Chinese robotaxis could be set to hit UK roads in 2026 as ride-sharing apps Uber and Lyft announce partnerships with Baidu to trial the tech. The two companies are hoping to obtain approval from regulators to test the autonomous vehicles in London. Baidu's Apollo Go driverless taxi service already operates in dozens of cities, mostly in China, and has accrued millions of rides without a human behind the wheel. Transport secretary Heidi Alexander said the news was another vote of confidence in our plans for self-driving vehicles - but many remain sceptical about their safety. We're planning for self-driving cars to carry passengers for the first time from spring, under our pilot scheme - harnessing this technology safely and responsibly to transform travel, Ms Alexander said in a post on X .

Tesla is being investigated by the US government after reports the firm's self-driving cars had broken traffic laws, including driving on the wrong side of the road and not stopping for red lights. It said it was aware of 58 reports where the electric cars had committed such violations, according to a filing from the National Highway Traffic Safety Administration (NHTSA). An estimated 2.9 million cars equipped with full self-driving tech will fall under the investigation. Tesla, whose boss Elon Musk recently became the world's first half-trillionaire, has been approached for comment. The NHTSA's preliminary evaluation will assess the scope, frequency, and potential safety consequences of the Full Self-Driving (Supervised) mode.

The world's addiction to fossil fuels is a "Frankenstein's monster sparing nothing and no one", the UN secretary general, António Guterres, told leaders at the World Economic Forum in Davos on Wednesday. "Our fossil fuel addiction is a Frankenstein's monster, sparing nothing and no one. All around us, we see clear signs that the monster has become master," Guterres said in a speech days after 2024 was revealed to have been the hottest year on record and Donald Trump began his second term as US president by pulling the country out of the Paris climate agreement and pledging to "drill, baby, drill" for more oil and gas. The fossil fuel industry gave 75m ( 60m) to Trump's campaign. Guterres said: "What we are seeing today – sea-level rise, heatwaves, floods, storms, droughts and wildfires – are just a preview of the horror movie to come."

In a binary classification problem, the hyperplane is a line that divides the data points into two classes. The distance between the hyperplane and the closest data points from each class is known as the margin. In a hard margin SVM, the goal is to find the hyperplane that can perfectly separate the data into two classes without any misclassification. However, this is not always possible when the data is not linearly separable or contains outliers. In such cases, the hard margin SVM will fail to find a hyperplane that can perfectly separate the data, and the optimization problem will have no solution.

Support Vector Machine (SVM) is an approach for classification which uses the concept of separating hyperplane. It was developed in the 1990s. It is a generalization of an intuitive and simple classifier called maximal margin classifier. In order to study Support Vector Machine (SVM), we first need to understand what is maximal margin classifier and support vector classifier. In maximal margin classifier, we use a hyperplane to separate the classes.

All you need to fool Tesla's autopilot into changing lane is a handful of stickers. Tesla's autopilot uses cameras to detect lane markings, so that it can position itself in the middle of the road and automatically change lanes when required. A team at Keen Security Labs, run by Chinese technology giant Tencent, managed to confuse the system onboard a Tesla Model S with just three stickers placed on the road. The car's autopilot system incorrectly classified the stickers, which were placed over road markings to make a jagged, rather than straight-edged. This caused the Tesla to move onto the wrong side of the road.

Imitation learning provides an appealing framework for autonomous control: in many tasks, demonstrations of preferred behavior can be readily obtained from human experts, removing the need for costly and potentially dangerous online data collection in the real world. However, policies learned with imitation learning have limited flexibility to accommodate varied goals at test time. Model-based reinforcement learning (MBRL) offers considerably more flexibility, since a predictive model learned from data can be used to achieve various goals at test time. However, MBRL suffers from two shortcomings. First, the predictive model does not help to choose desired or safe outcomes -- it reasons only about what is possible, not what is preferred. Second, MBRL typically requires additional online data collection to ensure that the model is accurate in those situations that are actually encountered when attempting to achieve test time goals. Collecting this data with a partially trained model can be dangerous and time-consuming. In this paper, we aim to combine the benefits of imitation learning and MBRL, and propose imitative models: probabilistic predictive models able to plan expert-like trajectories to achieve arbitrary goals. We find this method substantially outperforms both direct imitation and MBRL in a simulated autonomous driving task, and can be learned efficiently from a fixed set of expert demonstrations without additional online data collection. We also show our model can flexibly incorporate user-supplied costs as test-time, can plan to sequences of goals, and can even perform well with imprecise goals, including goals on the wrong side of the road.

Hey Everyone!!:-) I've got Ian William in my APC spotlight, today! Ian is an active and supportive member of the Authors-Professional Co-op Facebook group and his cyberpunk, sci-fi, suspense, action, thriller, novel, The Sentient Collector, looks great! Here's the description: In The Sentient Collector, our relationship with the first real AI is pushed to breaking point. The Isaac AI, when threatened with deactivation, does what any living being would do, it defends itself. But that is just the beginning.

Broiler farmers have used data as an aid to health and production management for over 40 years [1,2]. Food and water consumption, growth and mortality have been used to construct standard production curves to monitor and improve performance. Daily flock data are plotted graphically on broiler house'door charts' and deviations used as early indicators of flock health and welfare [3]. Increasingly, these and other sensor-recorded data are being collected electronically, giving birth to the concept of precision livestock farming [4]. Broiler flocks generate large datasets.

Of all the prejudices of pundits, presentism is the strongest. It is the assumption that what is happening now is going to keep on happening, without anything happening to stop it. If the West has broken down the Berlin Wall and McDonald's opens in St. Petersburg, then history is over and Thomas Friedman is content. If, by a margin so small that in a voice vote you would have no idea who won, Brexit happens; or if, by a trick of an antique electoral system designed to give country people more power than city people, a Donald Trump is elected, then pluralist constitutional democracy is finished. The liberal millennium was upon us as the year 2000 dawned; fifteen years later, the autocratic apocalypse is at hand. You would think that people who think for a living would pause and reflect that whatever is happening usually does stop happening, and something else happens in its place; a baby who is crying now will stop crying sooner or later. Exhaustion, or a change of mood, or a passing sound, or a bright light, something, always happens next. But for the parents the wait can feel the same as forever, and for many pundits, too, now is the only time worth knowing, for now is when the baby is crying and now is when they're selling your books.