Why Are Car Software Updates Still So Bad? Over-the-air upgrades can not only transform your ride, they can help carmakers slash costs. Despite years of effort and the outlay of billions of dollars, none of the world's automakers have yet to match Tesla's prowess in delivering over-the-air (OTA) software updates. Just like with your phone and laptop, these operating system refreshes allow owners to upgrade their cars remotely. Tesla introduced OTAs in 2012, but now Elon Musk's company pumps out these updates like no other automaker. "Tesla once issued 42 updates within six months," Jean-Marie Lapeyre, Capgemini's CTO for automotive, tells WIRED. But for many other automakers, says Lapeyre, OTAs ship "maybe once a year."

The first time you make anything, it probably won't come out perfect, so it wasn't a huge surprise when MSI's debut gaming handheld struggled out of the gate. And that's before you consider the unorthodox choice to go with an Intel chip instead of one from AMD like practically all of its rivals. However, MSI didn't give up, and now it's back with not one but two versions of its second-gen handheld, headlined by the Claw 8 AI . Not only is it bigger than before, it has twice as many Thunderbolt 4 ports, a way bigger battery and some of the best performance we've seen from any device in this category. But more importantly, as the follow-up to a device plagued by lackluster software and unfinished drivers, it feels like the Claw got its bite back. With its 8-inch screen, the Claw 8 AI is bigger than its predecessor and a number of its rivals like the ROG Ally X, though it's still smaller than Lenovo's chunky 8.8-inch Legion Go.

Large Language Models (LLMs) have demonstrated significant potential in causal discovery tasks by utilizing their vast expert knowledge from extensive text corpora. However, the multi-agent capabilities of LLMs in causal discovery remain underexplored. This paper introduces a general framework to investigate this potential. The first is the Meta Agents Model, which relies exclusively on reasoning and discussions among LLM agents to conduct causal discovery. The second is the Coding Agents Model, which leverages the agents' ability to plan, write, and execute code, utilizing advanced statistical libraries for causal discovery. The third is the Hybrid Model, which integrates both the Meta Agents Model and Coding Agents Model approaches, combining the statistical analysis and reasoning skills of multiple agents. Our proposed framework shows promising results by effectively utilizing LLMs' expert knowledge, reasoning capabilities, multi-agent cooperation, and statistical causal methods. By exploring the multi-agent potential of LLMs, we aim to establish a foundation for further research in utilizing LLMs multi-agent for solving causal-related problems.

Originating from cooperative game theory, Shapley values have become one of the most widely used measures for variable importance in applied Machine Learning. However, the statistical understanding of Shapley values is still limited. In this paper, we take a nonparametric (or smoothing) perspective by introducing Shapley curves as a local measure of variable importance. We consider two estimation strategies and derive the consistency and asymptotic normality both under independence and dependence among the features. We further propose a novel version of the wild bootstrap procedure specifically adjusted for Shapley curves. This allows us to construct confidence intervals and conduct inference. The asymptotic results are validated in extensive experiments. In an empirical application, we analyze which attributes drive the prices of vehicles.

Tesla had me convinced, for a while, that it was a cool company. It made cars that performed animatronic holiday shows using their lights and power-operated doors. It came up with dog mode (a climate control system that stays running for dogs in a parked car), a GPS-linked air suspension that remembers where the speed bumps are and raises the car automatically, and "fart mode" (where the car makes fart sounds). And, fundamentally, its cars had no competition. If you wanted an electric car that could go more than 250 miles between charges, Tesla was your only choice for the better part of a decade. The company's C.E.O., Elon Musk, came across as goofy and eccentric: You could build great cars and name each model such that the lineup spells "SEXY."

Hyundai's Genesis brand is now taking orders for its first electric vehicle, the GV60. The EV, which follows the G80 hybrid, starts at $58,890 and comes with three years of 30-minute charging sessions at Electrify America stations at no extra cost. US sales will be limited at the outset, however. To begin with, the GV60 will only be available for purchase at select retailers in California, Connecticut, New Jersey and New York. The EV will be available in two dual-motor trims, Advanced AWD and Performance AWD.

Of course, the simplest strategy is to use your intuition. Sometimes it's obvious that some columns will not be used in any form in the final model (columns such as "ID", "FirstName", "LastName" etc). If you know that a particular column will not be used, feel free to drop it upfront. In our data, none of the columns stand out as such, so I'm not removing any in this step. Having missing values is not acceptable in machine learning, so people apply different strategies to clean up missing data (e.g., imputation).

Regression-based machine learning models are used to predict the value of a continuous attribute. As with all supervised machine learning problems the model is trained using a set of features (X) to learn the mapping to a target variable (y). In the case of regression, the target is a continuous variable such as the price of a house. Probably the simplest regression algorithm is linear regression. Simple linear regression, where there is only one feature and one target, is represented by the equation shown below.

This document focuses on modeling a complex situations to achieve an advantage within a competitive context. Our goal is to devise the characteristics of games to teach and exercise non-easily quantifiable tasks crucial to the math-modeling process. A computerized game to exercise the math-modeling process and optimization problem formulation is introduced. The game is named The Formula 1 Championship, and models of the game were developed in the computerized simulation platform MoNet. It resembles some situations in which team managers must make crucial decisions to enhance their racing cars up to the feasible, most advantageous conditions. This paper describes the game's rules, limitations, and five Formula 1 circuit simulators used for the championship development. We present several formulations of this situation in the form of optimization problems. Administering the budget to reach the best car adjustment to a set of circuits to win the respective races can be an approach. Focusing on the best distribution of each Grand Prix's budget and then deciding how to use the assigned money to improve the car is also the right approach. In general, there may be a degree of conflict among these approaches because they are different aspects of the same multi-scale optimization problem. Therefore, we evaluate the impact of assigning the highest priority to an element, or another, when formulating the optimization problem. Studying the effectiveness of solving such optimization problems turns out to be an exciting way of evaluating the advantages of focusing on one scale or another. Another thread of this research directs to the meaning of the game in the teaching-learning process. We believe applying the Formula 1 Game is an effective way to discover opportunities in a complex-system situation and formulate them to finally extract and concrete the related benefit to the context described.

Standout titles include Grand Theft Auto III, Metal Gear Solid 2 and Final Fantasy X. It was also the year Xbox made its debut, while the Sega Dreamcast bowed out. But while all that was going on Nintendo was still going strong, releasing the Game Boy Advance in March of that year and a new home system in September. The GameCube was quite a console, an adorable box with a great wireless controller and fun add-ons like the Game Boy Player. Unfortunately, the system was plagued by a thin library, especially compared to the PlayStation's combined roster of PS1 and PS2 games.