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.
Telematics in Cars and Auto Insurance: Privacy or Savings?
on
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.
Stories of AI Failure and How to Avoid Similar AI Fails
on
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.
How to Use AI to Solve Real Business Problems
on
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?
on
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.
Top Applications of Text Analytics & NLP in Healthcare
on
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.
Build or Buy for Natural Language Processing?
on
8 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.
Cloud or On Premise for Data Analytics: How to Choose
on
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?
Machine Learning Micromodels: More Data is Not Always Better
on
6 m
Machine learning micromodels reduce the challenges of sourcing and annotating data while delivering better precision and accuracy than macromodels.
Fight Organizational Bias with Unstructured Data
on
2 m
Efforts to fight organizational bias often fall short. Lexalytics CEO Jeff Catlin explains how to use unstructured data to fight your own company’s biases.
Let’s Talk AI Cooperation (Don’t Call it an “Arms Race”)
on
6 m
Advances in artificial intelligence are usually framed as an arms race. Instead, let’s look at AI research through the sunnier lens of cooperation.
Machine Learning for Natural Language Processing
on
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.
Or call us at 1-800-377-8036