Watson on Jeopardy

Blog Summary: (AI Summaries by Summarizes)
  • IBM's Watson on Jeopardy is an impressive feat that combines several complex domains.
  • Watson's ability to reduce answers into questions was highlighted when it partially answered a question about a gymnast's anatomical oddity.
  • Watson's algorithm likely removes anything other than nouns or names for certain answer types.
  • If the Jeopardy exhibition had more questions that were not just nouns, definitions, and proper names, Watson may not have won.

IBM’s Watson on Jeopardy is an incredible accomplishment. The various domains of the problem are very interesting. It isn’t just a (un)natural language processor, a search engine, or a trivia machine. It is several very complex domains running to together and fast! You could see the frustration in Jennings’ and Rutter’s faces at being unable to buzz in most of the time.

It found one answer (Jeopardy reverses the normal question and answer format) very telling about Watson’s abilities. The answer was “It was the anatomical oddity of U.S. gymnast George Eyser, who won a gold medal on the parallel bars in 1904.” and Watson answered “What is leg?”. That was only partially true and Watson got the question wrong. Rutter answered “What is a missing leg?” and got it right. I think this shed a little bit of light into how Watson is reducing the answers into questions. Its algorithm probably removes anything other than noun or names for certain answer types.

This made me start thinking. If the Watson Jeopardy exhibition had more questions like this and fewer ones that are nouns, definitions, and proper names, would Watson still win? From that question, I would say no.

Related Posts

The Difference Between Learning and Doing

Blog Summary: (AI Summaries by Summarizes)There are several types of learning videos: hype, low effort, novice, and professional.It is important to avoid hype, low-effort, and

The Data Discovery Team

Blog Summary: (AI Summaries by Summarizes)The concept of a “data discovery team” is introduced, which focuses on searching for data in an enterprise data reality.Data

Black and white photo of three corporate people discussing with a view of the city's buildings

Current 2023 Announcements

Blog Summary: (AI Summaries by Summarizes)Confluent’s Current Conference featured several announcements that are important for both technologists and investors.Confluent has two existing moats (replication and

zoomed in line graph photo

Data Teams Survey 2023 Follow-Up

Blog Summary: (AI Summaries by Summarizes)Many companies, regardless of size, are using data mesh as a methodology.Smaller companies may not necessarily need a data mesh

Laptop on a table showing a graph of data

Data Teams Survey 2023 Results

Blog Summary: (AI Summaries by Summarizes)A survey was conducted between January 24, 2023, and February 28, 2023, to gather data for the book “Data Teams”

Black and white photo of three corporate people discussing with a view of the city's buildings

Analysis of Confluent Buying Immerok

Blog Summary: (AI Summaries by Summarizes)Confluent has announced the acquisition of Immerok, which represents a significant shift in strategy for Confluent.The future of primarily ksqlDB

Tall modern buildings with the view of the ocean's horizon

Brief History of Data Engineering

Blog Summary: (AI Summaries by Summarizes)Google created MapReduce and GFS in 2004 for scalable systems.Apache Hadoop was created in 2005 by Doug Cutting based on