https://youtu.be/c1pMbDAbcjo
UR5 Chess Game Demo
Published on Mar 28, 2017
Quote:
Positronics demonstration video of a robot programmed to play an interactive chess game against real people. Featuring a UR5 robot and Robotiq end effector.
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https://youtu.be/c1pMbDAbcjo
UR5 Chess Game Demo
Published on Mar 28, 2017
Quote:
Positronics demonstration video of a robot programmed to play an interactive chess game against real people. Featuring a UR5 robot and Robotiq end effector.
Article "DeepChess: End-to-End Deep Neural Network for Automatic Learning in Chess"
by Omid E. David, Nathan S. Netanyahu, Lior Wolf
https://youtu.be/K3cgBh3S1bs
Printer Fidelity, by Berger
Published on Jun 21, 2017
Article "Million Dollar Computer Chess Problem Will Take 'Thousands of Years' to Solve"
by Claire Downs
September 6, 2017
https://youtu.be/0pVa5djVzVg
Гарри Каспаров о компьютерных шахматах и искусственном интеллекте (in Russian)
Streamed live Feb 19, 2018
Article "Artificial intelligence and construction’s weak margins: learning the lesson of chess"
by Robert Brown
April 3, 2020
https://youtu.be/7pHaNMdWGsk
What happens when AI stops playing games?
May 6, 2020
"What happens when AI stops playing games?"Quote:
The question, “can a machine be made to think like a person?” has always been tied to strategy games. Games, with their clear rules and obvious winners and losers, were perfect proving grounds for early computer scientists, who could break them down into clearly defined problem sets. Audiences could marvel at the progress of man vs machine face offs, even if they didn’t fully understand the underlying technology.
The earliest AI game systems relied on a brute force, top-down approach: programmers downloaded every possible outcome into their AI systems, which were built around narrowly defined, rule-based criteria. In the 1950s, the earliest versions of neural networks and machine learning arrived. This represented a shift towards bottom-up programming, systems designed to determine the probability of various outcomes based on training data. One early system, a virtual rat solving a maze, was made up of vacuum tubes, motors, and clutches. As the rat navigated, the machine learned and shifted probabilities.
IBM debuted their first AI Grand Challenge — a multi-year effort meant to push the limits of artificial intelligence — in 1997. Deep Blue beat reigning chess world champion Gary Kasparov, and was seen as the final triumph for the top-down approach. In the last decade, advances in machine learning, deep learning, and natural language processing paved the way for AIs like Watson on Jeopardy!, AlphaGo, and Dota 2—increasingly complex systems that still existed within the framework of games.
Humans plus machine — AI shifts from games into something more subjective: human discourse.
May 6, 2020
https://youtu.be/jtNVYYJWtgo
Robot chess player PULSE
Jul 8, 2020
Quote:
A robotic arm is not only a tool for assembly, palletizing, and other routine production tasks. It has endless possibilities for creative automation. Our chess robot is an example of such a creative project.
Together with the SIA, we developed a PULSE robot chess player.
The metal instructor is already installed in one of the Belarusian chess schools where it helps children learn one of the oldest board games.
Feel the PULSE of automation.
https://youtu.be/wljgxS7tZVE
The strongest computer chess engines over time
May 20, 2020
Quote:
Computer Chess Engines have gotten stronger each year with current engines like Alphazero, Stockfish, Komodo, and Leela Chess Zero reaching new levels of perfection monthly! Check out how computer engines have developed in the last 30+ years, from weak machines, easily dominated by humans, to monsters!