Donald Trump signed four executive orders on Tuesday aimed at reviving coal, the dirtiest fossil fuel that has long been in decline, and which substantially contributes to planet-heating greenhouse gas emissions and pollution. Environmentalists expressed dismay at the news, saying that Trump was stuck in the past and wanted to make utility customers "pay more for yesterday's energy". The US president is using emergency authority to allow some older coal-fired power plants scheduled for retirement to keep producing electricity. The move, announced at a White House event on Tuesday afternoon, was described by White House officials as being in response to increased US power demand from growth in datacenters, artificial intelligence and electric cars. Trump, standing in front of a group of miners in hard hats, said he would sign an executive order "that slashes unnecessary regulations that targeted the beautiful, clean coal".

At least 10 people, including a child, have been killed and 45 injured following a Ukrainian attack on the centre of the Russian provincial capital of Belgorod, the Russian Emergencies Ministry has said. Governor Vyacheslav Gladkov said on Saturday that the attack on Belgorod, about 30km (19 miles) from the border with Ukraine, had hit a residential area. In a Telegram post, he urged all residents to move to air raid shelters as sirens sounded. Belgorod borders Ukraine's Luhansk, Sumy and Kharkiv regions, some of which were hit by Russian air raids on Ukraine on Friday, in what was one of the deadliest attacks since the war began in February 2022. The death toll has risen to 39 from those attacks.

Together, the consumerization of AI and advancement of AI use-cases for security are creating the level of trust and efficacy needed for AI to start making a real-world impact in security operation centers (SOCs). Digging further into this evolution, let's take a closer look at how AI-driven technologies are making their way into the hands of cybersecurity analysts today. After years of trial and refinement with real-world users, coupled with ongoing advancement of the AI models themselves, AI-driven cybersecurity capabilities are no longer just buzzwords for early adopters, or simple pattern- and rule-based capabilities. Data has exploded, as have signals and meaningful insights. The algorithms have matured and can better contextualize all the information they're ingesting--from diverse use cases to unbiased, raw data.

Click here to join the seminar. Research on machine learning (ML) algorithms, as well as on their ethical impacts, has focused largely on mathematical or computational questions. However, for algorithmic systems to be useful, reliable, and safe for human users, ML research must also wrangle with how users' psychology and social context affect how they interact with algorithms. This talk will address how novel research on how people interact with ML systems can benefit from decades-old ideas in social science. The first part of the talk will address how well-worn ideas from psychology and behavioral research methods can inform how ML researchers develop and evaluate algorithmic systems.

Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. Time series data is one of the most common data types in the industry and you will probably be working with it in your career. Therefore understanding how to work with it and how to apply analytical and forecasting techniques are critical for every aspiring data scientist. In this series of articles, I will go through the basic techniques to work with time-series data, starting from data manipulation, analysis, visualization to understand your data and prepare it for and then using statistical, machine, and deep learning techniques for forecasting and classification. It will be more of a practical guide in which I will be applying each discussed and explained concept to real data.

Over the past year, the sheer number of Ransomware attacks have increased dramatically, with organizations of all stripes being affected: government entities, educational institutions, healthcare facilities, retailers, and even agricultural groups. While the bulk of the media attention has been on critical infrastructure and large organizations, attackers are not limiting themselves to just those types of victims. "That's really just the tip of the iceberg," says Max Heinemeyer, director of threat hunting at Darktrace. "We see not just big names being hit. It's basically any company where adversaries think they can pay the ransom. Anybody who's got money and running some kind of digital business is basically in the crosshairs."

Over the past year, the sheer number of ransomware attacks have increased dramatically, with organizations of all stripes being affected: government entities, educational institutions, healthcare facilities, retailers, and even agricultural groups. While the bulk of the media attention has been on critical infrastructure and large organizations, attackers are not limiting themselves to just those types of victims. "That's really just the tip of the iceberg," says Max Heinemeyer, director of threat hunting at Darktrace. "We see not just big names being hit. It's basically any company where adversaries think they can pay the ransom. Anybody who's got money and running some kind of digital business is basically in the crosshairs."

Register Debate Welcome to the inaugural Register Debate in which we pitch our writers against each other on contentious topics in IT and enterprise tech, and you – the reader – decide the winning side. The format is simple: a motion is proposed, for and against arguments are published today, then another round of arguments on Wednesday, and we publish a concluding piece on Friday summarizing the brouhaha and the best reader comments. During the week you can cast your vote using the embedded poll below, choosing whether you're in favor or against the motion. The final score will be announced on Friday, revealing whether the for or against argument was most popular. It's up to our writers to convince you to vote for their side.

While global cybersecurity spending now surpasses $100 billion annually, 64 percent of enterprises were compromised in 2018, according to a study by the Ponemon Institute. The standard answer is that wily cyber-criminals are employing ever-evolving, increasingly sophisticated attack methods, part of a never-ending game of cat-and-mouse in which they all too often outsmart the good guys. This is undoubtedly true – but the root of the problem is that traditional machine learning-based cybersecurity solutions fail to keep up with the growing sophistication of today's cyber threats, both those that are created by hackers and AI alike. Why does machine learning so often come up short – and how should cybersecurity evolve to meet the scale and complexity of the challenge? There's no question that machine learning has driven significant improvements in cybersecurity.

AI brings mixed emotions and opinions when referenced in the context of jobs. If you ask the question "Do you think Artificial Intelligence will be a net job killer or net job creator?" to colleagues, friends, or strangers you're bound to get some very strong opinions on this subject. For sure you will hear an interesting and conflicting set of opinions that range from "AI will destroy all jobs as we know it" to "AI will enable us to work better and do new things we've never been able to do". If you look at various economic and analyst predictions, their assessments are all over the place, ranging from dramatic job losses across most economic sectors to large increases in employment due to dramatic increases in job productivity. Of course, as with everything, the true answer will be somewhere in the middle.