Manage

Manage

Learn to apply best practices and optimize your operations.

• 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

• 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

• AI vs. predictive analytics debate shows powerful combination

  Artificial intelligence and machine learning, when combined with predictive analytics, allow companies and organizations to get the most out of their data. Continue Reading

• Application of AI in robotics boosts enterprise potential

  The combination of AI and robotics has allowed companies to move past automation and tackle more complex and high-level tasks with their robots. Continue Reading

• 3 keys for the implementation of AI in the enterprise

  Organizations need to focus on diversity, proper scaling and augmentation capabilities when implementing artificial intelligence in order to keep the process pain-free. Continue Reading

• Machine learning and bias concerns weigh on data scientists

  Data scientists are forever vigilant in their desire to identify and eliminate the many forms of bias that can compromise the credibility of machine learning models.Continue Reading

• Responsible AI champions human-centric machine learning

  Encompassing ethics, transparency and human centricity, responsible AI is an effective approach to deploying machine learning models and achieving actionable insights.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

• 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

• 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

• 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

• 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

• Find out how smart you are about machine learning and AI

  Machine learning can help businesses gain powerful analytics value from their data -- but only if it's done right. How much do you know about machine learning and related forms of AI?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

• 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

• IBM Think 2020: AI, security, COVID-19 news, trends and analysis

  News at the IBM Think 2020 digital conference focused on paths to modernization -- including advanced AI and automation -- IoT, hybrid cloud infrastructure and cybersecurity.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

• 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

• 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

• 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

• 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

• 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

• 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

• How to create NLP metrics to improve your enterprise model

  As standardized NLP framework evaluations become popular, experts urge users to focus on individualized metrics for enterprise success.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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• How to create a data set for machine learning with limited data

  A shortage of data for machine learning training sets can halt a company's AI development in its tracks. Turning to external sources and hidden data can solve the problem.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

• 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

• 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

• 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

• 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

• 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

• 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

• AI in professional services revolutionizes white-collar jobs

  Professional services and consulting firms are adopting AI at a rapid rate, even though these types of jobs, which mainly focus on interpersonal interaction, may not seem like strong targets for automation.Continue Reading

• How to improve candidate matching with AI in HR recruitment

  AI in HR recruitment can source talent externally but can also be used to improve internal projects. AI platforms can match qualified existing employees to potential open projects.Continue Reading

• How to recycle data from AI for employee engagement efforts

  By reusing the data collected for AI algorithms and the insights they generate, enterprises can boost employee performance and improve business processes.Continue Reading

• Better together: Predictive analytics and AI boost each other

  Enterprises have long seen the value of predictive analytics, but now that AI is starting to influence forecasting tools, the benefits may start to go even deeper.Continue Reading

• Machine learning bots enable immediate paperless workplaces

  Document digitization allows for the automatic extraction of data through rapid bot technology, as companies aim for a cost-effective, eco-friendly paperless workplace.Continue Reading

• AI bias, for good or ill

  Bias in AI algorithms can produce harmful results, but it can also help train models. This third part of a three-part series on AI ethics explains the pros and cons of AI bias.Continue Reading

• Wield AI to personalize consumer products and aid logistics

  Companies using AI to sell consumer products seek brand loyalty by using personalization, online sales capabilities and optimized inventory managementContinue Reading

• Data scientists urged to take AI security threats more seriously

  AI security hasn't been the top concern of most data scientists using machine learning. But as these systems move closer to the core of the business, security is becoming critical.Continue Reading

• Address anonymity and data privacy in chatbot security

  A key to successful enterprise chatbot security is to program your chatbot to recognize personal or sensitive information and treat it accordingly.Continue Reading

• Chatbots in banking at Norway's largest financial company

  By deploying semantics technology over neural networks, startup Boost.ai has created a conversational AI product that is used by the largest financial services company in Norway.Continue Reading

