Tips

Tips

• Data science's ongoing battle to quell bias in machine learning

  Machine learning expert Ben Cox of H2O.ai discusses the problem of bias in predictive models that confronts data scientists daily and his techniques to identify and neutralize it.  Continue Reading

• AI vs. machine learning vs. deep learning: Key differences

  AI terms are often used interchangeably, but they are not the same. Understand the difference between artificial intelligence, machine learning and deep learning.  Continue Reading

• 4 main types of AI explained

  The emergence of artificial superintelligence will change humanity, but it's not happening soon. Here are the types of AI leading up to that new reality.  Continue Reading

• How to create NLP metrics to improve your enterprise model

  As standardized NLP framework evaluations become popular, experts urge users to focus on individualized metrics for enterprise success.  Continue Reading

• 5 benefits of AI in banking

  Despite risks, AI tools are helping banks overcome traditional customer service challenges and streamline back-end processes. Here are five benefits of AI in banking.  Continue Reading

• AI in the construction industry refurbishes trade procedures

  From design to reducing workplace injury, AI in the construction industry is changing manual labor jobs. Deploying cobots and AI systems is creating visible business value.  Continue Reading

• Perfect AI-defined infrastructure by analyzing your data center

  Before implementing AI, evaluate your IT team and data storage center. Experts explain the fundamental elements of data storage required to tailor an AI-defined infrastructure.  Continue Reading

• Artificial intelligence data storage planning best practices

  AI storage planning is similar to the storage planning you're used to: Consider capacity, IOPS and reliability requirements for source data and the application's database.  Continue Reading

• How to use machine learning to build a predictive algorithm

  Machine learning is an invaluable tool for solving business problems, but don't jump into it for predictive analytics without understanding these important factors.  Continue Reading

• Synthetic data could ease the burden of training data for AI models

  Sometimes it's better to manufacture training data for machine learning models than it is to collect it.  Continue Reading

• Dun & Bradstreet's chief data scientist: Don't ignore these eight AI topics

  Anthony Scriffignano's list of AI topics to watch in 2018 highlights the benefits and complications the widespread application of artificial intelligence technology will have on the enterprise in the coming year.  Continue Reading

• Machine learning and AI training paves new cloud career paths

  As cloud-based artificial intelligence gains momentum in the enterprise, business leaders will look to admins to implement and make the most of the technology.  Continue Reading

• Tensor Processing Units were purpose-built for machine learning: Pros, cons

  Google's Tensor Processing Units are built to train and run machine learning models. Experts discuss their plusses and minuses compared to CPUs and GPUs.  Continue Reading

• Microsoft Cognitive Services brings cloud AI to the enterprise

  Like other cloud-based offerings, Cognitive Services in Azure makes AI more accessible to the enterprise. Pricing and integration, however, can still be a challenge.  Continue Reading

• Three examples of machine learning methods and related algorithms

  Consultant David Loshin explains some widely used data analytics and machine learning techniques and details how the associated automated algorithms work.  Continue Reading

• Evaluate machine learning services in public cloud

  Cloud computing has pushed machine learning into the mainstream and made it more affordable for enterprises. See what services are available from top vendors AWS, Azure and Google.  Continue Reading

• AI in healthcare must overcome security, interoperability concerns

  Although AI has several useful applications in healthcare, the industry has yet to fully embrace the technology because of a lack of interoperability and privacy concerns.  Continue Reading

• Why machine learning models require a failover plan

  Flawed machine learning models lead to failures and user interruptions. Expert Judith Myerson explains the causes for failures and how a failover plan can improve user experience.  Continue Reading