's most controversial moment was years in the making. It took many more for the fallout to come into full view. One morning in 2010, Alex Trebek walked onto the IBM campus not far outside New York City and prepared to inspect what would become the most unusual player in's history. The trip, clear across the country from the show's Culver City set, had been carefully planned. David Ferrucci, a computer scientist at IBM, had spent years leading a team to develop what would become the first and, so far, last nonhuman ever to compete on Longtime host Trebek would watch three practice games played with "Watson," as the system was named, and two human contestants. Then the team would be taken to lunch nearby, and Trebek would ultimately take the stage and host two more Watson practice games himself. By then the preparations for a future televised contest with IBM's creation were well underway, but this was the first time Trebek would encounter the technology in person, and his approval was crucial. Ferrucci was eager to show off one element in particular: the display, which had been rigged to show Watson's top three guesses whenever it answered, along with the numerical confidence rate it had in each one. For Ferrucci, this feature was central to demonstrating the computer's language-processing capabilities, because it showed that Watson wasn't just spitting out answers--it was reasoning. If Watson were ever going to be deployed to industries like health care, its human users wouldn't just want to know its best guess. It would be infinitely more valuable to know if Watson was 95 percent confident or just 30 percent, and whether those confidence levels were in line with its actual accuracy rate. It also made for better viewing. Ferrucci had brought his young daughter to the lab earlier in the process and showed her Watson as it played against human opponents. When Watson declined to ring in, Ferrucci's daughter turned to him and asked if the computer had crashed. He struggled to explain that it hadn't--it just wasn't confident enough to hazard a guess.

In early 2011, Ken Jennings looked like humanity's last hope. Watson, an artificial intelligence created by the tech giant IBM, had picked off lesser Jeopardy players before the show's all-time champ entered a three-day exhibition match. At the end of the first game, Watson--a machine the size of 10 refrigerators--had Jennings on the ropes, leading $35,734 to $4,800. On day three, Watson finished the job. "I for one welcome our new computer overlords," Jennings wrote on his video screen during Final Jeopardy. Watson was better than any previous AI at addressing a problem that had long stumped researchers: How do you get a computer to precisely understand a clue posed in idiomatic English and then spit out the correct answer (or, as in Jeopardy, the right question)?

After a year's hiatus, the AI Debate hosted by Gary Marcus and Vincent Boucher returned with a gaggle of AI thinkers, this time including policy types and scholars outside of the discipline of AI such as Noam Chomsky. After a one-year hiatus, the annual artificial intelligence debate organized by Montreal.ai Learn about the leading tech trends the world will lean into over the next 12 months and how they will affect your life and your job. The debate this year, AI Debate 3: The AGI Debate, as it's called, focused on the concept of artificial general intelligence, the notion of a machine capable of integrating a myriad of reasoning abilities approaching human levels. While the previous debate featured a number of AI scholars, Friday's meet-up drew participation by 16 participants from a much wider gamut of professional backgrounds. In addition to numerous computer scientists and AI luminaries, the program included legendary linguist and activist Noam Chomsky, computational neuroscientist Konrad Kording, and Canadian parliament member Michelle Rempel Garner. Also: AI's true goal may no longer be intelligence Marcus was once again joined by his co-host, Vincent Boucher of Montreal.ai. The debate ran longer than planned. The full 3.5 hours can be viewed on the YouTube page for the debate. The debate Web site is agidebate dot com. In addition, you may want to follow the hashtag #agidebate. NYU professor emeritus and AI gadfly Gary Marcus resumed his duties hosting the multi-scholar face-off. Marcus started things off with a slide show of a "very brief history of AI," tongue firmly in cheek. Marcus said that contrary to enthusiasm in the decade following the landmark ImageNet success, the "promise" of machines doing various things had not paid off. He featured reference to his own New Yorker article throwing cold water on the matter.

