foundations create a ``physics'' of information, which enable these techniques to apply effectively to a wide variety of complex data types, including missing data. Empirical results indicate that this may be a viable alternative path to neural networks with regard to scalable machine learning and artificial intelligence that can maintain human understandability of the underlying mechanics.

Karen Price was just 15 when she vanished in 1981 and, had it not been for a chance discovery by two builders, her body might never have been found. Because no-one was looking for her. Dubbed Little Miss Nobody, Karen had not been seen for eight years when her skeletal remains, wrapped in a carpet, were uncovered by two unsuspecting builders in Cardiff city centre on 7 December 1989. Her body, found in a shallow grave outside a basement flat on Fitzhamon Embankment, was so badly decomposed it was impossible to establish the cause of her death. Now, more than 40 years on and after the release of her killer, a new documentary has examined how police put together the jigsaw to solve the killing of a teenager known to no-one and how it involved groundbreaking methods to bring two men to justice.

SGMSE [1] solves a stochastic differential equation with a learned score network, while the Schr odinger Bridge (SB) [3, 4] casts speech enhancement as an optimal transport problem. These approaches often outperform predictive baselines in terms of perceptual quality and robustness [2, 10]. A key limitation, however, is the emergence of generative artifacts. Unlike predictive models, which mainly distort or suppress existing speech, diffusion-based SE can "hallucinate" new content. Artifacts include phonetic errors such as insertions or substitutions, spurious breathing or hissing, robotic tones, and high-frequency attenuation [10]. These effects are most pronounced at low SNR as shown in Figure 1, where uncertainty drives the model to generate plausible but incorrect phonetic structures, leading to poor ASR performance despite high PESQ or STOI scores [9]. Existing metrics fail to fully capture these errors: intrusive metrics emphasize energy-based distortions at the signal-level, while non-intrusive predictors favor naturalness and overrate generative outputs. Complementary measures such as Levenshtein phoneme distance (LPD) and hallucination error rate (HER) have been proposed to address this gap [11, 12].

Samantha posted her story on TikTok and shared the scenario on a private Facebook group; many women responded--including her date's wife. Ultimately, as a result of this conversation, Samantha decided to report his profile to Hinge. The next day, the company contacted her to let her know it would be deleting his profile. Mandy and Samantha were pleased with Bumble's and Hinge's swift action to take down the profiles of the men they had matched with--but the experience was indelible. Neither of them plans to use dating apps again.

A new brain teaser reveals the most respected aspects of your personality. The puzzle features images that can be interpreted in different ways, depending on your personal experiences, traits and mental state. Hidden in the picture is a lion, panther and bunch of dandelions, and the one you see first means you are either a natural-born leader, a problem solver or have a strong sense of conviction. The puzzle features images that can be interpreted in different ways, depending on your personal experiences, traits and mental state. Did you see a lion, panther or dandelions first?

A Las Vegas teen claimed he witnessed an'eight-foot-tall demon creature with big shiny eyes' in his backyard last year. Skeptics have dismissed Angel Kenmore's video, saying the figures are merely smudges, but Scott Roder, a crime scene reconstruction veteran, believes it is'undeniable evidence that we are not alone.' Roder, who has stood as an expert on several high-profile criminal trials, claimed to use AI and computer software to recreate movement shown on April 30, 2023, outlining'anomalies' in the footage. He pinpointed to'smoky filters' that didn't match the background and speculated that the aliens used a'cloaking device' to shield themselves from human eyes. 'This is, as far as anyone should be concerned, this is the absolute end,' Roder told Fox News.

Over the last ten years the literature in computer science and philosophy has formulated different criteria of algorithmic fairness. One of the most discussed, classification parity, requires that the erroneous classifications of a predictive algorithm occur with equal frequency for groups picked out by protected characteristics. Despite its intuitive appeal, classification parity has come under attack. Multiple scenarios can be imagined in which - intuitively - a predictive algorithm does not treat any individual unfairly, and yet classification parity is violated. To make progress, we turn to a related principle, equal protection, originally developed in the context of criminal justice. Key to equal protection is equalizing the risks of erroneous classifications (in a sense to be specified) as opposed to equalizing the rates of erroneous classifications. We show that equal protection avoids many of the counterexamples to classification parity, but also fails to model our moral intuitions in a number of common scenarios, for example, when the predictor is causally downstream relative to the protected characteristic. To address these difficulties, we defend a novel principle, causal equal protection, that models the fair allocation of the risks of erroneous classification through the lenses of causality.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The former head of Britain's state-owned Post Office said Tuesday she will hand back a royal honor in response to mounting fury over a miscarriage of justice that saw hundreds of postmasters wrongfully accused of theft because of a faulty computer system. The British government is considering whether to offer a mass amnesty to more than 700 branch managers convicted of theft or fraud between 1999 and 2015, because Post Office computers wrongly showed that money was missing from their shops. The real culprit was a defective accounting system called Horizon, supplied by the Japanese technology firm Fujitsu.

Nonparametric learning is a fundamental concept in machine learning that aims to capture complex patterns and relationships in data without making strong assumptions about the underlying data distribution. Owing to simplicity and familiarity, one of the most well-known algorithms under this paradigm is the $k$-nearest neighbors ($k$-NN) algorithm. Driven by the usage of machine learning in safety-critical applications, in this work, we shed new light on the traditional nearest neighbors algorithm from the perspective of information theory and propose a robust and interpretable framework for tasks such as classification, regression, and anomaly detection using a single model. Instead of using a traditional distance measure which needs to be scaled and contextualized, we use a novel formulation of \textit{surprisal} (amount of information required to explain the difference between the observed and expected result). Finally, we demonstrate this architecture's capability to perform at-par or above the state-of-the-art on classification, regression, and anomaly detection tasks using a single model with enhanced interpretability by providing novel concepts for characterizing data and predictions.