Evaluate

Evaluate

Weigh the pros and cons of technologies, products and projects you are considering.

• 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

• 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

• 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

• How to choose between a rules-based vs. machine learning system

  Debating rules-based systems over machine learning comes down to the complexity of the task at hand. Machine learning dominates complex tasks, but requires more long-term expertise.Continue Reading

• In-depth guide to machine learning in the enterprise

  Enterprises are adopting machine learning technologies at rapid rates. In this machine learning guide, we break down what you need to know about this transformative technology.Continue Reading

• How neural network training methods are modeled after the human brain

  Neural networks are trained to loosely mirror the human brain, but this process is limited by complicated human learning processes, classifications and training approaches.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

• Supervised vs. unsupervised learning: Use in business

  Learn how LinkedIn, Zillow and others choose between supervised learning, unsupervised learning and semi-supervised learning for their machine learning projects.Continue Reading

• CNN vs. RNN: How they differ and where they overlap

  Convolutional neural networks and recurrent neural nets underlie many of the AI applications that drive business value. Learn about CNNs vs. RNNs in this primer.Continue Reading

• 10 common uses for machine learning applications in business

  Machine learning applications are unlocking value across business functions. Here are 10 examples of how machine learning applications are being used in business.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

• 9 types of machine learning algorithms with a cheat sheet

  Machine learning can assist enterprises by quickly modeling large data sets. Choosing the right algorithm depends on the desired outcome and the makeup of your data science team.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

• Ultimate guide to artificial intelligence in the enterprise

  AI in the enterprise will change how work is done, but companies must overcome several challenges to derive value from this powerful and rapidly evolving technology.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

• CPUs vs. GPUs for AI workloads

  GPUs are often presented as the vehicle of choice for running AI workloads, but the push is on to expand the number and types of algorithms that can run efficiently on CPUs.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

• 5 AI risks businesses must confront and how to address them

  Some risks associated with implementing AI systems are familiar to enterprise leaders; these five are unique to AI. Here's how to address them.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

• AI critical for 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

• AI use cases in banking create opportunities, improve systems

  AI has become increasingly more common in the banking industry and has found a home sifting through data, improving back-end systems and assisting with customer service.Continue Reading

• 4 main types of AI explained

  The emergence of artificial superintelligence will change humanity, but it's not happening soon. Here are the types of AI leading up to that new reality.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

• Use of AI in government makes agencies smarter

  Government agencies are starting to embrace some of the same AI technologies that typical enterprises use, and many are finding increased efficiencies along the way.Continue Reading

• Reinforcement learning and deep learning pairing pushes AI limits

  The pairing of reinforcement and deep learning is enabling researchers to push the boundaries of what AI can do and could help contribute to advanced applications.Continue Reading

• Mapping adoption of artificial intelligence in the enterprise

  As many enterprises focus on use case research, the mature uses of AI are concentrated in supervised learning, structured data extraction and using a small number of platforms.Continue Reading