Problem solve

Problem solve

Get help with specific problems with your technologies, process and projects.

• Expanding explainable AI examples key for the industry

  Improving AI explainability and interpretability are keys to building consumer trust and furthering the technology's success. Continue Reading

• Solving the AI black box problem through transparency

  Ethical AI black box problems complicate user trust in the decision-making of algorithms. As AI looks to the future, experts urge developers to take a glass box approach. Continue Reading

• Discover 2 unsupervised techniques that help categorize data

  Two unsupervised techniques -- category discovery and pattern discovery -- solve ML problems by seeking similarities in data groups, rather than a specific value. Continue Reading

• Combating racial bias in AI

  By employing a diverse team to work on AI models, using large, diverse training sets, and keeping a sharp eye out, enterprises can root out bias in their AI models. Continue Reading

• Addressing 3 infrastructure issues that challenge AI adoption

  One of the biggest problems enterprises run into when adopting AI infrastructure is using a development lifecycle that doesn't work when building and deploying AI models. Continue Reading

• 5 reasons NLP for chatbots improves performance

  Experts say chatbots need some level of natural language processing capability in order to become truly conversational. Without language capabilities, bots are simple order takers.Continue Reading

• Tackling the AI bias problem at the origin: Training data

  Though data bias may seem like a back-end issue, the enterprise implications of an AI software using biased data can derail model implementation.Continue Reading

• 9 data quality issues that can sideline AI projects

  The quality of your data affects how well your AI and machine learning models will operate. Getting ahead of these nine data issues will poise organizations for successful AI models.Continue Reading

• How to avoid overfitting in machine learning models

  Overfitting remains a common model error, but data scientists can combat the problem through automated machine learning, improving AI literacy and creating test data sets.Continue Reading

• How to troubleshoot 8 common autoencoder limitations

  Autoencoders' ability for automated feature extraction, data preparation, and denoising are complicated by their common problems and limitations in usage.Continue Reading

• Data science's ongoing battle to quell bias in machine learning

  Machine learning expert Ben Cox of H2O.ai discusses the problem of bias in predictive models that confronts data scientists daily and his techniques to identify and neutralize it.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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• How to overcome the data scientist shortage

  The industrywide data scientist shortage is leading companies like Schneider Electric to look internally for employees with a technical background and invest in training them.Continue Reading

• Automatic meeting transcription records business sessions

  By using an automated transcription platform from Fireflies.ai, employees at Zenatta Consulting can more easily find and read transcripts from important client calls.Continue Reading

• Berklee uses SnapLogic for its AI in higher education needs

  Berklee College of Music needed intelligent integration for its two student portals after a merger with the Boston Conservatory. The college chose SnapLogic to connect the systems.Continue Reading

• AI vendors attack data scientist shortage with trainings

  Internal data science training programs have helped vendors when colleges and universities have failed. Training is helping to fix the data scientist shortage.Continue Reading

• Clean data for machine learning is key to successful AI

  Many enterprises think their AI projects should start with massive amounts of data. But clean data for machine learning should be their first step toward AI.Continue Reading

• March Madness analytics, AI help data scientist fill bracket

  To create his March Madness bracket predictions, the head of data science at DataRobot uses a host of machine learning algorithms and some predictive analytics.Continue Reading

• How will the GDPR and AI clash affect enterprise applications?

  Compliance rules for GDPR and AI implementation may not seamlessly work together. Experts say AI's automated decisions on customer data are risky and need to be explainable.Continue Reading

• Arkose Labs puts AI in cybersecurity

  Cybersecurity vendor Arkose Labs uses fraud prevention AI to block web-based bots while allowing human users to safely get to the content they want.Continue Reading

• Common sense AI approaches point to more general applications

  The race to attain artificial general intelligence is on. Ranging from predictions of 10 to 200 years away, the one thing experts can agree on is that common sense AI is the next step in the journey.Continue Reading

• Debate over AI in the workforce requires a broad view

  Enterprises that are poised to implement AI in the workforce don't have to forfeit human jobs to do so. Read a Dun & Bradstreet analyst's take on the intent of enterprise AI.Continue Reading

• AI for accessibility helps people with disabilities

  AI for people with disabilities is making a meaningful difference in their ability to navigate the world and participate in all the activities of daily life.Continue Reading

• AI taking jobs is not a concern for certain workers

  AI and job loss may turn out to be among the biggest stories of this century, but workers in certain fields don't have nearly as much to worry about as others.Continue Reading

• Convert unstructured data to structured data with machine learning

  With access to powerful compute power and advances in machine learning, unstructured data is becoming easier and cheaper for businesses to turn into usable sources of insight.Continue Reading

• AI and jobs collide as automation looms

  AI automation will eliminate a broad swath of today's jobs over time, but some jobs are likely to disappear sooner than others due to the uneven pace of technology development.Continue Reading

• Algorithmic bias top problem enterprises must tackle

  No enterprise today would roll out an obviously biased AI tool, but many remain unaware of the risks of unconscious bias in algorithms, which can produce equally dangerous results.Continue Reading

• The threats of AI must be taken seriously to prevent harm

  The risks of AI use are growing as the technology becomes more pervasive. Rather than laugh off the threats, businesses should move to mitigate them before they become headaches.Continue Reading

• Combination of blockchain and AI makes models more transparent

  Blockchain technology could play an important role in helping enterprises develop more explainable AI applications, something that is frequently lacking today.Continue Reading

• GDPR regulations put premium on transparent AI

  As the EU's GDPR regulations go into effect, enterprises must focus on building transparency in AI applications so that algorithms' decisions can be explained.Continue Reading

• Machine vision makes paper a thing of the past for insurers

  The insurance industry is buried in paper-based processes. Former MetLife CIO Gary Hoberman aims to change that with a platform that runs on AI and machine vision.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