Popular media and hype cycles are leading us to expect radical changes and perfect results from artificial intelligence. But you don’t read perfectly, so why should your AI? In this blog, Tim Mohler explains when “good” is good enough for AI.
The Blog.
AI in Healthcare: Data Privacy and Ethics Concerns
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10 m
Artificial intelligence has rendered HIPAA obsolete. As AI in healthcare becomes commonplace, data privacy, security and ethical issues are growing. We explored the uses of AI in healthcare, the ethical issues involved, and why experts are raising flags about healthcare data storage and data security practices.
Text Analytics & NLP in Healthcare: Applications & Use Cases
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10 m
Healthcare databases are growing exponentially. Today, healthcare providers, drug makers and others are turning this data into value by using text analytics and natural language processing to mine unstructured healthcare data and then doing something with the results. Here are some examples.
Machine Learning (ML) for Natural Language Processing (NLP)
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14 m
Paul Barba explains the role of machine learning and AI in natural language processing, how to apply machine learning to solve problems in natural language processing and text analytics, and why a hybrid ML-NLP approach is best.
Voice of the Employee Explained: VoE Programs & More
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11 m
People Analytics and Voice of Employee programs take a data-driven approach to improving productivity and reducing turnover by analyzing employee feedback. This article explains what Voice of the Employee is, why it’s valuable and how to do it.
Build vs Buy: Text Analytics & NLP
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9 m
From customization to scalability, there’s a lot to consider when deciding whether to build or buy an NLP or system. In this article we outline the dilemma and explore the pros and cons and costs of both options.
Stories of AI Failure and How to Avoid Similar AI Fails
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11 m
Don’t fail prey to the AI hype machine. These high-profile stories of AI failure are alarming for consumers, embarrassing for the companies involved, and an important reality-check for us all.
Adding New NLP Capabilities for RPA: Build or Buy?
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7 m
Robotic Process Automation (RPA) is moving towards larger, transformational initiatives. As part of that shift, RPA vendors must improve their natural language processing (NLP) capabilities to support trending text analytics use cases. In this article, we explore your options as an RPA vendor looking to add new text analytics and NLP capabilities to your platform.
Telematics in Cars and Auto Insurance: Privacy or Savings?
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9 m
Is saving money on your car insurance reason enough to trade away your privacy? We researched how automakers, insurance companies and advertisers use telematics to collect, analyze and monetize data about your driving habits, and why maybe you shouldn’t be so quick to jump on the telematics bandwagon.
How to Use AI to Solve Real Business Problems
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5 m
Instead of going for a high-risk moon-shot, here’s how to effectively integrate AI into your business to solve tangible problems. In this article, originally posted on Forbes, Lexalytics CEO Jeff Catlin keeps it clear and concise.
Genetics Testing: What Happens to Your DNA Data?
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9 m
26 million people had already used take-home genetics testing kits by the start of 2019. But what happens to your data afterwards? We looked into it and found biotech firms, retailers, law enforcement agencies and others buying and accessing DNA data en masse.
Cloud or On Premise for Data Analytics: How to Choose
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5 m
These 4 factors are driving big names back to private cloud or on premise implementations for data storage and analytics. Should you move, too?