However, one of the main obstacles to overcome is common sense, which this technology lacks. This is something that David Ferrucci, the leader of the team responsible for ibm watson computer and who serve as executive directors of today Elemental Cognition, a startup that seeks to address the shortcomings of AI. "For me, the Watson Project was always just a small part of a bigger story about where we want to go with AI," he says in the statement. Now, its main purpose is to become humanity's "thinking companion", capable of suggesting and explaining. The system in which E is conservedMental Cognition combines the latest advances in machine learning with software modeled after human reasoning. New programs can recognize patterns and make predictions, sifting through vast amounts of data at breakneck speed to generate the most likely interpretations.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! In 2015, David Ferrucci – the award-winning artificial intelligence (AI) researcher who led the development of IBM Watson -- which won the television quiz show Jeopardy in 2011 against two of the game's top champions -- noticed that most AI systems failed to understand the meaning behind language. That meant they couldn't provide rich, reasoned explanations for any output. That's when Ferrucci founded New York City-based AI research and technology company Elemental Cognition, to tackle one of the most difficult challenges facing the future of AI: Developing the ability to reason and understand beyond statistical machine learning and data analytics, overcome bias and provide intelligence at scale.

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."

Machines can learn a lot of things--probably more than you can imagine. But can they learn common sense? At his company, Elemental Cognition, Ferrucci described how his AI team gave an advanced language program the sentence, "Zoey moves her plant to a sunny window. The AI program was tasked to complete the second sentence. SEE: An IT pro's guide to robotic process automation (free PDF) (TechRepublic) A human would likely complete the sentence by saying, "the sun will help the plant to grow and stay healthy." In the real world, it's common knowledge that plants need light. Unfortunately, the AI program couldn't deliver this common observation. Instead, the AI completed the sentence by analyzing statistical patterns. It came up with these possible answers: "she finds something, not pleasant," "fertilizer is visible in the window," and "another plant is missing from the bedroom." This story is an entry point to myriad "common sense" issues that face today's AI. It begins to explain why a self-driving vehicle may not be able to decipher the varying degrees of danger between striking a traffic cone or striking a pedestrian. "The great irony of common sense--and indeed AI itself--is that it is stuff that pretty much everybody knows, yet nobody seems to know what exactly it is or how to build machines that possess it," said Gary Marcus, CEO and founder of Robust.AI. "Solving this problem is, we would argue, the single most important step towards taking AI to the next level.

David Ferrucci, the man who built IBM's Jeopardy-playing machine, Watson, is explaining a children's story to his new creation. In the tale, Fernando and Zoey buy some plants. Fernando places his plant on a windowsill while Zoey tucks hers away in a darkened room. After a few days, Fernando's plant is green and healthy but the leaves of Zoey's have browned. She moves her plant to the windowsill, and it flourishes.

Dr. David Ferrucci is one of the few people who have created a benchmark in the history of AI because when IBM Watson won Jeopardy we reached a milestone many thought impossible. I was very privileged to have Ferrucci on my podcast in early 2012 when we spent an hour on Watson's intricacies and importance. Well, it's been almost 8 years since our original conversation and it was time to catch up with David to talk about the things that have happened in the world of AI, the things that didn't happen but were supposed to, and our present and future in relation to Artificial Intelligence. All in all, I was super excited to have Ferrucci back on my podcast and hope you enjoy our conversation as much as I did. During this 90 min interview with David Ferffucci, we cover a variety of interesting topics such as: his perspective on IBM Watson; AI, hype and human cognition; benchmarks on the singularity timeline; his move away from IBM to the biggest hedge fund in the world; Elemental Cognition and its goals, mission and architecture; Noam Chomsky and Marvin Minsky's skepticism of Watson; deductive, inductive and abductive learning; leading and managing from the architecture down; Black Box vs Open Box AI; CLARA – Collaborative Learning and Reading Agent and the best and worst applications thereof; the importance of meaning and whether AI can be the source of it; whether AI is the greatest danger humanity is facing today; why technology is a magnifying mirror; why the world is transformed by asking questions.

Give us your feedback Thank you for your feedback. Artificial intelligence is one of the important technological advances of the early 21st century. Already it has meant that machines can read medical images as well as a radiologist, and enabled the auto industry to develop autonomous cars. The technology is in danger of being overrated, however, and considerably more work is needed before we can reach the long-dreamt-of moment when machine intelligence matches the human variety. When we discuss AI today we are mainly referring to just one facet of it: deep learning.