Evaluate
Weigh the pros and cons of technologies, products and projects you are considering.
Evaluate
Weigh the pros and cons of technologies, products and projects you are considering.
ML model optimization with ensemble learning, retraining
Making ML models better post-deployment can be accomplished. Learn the ins and outs of two key techniques: ensemble learning and frequent model retraining. Continue Reading
What is data science? The ultimate guide
Data science is the process of using advanced analytics techniques and scientific principles to analyze data and extract valuable information for business decision-making, strategic planning and other uses. Continue Reading
Solving the AI black box problem through transparency
Ethical AI black box problems complicate user trust in the decision-making of algorithms. As AI looks to the future, experts urge developers to take a glass box approach. Continue Reading
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3 ways to evaluate and improve machine learning models
Training performance evaluation, prediction performance evaluation and baseline modeling can refine machine learning models. Learn how they work together to improve predictions. Continue Reading
5 ways AI bias hurts your business
A biased AI system can lead businesses to produce skewed, harmful and even racist predictions. It's important for enterprises to understand the power and risks of AI bias. Continue Reading
Wrangling data with feature discretization, standardization
A variety of techniques help make data useful in machine learning algorithms. This article looks into two such data-wrangling techniques: discretization and standardization.Continue Reading
Designing and building artificial intelligence infrastructure
Building an artificial intelligence infrastructure requires a serious look at storage, networking and AI data needs, combined with deliberate and strategic planning.Continue Reading
8 considerations for buying versus building AI
Business leaders should consider their employees' technical expertise, technology budgets and regulatory needs, among other factors, when deciding to build or buy AI.Continue Reading
Data scientists vs. machine learning engineers
The positions of data scientist and machine learning engineer are in high demand and are important for enterprises that want to make use of their data and use AI.Continue Reading
Moving beyond NLP to make chatbots smarter
Machine reasoning could help chatbots better understand context, which is crucial to understanding human emotions and formulating emotionally relevant responses.Continue Reading
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5 reasons NLP for chatbots improves performance
Experts say chatbots need some level of natural language processing capability in order to become truly conversational. Without language capabilities, bots are simple order takers.Continue Reading
Synthetic data for machine learning combats privacy, bias issues
Synthetic data generation for machine learning can combat bias and privacy concerns while democratizing AI for smaller companies with data set issues.Continue Reading
Businesses pivot back to AI adoption after year of slow growth
AI adoption has taken a step back when it comes to enterprise IT spending priority, but it remains a steady investment for enterprises across industries.Continue Reading
Artificial general intelligence in business holds promise
While AGI in business remains unattainable today, truly intelligent systems, chatbots and predictive analytics are potential use cases enterprises should keep their eyes on.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
Artificial general intelligence examples remain out of reach
Artificial general intelligence remains largely an aspiration goal of researchers, but as technologies advance, so too does the dream become more realistic.Continue Reading
Defining enterprise AI: From ETL to modern AI infrastructure
The promise of enterprise AI is built on old ETL technologies, and it relies on an AI infrastructure effectively integrating and processing loads of data.Continue Reading
KDD in data mining assists data prep for machine learning
While data scientists are often familiar with data mining, the deeper knowledge discovery in databases (KDD) procedure can help prepare data to train machine learning algorithms.Continue Reading
AI trends in 2020 marked by expectation shift and GPT-3
In the past year, AI hyperscalers got serious about their machine learning platforms, expectations were reset and transformer networks empowered the GPT-3 language model.Continue Reading
AI ROI questions to ask and the hidden costs of AI
While ROI can be difficult to show with AI projects, it is crucial for AI teams to anticipate costs and prove each investment is worth the enterprise's time.Continue Reading
How AI adoption by industry is being impacted by COVID-19
While COVID-19 has impacted budgets and businesses plans, some industries are seeing improved processes and consumer relationships due to new investments in AI and automation.Continue Reading
Reality check: Analysts check in on the AI hype cycle
AI applications still come with significant hype, but with a targeted approach, organizations can get the most out of their applications.Continue Reading
Why AI adoption in the enterprise continues to lag
In this episode of 'Today I Learned About Data,' we discuss AI adoption in the enterprise, and it's been slower than many have predicted.Continue Reading
8 examples of AI personalization across industries
Through AI content personalization, organizations can build unique profiles of users and customers and tailor their products, advertisements and services to better fit them.Continue Reading
Bayesian networks applications are fueling enterprise support
Cloud-based infrastructure has opened the door for enterprises to take advantage of the versatile predictive capability of Bayesian networks technology.Continue Reading
How AI can be used in agriculture: Applications and benefits
