Thomas Haigh (thomas.haigh@gmail.com) is a professor of history at the University of Wisconsin--Milwaukee, WI, USA, and a Comenius visiting professor at Siegen University, Germany.

Research and Markets estimated that annual global sales of information technology reached nearly $8.4 trillion in 2021. At that level, IT sales made up just less than 9% total estimated global annual gross domestic product (GDP). Global IT sales tend to grow about 6.6 percent annually. For the sake of argument, let's assume that the annual IT sales growth averages 6.6 percent from 2022 through 2030. This assumption includes global GDP growth for the period averaging just over 3.0 percent annually.

Although artificial intelligence (AI) has made great strides in recent years, it still struggles to provide useful guidance about unstructured events in the physical or social world. In short, computer programs lack common sense. "Think of it as the tens of millions of rules of thumb about how the world works that are almost never explicitly communicated," said Doug Lenat of Cycorp, in Austin, TX. Beyond these implicit rules, though, commonsense systems need to make proper deductions from them and from other, explicit statements, he said. "If you are unable to do logical reasoning, then you don't have common sense."

Doug Lenat has worked in diverse parts of AI – natural language understanding and generation, automatic program synthesis, expert systems, machine learning, etc. – for going on 40 years now, just long enough to dare to write this article. His 1976 Stanford PhD thesis, AM, demonstrated that creative discoveries in mathematics could be produced by a computer program (a theorem proposer, rather than a theorem prover) guided by a corpus of hundreds of heuristic rules for deciding which experiments to perform and judging "interestingness" of their outcomes. That work earned him the IJCAI Computers and Thought Award, and sparked a renaissance in machine learning research. Dr. Lenat was on the CS faculty at CMU and Stanford, was one of the founders of Teknowledge, and was in the first batch of AAAI Fellows. He worked with Bill Gates and Nathan Myhrvold to launch Microsoft Research Labs, and to this day he remains the only person to have served on the technical advisory boards of both Apple and Microsoft.

Over July 4th weekend in 1981, several hundred game nerds gathered at a banquet hall in San Mateo, California. Personal computing was still in its infancy, and the tournament was decidedly low-tech. Each match played out on a rectangular table filled with paper game pieces, and a March Madness-style tournament bracket hung on the wall. The game was called Traveller Trillion Credit Squadron, a role-playing pastime of baroque complexity. Contestants did battle using vast fleets of imaginary warships, each player guided by an equally imaginary trillion-dollar budget and a set of rules that spanned several printed volumes. If they won, they advanced to the next round of war games--until only one fleet remained. Doug Lenat, then a 29-year-old computer science professor at nearby Stanford University, was among the players. But he didn't compete alone. He entered the tournament alongside Eurisko, the artificially intelligent system he built as part of his academic research. Eurisko ran on dozens of machines inside Xerox PARC--the computer research lab just down the road from Stanford that gave rise to the graphical user interface, the laser printer, and so many other technologies that would come to define the future of computing. That year, Lenat taught Eurisko to play Traveller. Doug Lenat says his common-sense engine is a new dawn for AI. The rest of the tech world doesn't really agree with him. Lenat fed the massive Traveller rulebook into the system and asked it to find the best way of winning.

Goldbach conjecture is one of the most famous open mathematical problems. It states that every even number, bigger than two, can be presented as a sum of 2 prime numbers. In this work we present a deep learning based model that predicts the number of Goldbach partitions for a given even number. Surprisingly, our model outperforms all state-of-the-art analytically derived estimations for the number of couples, while not requiring prime factorization of the given number. We believe that building a model that can accurately predict the number of couples brings us one step closer to solving one of the world most famous open problems. To the best of our knowledge, this is the first attempt to consider machine learning based datadriven methods to approximate open mathematical problems in the field of number theory, and hope that this work will encourage such attempts.

WWTS (What Would Turing Say?) Turing's Imitation Game was a brilliant early proposed test of machine intelligence -- one that is still compelling today, despite the fact that in the hindsight of all that we've learned in the intervening 65 years we can see the flaws in his original test. And our field needs a good "Is it AI yet?" test more than ever today, with so many of us spending our research time looking under the "shallow processing of big data" lamppost. If Turing were alive today, what sort of test might he propose? If you are reading these words, surely you are already familiar with the Imitation Game proposed by Alan Turing (1950). Turing was heavily influenced by the World War II "game" of allied and axis pilots and ground stations each trying to fool the enemy into thinking they were friendlies.

Marvin Minsky, MIT professor and AI's founding father, says today's artificial-intelligence methods are fine for gluing together two or a few knowledge domains but still miss the "big" AI problem. Indeed, according to Minsky, the missing element is something so big that we can't see it: common sense. "To me the problem is how to get common sense into computers," said Minsky. "And part of that, it seems to me, is not how to solve any particular problem but how to quickly think of a new way to solve it-perhaps through a change in emotional state-when the usual method doesn't work." In his forthcoming book, The Emotion Machine, Minsky shares his accumulated knowledge on how people make use of common sense in the context of discovering that missing cognitive glue.

Despite Watson's tremendous performance, it struggles with simple questions most humans can answer easily. IBM's Watson computer doesn't really "think" anything; it struggles with simple questions Most of the clues on "Jeopardy!" IBM's Watson computer doesn't really "think" anything; it struggles with simple questions Watson's eventual commercial incarnation will be as a tool, not a human replacement Despite Watson's tremendous performance, the Final Jeopardy question at the end of Tuesday night's airing revealed the Achilles' heel that computer scientists have known all along: Watson doesn't really "think" anything, and it struggles with simple questions that most humans can answer without a second thought. "This gives the Watson algorithm a great deal of'traction.' To us viewing the show, it's impressive if it correctly knows that Franz Schubert's birth date was January 31, 1797. But if that date had been part of the clue, could Watson correctly pick out [Schubert's] maternal grandmother's birth date from a list where only one of the dates was earlier than 1797?"

Over July 4th weekend in 1981, several hundred game nerds gathered at a banquet hall in San Mateo, California. Personal computing was still in its infancy, and the tournament was decidedly low-tech. Each match played out on a rectangular table filled with paper game pieces, and a March Madness-style tournament bracket hung on the wall. The game was called Traveller Trillion Credit Squadron, a role-playing pastime of baroque complexity. Contestants did battle using vast fleets of imaginary warships, each player guided by an equally imaginary trillion-dollar budget and a set of rules that spanned several printed volumes. If they won, they advanced to the next round of war games--until only one fleet remained. Doug Lenat, then a 29-year-old computer science professor at nearby Stanford University, was among the players. But he didn't compete alone. He entered the tournament alongside Eurisko, the artificially intelligent system he built as part of his academic research. Eurisko ran on dozens of machines inside Xerox PARC--the computer research lab just down the road from Stanford that gave rise to the graphical user interface, the laser printer, and so many other technologies that would come to define the future of computing. That year, Lenat taught Eurisko to play Traveller. Doug Lenat says his common-sense engine is a new dawn for AI. The rest of the tech world doesn't really agree with him. Lenat fed the massive Traveller rulebook into the system and asked it to find the best way of winning.