Machine Intelligence with Michael Schmidt: Searching data for causation

Posted by Michael Schmidt

7/27/16 10:03 AM

The holy grail of data analytics is finding “causation” in data: identifying which variables, inputs, and processes are driving the outcome of a problem. The entire field of econometrics, for example, is dedicated to studying and characterizing where causation exists. Actually proving causation, however, is extremely difficult, typically involving carefully controlled experiments. To even get started, analysts need to know which variables are important to include in the evaluation, which need to be controlled for, and which to ignore. From there, they can build a model, design an experiment to test its causal predictions, and iterate until they arrive at a conclusion.

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Topics: Eureqa, Machine Intelligence, Causation

The “First Mover’s” Analytics Stack, 2015 vs. 2016

Posted by Jon Millis

7/1/16 10:00 AM

The irony of data science is the glacial and blazing speed at which the industry seems to move. It’s been more than 10 years since the origin of the phrase “big data”, and yet what we initially set out to accomplish – extracting valuable answers from data – is still a painstaking process. Some of this could be attributed to what Gartner refers to as the “Hype Cycle”, which hypothesizes that emerging technologies experience a predictable wave of hype, trials and tribulations before the they hit full-scale market maturity: technology trigger  peak of inflated expectations  trough of disillusionment  slope of enlightenment → plateau of productivity. 

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Topics: Eureqa, Big data, Machine Intelligence, Analytics stack

Machine Intelligence with Michael Schmidt: Analytical models predict and describe the world around us

Posted by Michael Schmidt

6/23/16 12:06 PM

Models are the foundation for predicting outcomes and forming business decisions from data. But all models are not created equal. Models range from simple trend analysis, to deep complex predictors and precise descriptions of how variables behave. One of the most powerful forms of model is an “analytical model” – that is, a model that can be analyzed, interpreted, and understood. In the past, analytical models have remained the most challenging type of model to obtain, requiring incredible skill and knowledge to create. However, modern AI today can infer these models directly from data.

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Topics: Machine learning, Analytical models, Deep learning

Machine Intelligence with Michael Schmidt: OpenAI and doomsday artificial intelligence

Posted by Michael Schmidt

6/1/16 9:30 AM

Speaking at the Open Data Science Conference (ODSC) last week, I discussed where artificial intelligence is going, what it will automate, and what its impact will be on science, business, and jobs. While the impact from Eureqa has been overwhelmingly positive, many are warning about a darker future:

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Topics: Artificial intelligence, Reinforcement learning, OpenAI

Machine Intelligence with Michael Schmidt: IBM’s Watson, Eureqa, and the race for smart machines

Posted by Michael Schmidt

5/16/16 11:12 AM

Three months ago I spoke at a conference affectionately titled “Datapalooza” sponsored by IBM. My talk covered how modern AI can infer the features and transformations that make raw data predictive. I’m not sure exactly how many IBM people were in the crowd, but two IBM database and analytics leads grabbed me after the talk:

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Topics: Machine Intelligence, IBM Watson, Artificial intelligence

Eureqa vs the Kentucky Derby: Triple the Hat, Triple the Fun

Posted by Jess Lin

5/7/16 1:07 PM


After 2 years in a row of coming up roses, we’ve got our sights set on a 3rd year of success with the Kentucky Derby. We’ve got our handicapping data from and we’ve prepped with plenty of mint juleps (drinks help you bet smarter, right?). Now we’ve spent the past couple days combining Eureqa’s data discovery horsepower with the raw horse power on the track to find out who’ll be in the winner’s circle for the 142nd running of the Kentucky Derby.
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Topics: Eureqa, Kentucky Derby

Using Machine Intelligence to Understand the Student Loan Problem

Posted by Jon Millis

5/6/16 12:30 PM

In March, the US Department of Education released its latest College Scorecard to “provide insights into the performance of schools eligible to receive federal financial aid, and offer a look at the outcomes of students at those schools.” Fortunately for us data-driven strategists (read: nerds) at Nutonian, the government also released the raw data it used to drive at its summary results and findings.

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Topics: Machine Intelligence, College Scorecard

Analyze This: 6 Ways Retailers Put Artificial Intelligence To Work

Posted by Jon Millis

5/4/16 12:08 PM

This week Retail TouchPoints, a leading retail analytics media company, ran an article by Scott Howser, our SVP of Products and Marketing. Scott is seemingly always on the cutting-edge of big data, data science, and AI, having spent time driving success at companies like Vertica (acquired by HP), Hadapt (acquired by Teradata), and now Nutonian. Retail TouchPoints sought Scott's expertise to find out how retailers are leveraging the latest hot technology: artificial intelligence. The article, "Analyze This: 6 Ways Retailers Put Artificial to Work," is reproduced below. 

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Topics: New store optimization, Sales forecasting, Artificial intelligence, Retail, Retail TouchPoints, Staffing

NSF Uses Artificial Intelligence to Tackle Illegal Tiger Poaching

Posted by Jon Millis

4/26/16 9:23 AM

AI to the rescue. Forget doomsday scenarios of robots transforming humans into paperclips. This time it’s more positive. The National Science Foundation (NSF) announced it’s turned to artificial intelligence as a critical weapon in the fight against poaching.

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Topics: Artificial intelligence

The Perils of Black Box Machine Learning: Baseball, Movies, and Predicting Deaths in Game of Thrones

Posted by Jon Millis

4/22/16 10:17 AM

Making predictions is fun. I was a huge baseball fan growing up. There was nothing quite like chatting with my dad and my friends, crunching basic statistics and watching games, reading scouting reports, and finally, expressing my opinion on what would happen (the Braves would win the World Series) and why things were happening (Manny Ramirez was on a hot streak because he was facing inexperienced left-handed pitchers). I was always right...unless I was wrong.*

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Topics: Machine Intelligence, Machine learning, Baseball, Game of Thrones

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