Technology, artificial intelligence and automation are supposed to solve our biggest problems, not create new ones or exacerbate existing issues. Unbeknownst to many, big tech is actually putting a huge burden on the environment. In a study assessing the energy consumption required to train several common large AI models, Researchers at the University of Massachusetts Amherst said artificial intelligence emissions can be over 626,000 pounds of carbon dioxide, which is about 5 times as much the lifetime emissions of an average car. According to research firm IDC, spending on AI systems is exploding, with the figure expected to hit nearly $98 billion in 2023, more than 3.5 times the $37.5 billion being spent this year. The U.S. is expected to deliver more than half of that spending through the forecast, which will be led by the retail and banking industries, according to IDC.

The artificial intelligence industry has skyrocketed in recent years, powering technologies behind smart speakers and self-driving cars, but that growth is coming at a cost. Researchers at the University of Massachusetts Amherst recently conducted a study assessing the energy consumption required to train several common large AI models. The study revealed that the training process can emit over 626,000 pounds of carbon dioxide, nearly 5x the lifetime emissions of an average car, or the equivalent of about 300 round-trip flights between New York and San Francisco. The benefits from the advancements in AI and other emerging technologies at the expense of the environment are simply not worth it, say many industry experts who are urging big tech companies to ramp up their sustainability efforts. Failing to do so could leave the companies' reputations at risk, they said.

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As with most iterations or applications of artificial intelligence, machine learning can either catapult the imagination to utopian climbs or conjure deeply dystopian anxieties. And while neither the stuff of Star Trek nor Terminator will come to pass anytime soon, machine learning will likely be to the post-industrial era what automation was once for the industrial, announcing both promise and perils across nearly every sector of business and society. Are business schools-- or more importantly, their graduates-- prepared for the changes ahead? Many schools are already integrating a deeper technical understanding of machine learning into their MBA curricula. In a world where computers perform more and more of the cognitive labors once reserved for humans, it makes sense that MBAs would tack closer to the role of engineers.

One of the world's biggest networks of hijacked computers has been dismantled after a four-year investigation, the EU law enforcement agency Europol says. The Avalanche network was used to target online bank customers with phishing and spam emails, it adds. More than a million emails were sent per week with malicious files or links. When users opened them, their infected computers became part of the network. Five people have been arrested, but Europol has not said where.

There exist two general forms of exact algorithms for updating probabilities in Bayesian Networks. The first approach involves using a structure, usually a clique tree, and performing local message based calculation to extract the belief in each variable. The second general class of algorithm involves the use of non-serial dynamic programming techniques to extract the belief in some desired group of variables. In this paper we present a hybrid algorithm based on the latter approach yet possessing the ability to retrieve the belief in all single variables. The technique is advantageous in that it saves a NP-hard computation step over using one algorithm of each type. Furthermore, this technique re-enforces a conjecture of Jensen and Jensen [JJ94] in that it still requires a single NP-hard step to set up the structure on which inference is performed, as we show by confirming Li and D'Ambrosio's [LD94] conjectured NP-hardness of OFP.