Evaluate

AI integration

• Data visualization process yields 360 AI-driven analytics view

  Data visualization tools find increasing uses as part of AI processes to explore data in the initial stages of model development and make outputs easier to digest. Continue Reading

• How the top open source AI software drives innovation

  In the world of AI, open source software is driving most of the innovation. But with vendor tools largely sidelined, what does this mean for things like security and technical support? Continue Reading

• 3 intelligent process automation use cases and how they work

  Enterprises are pursuing intelligent process automation to take their digital transformation and RPA applications to the next level. Here's a look at three use cases. Continue Reading

• How automated machine learning tools pave the way to AI

  Every enterprise is trying to get to machine learning and, ultimately, AI, but not every business has the level of skill in-house to make it happen. Is automated machine learning the answer for them? Continue Reading

• Choosing the right chip foundation for AI-optimized hardware

  Every enterprise is trying to implement AI and machine learning. But, before AI, before clean data and before platform comparison, enterprises need to find the best hardware to support AI. Continue Reading

• Key considerations for operationalizing machine learning

  Once a machine learning model is trained, developers need to operationalize it. This turns out to be a significant challenge for many enterprises.Continue Reading

• Causal deep learning teaches AI to ask why

  Most AI runs on pattern recognition, but as any high school student will tell you, correlation is not causation. Researchers are now looking at ways to help AI fathom this deeper level.Continue Reading

• Enterprise consumer relationships are building trust in AI

  Transparency is an increasingly important component of consumer trust. If you want to win over consumers whose data is being collected, start with explainability and collaboration.Continue Reading

• How AI in physical security makes public places safer

  Deep learning-based tools are increasingly finding a home in physical security to enhance the protection of real-world assets and make public spaces safer.Continue Reading

• The future of voice assistants is multiturn conversations

  The future looks promising for voice assistants, but for them to really live up to the hype, they are going to have to improve at true multiturn conversations.Continue Reading

• Applications of autonomous robots lead in the enterprise

  In enterprise AI, bot technology is leading the charge. Seamless integration and process streamlining are initial benefits, but the true profit lies in what comes next -- auto-AI.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

• AI at the edge spurs decentralization, IoT interconnectivity

  As AI spreads into most enterprises, it's imperative that devices or programs can make immediate smart decisions. Localized AI at the edge is aiming to tackle the lag.Continue Reading

• Inventory optimization machine learning tool sharpens pricing

  Retailers are chasing data-driven process automation in hopes of boosting sales and streamlining margins. But how do you decide which processes to automate or vendor to implement?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