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• AI breathes new life into IBM mainframes

  Often seen as the mammoths of IT shops well past their time, mainframes injected with AI are finding new opportunities for the massive data residing in the belly of the beast. Continue Reading

• How GenAI deployments are redefining everyday work routines

  GenAI deployments are making dramatic strides in boosting employee productivity, strategic analysis, software development cycles and customer service -- but not without risks. Continue Reading

• How CIOs should architect trust in AI -- not just govern it

  When designing trustworthy enterprise AI applications, platform architecture, not policy alone, is the best way to minimize long-term risk and ensure compliance and sustainability. Continue Reading

• Is your business ready for an agentic AI team?

  The race is on to implement agentic AI teams. But can your business successfully deploy and manage them before taking this next step in enterprise automation? Continue Reading

• Is neuromorphic computing the future of AI hardware?

  AI hardware is hitting its limits. Is neuromorphic computing with its reduced energy use and latency a viable alternative? Hear one expert's take on this emerging technology. Continue Reading

• Definitions to Get Started

  • What is OpenAI?
  • What is lemmatization?
  • What is GPT-3? Everything you need to know
  • What is chain-of-thought prompting (CoT)? Examples and benefits

  • What is a transformer model?
  • What is the Google Gemini AI model (formerly Bard)?
  • What is LangChain and how to use it: A guide
  • What is multimodal AI? Full guide

  View All Definitions

• How businesses can close the AI engineering gap

  Amid growing concerns that AI is replacing jobs in several sectors, demand for AI engineers is surging as businesses employ several methods to overcome a severe skills shortage.Continue Reading

• How agentic AI is changing work, strategy and competitiveness

  AI agents are transforming work, business strategy and global competitiveness by enabling autonomous workflows, innovation and value creation in enterprises.Continue Reading

• Battle of the bots: Best GenAI chatbots for business

  Businesses are deploying multiple GenAI tools to support productivity. Success requires choosing the right tools for the right use case and keeping a close eye on the outcomes.Continue Reading

• 35 AI content generators to explore in 2026

  Stay abreast of the growing selection of artificially generated media that businesses can use to produce text, images, music and code.Continue Reading

• Optimize AI models to generate more bang for your buck

  AI models must be fully optimized to align with business goals, provide valuable insights and produce positive ROI. Check out these proven, common sense model optimization methods.Continue Reading

• Best practices for building scalable AI infrastructure

  Scalability is vital for AI platforms. These 10 best practices can help businesses build scalable infrastructure that supports AI workloads and adapts to fluctuating demands.Continue Reading

• AI deployments gone wrong: The fallout and lessons learned

  AI's deployment landscape is littered with good intentions, crushed projects and unintended consequences due to misaligned business goals, mistrust in AI and weak management.Continue Reading

• How business leaders can prepare for quantum-AI integration

  AI completely changed the way we work, and quantum computing has the potential to upend our work completely. How can these technologies integrate to achieve further innovation?Continue Reading

• How to navigate data sovereignty for AI compliance

  As AI systems consume data for training and decision-making, businesses must navigate complex regulations, geopolitical boundaries and vendor risks.Continue Reading

• 5 skills needed to become a prompt engineer

  With the rise of generative AI, prompt engineering has emerged as a new profession. Desired skills include refining prompts, analyzing AI output and ensuring alignment with business goals.Continue Reading

• Generative models: VAEs, GANs, diffusion, transformers, NeRFs

  From powering conversational chatbots to realistic image creators, generative models are critical for many AI applications.Continue Reading

• How to use Perplexity AI: Tutorial, pros and cons

  AI-powered search engine Perplexity offers a conversational tone and much-needed source citations -- but it's not perfect. Learn how the tool works and how to start using it.Continue Reading

• What is OpenAI?

  OpenAI is a private research laboratory that aims to develop and direct artificial intelligence (AI) in ways that benefit humanity as a whole.Continue Reading

• GitHub Copilot vs. ChatGPT: How do they compare?

  Copilot and ChatGPT are generative AI tools that can help coders be more productive. Learn about their strengths and weaknesses, as well as alternative coding assistants.Continue Reading

• 12 prompt engineering best practices and tips

  Asking the right questions is crucial for effective generative AI use. Learn 12 tips for writing clear, useful prompts, including mistakes to avoid and advice for image generation.Continue Reading

• What is lemmatization?

