Evaluate

Neural networks and deep learning

• Interpretable AI has benefits beyond compliance

  Making AI models more interpretable has a wide range of benefits and shouldn't just be thought of as a compliance exercise, experts at the H2O World conference said. Continue Reading

• New deep learning techniques take center stage

  The days of simple, linear regressions powering machine learning are on their way out, as newer, more powerful deep learning techniques find a range of enterprise use cases. Continue Reading

• Davenport: AI-based projects should be focused in scope

  New AI projects in enterprises should focus on achievable goals rather than try to reinvent business processes or products and services, Tom Davenport says. Continue Reading

• The future of AI technology: 2019 and beyond

  Enterprise AI trends in 2019 will likely include more AI-powered tools for healthcare and fintech, as well as accelerating consolidation among smaller AI vendors. Continue Reading

• Explainability is no solution to problem of bias in AI

  Explainability has been touted as a solution to the problem of biased AI models, but experts say that approach only gets you part of the way to bias-free applications. Continue Reading

• Impact of AI on business still more limited than some hoped

  Most businesses have yet to feel the full effect of AI, as enterprises labor to determine where and under what conditions the technology can most effectively be deployed.Continue Reading

• AI in fashion design and retail takes center stage

  AI and fashion are a natural pairing as clothing designers and retailers are using technology to monitor what's hot and improve the efficiency of manufacturing and supply chains.Continue Reading

• Countries need national strategy for AI to stay competitive

  With the Chinese government funding artificial intelligence at an aggressive pace, the U.S. and other countries are facing substantial pressure to step up their investment.Continue Reading

• Nations sharpen AI strategies as global competition heats up

  The U.S. and China are the leading countries in AI research and development right now, but nations throughout the world are developing strategies to remain competitive.Continue Reading

• Healthcare and AI are a good pair -- until vendors overpromise

  Excessive promotion and grandiose promises from vendors like IBM are making it hard to accurately judge what the proper role of AI in healthcare should be.Continue Reading

• Current state of AI is poorly understood by the public

  Current applications of AI revolve entirely around machine learning. But researcher Zachary Lipton says that's not the message you get from most media coverage of AI.Continue Reading

• AI in policing is controversial but delivers results

  The last decade saw a drive toward more predictive policing and that trend is only growing stronger as more AI tools come on the scene. But not everyone is happy with the trend.Continue Reading

• IBM and AI: Researcher talks current and future projects

  The goal of numerous IBM AI projects is to ultimately create programs to augment human skills and jobs, not to take jobs away from humans, an IBM researcher says.Continue Reading

• Machine learning applications: From lab to enterprise

  The process from lab idea to enterprise reality may challenge machine learning applications. Learn how implementers are navigating the journey to practical use.Continue Reading

• AI in banking industry brings operational improvements

  Banks using AI tools are finding that the technology can help them overcome traditional customer service challenges, but barriers to widespread use remain.Continue Reading