Klu raises $1.7M to empower AI Teams  

What was the name of the first chess computer to beat a world champion?

by Stephen M. Walker II, Co-Founder / CEO

What was the name of the first chess computer to beat a world champion?

The name of the first chess computer to beat a world champion in AI was Deep Blue. Deep Blue was developed by IBM and was first used in competition in 1997. In May of 1997, Deep Blue beat world champion Garry Kasparov in a six-game match by a score of 3.5 to 2.5.

What year did Deep Blue first beat a world champion?

In 1997, Deep Blue, a chess-playing computer developed by IBM, defeated world champion Garry Kasparov in a six-game match. This was a landmark achievement in the field of artificial intelligence, as it showed that computers could outperform humans in a complex task that had long been considered a hallmark of human intelligence.

Who was the world champion Deep Blue beat in 1997?

In 1997, the world champion Deep Blue beat was Garry Kasparov, a Russian grandmaster.

How many processors did Deep Blue have?

Deep Blue was a chess-playing computer developed by IBM. Deep Blue is notable for being the first computer to win a chess match against a reigning world champion under regular time controls. Deep Blue defeated Garry Kasparov in 1997.

Deep Blue was an AI computer that had multiple processors. It is estimated that Deep Blue had around 30 processors. This gave Deep Blue the ability to calculate around 200 million moves per second.

What was the approximate cost of Deep Blue?

Deep Blue was a chess-playing computer developed by IBM. It is known for being the first computer to beat a world chess champion in a match under regular time controls. Deep Blue defeated Garry Kasparov in 1997.

The cost of Deep Blue is not publicly known, but is estimated to be around $10 million.

More terms

What is a naive Bayes classifier?

A naive Bayes classifier is a simple [machine learning](/glossary/machine-learning) algorithm that is used to predict the class of an object based on its features. The algorithm is named after the Bayes theorem, which is used to calculate the probability of an event occurring.

Read more

What is named-entity recognition (NER)?

Named-entity recognition (NER) is a sub-task of information extraction that seeks to locate and classify named entities in text into pre-defined categories such as the names of persons, organizations, locations, expressions of times, quantities, monetary values, percentages, etc.

Read more

It's time to build

Collaborate with your team on reliable Generative AI features.
Want expert guidance? Book a 1:1 onboarding session from your dashboard.

Start for free