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• stemming

  Stemming is the process of reducing a word to its stem that affixes to suffixes and prefixes or to the roots of words known as "lemmas." Continue Reading

• algorithmic transparency

  Algorithmic transparency is openness about the purpose, structure and underlying actions of the algorithms used to search for, process and deliver information. Continue Reading

• How AI in weather prediction can aid human intelligence

  AI and machine learning models are becoming more widely used in climate prediction and disaster preparedness to aid experts without replacing them. Continue Reading

• The white-box model approach aims for interpretable AI

  The white-box model approach to machine learning makes AI interpretable since algorithms are easy to understand. Ajay Thampi, author of 'Interpretable AI,' explains this approach. Continue Reading

• How hybrid chatbots improve customer experience

  Hybrid chatbots combine human intelligence with AI used in standard chatbots to improve customer experience. Learn how industries are using them to engage with customers. Continue Reading

• Definitions to Get Started

  • What is automated machine learning (AutoML)?
  • What are AI agents? Types and examples
  • What is an intelligent agent? Definition, use cases and benefits
  • What is natural language processing (NLP)?

  • What is a neural network?
  • What is edge AI?
  • What is AgentOps? What it does and how it powers AI agents
  • What is automated reasoning? How is it used in AI?

  View All Definitions

• Weighing quantum AI's business potential

  Quantum AI has the potential to revolutionize business computing, but logistic complexities create sizeable obstacles for near-term adoption and success.Continue Reading

• knowledge engineering

  Knowledge engineering is a field of artificial intelligence (AI) that tries to emulate the judgment and behavior of a human expert in a given field.Continue Reading

• Stochastic point processes and their practical value

  Data scientists learn and utilize stochastic point processes for myriad pragmatic uses. Data scientist Vincent Granville explains this in his new book.Continue Reading

• How AI ethics is the cornerstone of governance

  The concept of AI ethics ensures that AI systems provide accuracy and reliability. Businesses will benefit from adopting AI ethics strategies of their own.Continue Reading

• dropout

  Dropout refers to data, or noise, that's intentionally dropped from a neural network to improve processing and time to results.Continue Reading

• Training GANs relies on calibrating 2 unstable neural networks

  Understanding the complexities and theory of dueling neural networks can carve out a path to successful GAN training.Continue Reading

• Neuro-symbolic AI emerges as powerful new approach

  The unification of two antagonistic approaches in AI is seen as an important milestone in the evolution of AI. Read about the efforts to combine symbolic reasoning and deep learning by the field's leading experts.Continue Reading

• UX defines chasm between explainable vs. interpretable AI

  From deep learning to simple code, all algorithms should be transparent. The frameworks of AI interpretability and explainability aim to make machine learning understandable to humans.Continue Reading

• How to build a neural network from the ground floor

  Deep learning is powering the development of AI. To build your own neural network, start by understanding the basics: how neural networks learn, correlate and stack with data.Continue Reading

• How to develop a successful, modern AI infrastructure

  Before AI can revolutionize business processes or decision-making, companies need a strong foundation. These tools, platforms and applications help enterprises get started with AI.Continue Reading

• Neural network applications in business run wide, fast and deep

  Neural network uses are starting to emerge in the enterprise. This handbook examines the growing number of businesses reporting gains from implementing this technology.Continue Reading

• 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

• 3 ways to create an AI ethics framework for responsible tech

  AI can often reflect the biases and limits of its human developers. Experts say diversity, review boards and a strong AI ethics framework will lead the way toward ethical AI.Continue Reading

• RPA in banking gives fintech a competitive edge

  RPA in banking is setting its sights on fintech and flexible banking to compete with traditional banking. Community banks still see hurdles despite potential to wield RPA.Continue Reading

• AI as a service democratizes benefits of new tech tools

  The emergence of AI-as-a-service tools is helping more enterprises access the benefits of AI, not just the leading-edge tech companies that pioneered the technology.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

• Knowledge graph applications in the enterprise gain steam

  As the maturity of knowledge graphs improves, enterprises are finding new ways to incorporate them into business operations, though stumbling blocks remain.Continue Reading

• Convert unstructured data to structured data with machine learning

  With access to powerful compute power and advances in machine learning, unstructured data is becoming easier and cheaper for businesses to turn into usable sources of insight.Continue Reading

• AI in real estate smooths paper-based processes

  The use of AI applications in real estate aims to make the paper-based processes of buying and selling property more reliable and repeatable.Continue Reading

• AI in hospitality industry helps smooth travel turbulence

  A growing number of hotels using AI are reporting streamlined customer service and improved cross-sell opportunities, but the biggest benefits likely lay ahead.Continue Reading

• Customer support chatbots set to transform service functions

  AI-enabled chatbots are helping enterprises improve their customer service functions by automating some tasks, enabling human workers to focus on what really matters.Continue Reading

• GPU cloud tools take complexity out of machine learning infrastructure

  While talk of AI on GPUs is abuzz, actually building a machine learning infrastructure remains a dark art. A startup's PaaS is looking to automate parts of the process.Continue Reading

• Computer vision technology helps Trulia link buyers to homes

  In this podcast, Trulia's vice president of engineering discusses the importance of computer vision applications to the website's overall goal of helping buyers find homes.Continue Reading

• AI in insurance forces big changes to traditional industry

  Insurance companies using AI are forcing firms in this traditional industry to grapple with new technology and evaluate emerging risks that could impact their bottom lines.Continue Reading

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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