Evaluate

Neural networks and deep learning

• Computer vision AI looks beyond the narrow into the mainstream

  This handbook looks at computer vision in the enterprise, with examples of business applications and advice on deploying systems that incorporate the AI technology. Continue Reading

• Computer vision tools reach into test, healthcare, security

  Gaining a reputation as a viable technology in niche applications like X-ray scans, fingerprint matching and robotics, computer vision looks to mainstream, commodified apps. Continue Reading

• Deep learning recognition use cases grow as tech matures

  As deep learning image and voice recognition technology improves, enterprises are finding novel ways to apply the technology to sharpen and improve their operations. Continue Reading

• Use of AI in payments industry is set to explode

  Payment processors are making wider use of AI technologies as part of an effort to make better use of their vast troves of data and connect more directly with customers. Continue Reading

• Grammarly AI and an update to the writing tool

  A Grammarly update adds a new consistency checker tool to the writing platform that uses AI to automatically scan for multiple writing styles within a single document. Continue Reading

• 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