The use of agricultural AI optimizes the farming industry by decreasing workloads, analyzing harvesting data and improving accuracy through seasonal forecasting.Continue Reading
How 5G and artificial intelligence may influence each other
5G and AI can be combined to improve the network speed, responsiveness and efficiencies of organizations in the enterprise, but the former needs more time to mature.Continue Reading
Enterprise and home find use for intelligent virtual assistants
Intelligent virtual assistants have the capacity to augment employees, as well as improve convenience in homes, but only time will see their limitations resolved.Continue Reading
Advantages of AI in agriculture include increased efficiency
Artificial intelligence has the capacity to improve the supply chain and agricultural industry by improving demand forecasting and increasing productivity.Continue Reading
Explore the foundations of artificial neural network modeling
Dive into Giuseppe Bonaccorso's recent book 'Mastering Machine Learning Algorithms' with a chapter excerpt on modeling neural networks.Continue Reading
Combining AI and predictive analytics crucial for the enterprise
Predictive analytics, when combined with artificial intelligence, can assist organizations with their risk management, as well as their planning and optimization.Continue Reading
Future of autonomous vehicles depends on driver attitudes
Getting the public behind the idea of an autonomous vehicle means peeling back the black box nature of AI and proving the safety of self-driving technology.Continue Reading
Bias in machine learning examples: Policing, banking, COVID-19
Human bias, missing data, data selection, data confirmation, hidden variables and unexpected crises can contribute to distorted machine learning models, outcomes and insights.Continue Reading
Machine learning limitations marked by data demands
Machine learning has impressive capabilities in the enterprise, but with high-data requirements and struggles with explainability, it remains unable to reach widespread use.Continue Reading
4 ways AI and digital transformation enable deeper automation
Organizations that are going beyond the enterprise adoption of digitization are entering a new wave of AI-enabled digital transformation.Continue Reading
Reimagining creativity and AI to boost enterprise adoption
AI has yet to reach the point of creativity but continues to advance, while assisting humans in the production of their own creative works and improvement of their organizations.Continue Reading
Autoencoders' example uses augment data for machine learning
Autoencoders are neural networks that serve machine learning models -- from denoising to dimensionality reduction. Seven use cases explore the practical application of autoencoder technology.Continue Reading
Future of AI in video games focuses on the human connection
The future of gameplay is reliant on the usage and perfection of Emotional AI and its ability to create and emulate realistic and human relationships.Continue Reading
Applications of generative adversarial networks hold promise
Generative adversarial networks are tied to fake online content known as 'deepfakes,' but GANs can help data-poor enterprises supplement their data needs.Continue Reading
14 best machine learning platforms for 2020
Turn ever-growing volumes of data into enterprise insights with the right platform for machine learning. Learn more about the vendors and products in this cutting-edge market.Continue Reading
5 major benefits of machine learning in the enterprise
Businesses are inserting machine learning into processes wherever possible. Here are a few of the ways machine learning users are benefiting from machine learning.Continue Reading
Artificial intelligence content writing ramps up publishing
To ease the burden that is associated with content production, AI in content production has been deployed to augment writers' work and to help monitor and measure post engagement.Continue Reading
Deep learning's role in the evolution of machine learning
Machine learning has continued to evolve since its beginnings some seven decades ago. Learn how deep learning has catalyzed a new phase in the evolution of machine learning.Continue Reading
AI web scraping augments data collection
Web scraping automates the data gathering process and refines the data pipeline, but it requires careful attention to choosing the right tools, languages and programs.Continue Reading
Machine learning for fraud prevention keeps TrafficGuard agile
TrafficGuard uses machine learning to prevent ad fraud but has faced the challenges that come along with it. Full-scale commitment and investment have eased those obstacles.Continue Reading
Hospital IoT highlighted by AI use cases and wearable devices
AI in healthcare can provide patients and doctors with better care and support through prescriptive analytics, wearable devices and illness detection.Continue Reading
5 AI technologies in business that are making a big impact
Learn how image recognition, speech recognition, chatbots, natural language generation and sentiment analysis are changing how businesses operate.Continue Reading
Cloud computing for machine learning offers on-demand tools
Automated machine learning and MLaaS tools are now being developed for the cloud, and enterprises need better workflows and infrastructure to successfully integrate the technology.Continue Reading
AI document processing remains a subtle but powerful use case
Artificial intelligence has found strong use cases in content summarization and document categorization within the medical, marketing and legal fields.Continue Reading
AI COVID-19 tech bolsters social distancing, supply chains
As the world waits for staggered reopenings and a return to normal life, AI-based technology is assisting scientists with diagnosis, detection, research and remote-based workforces.Continue Reading
AI and augmented reality blur lines between virtual and reality
Using AI with augmented reality and virtual reality in industries as varied as gaming and conversational systems can enhance the already impressive technologies.Continue Reading
Are giant AI chips the future of AI hardware?
