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• How to build an enterprise generative AI tech stack

  Generative AI tech stacks consist of key components like LLMs, vector databases and fine-tuning tools. The right tech stack can help enterprises maximize their generative AI ROI. Continue Reading

• AI red teaming

  AI red teaming is the practice of simulating attack scenarios on an artificial intelligence application to pinpoint weaknesses and plan preventative measures. Continue Reading

• How to get started with machine learning

  Machine learning roles are rapidly evolving and require a diverse range of skills. Looking to join the field? Start by exploring job responsibilities and required experience. Continue Reading

• chain-of-thought prompting

  Chain-of-thought 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

• The need for common sense in AI systems

  Building explainable and trustworthy AI systems is paramount. To get there, computer scientists Ron Brachman and Hector Levesque suggest infusing common sense into AI development. Continue Reading

• Definitions to Get Started

  • AI red teaming
  • chain-of-thought prompting
  • Gemma
  • Google Gemini (formerly Bard)

  • augmented intelligence
  • BERT language model
  • natural language processing (NLP)
  • fine-tuning

  View All Definitions

• Gemma

  Gemma is a collection of lightweight open source generative AI models designed mainly for developers and researchers.Continue Reading

• Google Gemini (formerly Bard)

  Google Gemini -- formerly called 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

• Compare large language models vs. generative AI

  While large language models like ChatGPT grab headlines, the generative AI landscape is far more diverse, spanning models that are changing how we create images, audio and video.Continue Reading

• Prompt engineering tips for ChatGPT and other LLMs

  Master the art of prompt engineering -- from basic best practices to advanced strategies -- with practical tips to get more precise, relevant output from large language models.Continue Reading

• augmented intelligence

  Augmented intelligence is the use of technology to enhance a human's ability to execute tasks, perform analysis and make decisions.Continue Reading

• BERT language model

  BERT language model is an open source machine learning framework for natural language processing (NLP).Continue Reading

• natural language processing (NLP)

  Natural language processing (NLP) is the ability of a computer program to understand human language as it’s spoken and written -- referred to as natural language.Continue Reading

• Improve AI security by red teaming large language models

  Cyberattacks such as prompt injection pose significant security risks to LLMs, but implementing red teaming strategies can test models' resistance to various cyberthreats.Continue Reading

• fine-tuning

  Fine-tuning is the process of taking a pretrained machine learning model and further training it on a smaller, targeted data set.Continue Reading

• The role of trusted data in building reliable, effective AI

  Without quality data, creating and managing AI systems is an uphill battle. Methods such as zero-copy integration and primary key consistency can ensure trusted data for better AI.Continue Reading

• graph neural networks (GNNs)

  Graph neural networks (GNNs) are a type of neural network architecture and deep learning method that can help users analyze graphs, enabling them to make predictions based on the data described by a graph's nodes and edges.Continue Reading

• 8 top generative AI tool categories for 2024

  Need a generative AI-specific tool for your organization's development project? Explore the major categories these tools fall into and their capabilities.Continue Reading

• Retrieval-Augmented Language Model pre-training

  A Retrieval-Augmented Language Model, also referred to as REALM or RALM, is an artificial intelligence language model designed to retrieve text and then use it to perform question-based tasks.Continue Reading

• AI model optimization: How to do it and why it matters

  Challenges like model drift and operational inefficiency can plague AI models. These model optimization strategies can help engineers improve performance and mitigate issues.Continue Reading

• AgentGPT

  AgentGPT is a generative artificial intelligence tool that enables users to create autonomous AI agents that can be delegated a range of tasks.Continue Reading

• autonomous artificial intelligence (autonomous AI)

  Autonomous artificial intelligence (AI) is a branch of AI in which systems and tools are advanced enough to act with limited human oversight and involvement.Continue Reading

• Video guide to generative AI

  Generative AI has the potential to revolutionize technology. Learn about popular interfaces such as ChatGPT, the future of generative AI and its effects on businesses.Continue Reading

• vector embeddings

  Vector embeddings are numerical representations that capture the relationships and meaning of words, phrases and other data types.Continue Reading

