For the past few years, we\u2019ve applied core Google AI research and engineering to projects with positive societal impact, including forecasting floods<\/a>, protecting whales<\/a> and predicting famine<\/a>. Artificial intelligence has incredible potential to address big social, humanitarian and environmental problems, but in order to achieve this potential, it needs to be accessible to organizations already making strides in these areas. So, the Google AI Impact Challenge<\/a>, which kicked off in October 2018, was our open call to organizations around the world to submit their ideas for how they could use AI to help address societal challenges.<\/p>\n <\/p>\n \u201cAccelerating Insights from the Google AI Impact Challenge<\/a>\u201d sheds light on the range of organizations using AI to address big problems. It also identifies several trends around the opportunities and challenges related to using AI for social good. Here are some of the things\u00a0 we learned\u2014check out the report<\/a> for more details.<\/p>\n <\/p>\n AI is globally relevant\u00a0<\/b><\/p>\n We received 2,602 applications from six continents and 119 countries, with projects addressing a wide range of issue areas, from education to the environment. Some of the applicants had experience with AI, but 55 percent of not-for-profit organizations and 40 percent of for-profit social enterprises reported no prior experience with AI.\u00a0<\/p>\n <\/p>\n<\/div>\n<\/div>\n Similar projects can benefit from shared resources<\/b><\/p>\n When we reviewed all the applications, we saw that many people are trying to tackle the same problems and are even using the same approaches to do so. For example, we received more than 30 applications proposing to use AI to identify and manage agricultural pests. The report includes a list of common project submissions, which will hopefully encourage people to collaborate and share resources with others working to solve similar problems.\u00a0\u00a0<\/p>\n You don\u2019t need to be an expert to use AI for social good<\/b><\/p>\n AI is becoming more accessible as new machine learning libraries and other open-source tools, such as\u00a0Tensorflow<\/a>\u00a0and\u00a0ML Kit<\/a>, reduce the technical expertise required to implement AI. Organizations no longer need someone with a deep background in AI, and they don\u2019t have to start from scratch. More than 70 percent of submissions, across all sectors and organization types, used existing AI frameworks to tackle their proposed challenge.\u00a0<\/p>\n <\/p>\n Successful projects combine technical ability with sector expertise\u00a0<\/b><\/p>\n Few organizations had both the social sector and AI technical expertise to successfully design and implement their projects from start to finish. The most comprehensive applications established partnerships between nonprofits with deep sector expertise, and academic institutions or technology companies with technical experience.<\/p>\n ML isn\u2019t the only answer\u00a0<\/b><\/p>\n Some problems can be addressed by using alternative methods to AI\u2014and result in faster, simpler and cheaper execution. For example, several organizations proposed using machine learning to match underserved populations to legal knowledge and tools. While AI could be helpful, similar results could be achieved through a well-designed website. While we\u2019ve seen the impact AI can have in solving big problems, you shouldn\u2019t rule out more simple approaches as well.\u00a0<\/p>\n <\/p>\n Global momentum around AI for social good is growing\u2014and many organizations are already using AI to address a wide array of societal challenges. As more social sector organizations recognize AI\u2019s potential, we all have a role to play in supporting their work for a better world.\u00a0<\/p>\n <\/p>\n<\/div>\n<\/div>\n<\/div>\n Go to Source<\/a><\/p>\n","protected":false},"excerpt":{"rendered":" Author: For the past few years, we\u2019ve applied core Google AI research and engineering to projects with positive societal impact, including forecasting floods, protecting whales […] Read More<\/a><\/span><\/p>\n","protected":false},"author":1,"featured_media":2566,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_bbp_topic_count":0,"_bbp_reply_count":0,"_bbp_total_topic_count":0,"_bbp_total_reply_count":0,"_bbp_voice_count":0,"_bbp_anonymous_reply_count":0,"_bbp_topic_count_hidden":0,"_bbp_reply_count_hidden":0,"_bbp_forum_subforum_count":0,"footnotes":""},"categories":[24],"tags":[],"_links":{"self":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/posts\/2565"}],"collection":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/comments?post=2565"}],"version-history":[{"count":0,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/posts\/2565\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/media\/2566"}],"wp:attachment":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/media?parent=2565"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/categories?post=2565"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/tags?post=2565"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}![\"Goog\"](\"https:\/\/storage.googleapis.com\/gweb-uniblog-publish-prod\/images\/Google_AI_ImpactChallenge_Report_Charts_Gl.max-1000x1000.png\")
<\/figure>\n<\/div>\n
<\/b><\/p>\n
<\/b><\/p>\n