It's a rare thing to shoot yourself in the foot and win a marathon. For years, Elon Musk has managed to do something like that with Tesla, achieving monumental success in spite of a series of self-inflicted disasters. There was the time he heavily promoted the company's automated factory, only to later admit that its "crazy, complex network of conveyor belts" had thrown production of the Model 3 off track; and the time a tweet led him to be sued for fraud by the Securities and Exchange Commission; and the time he said that the Tesla team had "dug our own grave" with the massively delayed and overhyped Cybertruck. Tesla is nonetheless the most valuable car company in the world by a wide margin. Yesterday evening, Tesla reported first-quarter earnings for 2025, and they were abysmal: Profits dropped 71 percent from the same time last year.

In the early 1970s, programming computers involved punching holes in cards and feeding them to room-size machines that would produce results through a line printer, often hours or even days later. This is what computing had looked like for a long time, and it was against this backdrop that a team of 29 scientists and researchers at the famed Xerox PARC created the more intimate form of computing we know today: one with a display, a keyboard, and a mouse. This computer, called Alto, was so bewilderingly different that it necessitated a new term: interactive computing. Alto was viewed by some as absurdly extravagant because of its expensive components. But fast-forward 50 years, and multitrillion-dollar supply chains have sprung up to transform silica-rich sands into sophisticated, wondrous computers that live in our pockets.

Emerging scholarship suggests that the EU legal concept of direct discrimination - where a person is given different treatment on grounds of a protected characteristic - may apply to various algorithmic decision-making contexts. This has important implications: unlike indirect discrimination, there is generally no 'objective justification' stage in the direct discrimination framework, which means that the deployment of directly discriminatory algorithms will usually be unlawful per se. In this paper, we focus on the most likely candidate for direct discrimination in the algorithmic context, termed inherent direct discrimination, where a proxy is inextricably linked to a protected characteristic. We draw on computer science literature to suggest that, in the algorithmic context, 'treatment on the grounds of' needs to be understood in terms of two steps: proxy capacity and proxy use. Only where both elements can be made out can direct discrimination be said to be `on grounds of' a protected characteristic. We analyse the legal conditions of our proposed proxy capacity and proxy use tests. Based on this analysis, we discuss technical approaches and metrics that could be developed or applied to identify inherent direct discrimination in algorithmic decision-making.

Human life is inextricably linked to technology. Humans use machines to reduce the amount of work they have to do. Let's look at how it works in the Healthcare field. ARTIFICIAL INTELLIGENCE IS USED IN ALL ASPECTS OF LIFE. EHR -- In some situations, people may be diagnosed with numerous illnesses.

An epic number of citizens are video-conferencing to work in these lockdown times. But as they trade in a gas-burning commute for digital connectivity, their personal energy use for each two hours of video is greater than the share of fuel they would have consumed on a four-mile train ride. Add to this, millions of students'driving' to class on the internet instead of walking. Meanwhile in other corners of the digital universe, scientists furiously deploy algorithms to accelerate research. Yet, the pattern-learning phase for a single artificial intelligence application can consume more compute energy than 10,000 cars do in a day.