At an event held at IBM's Watson West site in San Francisco, a champion debater and IBM's AI system, Project Debater, began by preparing arguments for and against the statement: "We should subsidize space exploration."

Both sides then delivered a four-minute opening statement, a four-minute rebuttal, and a two-minute summary.

Project Debater pushes the frontiers of AI to facilitate intelligent debate so we can build well-informed arguments and make better decisions. it is an AI system that can debate humans on complex topics. Te system digests massive texts, constructs a well-structured speech on a given topic, delivers it with clarity and purpose, and rebuts its opponent. Eventually, Project Debater will help people reason by providing compelling, evidence-based arguments and limiting the influence of emotion, bias, or ambiguity.

Project Debater made an opening argument that supported the statement with facts, including the points that space exploration benefits human kind because it can help advance scientific discoveries and it inspires young people to think beyond themselves. Noa Ovadia, the 2016 Israeli national debate champion, opposed the statement, arguing that there are better applications for government subsidies, including subsidies for scientific research here on Earth. After listening to Noa's argument, Project Debater delivered a rebuttal speech, countering with the view that potential technological and economic benefits from space exploration outweigh other government spending. Following closing summaries from both sides, a snap poll showed that a majority of audience members thought Project Debater enriched their knowledge more than its human counterpart.

Later, IBM held a second debate between the system and another Israeli debate expert, Dan Zafrir, that featured opposing arguments on the statement: "We should increase the use of telemedicine."

For the initial demonstrations of this new technology, researchers at IBM selected from a curated list of topics to ensure a meaningful debate. But Project Debater was never trained on the topics. Over time, and in relevant business applications, we will naturally move toward using the system for issues that haven't been screened.

AI assistants have used to have become very good at certain tasks like searching for photos of loved ones, shuffling a playlist and dimming the lights. AI is also helping enterprises deliver better customer service and conduct deeper analysis. Project Debater is exploring different territory by using AI to engage in long-form discussion and provide impartial arguments on various topics that have no right or wrong answers.

Project Debater actually doesn't learn a topic, but it is very good at quickly creating a persuasive narrative based on available data. The Debater system was taught to debate unfamiliar topics. Hence, it can debate many different topics, as long as these are well covered in the massive corpus that the system mines, which includes hundreds of millions of articles from numerous well-known newspapers and magazines.

According to IBM, This is one of the many aspects that make Project Debater unique. It has been taught to understand the nuances of language and decide the stance of an argument given the topic. Imagine debating the pros and cons of the use of traffic enforcement cameras. When given the claim "the photo radar program fails to provide any clear safety benefit," a human debater instinctively understands it contests the use of traffic cameras. But this type of understanding is very hard for AI.

But how does the Debater system know if a claim is for or against the topic it's given?

According to IBM, this is one of the many aspects that make Project Debater unique. It has been taught to understand the nuances of language and decide the stance of an argument given the topic. Imagine debating the pros and cons of the use of traffic enforcement cameras. When given the claim "the photo radar program fails to provide any clear safety benefit," a human debater instinctively understands it contests the use of traffic cameras. But this type of understanding is very hard for AI.

The Debater system approaches this by breaking it down into smaller tasks. The system understands that "the photo radar program" is associated with "traffic enforcement cameras" and further understands that the rest of the sentence - even though it includes positive words like "safety" and "benefit" - is, in fact, contesting the use of traffic enforcement cameras.

Project Debater relies on three pioneering capabilities. The first is data-driven speech writing and delivery, or the ability to automatically generate a whole speech, reminiscent of an opinion article, and deliver it persuasively. The second is listening comprehension, which is the ability to understand a long spontaneous speech made by the human opponent in order to construct a meaningful rebuttal. The third is the system's ability to model human dilemmas and form principled arguments made by humans in different debates based on a unique knowledge graph. By combining these core capabilities, it can conduct a meaningful debate with human debaters.

And the system is actually listening to the human debater, using Watson Speech to Text. The system identifies key concepts and claims as its opponent is talking to prepare for rebuttal. Project Debater can listen and digest long, continuous spoken speech - up to four minutes of it.