MELBOURNE, July 26 (Reuters Breakingviews) - General Motors' (GM.N) latest journey in autonomous driving has taken it to the courthouse. Late on Friday, the $80 billion carmaker filed a suit accusing Ford Motor (F.N) of trademark infringement after the latter renamed its new driver-assist technology BlueCruise. The problem? GM has a similar system, in operation since 2017, dubbed Super Cruise, while its $30 billion self-driving division read more is called GM Cruise. The word has, of course, formed part of a generic industry term synonymous for decades with the beginnings of autonomous driving - "cruise control". Granted, Super Cruise, and Ford's new features, go beyond just speed control, allowing cars to maintain and change lanes and speed.

Increasingly, this message is finding a platform and it's beginning to shape AI's development meaningfully. The latest proposed regulations from the European Union, for example, take significant steps in the right direction by defining high-risk use cases, for example. The data science community wants to build models that align to societal values and improve outcomes. This well thought-out proposal from the EU will enable innovation and industry growth by standardising expectations among practitioners.

A man taking a selfie is silhouetted against the overcast sky along the Chicago skyline Wednesday, July 21, 2021, in Chicago. Clearview AI, the controversial facial recognition company that scrapes public images from social media to aid law enforcement probes, has subpoenaed internal documents from some of the groups that first exposed its activities. The firm served subpoenas in August to civil society coalition Open The Government, its policy analyst Freddy Martinez and the police accountability nonprofit that he'd previously founded, Lucy Parsons Labs -- demanding any correspondence they'd had with journalists about Clearview and its leaders, as well as information they'd uncovered about the company and its founders in public records requests, over the last four years. The subpoenas, obtained by POLITICO, could draw the groups into lengthy court battles and, they argue, dissuade others from taking on Clearview or other companies working on potentially problematic technologies.

AI or artificial intelligence could predict startup success to an impressive 90% accuracy, a study using machine learning models that look into tons of companies showed. As per Embroker, startups turn out to be a complete failure in most cases. To be precise, about 90% of them do not become successful. What's more, about 10% of startups end up being a failure every year, regardless of what industry it is in--whether it is from tech or retail. Not to mention that failure began at roughly the second to the fifth year of the firm. However, CBInsights learned in its recent data that 42% of the unsuccessful startups are due to misreading the market demand.

The European Union is getting back to work after the summer break, and one of the key files on everyone's mind is the EU Artificial Intelligence Act (AIA). Over the summer, the European Commission held a consultation on the AIA that received 304 responses, with everyone from the usual Big Tech players down to the Council of European Dentists having their say. Access Now submitted a response to the consultation in August that outlined a number of key issues that need to be addressed in the next stages of the legislative process. If you want to regulate something, you need to define it properly; if not, you're creating problematic loopholes. Unfortunately, the definitions of emotion recognition (Article 3(34)) and biometric categorisation (Article 3(35)) in the current draft of the EU Artificial Intelligence Act are technically flawed.

NEW YORK, September 23, 2021--(BUSINESS WIRE)--Helbiz Inc. (NASDAQ: HLBZ), a global leader in micro-mobility and the first in its industry to be publicly listed on Nasdaq, today announced a partnership with Drover AI to integrate its PathPilot safety technology onto Helbiz e-scooters. Helbiz will be the exclusive operator of PathPilot in Italy, with an initial deployment in Milan by end of the year. The company plans to expand the integration across other markets as the partnership grows. This press release features multimedia. The PathPilot technology is powered by artificial intelligence and computer vision, using onboard cameras to locate the surroundings of e-scooters.

At the ML Fairness Summit, we welcomed Fiddler Data Scientist, Léa Genuit to discuss intersectional group fairness. As more companies adopt AI, more people question the impact AI creates on society, especially on algorithmic fairness. Instead, they hold a binary view of fairness, e.g., protected vs. unprotected groups. In the below blog, Lea covers the latest research in research on intersectional group fairness. Before explaining why, the first question should be how do you detect and mitigate bias in European models to avoid a bad experience?

It has come to a point where artificial intelligence is also being used to enhance creativity. You give a phrase or two written by a human to a language model based on an AI and it can add on more phrases that sound uncannily human-like. They can be great collaborators for anyone trying to write a novel or a poem. Newsletter Click to get the day's best explainers in your inbox However, things aren't as simple as it seems. And the complexity rises owing to biases that come with artificial intelligence.

Artificial intelligence (AI) cannot be the inventor of new patents, the UK Court of Appeal has ruled. Patents assign the ownership of a new invention to its creator. At its core, the argument is about whether a law written for human inventors can be applied to machines. The appeal court ruled against Stephen Thaler, creator of a system called Dabus, who took a case against the UK's Intellectual Property Office (IPO) which refused patents to his AI. It is the latest such judgement in a long-running battle to grant machines the status of inventor.

The rampant adoption of ML methodologies has revealed that models are usually adopted to make decisions without taking into account the uncertainties in their predictions. More critically, they can be vulnerable to adversarial examples. Thus, we believe that developing ML systems that take into account predictive uncertainties and are robust against adversarial examples is a must for critical, real-world tasks. We start with a case study in retailing. We propose a robust implementation of the Nerlove-Arrow model using a Bayesian structural time series model. Its Bayesian nature facilitates incorporating prior information reflecting the manager's views, which can be updated with relevant data. However, this case adopted classical Bayesian techniques, such as the Gibbs sampler. Nowadays, the ML landscape is pervaded with neural networks and this chapter also surveys current developments in this sub-field. Then, we tackle the problem of scaling Bayesian inference to complex models and large data regimes. In the first part, we propose a unifying view of two different Bayesian inference algorithms, Stochastic Gradient Markov Chain Monte Carlo (SG-MCMC) and Stein Variational Gradient Descent (SVGD), leading to improved and efficient novel sampling schemes. In the second part, we develop a framework to boost the efficiency of Bayesian inference in probabilistic models by embedding a Markov chain sampler within a variational posterior approximation. After that, we present an alternative perspective on adversarial classification based on adversarial risk analysis, and leveraging the scalable Bayesian approaches from chapter 2. In chapter 4 we turn to reinforcement learning, introducing Threatened Markov Decision Processes, showing the benefits of accounting for adversaries in RL while the agent learns.