• masked language models (MLMs)

  Masked language models (MLMs) are used in natural language processing (NLP) tasks for training language models.Continue Reading

• knowledge graph in ML

  In the realm of machine learning (ML), a knowledge graph is a graphical representation that captures the connections between different entities.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

• Artificial intelligence vs. human intelligence: Differences explained

  Artificial intelligence is humanlike. There are differences, however, between natural and artificial intelligence. Here are three ways AI and human cognition diverge.Continue Reading

• How AI is advancing assistive technology

  Recent advances in generative AI could revolutionize assistive technology. For people relying on assistive tools, AI-powered devices could usher in a new era of accessibility.Continue Reading

• conversational AI (conversational artificial intelligence)

  Conversational AI (conversational artificial intelligence) is a type of AI that enables computers to understand, process and generate human language.Continue Reading

• What is generative AI? Everything you need to know

  Generative AI is a type of artificial intelligence technology that can produce various types of content, including text, imagery, audio and synthetic data.Continue Reading

• artificial intelligence (AI) governance

  Artificial intelligence governance is the legal framework for ensuring AI and machine learning technologies are researched and developed with the goal of helping humanity adopt and use these systems in ethical and responsible ways.Continue Reading

• convolutional neural network (CNN)

  A convolutional neural network (CNN) is a category of machine learning model, namely a type of deep learning algorithm well suited to analyzing visual data.Continue Reading

• What is sentiment analysis?

  Learn how AI is used to perform sentiment analysis, the different categories of sentiment that can be identified and how the analysis can be used to improve customer satisfaction.Continue Reading

• What is natural language processing (NLP)?

  NLP enables computers to understand language like humans. This video explores its techniques, applications and challenges, highlighting its importance in businesses.Continue Reading

• Learn how to create a machine learning pipeline

  Well-considered machine learning pipelines provide a structured approach to AI development in modern IT environments, ensuring uniformity, speed and business alignment.Continue Reading

• Explore the impact of data science in business workflows

  Data science and machine learning are reshaping business workflows and customer experiences, ushering in an era of highly tailored services and predictive strategies.Continue Reading

• 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

• computational linguistics (CL)

  Computational linguistics (CL) is the application of computer science to the analysis and comprehension of written and spoken language.Continue Reading

• How do big data and AI work together?

  Enterprises are leaning on big data to train AI algorithms and, in turn, are using AI to understand big data. The results are pushing operations forward.Continue Reading

• How an AI governance framework can strengthen security

  Learn how AI governance frameworks promote security and compliance in enterprise AI deployments with essential components such as risk analysis, access control and incident response.Continue Reading

• deep tech

  Deep technology, or deep tech, refers to advanced technologies based on some form of substantial scientific or engineering innovation.Continue Reading

• 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

• Compare 8 prompt engineering tools

  To get the most out of large language models, developers and other users rely on prompt engineering techniques to achieve their desired output. Review 8 tools that can help.Continue Reading

• adversarial machine learning

  Adversarial machine learning is a technique used in machine learning (ML) to fool or misguide a model with malicious input.Continue Reading

• How to become an MLOps engineer

  Explore the key responsibilities and skills needed for a career in MLOps, which focuses on managing ML workflows throughout the model lifecycle.Continue Reading

• A guide to ChatGPT Enterprise use cases and implementation

  ChatGPT Enterprise promises powerful generative AI capabilities for business use cases, but successful implementation requires careful planning for security, costs and integration.Continue Reading

• How to build a winning AI strategy, explained by experts

  Executives are aware of the value artificial intelligence in its many forms can bring to enterprises yet devising a viable AI strategy can be as complex as the technology itself.Continue Reading

• robo-advisor

  A robo-advisor is a virtual financial advisor powered by artificial intelligence (AI) that employs an algorithm to deliver an automated selection of financial advisory services.Continue Reading

• narrow AI (weak AI)

  Narrow AI is an application of artificial intelligence technologies to enable a high-functioning system that replicates -- and perhaps surpasses -- human intelligence for a dedicated purpose.Continue Reading

• artificial superintelligence (ASI)

  Artificial superintelligence (ASI) is a software-based system with intellectual powers beyond those of humans across a comprehensive range of categories and fields of endeavor.Continue Reading

• artificial general intelligence (AGI)

  Artificial general intelligence (AGI) is the representation of generalized human cognitive abilities in software so that, faced with an unfamiliar task, the AI system could find a solution.Continue Reading

• artificial intelligence (AI)

  Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems.Continue Reading

• How do LLMs like ChatGPT work?

