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

How to Master Business Planning with Time Series Forecasting

Posted by Jess Lin

1/17/17 10:15 AM

Every analyst report, news article, and business conference has drummed into our collective minds that predictive analytics is the way of the future. If you can predict future sales, you can funnel that knowledge into driving optimal sales and business operations. Smart businesses are leveraging big data insights to leave their competitors in the dust, or at least so they say.

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Topics: Eureqa, Time Series Forecasting, Business Planning

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: 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

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