Magazine article AI Magazine

Year One of the IBM Watson AI XPRIZE: Case Studies in "AI for Good"

Magazine article AI Magazine

Year One of the IBM Watson AI XPRIZE: Case Studies in "AI for Good"

Article excerpt

Investment in artificial intelligence has grown to more than $25 billion annually (Bughin et al. 2017), but these investments place higher priority on financial returns than the general welfare of humanity. To focus AI development on direct societal benefits, the IBM Watson AI XPRIZE (AIXP) issued a $5 million prize purse to award AI startups and researchers producing the greatest world-improving impact.

While the incentive for winning the AIXP is consistent with other XPRIZE competitions, the AIXP does not set a single shared objective for all teams. Rather, the AIXP invites teams to describe their own grand challenge and to demonstrate achievements over a four-year competition. This open prize structure allows teams to showcase a variety of approaches to the most significant problems faced by humanity. Problem flexibility also allows teams to discover unexpected opportunities. In many cases, a clever formulation may be the only requirement for improving millions of lives.

While all teams will ideally succeed in their efforts, both successes and failures present opportunities to focus research efforts in developing AI for Good. Our previous work outlined the complete AIXP process and year one team statistics (McGregor and Banifatemi, forthcoming); this work explores the problem domains and attributes of teams identified as top performers within the first year of the competition.

The AIXP began in 2017 with 148 teams working in the problem domains of table 1. The rows are ordered from domains with the highest advancement rate (top) to the lowest advancement rate (bottom). If left unaddressed, these problems pose significant negative consequences for humanity, including lack of access to basic human needs, lack of well-being, lack of education, environmental degradation, increased inequality, reduction in health, and loss of life.

After the first year of the competition, 59 of the starting teams remain. The competition closes after three annual judged rounds and a final round at TED 2020. The judges will award a $3,000,000 grand prize, a $1,000,000 second place prize, and a $500,000 third place prize. They will award an additional $500,000 to teams with noteworthy successes achieved during the annual reporting periods.

Teams began the competition by submitting solution proposals that were then read and categorized by the XPRIZE Foundation staff. The resulting team count within the team taxonomy of table 1 motivated the target list for judge recruitment. Appropriately judging 148 teams working towards different grand challenges required a judging panel with diverse technical, philosophical, and personal experiences. The 33 judges active in the first round of the AIXP have distinguished themselves either through their technical capacities within the field of AI or through their knowledge of the deployment of these systems in the real world. Among the judges are leaders from the labs of multinational corporations, AI startups, academic research labs, nongovernmental organizations, and public policy think tanks. Collectively these individuals have expertise in natural language processing, deep learning, adversarial learning, computer security, the social effects of technology, political campaigns, computational sustainability, ecology, robotics, and many other fields and applications of AI research. Judge biographies are available on the AIXP website.1

In September of 2017, competing teams submitted their first annual reports (FARs) as four-page extended abstracts detailing their problem areas, proposed solution, and the progress achieved to date. Of the 148 teams eligible to submit the FAR, only 118 teams opted to do so. This reduction shows significant selfselection that we consider for the purposes of analy- sis to be similar to a judged rejection. Judges followed a similar review process as with an academic AI conference, with two reviewers per submission.

The advancement criteria focused on the potential for world impact and indicators of technical progress. …

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