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Neural networks and deep learning

• Federated deep learning offers new approach to model training

  Training deep learning models puts a massive strain on enterprise infrastructure, but federated learning, which trains models on endpoint devices, could lessen some of the demand. Continue Reading

• Enterprises are crowdsourcing AI development

  By crowdsourcing AI development, enterprises can broaden the knowledge base of their machine learning applications, and early adopters are showing promising results. Continue Reading

• How to address the hidden risks of algorithmic decision making

  From biased customer interactions to harmful treatment recommendations, some risks of automated decisions have yet to be resolved. A Wharton School professor has a way out. Continue Reading

• Data preparation for machine learning still requires humans

  Looking to AI to automate more of your processes? Don't overlook the labor that's still needed to prepare data for training machine learning and AI algorithms. Continue Reading

• Enterprises need to develop an AI strategy now

  Business leaders who approach AI as a future concern are likely to miss the boat on this influential technology. The time to explore AI implementation is now. Continue Reading

• New uses for GAN technology focus on optimizing existing tech

  GAN technology has emerged as the latest facet of AI that can be applied to existing technology to stack learning. What else can it do? Here are emerging use cases for GANs.Continue Reading

• 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 for machine learning are giving way to more powerful deep learning techniques that point the way toward general AI capabilities.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