Project Debater is an IBMartificial intelligence project, designed to participate in a full live debate with expert human debaters. It follows on from the Watson project which played Jeopardy!.
Development
Project Debater was developed at IBM's lab in Haifa, Israel. The project was proposed by in 2011 as the IBM Research next Grand Challenge, following Deep Blue and the victory of Watson in Jeopardy! It was exposed for the first time in a closed media event at June 18, 2018, in San Francisco, under the leadership of and Slonim, both from the IBM Research lab in Haifa, Israel. The AI technology debated two human debaters, Noa Ovadia, who was the 2016 Israeli debate champion and Dan Zafrir. The two debated on the topics "We should subsidize space exploration" and "Should we increase the use of telemedicine." A demonstration of Project Debater also aired on the Discovery Channel in June 2018 debating the question of whether sports gambling should be legalized. On February 11, 2019, Project Debater debated Harish Natarajan, who holds the world record in number of debate competition victories. Both sides debated the topic “We should subsidize preschools”. The debate took place in San Francisco, in front of live audience of around 800 people, and was hosted by Intelligence Squared and moderated by John Donvan. To develop Project Debater, the IBM Research team had to endow the system with three AI capabilities:
Data-driven speech writing and delivery: Project Debater is the first demonstration of a computer that can digest massive corpora, and given a short description of a controversial topic, write a well-structured speech, and deliver it with clarity and purpose, while even incorporating humor where appropriate.
Listening comprehension: the ability to identify the key concepts and claims hidden within long continuous spoken language.
Four minutes of persuasive speech: the guarantee of producing four minutes of persuasive speech.
Modeling human dilemmas: modeling the world of human controversy and dilemmas in a unique knowledge representation, enabling the system to suggest principled arguments as needed.
