Breakthroughs, discoveries, and DIY tips sent every weekday. At 95 degrees, the heat rising off the track at the Circuit of the Americas in Austin, Texas, makes it impossible to see the 40-mph left turn at the end of the 170-mph straight before you need to brake for the turn. This makes every lap a leap of faith of sorts as you brake at the appointed spot and pray to Brembo, the patron saint of deceleration, that you'll slow in time to make the turn you know is coming but cannot see clearly through shimmering heat waves. The Brembo-supplied carbon ceramic brakes feature six-piston monobloc front calipers gripping 15.7-inch rotors and four-piston monobloc rear calipers squeezing 15.4-inch rotors. Pounding around COTA for lap after lap, the brakes continue to deliver, with no fade or hair-raising long pedal as exhibited by the Aston Martin Vantage during last year's track test.

Recent advancements in multimodal reasoning have largely overlooked the audio modality. We introduce Audio-Reasoner, a large-scale audio language model for deep reasoning in audio tasks. We meticulously curated a large-scale and diverse multi-task audio dataset with simple annotations. Then, we leverage closed-source models to conduct secondary labeling, QA generation, along with structured COT process. These datasets together form a high-quality reasoning dataset with 1.2 million reasoning-rich samples, which we name CoTA. Following inference scaling principles, we train Audio-Reasoner on CoTA, enabling it to achieve great logical capabilities in audio reasoning. Experiments show state-of-the-art performance across key benchmarks, including MMAU-mini (+25.42%), AIR-Bench chat/foundation(+14.57%/+10.13%), and MELD (+8.01%). Our findings stress the core of structured CoT training in advancing audio reasoning.

Causal abstraction (CA) theory establishes formal criteria for relating multiple structural causal models (SCMs) at different levels of granularity by defining maps between them. These maps have significant relevance for real-world challenges such as synthesizing causal evidence from multiple experimental environments, learning causally consistent representations at different resolutions, and linking interventions across multiple SCMs. In this work, we propose COTA, the first method to learn abstraction maps from observational and interventional data without assuming complete knowledge of the underlying SCMs. In particular, we introduce a multi-marginal Optimal Transport (OT) formulation that enforces do-calculus causal constraints, together with a cost function that relies on interventional information. We extensively evaluate COTA on synthetic and real world problems, and showcase its advantages over non-causal, independent and aggregated COTA formulations. Finally, we demonstrate the efficiency of our method as a data augmentation tool by comparing it against the state-of-the-art CA learning framework, which assumes fully specified SCMs, on a real-world downstream task.

While there exist several reasoners for Description Logics, very few of them can cope with uncertainty. BUNDLE is an inference framework that can exploit several OWL (non-probabilistic) reasoners to perform inference over Probabilistic Description Logics. In this chapter, we report the latest advances implemented in BUNDLE. In particular, BUNDLE can now interface with the reasoners of the TRILL system, thus providing a uniform method to execute probabilistic queries using different settings. BUNDLE can be easily extended and can be used either as a standalone desktop application or as a library in OWL API-based applications that need to reason over Probabilistic Description Logics. The reasoning performance heavily depends on the reasoner and method used to compute the probability. We provide a comparison of the different reasoning settings on several datasets.

Millions of tickets arrive at Uber's customer service department every week from its riders, drivers, eaters, etc. It is important for Uber to handle these tickets in a quick and efficient manner to retain its customers and fuel the companies growth. For this purpose, Uber has designed COTA or'Customer Obsession Ticket Assistant'. COTA is a Machine Learning and NLP powered tool that enables quick and efficient issue resolution of more than 90 per cent of Uber's inbound support tickets. For detailed information about different processes in the pipeline, please refer to this article by Uber. Uber is known to organize its processes using Machine Learning to achieve high speed and accuracy.

For a company looking to provide delightful user experiences, it is of paramount importance to take care of any customer issues. This paper proposes COTA, a system to improve speed and reliability of customer support for end users through automated ticket classification and answers selection for support representatives. Two machine learning and natural language processing techniques are demonstrated: one relying on feature engineering (COTA v1) and the other exploiting raw signals through deep learning architectures (COTA v2). COTA v1 employs a new approach that converts the multi-classification task into a ranking problem, demonstrating significantly better performance in the case of thousands of classes. For COTA v2, we propose an Encoder-Combiner-Decoder, a novel deep learning architecture that allows for heterogeneous input and output feature types and injection of prior knowledge through network architecture choices. This paper compares these models and their variants on the task of ticket classification and answer selection, showing model COTA v2 outperforms COTA v1, and analyzes their inner workings and shortcomings. Finally, an A/B test is conducted in a production setting validating the real-world impact of COTA in reducing issue resolution time by 10 percent without reducing customer satisfaction.

According to a local report in Belgium, an owner of a Tesla Model S claimed it started on its own, without a driver, and drove into five other cars in the municipality of Saint-Gilles, Brussels. The article said the strange journey, explained by the owner to the police, met its end with a Dacia Logan across the street. It is unclear if there were any injuries and as to what the extent of damages were. This comes on the back of the fact that on Tuesday morning a Tesla Model S running on Autopilot mode crashed into a parked police vehicle in Laguna Beach, California, according to a report in the Los Angeles Times. Laguna Police Sgt. Jim Cota explained the collision happened at 20652 Laguna Canyon Road and said "Thankfully there was not an officer at the time in the police car."

But investigators also noted the autopilot system isn't designed to reliably prevent crashes. A Tesla that the driver said was in Autopilot mode struck a parked police vehicle in Laguna Beach, Calif. LOS ANGELES -- The latest crash involving a Tesla in Autopilot mode didn't turn tragic, as some past ones have, but certainly was embarrassing. A Tesla Model S veered into a parked police cruiser Tuesday, severely damaging both vehicles in Laguna Beach, Calif., a coastal community south of Los Angeles. The driver, a 65-year-old from Laguna Niguel, Calif., told officers that he had engaged the car's partial self-driving system, called Autopilot.

A Tesla car operating in "autopilot" mode crashed into a stationary police car in Laguna Beach, California, leaving the driver injured and the patrol vehicle "totalled", according to an official. Sgt Jim Cota, the public information officer for the Laguna Beach police department, tweeted photos of the accident, which was reported at 11.07am on Tuesday. The driver of the Tesla, who suffered minor lacerations to the face from his glasses, told police officers the Tesla was in the semi-autonomous mode, although further investigation is needed to confirm this. This morning a Tesla sedan driving outbound Laguna Canyon Road in "autopilot" collides with a parked @LagunaBeachPD unit. Officer was not in the unit at the time of the crash and minor injuries were sustained to the Tesla driver.

To facilitate the best end-to-end experience possible for users, Uber is committed to making customer support easier and more accessible. Working toward this goal, Uber's Customer Obsession team leverages five different customer-agent communication channels powered by an in-house platform that integrates customer support ticket context for easy issue resolution. With hundreds of thousands of tickets surfacing daily on the platform across 400 cities worldwide, this team must ensure that agents are empowered to resolve them as accurately and quickly as possible.