  AI expert Ronald Kneusel explains how transformer neural networks and extensive pretraining enable large language models like GPT-4 to develop versatile text generation abilities.Continue Reading

• Demystifying AI with a machine learning expert

  In this interview, author Ronald Kneusel discusses his new book 'How AI Works,' the recent generative AI boom and tips for those looking to enter the AI field.Continue Reading

• Tips for planning a machine learning architecture

  When planning a machine learning architecture, organizations must consider factors such as performance, cost and scalability. Review necessary components and best practices.Continue Reading

• AI watermarking

  AI watermarking is the process of embedding a recognizable, unique signal into the output of an artificial intelligence model, such as text or an image, to identify that content as AI generated.Continue Reading

• data dignity

  Data dignity, also known as data as labor, is a theory positing that people should be compensated for the data they have created.Continue Reading

• backpropagation algorithm

  Backpropagation, or backward propagation of errors, is an algorithm that is designed to test for errors working back from output nodes to input nodes.Continue Reading

• Machine learning vs. neural networks: What's the difference?

  Though machine learning and neural networks are both forms of AI, neural networks are a specific type of ML algorithm. Learn more about their similarities and differences.Continue Reading

• ambient intelligence (AmI)

  Ambient intelligence, sometimes referred to as AmI, is the element of a pervasive computing environment that enables it to interact with and respond appropriately to the humans in that environment.Continue Reading

• neural net processor

  A neural net processor is a central processing unit (CPU) that holds the modeled workings of how a human brain operates on a single chip.Continue Reading

• prompt engineering

  Prompt engineering is an AI engineering technique encompassing the process of refining LLMs with specific prompts and recommended outputs, as well as the process of refining input to various generative AI services to generate text or images.Continue Reading

• How to source AI infrastructure components

  Rent, buy or repurpose AI infrastructure? The right choice depends on an organization's planned AI projects, budget, data privacy needs and technical personnel resources.Continue Reading

• neurosynaptic chip

  A neurosynaptic chip, also known as a cognitive chip, is a computer processor that is designed to function more like a biological brain than a typical central processing unit (CPU).Continue Reading

• retrieval-augmented generation

  Retrieval-augmented generation (RAG) is an AI framework that retrieves data from external sources.Continue Reading

• IBM Watson supercomputer

  Watson was a supercomputer designed and developed by IBM. This advanced computer combined artificial intelligence (AI), automation and sophisticated analytics capabilities to deliver optimal performance as a 'question answering' machine.Continue Reading

• language modeling

  Language modeling, or LM, is the use of various statistical and probabilistic techniques to determine the probability of a given sequence of words occurring in a sentence. Language models analyze bodies of text data to provide a basis for their word...Continue Reading

• Amazon Bedrock (AWS Bedrock)

  Amazon Bedrock -- also known as AWS Bedrock -- is a machine learning platform used to build generative artificial intelligence (AI) applications on the Amazon Web Services cloud computing platform.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

• AI prompt

  An artificial intelligence (AI) prompt is a mode of interaction between a human and a large language model that lets the model generate the intended output.Continue Reading

• Why and how to use Google Colab

  Whether you're looking to gain experience or you're already an expert data scientist, Google Colab can help boost ML and AI initiatives. Follow this tutorial to learn the basics.Continue Reading

• image-to-image translation

  Image-to-image translation is a generative artificial intelligence (AI) technique that translates a source image into a target image while preserving certain visual properties of the original image.Continue Reading

• 10 prompt engineering tips and best practices

  Asking the right questions is key to using generative AI effectively. Learn 10 tips for writing clear, useful prompts, including mistakes to avoid and advice for image generation.Continue Reading

