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How Biotech and Pharma Use AI Today
When you ask who is “doing” AI the best, the answer is rarely a Fortune 500 Pharma company, and there are a few reasons for this. Most people think of genomics for healthcare AI applications. Another common one is drug discovery, which is very linear.  According... Read more
Improving Data Quality for Superior Results
When you’re a data scientist, you see a problem, and you build a model to solve it. If it’s not as accurate as you were hoping, you tweak the model. But what if it’s the quality of your data causing skewed or flawed results? Kaitlin Andryauskas... Read more
DeepOps for Business: Building an AI-First Company
Enterprises and large companies like Facebook have had AI-first capability for years, but it’s only recently that small businesses could make the transition. Yuval Greenfield of missinglink.ai has developed a ten-point checklist for companies wanting to make the change, giving them both AI capability and the... Read more
Big Data and Mobility Analytics
One of the best ways to measure mobility data is through humans themselves. Turns out — we’re great sensors for this kind of thing. Once you’ve anonymized and segmented mobility data taken from our movements, the wealth of information is staggering. Dr. Arturo Amador of Capgemini... Read more
Artificial Intelligence to Revolutionize Child Behavioral Diagnostics
jThe CDC estimates that a variety of disorders affect children in the US. ADHD could be as high as 10% of the childhood population, while speech disorders could affect between 5 and 12% of young children. And these aren’t the only conditions researchers are exploring. The... Read more
Validating AI & Machine Learning Models—Lessons Learned from the Banking Industry
Sectors that deal with sensitive data are very familiar with compliance standards that require transparency. If you request a loan, the company is required to disclose the reasoning behind your denial or approval. Other regulations, such as the recent GDPR amendment, require companies to disclose how... Read more
Building an Effective Data Science Portfolio For Your Business
Businesses that want effective data science strategies must first build effective data science roadmaps. Kerstin Frailey, formerly of Metis and now Numerator, uses her expertise in for-profit, non-profit, and government data science to outline strategies for building that essential piece of applied data science in her... Read more
Top 10 Big Data Blunders, Part 2
In the first part of the series, Dr. Stonebraker outlined five ways to know for sure companies are making mistakes in their big data plans and adoptions. He focused a lot on the missed opportunity businesses would have in not hiring the best talent and embracing... Read more
Building Data Science Teams: What Do You Need to Know?
Building a data science team from scratch involves more than just a list of requirements scraped from HR. Your data science team needs to solve problems and provide real business value, necessities that are difficult to describe in the traditional job posting. At ODSC East 2019,... Read more
Top 10 Big Data Blunders Part 1
Your company is making the move to big data initiatives, but with so many organizations launching half-baked data initiatives, how do you know your organization is going to succeed? Dr. Michael Stonebraker, a co-founder of Tamr and co-director of the Intel Science and Technology Center for... Read more