Ladies and gentlemen, start your algorithms: Google is creating a $25 million fund for artificial intelligence research around the world, to address social and economic problems. Organizations chosen by the Mountain View digital advertising giant will receive not only financial assistance, but help from Google AI experts and computing resources, said Jacquelline Fuller, president of Google's charity arm, Google.org. Grants for the "Google AI Impact Challenge" are expected to range from about $500,000 to $2 million, Fuller said. "AI can help us revisit problems that were previously seen as unsolvable," Fuller said. "We're optimistic that AI can accelerate research and engineering efforts to tackle the world's biggest humanitarian, environmental and social problems. We want to work with organizations and developers globally."

One of the biggest hurdles in the field of artificial intelligence is preventing it from developing the same intrinsic faults and biases as its human creators, and in using AI to solve social issues instead of simply automating tasks. Now, Google, one of the world's leading organizations developing AI software today, is launching a global competition to help spur the development of applications and research that have positive impacts on the field and society at large. The competition, called the Google AI Impact Challenge, was announced today at an event called AI for Social Good held at the company's Sunnyvale, California office. Google is positioning it as a way to integrate nonprofits, universities, and other organizations not within the corporate and profit-driven world of Silicon Valley into the future-looking development of AI research and applications. The company says it will award up to $25 million to a number of grantees to "help transform the best ideas into action."

Much of current machine learning (ML) research has lost its connection to problems of import to the larger world of science and society. From this perspective, there exist glaring limitations in the data sets we investigate, the metrics we employ for evaluation, and the degree to which results are communicated back to their originating domains. What changes are needed to how we conduct research to increase the impact that ML has? We present six Impact Challenges to explicitly focus the field's energy and attention, and we discuss existing obstacles that must be addressed. We aim to inspire ongoing discussion and focus on ML that matters.