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• Dall-E

  Dall-E is a generative AI technology that enables users to create new images with text to graphics prompts. Continue Reading

• face detection

  Face detection, also called facial detection, is an artificial intelligence (AI)-based computer technology used to find and identify human faces in digital images and video. Continue Reading

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

  Choosing the right GenAI model for the task requires understanding the techniques each uses and their specific talents. Learn about VAEs, GANs, diffusion, transformers and NerFs. Continue Reading

• deconvolutional networks (deconvolutional neural networks)

  Deconvolutional networks are convolutional neural networks (CNN) that work in a reversed process. Continue Reading

• cognitive modeling

  Cognitive modeling is an area of computer science that deals with simulating human problem-solving and mental processing in a computerized model. Continue Reading

• Definitions to Get Started

  • natural language generation (NLG)
  • OpenAI
  • adversarial machine learning
  • robo-advisor

  • narrow AI (weak AI)
  • artificial intelligence (AI)
  • artificial general intelligence (AGI)
  • artificial superintelligence (ASI)

  View All Definitions

• expert system

  An expert system is a computer program that uses artificial intelligence (AI) technologies to simulate the judgment and behavior of a human or an organization that has expertise and experience in a particular field.Continue Reading

• 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

• CNN vs. GAN: How are they different?

  Convolutional neural networks and generative adversarial networks are both deep learning models but differ in how they work and are used. Learn the ins and outs of CNNs and GANs.Continue Reading

• intelligent process automation (IPA)

  Intelligent process automation (IPA) is a combination of technologies used to manage and automate digital processes.Continue Reading

• crypto-agility

  Crypto-agility, or cryptographic agility, is a data encryption practice used by organizations to ensure a rapid response to a cryptographic threat.Continue Reading

• neural radiance field (NeRF)

  Neural radiance fields (NeRF) are a technique that generates 3D representations of an object or scene from 2D images by using advanced machine learning.Continue Reading

• What do large language models do in AI?

  To capitalize on generative AI, business IT leaders must understand the features of large language models.Continue Reading

• sustainable AI

  Sustainable AI is the use of artificial intelligence systems that operate in ways contingent with sustainable business practices.Continue Reading

• assistive technology (adaptive technology)

  Assistive technology is a set of devices intended to help people who have disabilities.Continue Reading

• generative modeling

  Generative modeling is the use of artificial intelligence (AI), statistics and probability in applications to produce a representation or abstraction of observed phenomena or target variables that can be calculated from observations.Continue Reading

• How AI has cemented its role in telemedicine

  Many healthcare clinicians rely on AI when performing daily tasks and see benefits that outweigh the drawbacks.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

• lemmatization

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

• Exploring GPT-3 architecture

  With 175 billion parameters, GPT-3 is one of the largest and most well-known neural networks available for natural language applications. Learn why people are so pumped about it.Continue Reading

• generative adversarial network (GAN)

  A generative adversarial network (GAN) is a machine learning (ML) model in which two neural networks compete with each other by using deep learning methods to become more accurate in their predictions.Continue Reading

• How AI serves as a cornerstone of Industry 4.0

  For manufacturing environments to be included in Industry 4.0, they must adopt up-to-date technologies to improve operations. AI should be foremost among them.Continue Reading

• self-driving car (autonomous car or driverless car)

  A self-driving car (sometimes called an autonomous car or driverless car) is a vehicle that uses a combination of sensors, cameras, radar and artificial intelligence (AI) to travel between destinations without a human operator.Continue Reading

• natural language processing (NLP)

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

• Recent developments show us the future of chatbots

  Experts in conversational AI are optimistic about what recent advancements in chatbot technology mean for the future. Despite challenges, these advancements can point the way forward.Continue Reading

• How AI can assist industries in environmental protection efforts

  While technology for environmental protection isn't a new concept, AI advancements empower businesses to achieve sustainable operations.Continue Reading

• How significant is AI's role in Industry 4.0?

  Many examples exist to demonstrate the effectiveness of AI in Industry 4.0. It entails the newest revolution in manufacturing, so naturally advanced tech like AI will play a crucial role.Continue Reading

• facial recognition

  Facial recognition is a category of biometric software that maps an individual's facial features mathematically and stores the data as a faceprint.Continue Reading

• 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

• robotic surgery (robot-assisted surgery)

  Robotic surgery is the use of computer technologies working in conjunction with robot systems to perform medical procedures.Continue Reading

• social robot

  A social robot is an artificial intelligence (AI) system that is designed to interact with humans and other robots.Continue Reading

• PyTorch

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

• Why and when to consider a feature store in machine learning

  Feature stores exist to make data for training machine learning models reusable. Explore both the benefits and challenges of feature stores that organizations can experience.Continue Reading

• data scientist

  A data scientist is an analytics professional who is responsible for collecting, analyzing and interpreting data to help drive decision-making in an organization.Continue Reading

• Industries leading the way in conversational AI

  Learn how companies in vertical markets are using conversational AI and even partnering with AI developers for software that's tailored to their unique business needs.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

• AI winter

  AI winter is a quiet period for artificial intelligence research and development.Continue Reading

• How enterprises can establish an AI-first data strategy

  Enterprises looking to mature in their use of AI must focus on the information they're putting into their models. Their models should create trust in their business.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

• 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

• data splitting

  Data splitting is when data is divided into two or more subsets. Typically, with a two-part split, one part is used to evaluate or test the data and the other for training the model.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

• Learn the benefits of interpretable machine learning

  In this excerpt from 'Interpretable Machine Learning with Python,' read how machine learning models and algorithms add value when they are both interpretable and explainable.Continue Reading

