What programming language for artificial intelligence is the best? (2022) - Dataconomy


What programming language for artificial intelligence is suitable for you? It is a crucial question for your company's future. Every major tech business and even startups are working on artificial intelligence (AI), which has emerged as one of the hottest issues and largest study disciplines. It's a tremendously broad topic that covers anything from simple calculators and self-driving cars to intelligent robots that could fundamentally alter the course of human history. The core of AI is creating machines that are as intelligent as or more intelligent than humans. Better AI solutions are continuously being sought after by businesses. IDC projects that the market for artificial intelligence will reach $500 billion by 2024, with a five-year CAGR of 17.5% and total revenue of $554.3 billion.

Red Hat Linux is coming to your Vette and Caddy Escalade


Steven J. Vaughan-Nichols, aka sjvn, has been writing about technology and the business of technology since CP/M-80 was the cutting edge, PC operating system; 300bps was a fast Internet connection; WordStar was the state of the art word processor; and we liked it. Linux has long played a role in cars. Some companies, such as Tesla, run their own homebrew Linux distros. Audi, Mercedes-Benz, Hyundai, and Toyota all rely on Automotive Grade Linux (AGL). AGL is a collaborative cross-industry effort developing an open platform for connected cars with over 140 members.

Technology Ethics in Action: Critical and Interdisciplinary Perspectives Artificial Intelligence

This special issue interrogates the meaning and impacts of "tech ethics": the embedding of ethics into digital technology research, development, use, and governance. In response to concerns about the social harms associated with digital technologies, many individuals and institutions have articulated the need for a greater emphasis on ethics in digital technology. Yet as more groups embrace the concept of ethics, critical discourses have emerged questioning whose ethics are being centered, whether "ethics" is the appropriate frame for improving technology, and what it means to develop "ethical" technology in practice. This interdisciplinary issue takes up these questions, interrogating the relationships among ethics, technology, and society in action. This special issue engages with the normative and contested notions of ethics itself, how ethics has been integrated with technology across domains, and potential paths forward to support more just and egalitarian technology. Rather than starting from philosophical theories, the authors in this issue orient their articles around the real-world discourses and impacts of tech ethics--i.e., tech ethics in action.

Tesla to disable 'rolling stop' feature as it may increase risk of collisions


Tesla is issuing a recall of 53,822 vehicles in the U.S. due to an experimental feature that may be dangerous. The recall boils down to the removal of the'rolling stop' feature in the optional Full Self Driving software package, introduced as a beta feature in October 2021. According to the National Highway Traffic Safety Administration (via CNBC), the feature may allow vehicles to travel through an all-way stop intersection without first coming to a stop. The recall covers 2016-2022 Model S and Model X vehicles, 2017-2022 Model 3 vehicles, and 2020-2022 Model Y vehicles. Tesla did not comment on the recall, though the CNBC says the company said it was not aware of any warranty claims, crashes, injuries, or fatalities related to the recall as of Jan. 27.

A Probabilistic Framework for Dynamic Object Recognition in 3D Environment With A Novel Continuous Ground Estimation Method Artificial Intelligence

In this thesis a probabilistic framework is developed and proposed for Dynamic Object Recognition in 3D Environments. A software package is developed using C++ and Python in ROS that performs the detection and tracking task. Furthermore, a novel Gaussian Process Regression (GPR) based method is developed to detect ground points in different urban scenarios of regular, sloped and rough. The ground surface behavior is assumed to only demonstrate local input-dependent smoothness. kernel's length-scales are obtained. Bayesian inference is implemented sing \textit{Maximum a Posteriori} criterion. The log-marginal likelihood function is assumed to be a multi-task objective function, to represent a whole-frame unbiased view of the ground at each frame because adjacent segments may not have similar ground structure in an uneven scene while having shared hyper-parameter values. Simulation results shows the effectiveness of the proposed method in uneven and rough scenes which outperforms similar Gaussian process based ground segmentation methods.

New York Times ad warns against Tesla's "Full Self-Driving" – TechCrunch


A full page advertisement in Sunday's New York Times took aim at Tesla's "Full Self-Driving" software, calling it "the worst software ever sold by a Fortune 500 company" and offering $10,000, the same price as the software itself to the first person who could name "another commercial product from a Fortune 500 company that has a critical malfunction every 8 minutes." The ad was taken out by The Dawn Project, a recently founded organization aiming to ban unsafe software from safety critical systems that can be targeted by military-style hackers, as part of a campaign to remove Tesla Full Self-Driving (FSD) from public roads until it has "1,000 times fewer critical malfunctions." The founder of the advocacy group, Dan O'Dowd, is also the CEO of Green Hill Software, a company that builds operating systems and programming tools for embedded safety and security systems. At CES, the company said BMW's iX vehicle is using its real-time OS and other safety software, and it also announced the availability of its new over-the-air software product and data services for automotive electronic systems. Despite the potential competitive bias of The Dawn Project's founder, Tesla's FSD beta software, an advanced driver assistance system that Tesla owners can access to handle some driving function on city streets, has come under scrutiny in recent months after a series of YouTube videos that showed flaws in the system went viral.