• Researchers race for quantum AI as quantum computing advances

  Machine learning is likely to be an early application of quantum computers, as researchers and developers look for the key to a more human-like artificial intelligence.Continue Reading

• AI in accounting boosts compliance and fraud detection

  Accounting and finance teams are using AI tools to speed document review and other error-prone processes, which gives a boost to fraud detection and compliance efforts.Continue Reading

• Enterprise AI tools help cultivate better business health

  AI technologies can give companies a business boost, enough so that the number of available jobs for humans may increase instead of declining due to more automation.Continue Reading

• Recruiting data scientists for AI and machine learning

  When hiring data scientists, be sure to include your data science team in the interview process, and strive to build a data-literate HR department. AI tools may also help.Continue Reading

• Using AI in manufacturing processes surges quality and design

  The addition of AI in manufacturing leads to increased workflow -- from design to production. Production plants are turning to technology to supplement the manufacturing skills gap.Continue Reading

• Machine learning in production challenges developers' skills

  Deploying machine learning models requires an entirely different skill set than developing them, and data scientists and engineering teams need to be ready to bridge this gap.Continue Reading

• Data science skills spawn success in AI, big data analytics

  AI and big data analytics initiatives require a broad set of skills, and putting those skills in place often takes teams of data scientists with different strengths.Continue Reading

• Reskilling the analytics team: Math, science and creativity

  Technical skills are a must for data scientists. But to make analytics teams successful, they also need to think creatively, work in harmony and be good communicators.Continue Reading

• AI for business operations starts to offer value

  Businesses are starting to implement AI in operations to smooth out back-office processes and streamline repetitive tasks that currently take too much time for human workers.Continue Reading

• Threats of AI include malicious use against enterprises

  As sophisticated tools become easier to use, enterprises need to protect themselves against AI threats to ensure they do not become the victims of malicious attacks.Continue Reading

• How one company thinks chatbots and AI can change insurance

  Insurance agency management company In-Fi is hoping AI chatbots can streamline homeowner insurance applications and bring the process in line with customers' expectations.Continue Reading

• Harman's plan for AI in cars moving full speed ahead

  Can an AI virtual assistant in every car save the auto industry from sluggish sales and an uncertain future? Audio component manufacturer Harman argues that it can help.Continue Reading

• More curiosity could help narrow AI tools handle broader uses

  Today, engineers are developing AI tools primarily for individual applications, but programming a facsimile of curiosity into algorithms could help make them more general purpose.Continue Reading

• AI virtual assistant tools prove better for customer service than chatbots

  AI virtual assistant software is increasingly surpassing chatbots and natural language search, as enterprises see deeper value in enabling true conversation.Continue Reading

• Humans and AI tools go hand in hand in analytics applications

  Companies are keeping data analysts and other workers in the loop with AI applications to check the results generated by automated algorithms for accuracy, relevance and missing info.Continue Reading

• Insurer's machine learning use case: Changing driver behavior

  Machine learning tools can be put to use for more than targeted marketing and product recommendations. Auto insurer HiRoad is using them to help create safer drivers.Continue Reading

• Artificial intelligence data storage planning best practices

  AI storage planning is similar to the storage planning you're used to: Consider capacity, IOPS and reliability requirements for source data and the application's database.Continue Reading

• Chatbot applications must get better at chatting to engage users

  Today's AI chatbots are good at taking orders and delivering scripted responses, but experts say tomorrow's chatbots need to be more conversational in order to deliver bigger value.Continue Reading

• Wayfair's chief architect talks AI-driven innovation, impactful IT

  Wayfair sells home furnishings, but under the covers, it's a tech juggernaut. Chief Architect Ben Clark explains how AI-driven innovation propels the business.Continue Reading

• Why machine learning models require a failover plan

  Flawed machine learning models lead to failures and user interruptions. Expert Judith Myerson explains the causes for failures and how a failover plan can improve user experience.Continue Reading