Features

Features

• 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

• AI in retail has helped retailers during COVID-19

  During the Ai4 2020 virtual conference, a panel of retail experts discusses how AI and analytics have helped retailers deal with the economic fallout of the COVID-19 pandemic.  Continue Reading

• Automated document processing helps PPP applications

  Using its own AI models, as well as technology from Abbyy, PwC can rapidly and correctly file a large volume of PPP loan applications.  Continue Reading

• AI, robotics help businesses pivot supply chain during COVID-19

  Big enterprises such as Wayfair, UPS, Unilever and Siemens move to automate more of their supply chains with AI as the coronavirus pandemic disrupts business operations.  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

• Process automation in education helps school during COVID-19

  Using process automation, a school in Sydney helped keep classes going online by automating the daily roll call process. Organizations have increasingly turned to automation and AI during COVID-19.  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

• AIoT applications prove the technology's adaptability

  The combination of artificial intelligence and IoT has led to better predictive capabilities for devices, informed data storage, and enterprise machine optimization.  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

• AI in operations management relieves pressure on IT teams

  AI, when combined with IT operations and DevOps teams, forms AIOps that can greatly improve how IT assets are developed, produced and managed.  Continue Reading

• The state of AI in 2020 likely sees more adoption

  AI adoption has appeared to grow this year, as organizations deploy automation to augment dwindling workforces and help deal with growing demand during the COVID-19 pandemic.  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

• Accounts payable automation eliminates invoice backlog

  Purple, a mattress company, struggled each week to manually process its myriad invoices and bills. It found relief in automated accounts payable software from a startup vendor.  Continue Reading

• Care provider uses automated machine learning in healthcare

  An assisted living and transitional care provider uses DataRobot to automate the process of building and deploying machine learning models, enabling it to deploy models quickly.  Continue Reading

• AI in tax preparation gets a boost from classification tech

  Tax filers and tax collectors are using AI tools to make the process of paying and collecting taxes simpler. The data-rich, complex processes of tax collection are an ideal use case for AI.  Continue Reading

• Warehouse AI and robotics help train employees

  Workers at a logistics and transportation company learn on the job with an autonomous cart, Chuck. The robot teaches employees the layout of the warehouses and where items are.  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

• 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

• Accelerating digital transformation through remote work

  As working remotely became a near-global mandate, companies have been thrust headfirst into a digital transformation. AI is helping to smooth the journey.  Continue Reading

• Understanding how deep learning black box training creates bias

  Bias in AI is a systematic issue that derails many projects. Dismantling the black box of deep learning algorithms is crucial to the advancement and deployment of the technology.  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

• NLP, chatbot development reignite conversational commerce AI

  The world of e-commerce has been focused on the adoption of conversational commerce for years to boost online sales. COVID-19 might just be the catalyst for widespread use.  Continue Reading

• 6 ways to reduce different types of bias in machine learning

  As adoption of machine learning grows, companies must become data experts -- or risk results that are inaccurate, unfair or even dangerous. Here's how to combat ML bias.  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

• 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

• Contact tracing apps seem effective, but have privacy concerns

  Contact tracing mobile applications appear to offer an easier, safer way to track where an infected person has been, but technology could cross a data privacy boundary.  Continue Reading

• Enterprises should not neglect AI digital transformation

  Enterprises should focus on automation to augment their workforces as they recover from the COVID-19 economic downturn, and not lose sight of larger digital transformation projects.  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 in sales can make smart lead recommendations

  For one B2B sales tech company, a self-service AI startup founded by a former Google employee helps create lead recommendations using machine learning.  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's impact on business: The quest to make money

  'Bionic' companies have cracked the code on using AI to make money. Here is what IT and business leaders need to do to maximize AI's impact on the business.  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

• AI and DevOps team up in remote work model

  Communication and network security are increasingly difficult in DevOps teams as the workforce goes digital. Using AI and machine learning helps companies adjust to the remote work model.  Continue Reading

• AI in digital transformation vital for enterprises

  Without using AI, enterprises will not be able to compete effectively, according to the CEO of Box. Companies need to infuse AI into their digital transformation efforts now.  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

• 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

• 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

• MLOps tools hope to boost enterprise model implementation

  Taking notes from DevOps lifecycle management, machine learning operations tools and platforms seek to improve accuracy, ease integration problems and keep models trained.  Continue Reading

• AI helping higher education during the pandemic

  By using AI and analytics, higher education schools can gauge how their students are faring at scale during the coronavirus pandemic.  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

• 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

• 7 last-mile delivery problems in AI and how to solve them

  Enterprises are discovering it's easier to build AI than it is to integrate it into existing processes. We examine seven 'last-mile' deployment problems when delivering AI.  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

• Safety vs. privacy in the age of coronavirus raises tech questions

  To slow the spread of the coronavirus, governments and organizations are using contact tracing and thermal imaging for fever detection, but these methods carry privacy concerns.  Continue Reading