Artificial Intellgence -- Application in Life Sciences and Beyond. The Upper Rhine Artificial Intelligence Symposium UR-AI 2021 Artificial Intelligence

The TriRhenaTech alliance presents the accepted papers of the 'Upper-Rhine Artificial Intelligence Symposium' held on October 27th 2021 in Kaiserslautern, Germany. Topics of the conference are applications of Artificial Intellgence in life sciences, intelligent systems, industry 4.0, mobility and others. The TriRhenaTech alliance is a network of universities in the Upper-Rhine Trinational Metropolitan Region comprising of the German universities of applied sciences in Furtwangen, Kaiserslautern, Karlsruhe, Offenburg and Trier, the Baden-Wuerttemberg Cooperative State University Loerrach, the French university network Alsace Tech (comprised of 14 'grandes \'ecoles' in the fields of engineering, architecture and management) and the University of Applied Sciences and Arts Northwestern Switzerland. The alliance's common goal is to reinforce the transfer of knowledge, research, and technology, as well as the cross-border mobility of students.

Querying Labelled Data with Scenario Programs for Sim-to-Real Validation Artificial Intelligence

Simulation-based testing of autonomous vehicles (AVs) has become an essential complement to road testing to ensure safety. Consequently, substantial research has focused on searching for failure scenarios in simulation. However, a fundamental question remains: are AV failure scenarios identified in simulation meaningful in reality, i.e., are they reproducible on the real system? Due to the sim-to-real gap arising from discrepancies between simulated and real sensor data, a failure scenario identified in simulation can be either a spurious artifact of the synthetic sensor data or an actual failure that persists with real sensor data. An approach to validate simulated failure scenarios is to identify instances of the scenario in a corpus of real data, and check if the failure persists on the real data. To this end, we propose a formal definition of what it means for a labelled data item to match an abstract scenario, encoded as a scenario program using the SCENIC probabilistic programming language. Using this definition, we develop a querying algorithm which, given a scenario program and a labelled dataset, finds the subset of data matching the scenario. Experiments demonstrate that our algorithm is accurate and efficient on a variety of realistic traffic scenarios, and scales to a reasonable number of agents.

Career Growth for Automotive Software Engineer: A Complete Guide for You


Roles and Responsibilities: Many software developers and engineers working in the autonomous vehicle-making sector go through a tough time to find the apt software that works well on the system. Therefore, a disruptive course called Automotive Software Engineer, combining the perspective of autonomous vehicle making and the software used in it has emerged. They control the functions of cars, supports, and assist the driver, and realize systems for information and entertainment. Automotive Software Engineers are responsible for the design and development of software systems using in-car technology. Automobile Engineering: Vehicle Dynamic for Beginners at Udemy: Automobile Engineering course offered by Mufaddal Rasheed at Udemy is an introductory course on the mechanics of vehicle behavior and suspension design concepts.

A Scenario-Based Platform for Testing Autonomous Vehicle Behavior Prediction Models in Simulation Artificial Intelligence

Behavior prediction remains one of the most challenging tasks in the autonomous vehicle (AV) software stack. Forecasting the future trajectories of nearby agents plays a critical role in ensuring road safety, as it equips AVs with the necessary information to plan safe routes of travel. However, these prediction models are data-driven and trained on data collected in real life that may not represent the full range of scenarios an AV can encounter. Hence, it is important that these prediction models are extensively tested in various test scenarios involving interactive behaviors prior to deployment. To support this need, we present a simulation-based testing platform which supports (1) intuitive scenario modeling with a probabilistic programming language called Scenic, (2) specifying a multi-objective evaluation metric with a partial priority ordering, (3) falsification of the provided metric, and (4) parallelization of simulations for scalable testing. As a part of the platform, we provide a library of 25 Scenic programs that model challenging test scenarios involving interactive traffic participant behaviors. We demonstrate the effectiveness and the scalability of our platform by testing a trained behavior prediction model and searching for failure scenarios.