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• 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

• Definitions to Get Started

  • What is Gemma? Google's open sourced AI model explained
  • What is neuro-symbolic AI?
  • What is AI red teaming?
  • What is data poisoning (AI poisoning) and how does it work?

  • What is Q-learning?
  • What is Fréchet inception distance (FID)?
  • What is computational linguistics? Definition and career info
  • What is Dall-E and how does it work?

  View All Definitions

• 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 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

• 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

• 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

• 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

• How to build better conversational AI bots for business uses

  Conversational AI developers from Google, Uber and Autodesk detail dos and don'ts for designing chatbots and AI assistants that can interact effectively with users.Continue Reading

• AI for customer experience powers gains at enterprises

  Customer experience is growing more central to enterprises' digital strategies, and AI is increasingly driving much of their engagement and retention efforts.Continue Reading

• Implementing RPA boosts company growth and employee satisfaction

  Implementing RPA to augment your workforce can feel overwhelming. Ditch theoretical application conversations, and read a rundown of one executive's experience implementing RPA.Continue Reading

• The new AI frontier: Hyperpersonalized automated advertising

  AI-powered automated advertising is being utilized to connect consumer to products, leading to more sales. Simply put: Hyperpersonalized content is taking over the ad space.Continue Reading

• Enterprises deploy AI in mining projects to improve analytics

  By implementing AI in mining processes, enterprises are able to utilize data algorithms and automated machines to preserve the sensitive environment and their human workforce.Continue Reading

• Automated help desk processes improve enterprise-level ITSM

  By using AI-enabled systems to create an automated help desk, enterprises can streamline IT support while predicting, managing and solving common user issues.Continue Reading

• Using AI in manufacturing processes surges quality and design

  The addition of AI in manufacturing leads to increased workflow -- from design to production. Production plants are turning to technology to supplement the manufacturing skills gap.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

• Enterprises put AI in supply chain to streamline processes

  Supply chain AI is helping enterprises that rely on the movement of physical parts and products streamline their operations and automate tricky last-mile problems.Continue Reading

• AI use in healthcare ramps up for app maker Cognoa

  Applications of AI in healthcare have been relatively restricted due to regulatory and data challenges, but one startup is finding ways to make AI effective.Continue Reading

• Business robotics moves off of the manufacturing floor

  New uses of robotics are opening up in businesses, as applications for the technology begin to expand beyond its traditional place in manufacturing plants.Continue Reading

• Social media and AI beef up personalization in marketing

  Enterprises are increasing their use of AI in social media marketing, helping produce more targeted content. As applications evolve, some surprising use cases are emerging.Continue Reading

• How AI process automation helps simplify enterprise workflows

  The growing trend of putting AI in process automation tools is helping companies make their workflows more intelligent, offering an upgrade over traditional process automation tools.Continue Reading

• Use of AI in pharma grows as drug-makers see big benefits

  The use of AI in clinical trials, drug discovery and manufacturing is growing and, despite a few barriers, drug companies are expected to continue rolling out the technology.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

• RPA strategy takes advantage of fast-growing market

  The RPA market is rapidly growing, and it's little wonder why. Using RPA best practices, businesses can deploy RPA quickly and potentially use it to save time and money.Continue Reading

• AI for education brings benefits to burdened school staff

  Using AI in education can have a dramatic impact on the way teachers use their time and the manner in which students are served individually. Expect the trend to continue.Continue Reading

• AI and jobs collide as automation looms

  AI automation will eliminate a broad swath of today's jobs over time, but some jobs are likely to disappear sooner than others due to the uneven pace of technology development.Continue Reading

• Automated journalism creeps into newsrooms leaning on AI

  The use of AI in journalism is gaining steam and, at this early stage, newsrooms are still looking at how the tools can be used to help reporters tell deeper stories.Continue Reading

• How AI in e-commerce makes vendors more responsive to customers

  AI tools are giving a boost to personalization in e-commerce as vendors find machine learning tools can make ads and experiences more relevant to their customers.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

• AI in restaurants takes customer service to the next level

  Restaurants face challenges that include low margins and high turnover, but the use of chatbots is helping some restaurant chains address these perennial difficulties.Continue Reading

• Data science in healthcare demands dual focus, expert says

  Symphony Post Acute Network is seeing success blending analytics for clinical and business operations, a change compared to how healthcare systems have traditionally used analytics.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

• The threats of AI must be taken seriously to prevent harm

  The risks of AI use are growing as the technology becomes more pervasive. Rather than laugh off the threats, businesses should move to mitigate them before they become headaches.Continue Reading

• Labeled data brings machine learning applications to life

  The types of data being collected for analytics use are increasing, but traditional structured data is a good match for machine learning. Gartner's Svetlana Sicular explains why.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

• AI for recruiting helps companies land top job candidates

  More enterprises are using AI in hiring today, a practice that can help surface strong applicants. But there are several pitfalls businesses need to watch for.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

• AI applications in healthcare smooth providers' operations

  Healthcare providers are embracing AI systems at an increasing rate as the potential benefits for both patient care and operational management become more apparent.Continue Reading

• Deep learning use cases aren't limited to big tech companies

  Industries that are not traditionally technology-driven are starting to find ways to use deep learning, proving the tools aren't just for large tech companies.Continue Reading

• Tech experts weigh in on the AI hype cycle

  AI expectations couldn't be any higher. Read why leading industry experts believe the hype is deserved and what developers can do to deliver on the technology's weighty promise.Continue Reading

• Industries evaluate collaborative robot applications

  Robotics has traditionally been viewed as a threat to jobs, but putting AI in robots can make machines more collaborative and empower human workers to be more effective.Continue Reading

• Enterprises explore AI voice assistant technology

  Intelligent voice assistant devices, so popular among consumers, are starting to make their way into enterprises, but businesses need to be mindful of several challenges.Continue Reading