• 8 emerging AI use cases in the enterprise

  New approaches to AI model building, more system thinkers -- amid a global pandemic, the landscape has changed for AI applications in the enterprise. Here are eight emerging AI use cases.  Continue Reading

• 4 explainable AI techniques for machine learning models

  At its core, AI is a complex modeling process with layers of information. In order to be able to explain the algorithm's decision-making process, start with its input data.  Continue Reading

• Using ModelOps, a financial services company scales out

  Using model operations (ModelOps), a fintech startup was able to scale up its model deployment quickly, while also maintaining model governance at scale.  Continue Reading

• Here's how one lawyer advises removing bias from AI

  Avoiding bias in AI applications is one of the central challenges in using the technology. Here's some advice on deploying AI technologies in a way that is fair.  Continue Reading

• 5 deep learning model training tips

  Deep learning model training requires not only the right amount of data, but the right type of data. Enterprises must be inventive and careful when training their models.  Continue Reading

• Supercomputer consortium powering COVID-19 treatment research

  Supercomputers, AI and high-end analytic tools are each playing a key role in the race to find answers, treatments and a cure for the widespread COVID-19.  Continue Reading

• Text search tool makes finding documents easier

  Using a visually appealing approach, Keeper.fyi enables users to search text documents, images, and graphs and charts quickly and efficiently.  Continue Reading

• AI for supply chain can help companies handle disruptions

  Even faced with the unpredictability of the coronavirus pandemic, enterprises can generate some stability using AI and machine learning in their supply chains.  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

• City of Knoxville deploys a web and SMS chatbot

  By using a chatbot from text messaging vendor Quiq, the city of Knoxville, Tenn. can give its residents information on the coronavirus, the 2020 census, and the city's social services.  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

• Using AI in AML fights fraud while protecting privacy

  Money laundering and fraud remain a risk for financial institutions, but AI can act as a useful tool against a constantly evolving financial enemy.  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

• How to identify projects that create AI business value

  When AI projects lead with technology, they rarely have a business impact. Instead, business leaders should target projects to make a meaningful improvement in processes.  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

• AI in mining takes root in the industry

  Executives from data science vendors Kespry and Descartes Labs discuss the importance of AI in the mining industry, a sector that is still fairly new to AI technologies.  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

• Using automated machine learning for AI in insurance

  Using dotData, an automated machine learning vendor, one of the largest insurance firms in Japan built out an AI platform that provides a personalized experience to customers.  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

• AI and chatbots: Conversational app platforms are maturing

  Chatbots have long been reliant on pre-written responses to common questions. With the incorporation of AI, however, chatbots can take an important step forward.  Continue Reading

• Data Science Central co-founder talks AI, data science trends

  In a Q&A, Vincent Granville, executive data scientist and co-founder of Data Science Central, discusses how AI has changed the data science field and the ways in which it will continue to do so.  Continue Reading

• Advanced AI in financial services boosts fraud detection, efficiency

  Financial firms plan to invest more into R&D on AI and plan to deploy advanced AI, like deep learning, within the next two years, according to a new survey.  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

• Customer experience chatbot provides answers

  Upwork has slowly been rolling out a customer experience chatbot service from vendor Ada, which it uses in conjunction with Zendesk to help provide customers with support.  Continue Reading

• Forest fire sensors and AI help detect fires in Chile

  Entel Ocean uses DataRobot's automated machine learning platform, as well as IoT sensors, to automatically detect forest fires in Chile. The platform can detect fires faster than manual counterparts.  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

• Merging DevOps and machine learning requires restructuring

  Companies that are restructuring in order to merge their traditional DevOps teams with their machine learning efforts to aid with accessibility need to include voices from multiple teams.  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

• The future state of machine learning needs improved frameworks

  Utilizing machine learning in the collection and processing of data would most likely lead to more widespread adoption of AI based on the technology.  Continue Reading

• Employee AI readiness is fairly low

  Enterprise employees are largely lacking in AI skills, and enterprises need to work to reskill or upskill employees to improve their skills and help reduce AI job loss fears.  Continue Reading

• Customer experience vendor benefits from AI in forecasting

  Using Anodot, customer experience software vendor Gainsight can automatically surface anomalies in its data and fix them, often before they become major problems.  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

• Social media marketing vendor uses sentiment analysis

  Falcon.io, a social media marketing vendor, turned to AI vendor Lexalytics to add automatic social media sentiment analysis capabilities to its platform.  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

• RPA in payroll automates finance processes for NYP hospital

  NewYork-Presbyterian, one of the largest hospitals in the U.S., looked to WorkFusion to automate some payroll processes, including filling out missed punch forms.  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

• AI in hiring gets companies more talent, faster

  Through processes like automated candidate sourcing and candidate rediscovery, AI sifts through many resumes to find those most suited to a position.  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

• GumGum uses machine learning annotation service Figure Eight

  Computer vision vendor GumGum gets its training data from Figure Eight, a machine learning training data vendor. Figure Eight uses crowdsourcing to annotate training data.  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