• predictive modeling

  Predictive modeling is a mathematical process used to predict future events or outcomes by analyzing patterns in a given set of input data.Continue Reading

• Tips and tricks for deploying TinyML

  A typical TinyML deployment has many software and hardware requirements, and there are best practices that developers should be aware of to help simplify this complicated process.Continue Reading

• edge AI

  Edge artificial intelligence (edge AI) is a paradigm for crafting AI workflows that span centralized data centers (the cloud) and devices outside the cloud that are closer to humans and physical things (the edge).Continue Reading

• telepresence robot

  A telepresence robot is a remote-controlled, wheeled device that has wireless internet connectivity.Continue Reading

• robot economy

  The robot economy is a scenario in which most of the labor required to sustain human life is automated.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

• Why transparency in AI matters for businesses

  To ensure model accuracy, businesses need to understand why their machine learning models make their decisions. Certain tools and techniques can help with that.Continue Reading

• The benefits of an AI-first strategy

  Enterprises should put AI first in their business strategies by constantly collecting and using new data to power AI models, argues startup investor Ash Fontana.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

• fuzzy logic

  Fuzzy logic is an approach to computing based on "degrees of truth" rather than the usual "true or false" (1 or 0) Boolean logic on which the modern computer is based.Continue Reading

• 10 AI tech trends data scientists should know

  The rising environmental and monetary costs of deep learning are catching enterprises' attention, as are new AI techniques like graph neural networks and contrastive learning.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

• 11 data science skills for machine learning and AI

  As companies realize the power of data, they're tasked with finding data science practitioners with AI and ML skill sets to help them use the data to make better business decisions.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

• dropout

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

• AWS SageMaker training, making machine learning accessible

  Making machine learning more accessible and helping developers with AWS SageMaker training is at the core of Julien Simon's book, 'Learn Amazon SageMaker.'Continue Reading

• 5 examples of effective NLP in customer service

  Through use cases such as chatbots, recommendation systems and customer relationship management, NLP and AI are playing an important role in enterprise customer service.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

• 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

• Do you have competitive data science key skills?

  Data scientists should be familiar with a variety of programming languages, machine learning algorithms and databases and must be able to communicate these skills across teams.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

• General AI vs. narrow AI comes down to adaptability

  AI today has limited and specific applications, but the continual growth of the technology may just lead to the replication of human intelligence through general AI.Continue Reading

• Understanding motion analytics, where it is and where it's going

  Machine learning is helping make motion analysis more usable for the average enterprise, creating new use cases and applications that can drive value.Continue Reading

• GPT-3 AI language model sharpens complex text generation

  GPT-3 is the latest natural language generation model, but its acquisition by Microsoft leaves developers wondering when, and how, they'll be able to use the model.Continue Reading

• Modern AI evolution timeline shows a decade of rapid progress

  AI has become an asset for organizations to better understand their business position, and its capabilities have improved dramatically over the past decade.Continue Reading

• Learn the business value of AI's various techniques

  To drive business value from AI, business managers need to distinguish between the various AI techniques, starting with the many flavors of machine learning.Continue Reading

• Use of AI-assisted surgery remains limited despite its benefits

  While AI adoption to assist with surgeries remains limited, the technology holds great potential to increase quality of care and decrease patient risk.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

• Science fiction vs. reality: A robotics industry overview

  Robots have made their way into industrial, manufacturing and military settings, but the robots of science fiction remain a long-term goal rather than a reality.Continue Reading

• How and why are our devices listening to us?

  Consumers are utilizing digital voice assistants and smartphones but may not realize how frequently companies listen in and sift through all the data these devices create.Continue Reading

• BERT language model

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

• How to overcome 4 major challenges in AI adoption

  While companies are stuck in the research phase of AI, a few simple infrastructure analyzations can jumpstart the process -- and ensure successful deployment.Continue Reading

• The peaks and pitfalls of hyper-personalization marketing

  As consumers begin to revolt against unlimited personal data collection and usage, the longevity of hyper-personalized communication may be cut short.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

• Data visualization process yields 360 AI-driven analytics view

  Data visualization tools find increasing uses as part of AI processes to explore data in the initial stages of model development and make outputs easier to digest.Continue Reading

• 3 in-demand AI skills that boost data scientists' development

  AI encompasses a wide range of disciplines, from advanced math to application development, and building a strong AI team starts with incredibly skilled data scientists.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

• Machine learning platform architecture demands deep analysis

  This handbook offers advice on choosing machine learning platforms and using them to get accurate and meaningful information from analytics applications.Continue Reading

• How pattern matching in machine learning powers AI

  Pattern matching may sound like a simple idea, but it's being used to create some highly advanced AI tools, powering everything from image recognition to natural language applications.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

• Reinforcement learning applications provide focused models

  Goal-driven AI uses trial-and-error learning methods to find optimal solutions to enterprise problems, while distancing themselves from requiring human maintenance.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

• Enterprise AI collaboration tools take tips from dating apps

  Enterprise AI collaboration is turning to an unlikely source for inspiration: dating apps that have long used machine-learning based personalization and communication.Continue Reading

• Capital One AI VP discusses AI assistant Eno

  Eno from Capital One is an AI assistant that can give customers real-time banking updates and alerts to possible fraud attempts. In this Q&A, Capital One's VP of conversational AI goes over the basics of Eno.Continue Reading

• Data science and machine learning platforms advance analytics

  Data science platforms include a variety of technologies for machine learning and other advanced analytics uses. This handbook examines them and how they can be used.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 in fitness offers virtual trainers and customized wearables

  From Fitbits to virtual support, wellness enterprises are positioning AI as a useful tool. Using AI in fitness clubs and products can enhance user comfort and personalization.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