Giant AI chips like the Cerebras WSE are dazzlingly fast and could transform AI models, but how soon is the question for CIOs. Experts mull the merits of small vs. big AI chips.Continue Reading
AI and edge computing security
While boasting benefits such as reduced latency, edge computing can increase a company's vulnerability. Ensuring security is crucial to getting the most out of the technology.Continue Reading
Can the future of voice assistants include enterprise use?
Voice assistants haven't had the success that companies originally expected. AI and machine learning could go a long way to boost this technology.Continue Reading
Value of AI in capitalizing on big data, expanding automation
The strategic value of AI in business is growing as companies learn how to use AI technologies to capitalize on big data, drive automation and better serve customers.Continue Reading
How far are we from artificial general intelligence?
Developers and researchers are currently debating the extent to which artificial general intelligence needs to mimic the human brain. Explore the two schools of thought.Continue Reading
AI and RPA are here to stay
The coronavirus pandemic has caused enterprises to rely more on AI technologies as they are forced to lay off employees or temporarily close their offices.Continue Reading
Comparing MLaaS providers by cost, UX and ease of use
MLaaS allows companies to add machine learning capabilities without software development. There are still some barriers to entry, however, and providers are not one-size-fits-all.Continue Reading
Common sense in AI remains elusive
While AI and machine learning have made major improvements and advancements to computers, common sense in AI has proven to be a significant challenge.Continue Reading
How to optimize hyperparameter tuning for machine learning models
Adding hyperparameters tuning to your organization's research and design modelling process enables use case, region or data-specific model specifications.Continue Reading
Data center energy usage combated by AI efficiency
Though often forgotten by the general public, data centers account for 1% of the world's energy consumption. Explore what brought about data centers and how AI can be used to help mitigate their presence.Continue Reading
RPA adoption trends point to broadening use cases
RPA is frequently discussed, but here we sort out the potential of this technology -- and the limiting factors that prevent it from living up to the hype.Continue Reading
The state of AI defined by global adoption and regulation
Cognilytica reports on AI adoption by both countries and companies across the globe, as well as the former's overall strategies and regulation frameworks.Continue Reading
The AI accelerator chip can make AI accessible to all
Specialized AI chips released by companies like Amazon, Intel and Google tackle model training efficiently and generally make AI solutions more accessible.Continue Reading
Edge computing use cases led by autonomous cars and coffee bars
Edge computing can decrease latency times dramatically and has found its place in autonomous vehicles, manufacturing plants and retail.Continue Reading
How stock market prediction using AI impacts the trading floor
Robo-advisors and stock monitoring bots are changing the way traders and investors take on the stock market.Continue Reading
Standards for data sharing should guide AI government regulation
The White House has taken a deregulatory approach to AI and aims to inspire innovation. An expert weighs in on the role of government in AI and where the industry stands.Continue Reading
Where are we with machine translation in AI?
Machine translation has received a boost from cutting-edge technology like deep learning but continues to struggle with the complexities and nuances of human languages.Continue Reading
Comparing semi-supervised machine learning vs. one-shot learning
Machine learning models require massive amounts of data -- labeled or unlabeled. Two new approaches are hoping to curtail the need for large data sets and overarching human interference.Continue Reading
What do NLP benchmarks like GLUE and SQuAD mean for developers?
