Features

Features

• 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

• 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

• 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

• AI use cases in the enterprise undergoing rapid change

  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

• The rising use of AI in the energy sector

  The energy industry has begun to ramp up the adoption of AI with the usage of smart grids among other technologies and has seen the benefits of the technology.  Continue Reading

• AI in e-commerce helps product sales

  EBay uses AI to power tools and services it built in-house to attract and retain users, such as automatic translation, image recognition and photo cleanup.  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

• Hair salon uses front desk AI to boost appointments

  A hair salon in Florida uses conversational AI to automatically engage clients over text. The bot answers questions and schedules appointments without human input.  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

• 5 mistakes that disrupt data science best practices

  Through asking questions and understanding the real-world issues other parts of the company face, data scientists polish their enterprise contribution.  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

• 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

• The complex nature of regulating AI

  Regulating AI is a difficult task because the technology changes rapidly. Governments must be able to employ preventative regulation to prevent any misuse.  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

• AI vendors to watch in 2020 and beyond

  The past 10 years have seen a surge of new AI vendors, and the trend isn't likely to end anytime soon, as investors continue to pour money into artificial intelligence.  Continue Reading

• Engage employees for successful enterprise AI strategy creation

  Existing employees are in the best position to suggest process improvements through automation, and executives need to utilize the employee experience to drive AI strategy.  Continue Reading

• Machine learning ops to lead AI in 2020

  The increased usage of pre-trained models, machine learning ops coming to the fore and increased transparency are all poised to lead the new year in AI.  Continue Reading

• AI trends 2020 outlook: More automation, explainability

  Top AI trends for 2020 are increased automation to extend traditional RPA, deeper explainable AI with more natural language capacity, and better chips for AI on the edge.  Continue Reading

• A year of AI events points to AI predictions for 2020

  A thoughtful reflection on the most important AI technology news of 2019 as well as a look to what 2020 may have in store for the world of AI.  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

• AI in networking helps keep systems running

  Network administrators are increasingly using AI tools to help them manage the growing complexity of their network infrastructure, a task that's getting more complicated by the day.  Continue Reading

• Vendor 3PM uses AI and analytics to prevent Black Friday fraud

  Using AI, analytics and Google Cloud tools, e-commerce intelligence vendor 3PM Solutions helps identify and take down counterfeit products for major e-commerce players.  Continue Reading

• Supervise data and open the black box to avoid AI failures

  As AI blooms, marketers and vendors are quick to highlight easy positive use cases. But implementation can -- and has -- gone wrong in cases that serve as warnings for developers.  Continue Reading

• Tailored content heads machine learning in digital marketing

  Consumers are no longer engaged with content alone -- companies need to create a robust digital marketing strategy personalized to each consumer. Machine learning is here to help.  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

• AI in advertising captures audiences with personalized ads

  Using AI technology, AdGreetz generates millions of personalized ads over the web and social media for its client Flipkart, an e-commerce vendor based in India.  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 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

• AI and climate change: The mixed impact of machine learning

  While AI can play a role in helping researchers and conservationists address the impacts of climate change, it can also contribute to carbon emissions, making it a mixed blessing.  Continue Reading

• Medline streamlines workflow by automating accounts payable

  By using Abbyy for OCR and UiPath for RPA, Medline is automating accounts payable. This speeds up the filing of the 2,000 invoices the healthcare distributor receives daily.  Continue Reading

• Using small data sets for machine learning models sees growth

  While massive data sets allow for easy training, developers are using new techniques to mine and transfer data that allows for training on limited labeled information.  Continue Reading

• Training data in facial recognition use cases reveals bias

  Applications of facial recognition technology show incredible promise in law enforcement and personal privacy, but the risks are holding enterprises back from adoption.  Continue Reading

• Fintech and retail lead the fray in AI adoption by industry

  Though AI enhances and drives the financial and manufacturing industries, others remain wary of the investment capital and research needed to insert AI into their enterprise.  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

• 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

• The importance of AI for fraud prevention

  As fraudsters become increasingly more professional and technologically advanced, financial organizations need to rely on products that use AI for to prevent fraud.  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

• Brands must allay worries for AI in transportation to take hold

  The personal mobility market is turning to emotional analysis and AI to negate fear and trepidation around emerging vehicle technology and the future of transportation.  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

• A chemical company simplifies workflows using RPA

  Software from Automation Anywhere, an RPA vendor, was easy for Eastman Chemical Company employees to use at a desktop level to automatically handle daily tasks.  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

• Government AI strategy includes tech transfer to private sector

  The Department of Energy's chief commercialization officer works with the DoE's national laboratories on transferring AI and other technologies to the private sector.  Continue Reading

• AI in content management revolutionizes unstructured data

  Managing content and data can take business process from unrefined to streamlined. Enterprises can slowly apply technologies to climb the ladder of cognitive content intelligence.  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

• John Deere's software and AI journey

  John Deere began a software journey more than a decade ago, hiring technology teams and developing new technologies, including AI, to help drive innovation in its machinery.  Continue Reading