At a moment when vaccines promise to end the coronavirus pandemic, emerging new variants threaten to accelerate it. The astonishingly fast development of safe and effective vaccines is being stymied by the glacial pace of actual vaccinations while 3,000 Americans die each day. Minimizing death and suffering from COVID-19 requires vaccinating the most vulnerable Americans first and fast, but the vaccine rollout has been slow and inequitable. Prioritization algorithms have led to the most privileged being prioritized over the most exposed, and strict adherence to priority pyramids has been disastrously slow. Yet without prioritization, vaccines go to those with greatest resources rather than to those at greatest risk.
For 23 years, Larry Collins worked in a booth on the Carquinez Bridge in the San Francisco Bay Area, collecting tolls. The fare changed over time, from a few bucks to $6, but the basics of the job stayed the same: Collins would make change, answer questions, give directions and greet commuters. "Sometimes, you're the first person that people see in the morning," says Collins, "and that human interaction can spark a lot of conversation." But one day in mid-March, as confirmed cases of the coronavirus were skyrocketing, Collins' supervisor called and told him not to come into work the next day. The tollbooths were closing to protect the health of drivers and of toll collectors. Going forward, drivers would pay bridge tolls automatically via FasTrak tags mounted on their windshields or would receive bills sent to the address linked to their license plate. Collins' job was disappearing, as were the jobs of around 185 other toll collectors at bridges in Northern California, all to be replaced by technology.
Decades of research in artificial intelligence (AI) have produced formidable technologies that are providing immense benefit to industry, government, and society. AI systems can now translate across multiple languages, identify objects in images and video, streamline manufacturing processes, and control cars. The deployment of AI systems has not only created a trillion-dollar industry that is projected to quadruple in three years, but has also exposed the need to make AI systems fair, explainable, trustworthy, and secure. Future AI systems will rightfully be expected to reason effectively about the world in which they (and people) operate, handling complex tasks and responsibilities effectively and ethically, engaging in meaningful communication, and improving their awareness through experience. Achieving the full potential of AI technologies poses research challenges that require a radical transformation of the AI research enterprise, facilitated by significant and sustained investment. These are the major recommendations of a recent community effort coordinated by the Computing Community Consortium and the Association for the Advancement of Artificial Intelligence to formulate a Roadmap for AI research and development over the next two decades.
Rolnick, David, Donti, Priya L., Kaack, Lynn H., Kochanski, Kelly, Lacoste, Alexandre, Sankaran, Kris, Ross, Andrew Slavin, Milojevic-Dupont, Nikola, Jaques, Natasha, Waldman-Brown, Anna, Luccioni, Alexandra, Maharaj, Tegan, Sherwin, Evan D., Mukkavilli, S. Karthik, Kording, Konrad P., Gomes, Carla, Ng, Andrew Y., Hassabis, Demis, Platt, John C., Creutzig, Felix, Chayes, Jennifer, Bengio, Yoshua
Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. Here we describe how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by machine learning, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the machine learning community to join the global effort against climate change.