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Business Analytics: Simple is Better. Always.

Posted by Ben Israelite

1/31/17 10:35 AM

Occam’s Razor, a well-known principle stating the simplest solution for a problem is often the best, has been utilized by businesses for decades to solve their most significant and complicated problems. The integration of data analytics – the pursuit of extracting meaning from raw data – into an enterprise’s decision-making process should aid in this effort. Yet, as organizations ramp up their data analytics capabilities, black box algorithms and highly convoluted predictions have been favored over concise and actionable insights.

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Topics: Machine Intelligence, Occam's Razor, Parsimonious models

Eureqa Hits Wall Street; Automatically Identifies Key Predictive Relationships

Posted by Jason Kutarnia

12/1/16 10:46 AM

As a team of data scientists, analysts and software developers, we didn’t expect to be praised as financial gurus. But in an industry of ever-present uncertainly and huge financial gains and losses at stake, Eureqa, the dynamic modeling engine, displays a unique competitive advantage in the technology stack: the ability to quickly derive extremely accurate and simple-to-understand models that predict what will happen in the future, and why.

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Topics: Eureqa, Machine Intelligence, Financial Analysis

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: 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

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

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

Machine Intelligence Strips Off Our Data Science Blinders

Posted by Guest Author

10/7/15 10:00 AM

by Dan Woods

In our increasingly digital lives, we have been trained to trust the way that technology works. That is, right up until it doesn’t.

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Topics: Eureqa, Machine Intelligence, Golden Age of Analytics

Intelligent Partners: Man and the Machine

Posted by Guest Author

9/30/15 10:30 AM

by Dan Woods

When it comes to the creative processes inherent in predictive modeling, it is time for a new paradigm, one in which the user and machine learning work in tandem to achieve better results than could be achieved working separately. Nutonian’s vision for this is machine intelligence.

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Topics: Eureqa, Machine Intelligence, Golden Age of Analytics

Are Machines Partners or Foes?

Posted by Guest Author

9/22/15 10:30 AM

by Dan Woods

The exploitation of data in the business world demands a new data-driven approach to innovation. Human-driven data analysis needs to make way for new machine-driven methods capable of handling access to the new abundance of data. However, much hysteria has been recently directed at the dangers of big data and over-reliance on Artificial Intelligence (AI). Is this fear warranted, or is it just much ado about nothing?

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Topics: Eureqa, Machine Intelligence, Golden Age of Analytics

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