If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
For those of us involved in programming computers to play chess, it has been a great adventure. ACM annual tournaments began in 1970 (50 years ago!) and were hosted year after year for a quarter-century by the organization. They were terrific catalysts for progress in the field, and deserve major credit for the eventual 1997 defeat of then-World Champion Garry Kasparov. I feel human intelligence has been vastly overrated. We humans haven't learned how not to fight wars over various explanations of how the universe or man came into being.
Numerai is a machine learning stock market prediction platform seeking to build the world's largest hedge fund. The project continuously runs "the hardest data science tournament on the planet" with the goal of crowdsourcing an excellent financial model for predicting the stock market, among other things. Now, before we dive in, the following piece is similar to my latest articles on Hegic (HEGIC), Ocean Protocol (OCEAN), and Quantstamp (QSP), so if you haven't already seen those, be sure to check them out as well. Numerai is a unique project that's tackling a complicated data science problem by crowdsourcing data scientists who are provided with clean and regularized stock market data that has been encrypted and obfuscated so it can be given out for free. Users (data scientists) who sign up with Numerai can download their cleaned data to create models that predict stock market movements.
Chess has a reputation for cold logic, but Vladimir Kramnik loves the game for its beauty. "It's a kind of creation," he says. His passion for the artistry of minds clashing over the board, trading complex but elegant provocations and counters, helped him dethrone Garry Kasparov in 2000 and spend several years as world champion. Yet Kramnik, who retired from competitive chess last year, also believes his beloved game has grown less creative. He partly blames computers, whose soulless calculations have produced a vast library of openings and defenses that top-flight players know by rote.
Prisoner's Dilemma mainly treat the choice to cooperate or defect as an atomic action. We propose to study online learning algorithm behavior in the Iterated Prisoner's Dilemma (IPD) game, where we explored the full spectrum of reinforcement learning agents: multi-armed bandits, contextual bandits and reinforcement learning. We have evaluate them based on a tournament of iterated prisoner's dilemma where multiple agents can compete in a sequential fashion. This allows us to analyze the dynamics of policies learned by multiple self-interested independent reward-driven agents, and also allows us study the capacity of these algorithms to fit the human behaviors. Results suggest that considering the current situation to make decision is the worst in this kind of social dilemma game. Multiples discoveries on online learning behaviors and clinical validations are stated.
International Business Machines Corporation IBM recently announced that it will be leveraging its artificial intelligence ("AI") capabilities of Watson as well as open hybrid cloud architecture, to provide tennis fans with enriched experiences. United States Tennis Association ("USTA") is conducting this year's US Open without fans present at the stadium due to the coronavirus outbreak. Markedly, the US Open 2020 will be held from Aug 31 to Sep 13. Nevertheless, USTA, with IBM's help, will provide fans with an interactive and engaging digital experience to enjoy the tournament. IBM has been USTA's technology partner for almost three decades.
Common methods applied in the evaluation of model performance share several limitations. There are many approaches to verify whether a new algorithm improves the performance compared to the previous state-of-the-art algorithms. The majority of them are testing procedures. In his paper Statistical Comparisons of Classifiers over Multiple Data Sets, Janez Demšar reviewed commonly used practices and pointed out the vast amount of problems with them. He analyzed papers from five International Conferences on Machine Learning (1999-2003) that compared at least two classification models.
Fans can become instant "experts" about the players and the tournament match-ups with new AI-powered insights. This year, IBM is partnering again with the United States Tennis Association (USTA) and has developed three new tennis-based digital experiences for fans of the US Open. Spectators won't be allowed at the Arthur Ashe Stadium at the Billie Jean King National Tennis Center in Flushing, NY when the Grand Slam event begins on Aug. 31, due to the COVID-19 pandemic, but they will be able to participate remotely with new fan experiences that use artificial intelligence (AI) underpinned by hybrid cloud technologies. IBM has partnered with the USTA for 29 years, but 2018 was the first year that AI-powered tools were used by players and coaches. Last year, IBM introduced the IBM Coach Advisor and IBM Watson OpenScale.
The decision led to sweeping changes to almost every aspect of the competition, from playing matches with electronic line calling to having athletes use food-ordering apps for meal deliveries to their hospitality suites at the Billie Jean King National Tennis Center. However, the absence of fans immediately presented a problem for some of the USTA's recent AI projects. Last year, for instance, the USTA worked with International Business Machines Corp. to introduce a number of AI-powered additions to the tournament, including machine learning algorithms that rapidly compose broadcast highlight reels based on crowd reaction. "June 17th was really a pivotal moment, a lot of the solutions that we had in the pipeline were no longer going to be viable," said Kristi Kolski, marketing program director for IBM's sports and entertainment partnerships unit. "No crowd, no roar, no AI highlights."
An AI-controlled fighter jet will battle a US Air Force pilot in a simulated dogfight next week -- and you can watch the action online. The clash is the culmination of DARPA's AlphaDogfight competition, which the Pentagon's "mad science" wing launched to increase trust in AI-assisted combat. DARPA hopes this will raise support for using algorithms in simpler aerial operations, so pilots can focus on more challenging tasks, such as organizing teams of unmanned aircraft across the battlespace. The three-day event was scheduled to take place in-person in Las Vegas from August 18-20, but the COVID-19 pandemic led DARPA to move the event online. Before the teams take on the Air Force on August 20, the eight finalists will test their algorithms against five enemy AIs developed by Johns Hopkins Applied Physics Laboratory.
An upcoming event to display and test AI-powered jet fighters will now be held virtually due to COVID-19. "We are still excited to see how the AI algorithms perform against each other as well as a Weapons School-trained human and hope that fighter pilots from across the Air Force, Navy, and Marine Corps, as well as military leaders and members of the AI tech community will register and watch online," said Col. Dan Javorsek, program manager in DARPA's Strategic Technology Office. "It's been amazing to see how far the teams have advanced AI for autonomous dogfighting in less than a year." DARPA (Defense Advanced Research Projects Agency) is using the AlphaDogfight Trial event to recruit more AI developers for its Air Combat Evolution (ACE) program. The upcoming event is the final in a series of three and will finish with a bang as the AI-powered F-16 fighter planes virtually take on a human pilot.