84% of marketing leaders use predictive analytics, but struggle with data-driven decisions
By VB Staff, contributing writers for Venturebeat.com
Artificial intelligence (AI) holds great promise for businesses today, especially for marketing teams who must anticipate customers’ interests and behavior to achieve their goals. Despite the growing availability of AI-powered technologies, many marketers are still in the early days of formulating their AI strategies.
ML of binary ‘yes/no’ systems may improve medical diagnoses, financial risk analysis, and more
By Embry-Riddle Aeronautical University, contributing writers for Techxplore.com
Similar to a mouse racing through a maze, making “yes” or “no” decisions at every intersection, researchers have developed a way for machines to swiftly learn all the twists and turns in a complex data system.
Why data remains the greatest challenge for ML projects
By Ben Dickson, contributing writer for Venturebeat.com
Quality data is at the heart of the success of enterprise artificial intelligence (AI). And accordingly, it remains the main source of challenges for companies that want to apply machine learning (ML) in their applications and operations.
Feeding the world by AI, ML and the cloud
By MIT Technology Review Insights, contributing writers for Technologyreview.com
Although the world population has continued to steadily increase, farming practices have largely remained the same. Amid this growth, climate change poses great challenges to the agricultural industry and its capacity to feed the world sustainably. According to the World Bank, 70% of the world’s fresh water is used in agriculture and droughts and heat waves continue to threaten crops. And that is where the challenge arises to feed the world while mitigating the environmental effects of agricultural practices.
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