Evaluate

Neural networks and deep learning

• 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

• 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

• 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

• Despite risks, deepfake AI technology has enterprise potential

  The use of GANs to duplicate data has been portrayed as one of AI's biggest potential risks -- but enterprises can also use deepfake technology for positive content production. 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• How AI in physical security makes public places safer

  Deep learning-based tools are increasingly finding a home in physical security to enhance the protection of real-world assets and make public spaces safer.Continue Reading

• 5 benefits of AI in banking

  Despite risks, AI tools are helping banks overcome traditional customer service challenges and streamline back-end processes. Here are five benefits of AI in banking.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

• Computer vision tools reach into test, healthcare, security

  Gaining a reputation as a viable technology in niche applications like X-ray scans, fingerprint matching and robotics, computer vision looks to mainstream, commodified apps.Continue Reading

• Deep learning recognition use cases grow as tech matures

  As deep learning image and voice recognition technology improves, enterprises are finding novel ways to apply the technology to sharpen and improve their operations.Continue Reading

• Use of AI in payments industry is set to explode

  Payment processors are making wider use of AI technologies as part of an effort to make better use of their vast troves of data and connect more directly with customers.Continue Reading

• Grammarly AI and an update to the writing tool

  A Grammarly update adds a new consistency checker tool to the writing platform that uses AI to automatically scan for multiple writing styles within a single document.Continue Reading

• Federated deep learning offers new approach to model training

  Training deep learning models puts a massive strain on enterprise infrastructure, but federated learning, which trains models on endpoint devices, could lessen some of the demand.Continue Reading

• Enterprises are crowdsourcing AI development

  By crowdsourcing AI development, enterprises can broaden the knowledge base of their machine learning applications, and early adopters are showing promising results.Continue Reading

• How to address the hidden risks of algorithmic decision making

  From biased customer interactions to harmful treatment recommendations, some risks of automated decisions have yet to be resolved. A Wharton School professor has a way out.Continue Reading

• Data preparation for machine learning still requires humans

  Looking to AI to automate more of your processes? Don't overlook the labor that's still needed to prepare data for training machine learning and AI algorithms.Continue Reading

• Enterprises need to develop an AI strategy now

  Business leaders who approach AI as a future concern are likely to miss the boat on this influential technology. The time to explore AI implementation is now.Continue Reading

• New uses for GAN technology focus on optimizing existing tech

  GAN technology has emerged as the latest facet of AI that can be applied to existing technology to stack learning. What else can it do? Here are emerging use cases for GANs.Continue Reading

• Interpretable AI has benefits beyond compliance

  Making AI models more interpretable has a wide range of benefits and shouldn't just be thought of as a compliance exercise, experts at the H2O World conference said.Continue Reading

• New deep learning techniques take center stage

  The days of simple linear regressions for machine learning are giving way to more powerful deep learning techniques that point the way toward general AI capabilities.Continue Reading

• Davenport: AI-based projects should be focused in scope

  New AI projects in enterprises should focus on achievable goals rather than try to reinvent business processes or products and services, Tom Davenport says.Continue Reading

• The future of AI technology: 2019 and beyond

  Enterprise AI trends in 2019 will likely include more AI-powered tools for healthcare and fintech, as well as accelerating consolidation among smaller AI vendors.Continue Reading

• Explainability is no solution to problem of bias in AI

  Explainability has been touted as a solution to the problem of biased AI models, but experts say that approach only gets you part of the way to bias-free applications.Continue Reading

• Impact of AI on business still more limited than some hoped

  Most businesses have yet to feel the full effect of AI, as enterprises labor to determine where and under what conditions the technology can most effectively be deployed.Continue Reading

• AI in fashion design and retail takes center stage

  AI and fashion are a natural pairing as clothing designers and retailers are using technology to monitor what's hot and improve the efficiency of manufacturing and supply chains.Continue Reading

• Countries need national strategy for AI to stay competitive

  With the Chinese government funding artificial intelligence at an aggressive pace, the U.S. and other countries are facing substantial pressure to step up their investment.Continue Reading

• Nations sharpen AI strategies as global competition heats up

  The U.S. and China are the leading countries in AI research and development right now, but nations throughout the world are developing strategies to remain competitive.Continue Reading

• Healthcare and AI are a good pair -- until vendors overpromise

  Excessive promotion and grandiose promises from vendors like IBM are making it hard to accurately judge what the proper role of AI in healthcare should be.Continue Reading

• Current state of AI is poorly understood by the public

  Current applications of AI revolve entirely around machine learning. But researcher Zachary Lipton says that's not the message you get from most media coverage of AI.Continue Reading

• AI in policing is controversial but delivers results

  The drive toward more predictive policing is only growing stronger as computer vision and other AI tools are put to use. But not everyone is happy with the trend.Continue Reading

• IBM and AI: Researcher talks current and future projects

  The goal of numerous IBM AI projects is to ultimately create programs to augment human skills and jobs, not to take jobs away from humans, an IBM researcher says.Continue Reading

• Machine learning applications: From lab to enterprise

  The process from lab idea to enterprise reality may challenge machine learning applications. Learn how implementers are navigating the journey to practical use.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

• 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

• 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

• Limits of AI today push general-purpose tools to the horizon

  The future of AI should be focused on more general-purpose tools, but developers have a long way to go before achieving the kind of AI movies taught us to expect.Continue Reading