Features

Features

• 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

• 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

• Clearsense uses Unravel Data for AI in performance management

  Unravel Data uses AI in performance management to power its APM platform. Clearsense turned to Unravel to get automated optimizations and enable multi-cloud support.  Continue Reading

• Serverless machine learning reduces development burdens

  Getting started with machine learning throws multiple hurdles at enterprises. But the serverless computing trend, when applied to machine learning, can help remove some barriers.  Continue Reading

• Collaborative robots' safety stalls enterprise implementation

  Cobots are promising big gains, especially in enterprises utilizing manual labor. However, due to a number of safety concerns, human workers are still at risk.  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

• U.S. spends more on AI as AI in China continues to grow

  Research and development of AI in China continue to grow. Meanwhile, the U.S. plans to up its spending on non-defense AI projects by close to $1 billion in 2020.  Continue Reading

• Use of AI in business requires education, understanding

  AI adoption requires business leaders to have a clear understanding of the technology and its capabilities, as well as how AI can help automate and aid specific functions.  Continue Reading

• Using enterprise intelligent automation for cognitive tasks

  RPA is no longer comprised of simple chatbots or repetitive programmed tasks. Enterprises are looking at RPA to move up the ladder of cognitive automation.  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

• How to solve deep learning challenges through interoperability

  The challenges of training and overseeing advanced neural networks is leading to an implementation bottleneck in deep learning technology.  Continue Reading

• The use of technology in education has pros and cons

  Vendors continue to develop AI in education applications and technologies as students see benefits in using technology in the classrooms and when doing their homework.  Continue Reading

• Consumer goods company Unilever uses Google AI for marketing

  Using Google Cloud products, including Google's Vision API and Natural Language API, Unilever creates innovative and personalized social media marketing campaigns.  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

• With ThoughtSpot, GlobalTranz makes AI in logistics platform

  GlobalTranz, a logistics company, uses AI and analytics in logistics to predict driver behaviors and plan shipping routes that will keep the shippers and the drivers happy.  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

• Bibb County School District cybersecurity efforts use AI

  Using AI-driven cybersecurity from ManagedMethods, a Georgia school district blocks external threats and identifies potentially harmful language in student documents.  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

• The future of data science and AI points to automatic tools

  The relationship between data scientists and companies using AI is evolving rapidly, shifting from a focus on trained professionals to experienced employees with automated tools.  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

• AI in law enforcement is growing, but needs work

  AI for police includes numerous different analytics, machine learning and natural language processing technologies, including facial recognition and automated transcription tools.  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

• Enterprises need to create an AI culture for success

  Enterprises can resist using AI because of the cultural changes employees feel it will bring, including changes to employee job descriptions and elimination of outdated jobs.  Continue Reading

• Analyst, author talks enterprise AI expectations

  The author of the upcoming book about enterprise AI talks about realistic AI deployment, dispelling some of the AI hype myths that can be harmful to enterprises.  Continue Reading

• Augmented intelligence applications showing ROI, broad success

  Enterprise uses have shown that utilizing augmented intelligence technology increases ROI, productivity and linear success as compared to general AI or AGI.  Continue Reading

• More to machine learning platforms than meets the AI

  To reach full analytics potential, machine learning platforms powered by AI must provide scalability, handle multiple models, integrate with data sources and be cloud-friendly.  Continue Reading

• Agricultural equipment maker uses AI for customer experience

  Using AWS services Redshift, S3 and SageMaker, as well as third-party tools, AGCO has created new AI marketing tools and a customer portal to better compete in a tight market.  Continue Reading

• AI for digital marketing heavily used in the gaming industry

  AI in gaming has evolved beyond automating character and fictional world development. Gaming companies are now using AI to better market to existing and potential players.  Continue Reading

• Enterprise consumer relationships are building trust in AI

  Transparency is an increasingly important component of consumer trust. If you want to win over consumers whose data is being collected, start with explainability and collaboration.  Continue Reading

• AI in financial services helps speed consumer interaction

  By using AI in finance, financial organizations are able to speed processes that otherwise would require more manual input from employees or consumers.  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

• Automated transcription services for adaptive applications

  NLP technologies have advanced in recent years. Using them, startups have been able to create automatic transcription software for adaptive applications.  Continue Reading

• GPU analytics speeds up deep learning, other data insights

  GPU-based systems have become a popular platform for deep learning applications, and they're now also being used to accelerate analysis of IoT and geospatial data.  Continue Reading

• AI in the legal industry focuses on augmenting research

  From billable time invoice generation to patent attribute data mining, the implementation of AI in law firms has aided in reducing low-value, time-consuming paralegal work.  Continue Reading

• Consider these 12 RPA software vendors for deployment

  RPA technology can help companies successfully automate their tasks and processes if they can sift through the options to determine the right system for their enterprise.  Continue Reading

• Pushing past chatbot challenges will secure their longevity

  For chatbots to remain in enterprise futures, developers and data scientists need to get flexible. From open source to intelligent sharing, chatbot collaboration will boost benefits.  Continue Reading

• Experts discuss pressing data science problems and solutions

  Most data science projects end up facing similar problems, such as lack of robustness and data quality issues. In this feature, experts offer tips on how to overcome these challenges.  Continue Reading

• AI and big data go perfectly together -- sometimes

  The combination of big data and AI tools enables new forms of analytics and automation, but use of the technologies in enterprise applications is still evolving.  Continue Reading

• The future of voice assistants is multiturn conversations

  The future looks promising for voice assistants, but for them to really live up to the hype, they are going to have to improve at true multiturn conversations.  Continue Reading

• AI acquisitions lead to consolidation

  Analytics and AI startups emerge regularly and grow quickly. Frequent acquisitions, however, seem to be creating a more consolidated industry, cementing some vendors at the top.  Continue Reading

• Natural language processing drives conversational AI trends

  Since the first conversational interfaces, users have desired human-like conversation. Now, AI sentiment analysis, emotion and unique generation are bringing us one step closer.  Continue Reading

• The future scope of chatbots begins with addressing flaws

  Chatbots are hot software in the enterprise, but to maintain longevity and relevance, developers need to take a look at the barriers to entry, interface options and NLP issues.  Continue Reading

• Successful retail robots improve the augmented workforce

  The future of AI in the workplace relies on the successful integration of humans and bots. Retail use cases for cobots point to customer engagement and labor tasks.  Continue Reading

• AI network security tool autonomously does microsegmentation

  To ensure network security, a U.S. law firm has turned to automated network microsegmentation vendor Edgewise. The startup uses machine learning to deploy microsegmentation.  Continue Reading