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• Generative adversarial networks could be most powerful algorithm in AI

  The emergence of generative adversarial networks has been called one of the most interesting successes in recent AI development and could make AI applications more creative. Continue Reading

• Limits of AI today push general-purpose tools to the horizon

  The future of AI should be focused on more general-purpose tools, but developers have a long way to go before achieving the kind of AI movies taught us to expect. Continue Reading

• New data science platforms aim to be workflow, collaboration hubs

  Oracle's acquisition of DataScience.com is shining a spotlight on workbench-style platforms designed to centralize advanced analytics work by teams of data scientists. Continue Reading

• Addressing the ethical issues of AI is key to effective use

  Enterprises must confront the ethical implications of AI use as they increasingly roll out technology that has the potential to reshape how humans interact with machines. Continue Reading

• Machine learning still big at Stripe despite deep learning hype

  Classical machine learning methods are getting overshadowed in today's AI landscape, but problems with deep learning are keeping them relevant at payment processor Stripe. Continue Reading

• Definitions to Get Started

  • What is Gemma? Google's open sourced AI model explained
  • What is neuro-symbolic AI?
  • What is AI red teaming?
  • What is data poisoning (AI poisoning) and how does it work?

  • What is Q-learning?
  • What is Fréchet inception distance (FID)?
  • What is computational linguistics? Definition and career info
  • What is Dall-E and how does it work?

  View All Definitions

• Combination of blockchain and AI makes models more transparent

  Blockchain technology could play an important role in helping enterprises develop more explainable AI applications, something that is frequently lacking today.Continue Reading

• New Intel toolkit OpenVINO supports deep learning on CPUs

  A new developer kit from Intel seeks to lower the bar for doing deep learning on CPUs and other types of chips to extract more intelligence from video.Continue Reading

• Implementing deep learning requires a creative approach

  Using deep learning in an effective way requires creative problem-solving and a team approach that goes beyond simply hiring data scientists, experts say.Continue Reading

• Avoid bias in algorithms for best AI results

  In this podcast, we examine leading thoughts on the problem of AI bias and how to mitigate some of the most common sources of unfair treatment of users of AI applications.Continue Reading

• AI in call centers amplifies customer voice

  Speech analytics use cases involving customer contact centers show how AI technology can make sense out of messy human language, helping businesses along the way.Continue Reading

• Limitations of neural networks grow clearer in business

  AI often means neural networks, but intensive training requirements are prompting enterprises to look for alternatives to neural networks that are easier to implement.Continue Reading

• AI implementation is a winner-take-all race, analyst says

  Your AI strategy should focus on growth rather than efficiency, says McKinsey analyst Jacques Bughin -- advice that enterprises rarely hear when launching projects.Continue Reading

• Value of NLP applications varies for different AI uses

  Chatbots and virtual assistants are built on sophisticated component pieces, like NLP tools and automated bot technology, which can be implemented on their own in some use cases.Continue Reading

• Why Intuit aims chatbot design at a narrow set of tasks

  A data scientist at Intuit details the finance software vendor's approach to building chatbots -- and explains why it's limiting them to some basic customer service activities.Continue Reading

• How to keep your implementation of AI free from algorithm bias

  When implementing AI, it's important to focus on the quality of training data and model transparency in order to avoid potentially damaging bias in models.Continue Reading

• Humans and AI tools go hand in hand in analytics applications

  Companies are keeping data analysts and other workers in the loop with AI applications to check the results generated by automated algorithms for accuracy, relevance and missing info.Continue Reading

• Artificial intelligence in business strategies, uses

  SearchEnterpriseAI delivers news, tips and strategic advice on applying artificial intelligence technologies in the enterprise to improve products, services and operations.Continue Reading

• Slow pace for AI implementation is a prudent business strategy

  Enterprises eyeing AI development need to keep expectations under control and make sure projects align with business priorities to get real value from the technology.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

• Gauge your knowledge of cloud providers' AI technologies

  As enterprise interest grows, major cloud providers continue to unveil machine learning and AI services. See how much you know about their offerings with this brief quiz.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

• Machine learning models require DevOps-style workflows

  Big data is driving the use of AI and machine learning. But teams must be swift to embrace DevOps and re-evaluate models, according to Wikibon's James Kobielus.Continue Reading

• AI components make tools more than the sum of their parts

  AI applications, rather than being one monolithic tool, are built around a diverse collection of tools and techniques that combine to produce advanced functionality.Continue Reading