• AI prompt engineer

  An AI prompt engineer is an expert in creating text-based prompts or cues that can be interpreted and understood by large language models and generative AI tools.Continue Reading

• LangChain

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

• Lessons on integrating generative AI into the enterprise

  At Generative AI World 2023, various industries convened to explore existing and potential generative AI use cases. Review insights from one company's implementation experience.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

• How to build a machine learning model in 7 steps

  Building a machine learning model is a multistep process involving data collection and preparation, training, evaluation, and ongoing iteration. Follow these steps to get started.Continue Reading

• anomaly detection

  Anomaly detection is the process of identifying data points, entities or events that fall outside the normal range.Continue Reading

• machine vision

  Machine vision is the ability of a computer to see; it employs one or more video cameras, analog-to-digital conversion and digital signal processing.Continue Reading

• What is regression in machine learning?

  Regression in machine learning helps organizations forecast and make better decisions by revealing the relationships between variables. Learn how it's applied across industries.Continue Reading

• Machine learning regularization explained with examples

  Regularization in machine learning refers to a set of techniques used by data scientists to prevent overfitting. Learn how it improves ML models and prevents costly errors.Continue Reading

• Build a natural language processing chatbot from scratch

  In this excerpt from the book 'Natural Language Processing in Action,' you'll walk through the steps of creating a simple chatbot to understand how to start building NLP pipelines.Continue Reading

• Q&A: How to start learning natural language processing

  In this Q&A, 'Natural Language Processing in Action' co-author Hobson Lane discusses how to start learning NLP, including benefits and challenges of building your own pipelines.Continue Reading

• What are machine learning models? Types and examples

  Training data and algorithms are key, but there are many learning techniques, processes and practices that influence the selection, care and feeding of machine learning models.Continue Reading

• Attributes of open vs. closed AI explained

  What's the difference between open vs. closed AI, and why are these approaches sparking heated debate? Here's a look at their respective benefits and limitations.Continue Reading

• decision tree in machine learning

  A decision tree is a flow chart created by a computer algorithm to make decisions or numeric predictions based on information in a digital data set.Continue Reading

• Prompt engineering vs. fine-tuning: What's the difference?

  Prompt engineering and fine-tuning are both practices used to optimize AI output. But the two use different techniques and have distinct roles in model training.Continue Reading

• neural network

  A neural network is a machine learning (ML) model designed to mimic the function and structure of the human brain.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

• GPT-3

  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

• Compare machine learning vs. software engineering

  Although machine learning has a lot in common with traditional programming, the two disciplines have several key differences, author and computer scientist Chip Huyen explains.Continue Reading

• clustering in machine learning

  Clustering is a data science technique in machine learning that groups similar rows in a data set.Continue Reading

• The history of artificial intelligence: Complete AI timeline

  From the Turing test's introduction to ChatGPT's celebrated launch, AI's historical milestones have forever altered the lifestyles of consumers and operations of businesses.Continue Reading

• reinforcement learning

  Reinforcement learning is a machine learning training method based on rewarding desired behaviors and punishing undesired ones.Continue Reading

• linear regression

  Linear regression identifies the relationship between the mean value of one variable and the corresponding values of one or more other variables.Continue Reading

• natural language understanding (NLU)

  Natural language understanding (NLU) is a branch of artificial intelligence (AI) that uses computer software to understand input in the form of sentences using text or speech.Continue Reading

• 4 types of learning in machine learning explained

  Factoring performance, accuracy, reliability and explainability, data scientists consider supervised, unsupervised, semi-supervised and reinforcement models to reach best outcomes.Continue Reading

• What is boosting in machine learning?

  Boosting is a technique used in machine learning that trains an ensemble of so-called weak learners to produce an accurate model, or strong learner. Learn how it works.Continue Reading

• CNN vs. RNN: How are they different?

  Convolutional and recurrent neural networks have distinct but complementary capabilities and use cases. Compare each model architecture's strengths and weaknesses in this primer.Continue Reading