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Bring yourself up to speed with our introductory content.
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Bring yourself up to speed with our introductory content.
supervised learning
Supervised learning is an approach to creating artificial intelligence (AI) where a computer algorithm is trained on input data that has been labeled for a particular output. Continue Reading
deep learning
Deep learning is a type of machine learning and artificial intelligence (AI) that imitates the way humans gain certain types of knowledge. Continue Reading
machine learning engineer (ML engineer)
A machine learning engineer (ML engineer) is a person in IT who focuses on researching, building and designing self-running artificial intelligence (AI) systems to automate predictive models. Continue Reading
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Artificial Intelligence as a Service (AIaaS)
Artificial Intelligence as a Service (AIaaS) is the third-party offering of artificial intelligence (AI) outsourcing. Continue Reading
How data quality shapes machine learning and AI outcomes
Data quality directly influences the success of machine learning models and AI initiatives. But a comprehensive approach requires considering real-world outcomes and data privacy. Continue Reading
cognitive computing
Cognitive computing is the use of computerized models to simulate the human thought process in complex situations where the answers might be ambiguous and uncertain.Continue Reading
Fréchet inception distance (FID)
Fréchet inception distance (FID) is a metric for quantifying the realism and diversity of images generated by generative adversarial networks (GANs).Continue Reading
machine teaching
Machine teaching is the practice of infusing context -- and often business consequences -- into the selection of training data used in machine learning (ML) so that the most relevant outputs are produced by the ML algorithms.Continue Reading
automated machine learning (AutoML)
Automated machine learning (AutoML) is the process of applying machine learning (ML) models to real-world problems using automation.Continue Reading
15 top applications of artificial intelligence in business
The use of AI in business applications and operations is expanding. Learn about where enterprises are applying AI and the benefits AI applications are driving.Continue Reading
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variational autoencoder (VAE)
A variational autoencoder (VAE) is a generative AI algorithm that uses deep learning to generate new content, detect anomalies and remove noise.Continue Reading
The role of AI parameters in the enterprise
What is the correlation between the number of parameters and an AI model's performance? It's not as straightforward as the parameter-rich generative AI apps would have us believe.Continue Reading
responsible AI
Responsible AI is an approach to developing and deploying artificial intelligence (AI) from both an ethical and legal point of view.Continue Reading
inception score (IS)
The inception score (IS) is a mathematical algorithm used to measure or determine the quality of images created by generative AI through a generative adversarial network (GAN).Continue Reading
machine learning bias (AI bias)
Machine learning bias, also sometimes called algorithm bias or AI bias, is a phenomenon that occurs when an algorithm produces results that are systemically prejudiced due to erroneous assumptions in the machine learning process.Continue Reading
unsupervised learning
Unsupervised learning is a type of machine learning (ML) technique that uses artificial intelligence (AI) algorithms to identify patterns in data sets that are neither classified nor labeled.Continue Reading
singularity
In technology, the singularity describes a hypothetical future where technology growth is out of control and irreversible.Continue Reading
What is trustworthy AI and why is it important?
What are the tenets of trustworthy AI and how do the funders and developers of AI ensure they're upheld?Continue Reading
multimodal AI
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
Q-learning
Q-learning is a machine learning approach that enables a model to iteratively learn and improve over time by taking the correct action.Continue Reading
Use cases show the combined potential of AI and blockchain
AI and blockchain are both hot topics in IT, yet used for different purposes. However, enterprises across various sectors can now combine both technologies to their advantage.Continue Reading
automated reasoning
Automated reasoning is the area of computer science concerned with applying reasoning in the form of logic to computing systems.Continue Reading
AI art (artificial intelligence art)
AI art (artificial intelligence art) is any form of digital art created or enhanced with AI tools.Continue Reading
cognitive search
Cognitive search represents a new generation of enterprise search that uses artificial intelligence (AI) technologies to improve users' search queries and extract relevant information from multiple diverse data sets.Continue Reading
History of generative AI innovations spans 9 decades
ChatGPT's debut has prompted widespread publicity and controversy surrounding generative AI, a subset of artificial intelligence that's deep-rooted in historic milestones.Continue Reading
case-based reasoning (CBR)
Case-based reasoning (CBR) is an experience-based approach to solving new problems by adapting previously successful solutions to similar problems.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
Turing Test
A Turing Test is a method of inquiry in artificial intelligence (AI) for determining whether or not a computer is capable of thinking like a human being.Continue Reading
neuromorphic computing
Neuromorphic computing is a method of computer engineering in which elements of a computer are modeled after systems in the human brain and nervous system.Continue Reading
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
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
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