  Lemmatization is the process of grouping together different inflected forms of the same word.Continue Reading

• What is GPT-3? Everything you need to know

  GPT-3, or the third-generation Generative Pre-trained Transformer, is a neural network machine learning model trained using internet data to generate any type of text.Continue Reading

• What is chain-of-thought prompting (CoT)? Examples and benefits

  Chain-of-thought (CoT) prompting is a prompt engineering technique that aims to improve language models' performance on tasks requiring logic, calculation and decision-making by structuring the input prompt in a way that mimics human reasoning.Continue Reading

• What is a transformer model?

  A transformer model is a neural network architecture that can automatically transform one type of input into another type of output.Continue Reading

• What is the Google Gemini AI model (formerly Bard)?

  Google Gemini -- formerly known as Bard -- is an artificial intelligence (AI) chatbot tool designed by Google to simulate human conversations using natural language processing (NLP) and machine learning.Continue Reading

• What is LangChain and how to use it: A guide

  LangChain is an open source framework that enables software developers working with artificial intelligence (AI) and its machine learning subset to combine large language models with other external components to develop LLM-powered applications.Continue Reading

• What is multimodal AI? Full guide

  Multimodal AI is artificial intelligence that combines multiple types, or modes, of data to create more accurate determinations, draw insightful conclusions or make more precise predictions about real-world problems.Continue Reading

• What is Dall-E and how does it work?

  Dall-E is a generative artificial intelligence (AI) technology that enables users to create images by submitting text-based prompts.Continue Reading

• Generative AI vs. predictive AI: Understanding the differences

  Generative AI and predictive AI vary in how they handle use cases and unstructured and structured data, respectively. Explore the benefits and limitations of each.Continue Reading

• What is an AI prompt?

  An artificial intelligence (AI) prompt is the input submitted to a large language model (LLM) via a generative AI platform, like OpenAI's ChatGPT or Microsoft Copilot.Continue Reading

• Types of AI algorithms and how they work

  AI algorithms can help businesses gain a competitive advantage. Learn the main types of AI algorithms, how they work, and why companies must thoroughly evaluate benefits and risks.Continue Reading

• What is natural language generation (NLG)?

  Natural language generation (NLG) is the use of artificial intelligence (AI) programming to produce written or spoken narratives from a data set.Continue Reading

• ChatGPT explained in a minute

  ChatGPT is an AI-powered chatbot developed by OpenAI. With its ability to communicate in natural language patterns, it can create various types of content for many use cases.Continue Reading

• Why and how to develop a set of responsible AI principles

  Enterprise AI use raises a range of pressing ethical issues. Learn why responsible AI principles matter and explore best practices for enterprises developing an AI framework.Continue Reading

• image recognition

  Image recognition, in the context of machine vision, is the ability of software to identify objects, places, people, writing and actions in digital images.Continue Reading

• PyTorch

  PyTorch is an open source machine learning (ML) framework based on the Python programming language and the Torch library.Continue Reading

• Model optimization methods to cut latency, adapt to new data

  This last part of the series on machine learning explains two final model optimization techniques: lightweight model implementation and incremental model learning.Continue Reading

• 2 supervised learning techniques that aid value predictions

  Learn how two supervised machine learning techniques -- numerical prediction and category prediction -- work to predict values and, thus, can aid model training.Continue Reading

• 2 data-wrangling techniques for better machine learning

  Before data can be usefully inputted into algorithms, it must first be prepared. Learn two of the techniques that do the job and make machine learning work.Continue Reading

• How feature selection, extraction improve ML predictions

  In this discussion of machine learning patterns, learn how feature selection and feature extraction help make data more useful and, thus, improve predictions.Continue Reading

• Associativity, graphical summary computations aid ML insights

  Associativity computation and graphical summary computation allow for more complex insights, and in turn improve predictions. Explore how these ML techniques work in practice.Continue Reading

• Common ML patterns: Central tendency and variability

  Four common patterns provide approaches to solving machine-learning problems. Learn how two -- central tendency computation and variability computation -- work.Continue Reading

• The supervised approach to machine learning

  In part 2 of our machine learning tutorial, learn how to use the supervised learning approach to machine learning to produce the best predictions.Continue Reading

• Introduction to using machine learning

  The first part of our machine learning series, excerpted from training materials for Arcitura's Machine Learning Specialist certification, introduces algorithms, models and model training.Continue Reading

• A basic design pattern for image recognition

  Learn how a design pattern based on convolutional neural networks can be adapted to create a visual graphics generator model for image recognition.Continue Reading