I began reading Barron's when I was student at The Ohio State University in Columbus though as a nine-year-old child, I recall seeing a copy at the local drug store news stand in my small Northern Ohio hometown, Avon Lake. For four decades, I have advocated that reading Barron's is an essential element for business higher education. I practice what I believe. Since 2007, I quote Barron's daily on my blog that now has 11,500 posts. The accumulated insight deep into America and the world on an array of levels is of great personal value to me.

The last year has been tricky for electric vehicle startups. After a burst of investment mania in which companies raised billions on the mere promise of battery propulsion, valuations have come back down to earth. One of the loudest thuds has come from Arrival, the closest to what could be called a British electric vehicle champion. Its market value on the Nasdaq has fallen from $15bn (£11.6bn) in March 2021, when it first completed a merger with a listed cash shell, to about $1.75bn. Almost all its startup rivals have suffered similar plunges, but Arrival is arguably a special case.

Natalie Lao was set on becoming an electrical engineer, like her parents, until she stumbled on course 6.S192 (Making Mobile Apps), taught by Professor Hal Abelson. Here was a blueprint for turning a smartphone into a tool for finding clean drinking water, or sorting pictures of faces, or doing just about anything. "I thought, I wish people knew building tech could be like this," she said on a recent afternoon, taking a break from writing her dissertation. After shifting her focus as an MIT undergraduate to computer science, Lao joined Abelson's lab, which was busy spreading its App Inventor platform and do-it-yourself philosophy to high school students around the world. App Inventor set Lao on her path to making it easy for anyone, from farmers to factory workers, to understand AI, and use it to improve their lives.

With a box of popcorn in one hand, Hal Abelson, a renowned computer scientist, strolled through the first floor of the Ray and Maria Stata Center studying the machine learning exhibits that surrounded him on the afternoon of Feb. 26. Everywhere he looked he saw evidence of the remarkable things MIT students can do when given access to computing resources. "Computing tools and infrastructure have gotten to a place where students can outperform professional researchers. You are constrained mostly by your imagination. It's just an amazing time," said Abelson, the Class of 1922 Professor of Computer Science and Engineering.

Hal Abelson, an MIT computer scientist, talks to senior policymakers from countries in the Organization for Economic Cooperation and Development. Hal Abelson, an MIT computer scientist, talks to senior policymakers from countries in the Organization for Economic Cooperation and Development. Hal Abelson, an MIT computer scientist, talks to senior policymakers from countries in the Organization for Economic Cooperation and Development. Hal Abelson, an MIT computer scientist, talks to senior policymakers from countries in the Organization for Economic Cooperation and Development. The subject was artificial intelligence, and his students last week were mainly senior policymakers from countries in the 36-nation Organization for Economic Cooperation and Development.

Hal Abelson, a renowned computer scientist at the Massachusetts Institute of Technology, was working the classroom, coffee cup in hand, pacing back and forth. The subject was artificial intelligence, and his students last week were mainly senior policymakers from countries in the 36-nation Organization for Economic Cooperation and Development. Mr. Abelson began with a brisk history of machine learning, starting in the 1950s. Next came a description of how the technology works, a hands-on project using computer-vision models and then case studies. The goal was to give the policymakers from countries like France, Japan and Sweden a sense of the technology's strengths and weaknesses, emphasizing the crucial role of human choices.

General Motors, which plans a ride-sharing fleet of robotaxis for next year, is looking at further business opportunities for self-driving cars, such as custom designs that could be almost unrecognizable from those of today, a top executive said on Thursday. Custom-designed vehicles could be owned or leased by individual customers, including those outside of large cities, and used in peer-to-peer car sharing applications, said Mike Abelson, GM's vice president of global strategy. GM is'thinking about several models' to drive revenue from self-driving cars, according to Abelson, speaking at Citi's Car of the Future conference in New York. Abelson, one of the key architects of GM's future transportation business, said self-driving cars used for ride sharing'are going to evolve quickly into purpose-built vehicles' that do not look like conventional cars. While GM initially is using specially modified versions of the Chevrolet Bolt EV for its robotaxi fleet in 2019, the U.S. automaker's next wave of driverless vehicles will do away with the steering wheel and pedals, and could feature different seating arrangements.

Automakers Toyota and GM and ride-sharing firm Lyft called on the US government to create US-wide standards to ease the testing and adoption of autonomous cars. The problem, they say, is that rules vary widely across the country are more restrictive in some states (like California) than others (Michigan). "Self-driving cars won't drive while impaired by drugs or alcohol [and] they won't be distracted by a cell phone," GM VP Michael Abelson told a subcommittee on Tuesday. "We have the further opportunity to avoid crashes altogether." It's not as though the US Department of Transport (DoT) is doing nothing, as it released a policy document last September.

A longstanding network of bogus "think tanks" raise disinformation to a pseudoscience, and their studies' pull quotes and flashy stats become the "evidence" driving viral, fact-free stories. Not to mention President Trump's tweets. These organizations have always existed: they're old-school propagandists with new-school, tech-savvy reach. They've been ginning up so-called research for everyone from shady corporations to anti-LGBTQ groups to white supremacists for decades--they're practiced, and their faux-academic veneer is thick and glossy. Which makes them harder to brush off than your garden-variety liar.

Machine learning algorithms are worked into a variety of popular products on the market today and used by the biggest technology companies, such as Microsoft, Amazon, Google, Oracle, and IBM, to name a few. But while machine learning algorithms are fairly routine and practical, there is the primary subject from which it branches: artificial intelligence (AI). After decades of popular films and books, what has been achieved from AI? First, the benefits of AI research have yielded various applications, from Apple's Siri to IBM's prototype diagnostic app, Watson. Apps are useful to anyone with a smart phone, but at this point consumers and programmers alike have grown accustomed to them. There are those who would like to push the envelope beyond what we have yet seen.