AI models for various language understanding tasks have been dramatically improved due to the rise in scale and scope of NLP data sets and have set the benchmark for other models.Continue Reading
Patience is pivotal for the autonomous vehicle future
The fatal collision between an Uber ATG vehicle and a pedestrian was a reminder that autonomous vehicles are not ready and that a difficult technological hill remains.Continue Reading
The uses of AI in medical imaging
Medical imaging is being deployed with the assistance of AI in order to reduce the strain on medical professionals and hopefully improve patient care.Continue Reading
Autonomous retail cuts operational costs, personalizes shopping
Companies are starting to deploy popular AI technologies like RFID, facial recognition and sensor tracking to provide consumers with autonomous shopping experiences.Continue Reading
Companies focused on building sustainable AI in 2020
Fewer companies are deploying AI enterprise-wide in 2020 because of universal underestimation of investments and transformation required to have a rounded AI strategy.Continue Reading
How GE uses a 'Humble AI' approach to manufacturing
GE executive Colin Parris explains why a deliberate approach to deploying AI is needed when dealing with products that cost hundreds of millions of dollars to make.Continue Reading
How to build a chatbot with personality and not alienate users
Adding personality to a chatbot can push it toward the uncanny valley and raises ethical questions. But enterprises can make their bots more engaging, while avoiding these hurdles.Continue Reading
How Getty Images reduces bias in AI algorithms to avoid harm
In applications from internal job recruiting to law enforcement technology, AI bias is a widespread issue. Here's what enterprises can do to reduce bias in training and deployment.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
Data visualization in machine learning boosts data scientist analytics
Data scientists offer practical insights into the role of visualization tools in building, exploring, deploying and monitoring their machine learning models.Continue Reading
Building a better conversational AI assistant requires emotion
Industry after industry is seeing benefits from chatbot implementation, but customers and developers are looking toward a future of more connected, intelligent conversational agents.Continue Reading
How the top open source AI software drives innovation
In the world of AI, open source software is driving most of the innovation. But with vendor tools largely sidelined, what does this mean for things like security and technical support?Continue Reading
3 intelligent process automation use cases and how they work
Enterprises are pursuing intelligent process automation to take their digital transformation and RPA applications to the next level. Here's a look at three use cases.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
Deep learning and neural networks gain commercial footing
Deep learning and neural networks are picking up steam in applications like self-driving cars, radiology image processing, supply chain monitoring and cybersecurity threat detection.Continue Reading
Ethical concerns of AI call growing adoption into question
AI tools are getting easier to use every day, putting powerful tools into the hands of potentially malicious users. The time to think about the ethics of AI advances is now.Continue Reading
Human-AI collaboration produces top results
Humans and machines have different -- and often complementary -- strengths and weaknesses. That's why we're not seeing automation leading to mass job losses, at least for now.Continue Reading
How automated machine learning tools pave the way to AI
Every enterprise is trying to get to machine learning and, ultimately, AI, but not every business has the level of skill in-house to make it happen. Is automated machine learning the answer for them?Continue Reading
Is artificial general intelligence possible in our lifetime?
Artificial general intelligence aims to create a wide-reaching, common-sense AI that behaves in a human fashion, but researchers and experts are questioning its plausibility.Continue Reading
AI gig economy sets workers and bots on collision course
The future of work has shifted toward a gig economy, with high-value, short-term workers on demand for organizations. The fast turnover and high volume demand AI to reduce friction.Continue Reading
Clashes between AI and data privacy affect model training
Enterprises' lax data rules reveal weaknesses around AI and model training -- particularly machine learning's reliance on unrestrained big data collection.Continue Reading
Full benefits of voice assistant tech yet to be realized
Voice assistant technology is advancing rapidly, thanks to substantial vendor investment. But a new benchmark report reveals the most popular assistants still leave much to be desired.Continue Reading
How to choose the right autoML platform for your enterprise
Before autoML can improve model building and deployment, enterprises need to choose a platform. Here, we evaluate autoML platforms by category, key features and accessibility.Continue Reading
Enterprises work toward AI trust and transparency
If ignored, a lack of trust in AI algorithms could diminish user adoption. To remedy this risk, enterprises are working to make their applications more transparent and explainable.Continue Reading
Choosing the right chip foundation for AI-optimized hardware
Every enterprise is trying to implement AI and machine learning. But, before AI, before clean data and before platform comparison, enterprises need to find the best hardware to support AI.Continue Reading
3 GAN use cases that showcase their positive potential
GANs' ability to create realistic images and deepfakes have caused industry concern. But, if you dig beyond fear, GANs have practical applications that are overwhelmingly good.Continue Reading
Key considerations for operationalizing machine learning
Once a machine learning model is trained, developers need to operationalize it. This turns out to be a significant challenge for many enterprises.Continue Reading
Chatbots in customer service find success with focused goals
Chatbots can be a great adjunct to customer service, but a successful rollout requires careful planning, flexibility and clear objectives.Continue Reading
Wayfair takes a dip into NLP image processing technology
At Wayfair, using computer vision and NLP to understand the meaning behind images and searches is the key to customer recommendation, satisfaction and easy substitutability.Continue Reading
Causal deep learning teaches AI to ask why
Most AI runs on pattern recognition, but as any high school student will tell you, correlation is not causation. Researchers are now looking at ways to help AI fathom this deeper level.Continue Reading
AI for retailers is progressing
AI in retail adoption has been relatively slow, but it's starting to pick up as retailers see the benefits of AI technologies and the realities of e-commerce competition.Continue Reading