In the AI arms race, all the major players say they want to go nuclear. Over the past year, the likes of Meta, Amazon, Microsoft, and Google have sent out a flurry of announcements related to nuclear energy. Some are about agreements to purchase power from existing plants, while others are about investments looking to boost unproven advanced technologies. These somewhat unlikely partnerships could be a win for both the nuclear power industry and large tech companies. Tech giants need guaranteed sources of energy, and many are looking for low-emissions ones to hit their climate goals.

For months, my colleague Will Douglas Heaven has been on a quest to go deeper to understand why everybody seems to disagree on exactly what AI is, why nobody even knows, and why you're right to care about it. He's been talking to some of the biggest thinkers in the field, asking them, simply: What is AI? It's a great piece that looks at the past and present of AI to see where it is going next. You can read it here. Here's a taste of what to expect: Artificial intelligence almost wasn't called "artificial intelligence" at all. The computer scientist John McCarthy is credited with coming up with the term in 1955 when writing a funding application for a summer research program at Dartmouth College in New Hampshire.

Web search is such a routine part of daily life that it's easy to forget how marvelous it is. Type into a little text box and a complex array of technologies--vast data centers, ravenous web crawlers, and stacks of algorithms that poke and parse a query--spring into action to serve you a simple set of relevant results. The age of generative AI threatens to sprinkle epistemological sand into the gears of web search by fooling algorithms designed for a time when the web was mostly written by humans. Take what I learned this week about Claude Shannon, the brilliant mathematician and engineer known especially for his work on information theory in the 1940s. Microsoft's Bing search engine informed me that he had also foreseen the appearance of search algorithms, describing a 1948 research paper by Shannon called "A Short History of Searching" as "a seminal work in the field of computer science outlining the history of search algorithms and their evolution over time."

The weird and wonderful art created when AI and humans unite BBC Will AI kill art? Not likely, says the artist Alexander Reben, who has been working with AI for years. "I knew I had hit upon the right recipe when I got the following output by GPT-3 (which made me laugh a little too hard alone in my studio in lockdown):" "The sculpture contains a plunger, a toilet plunger, a plunger, a plunger, a plunger, and a plunger, each of which has been modified. The first plunger is simply a normal plunger, but the rest represent a series of plungers with more and more of the handle removed until just the rubber cup is left. The title of the artwork is "A Short History of Plungers and Other Things That Go Plunge in the Night" by the artists known as "The Plungers" (whose identity remains unknown). "The Plungers", were a collective of anonymous artists, founded in 1972. They were dedicated to the "conceptualization and promotion of a new art form called Plungism." Plungism was a creative interpretation of the idea of Plungerism, which was defined by The Plungers as "a state of mind wherein the mind of an artist is in a state of flux and able to be influenced by all things, even plungers." The Plungers' works were displayed in New York galleries and included such titles as "Plunger's Progress," "The Plungers," "The Plungers Strike Back," and "Big Plunger 4: The Final Plunger," all of which featured plungers, and "Plungers on Parade," which showed images of plungers in public spaces. The Plungers disappeared and left no trace of their identity."

The story of how data scientists became sexy is mostly the story of the coupling of the mature discipline of statistics with a very young one--computer science. The term "Data Science" has emerged only recently to specifically designate a new profession that is expected to make sense of the vast stores of big data. But making sense of data has a long history and has been discussed by scientists, statisticians, librarians, computer scientists and others for years. The following timeline traces the evolution of the term "Data Science" and its use, attempts to define it, and related terms. But as I have watched mathematical statistics evolve, I have had cause to wonder and doubt… I have come to feel that my central interest is in data analysis… Data analysis, and the parts of statistics which adhere to it, must…take on the characteristics of science rather than those of mathematics… data analysis is intrinsically an empirical science… How vital and how important… is the rise of the stored-program electronic computer? In many instances the answer may surprise many by being'important but not vital,' although in others there is no doubt but what the computer has been'vital.'"

Inspired by A New History of Modern Computing by Thomas Haigh and Paul E. Ceruzzi. But the selection of key events in the journey from ENIAC to Tesla, from Data Processing to Big Data, is mine. This was the first computer made by Apple Computers Inc, which became one of the fastest growing ... [ ] companies in history, launching a number of innovative and influential computer hardware and software products. Most home computer users in the 1970s were hobbyists who designed and assembled their own machines. The Apple I, devised in a bedroom by Steve Wozniak, Steven Jobs and Ron Wayne, was a basic circuit board to which enthusiasts would add display units and keyboards. April 1945 John von Neumann's "First Draft of a Report on the EDVAC," often called the founding document of modern computing, defines "the stored program concept." July 1945 Vannevar Bush publishes "As We May Think," in which he envisions the "Memex," a memory extension device serving as a large personal repository of information that could be instantly retrieved through associative links.

Machine learning has done magic, such as beating human chess champions. But in finance, expectations for the technology may need to come down a notch or two, according to quantitative firm AQR. In a report published Monday, AQR argues that the benefits of machine learning will likely apply to problems involving optimizing portfolio construction, such as risk management, transaction cost analysis, and factor construction -- at least at first. That's because markets are different from other areas where machine learning has come to offer up breakthrough research, according to "Can Machines Learn Finance?" Machine learning changes the way problems are solved. Traditional computer programmers define all of the rules or parameters of a game.

When thinking back to the era of ancient civilisations, it's unlikely you'd consider insurance and artificial intelligence staples of the time. Rather, they fit much better into the modern day, where technological innovation goes hand-in-hand with better business practices. Yet, the idea of giving artificial beings a form of mind goes back to antiquity, seen in folklore, myths and stories. As Pamela McCorduck, a writer and novelist on artificial intelligence, wrote, AI stemmed from "an ancient wish to forge the gods" – making it just a bit older than Google, really. Civilisations have long-held beliefs and folklore surrounding bringing inanimate objects to life in Greek, Chinese and Jewish folklore, from Pygmalion's Galatea, an ivory sculpture brought to life to be his wife, to rabbinic golems and a lifelike robot performing to King Mu of Zhou.

The computer can't tell you the emotional story. It can give you the exact mathematical design, but what's missing is the eyebrows. Analytics has never been sexier in the world of business. Big data, artificial intelligence (AI) and machine learning are all terms that fill executives with excitement at their potential, or with dread at falling behind. Yet as recently as three years ago, an online job search would have returned very few AI-titled jobs.

Artificial intelligence is not fiction. It is already our reality and has been for a very long time. Its origins date back to the 1950s. How has it changed since then? Are computers able to teach themselves? What does a smartphone have to do with the landing on the moon, and could machines be smarter than humans?