× Business
TelecomHealthcareDigital MarketingERPRetailMedia and EntertainmentOil and GasFood and BeveragesMarketing and AdvertisingBanking and InsuranceMetals and MiningLegalComplianceCryptocurrency
Big DataCloudIT ServiceSoftwareMobileSecurityNetworkingStorageCyber SecuritySAPData AnalysisloTBio TechQuality AssuranceEducationE-commerceGaming and VFXArtificial Intelligencescience-and-technology
Cisco DATABASE Google IBM Juniper Microsoft M2M Oracle Red hat Saas SYMANTEC
CEO ReviewCMO ReviewCFO ReviewCompany Review
Startups Opinion Yearbook Readers Speak Contact Us

One of Microsoft’s AI systems has made the perfect score in the 1980s video game Ms. Pac-Man

siliconreview One of Microsoft’s AI systems has made the perfect score in the 1980s video game Ms. Pac-Man

Microsoft’s AI is the first player to have claimed the top score of 999,990 points in Ms. Pac-Man. After Google’s Deep Mind AI beat humans at the complex game of Go, an AI from Microsoft’s Maluuba team, a Canadian deep learning startup, has wiped the floor by four times of the human record.

Ms. Pac-Man, the Atari game that involves eating pellets and being chased by ghosts, was developed in 1980s and was programmed to be less predictable than the original Pac-Man making it one of the toughest games for an AI to beat. According to Microsoft, the method used in the game can enable AI agents to perform complex tasks to help humans. The challenge of completing all 256 levels with a perfect score was taken on by 150 agents working as a hive mind and on different problems. Maluuba likes to call this approach as “divide and conquer”.

 “This idea of breaking things down into smaller problems is the basis of how humans solve problems”, said Kaheer Suleman, the Co-founder and CTO of Maluuba. “A company doing product development is a good example. The goal of the whole organization is to develop a product, but individually, there are groups that have their own reward and goal for the process”, he added. It is suggested that such a system could be used in retail to prioritize customers according to need in order to maximize their own revenue.