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Machine Intelligence Strips Off Our Data Science Blinders

Posted by Guest Author

07.10.2015 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.

Consider a GPS. A lot of powerful technology is used to correctly make an optimal GPS route. Few people understand why their GPS system chooses the routes that it does, but we’ve come to simply accept its recommended navigation directions because they tend to be good enough. It’s OK even when the predicted route doesn’t work – say, it prompts you to turn the wrong way on a one-way street or you run into construction and need to make a detour – we have corrective mechanisms in place to override its instructions.

However, accepting blinders on data-driven solutions can be dangerous. The higher the cost of a mistake, the higher the consequences are for false positives and false negatives. Have you ever started internet sleuthing and found a symptom checker that declared that your runny nose and painful headache meant you had cancer? Instead of being gently let down by your exasperated doctor the next morning, imagine if the hospital immediately enrolled you in chemotherapy treatment based solely on this output. While this is an extreme example, outsourcing too much responsibility to machines could lead to mistakes just as costly.

A fundamentally new approach to data science is needed to accomplish this partnership – one that allows each side to equally communicate ideas and strategies to each other, rather than one side dictating the constraints of the connection. This approach is machine intelligence, with the driving philosophy that the partnership between man and machines is greater than the sum of its parts.

Nutonian’s machine intelligence system, Eureqa, doesn’t put blinders on users. In fact, the system purposefully shows its work, surfaces user-friendly ways to reach advanced results, and encourages rapid iteration to incorporate the user’s domain expertise into the results. Regardless of technical expertise, users all across the organization can use Eureqa to discover new business strategies, while retaining the ability to audit and correct sub-optimal paths before committing to them.

The abundance of data in the business world needs more than a one-sided discussion. Use machine intelligence to open up a new horizon of possibilities in the golden age of analytics.

 


ABOUT THE AUTHOR

Dan Woods is CTO and founder of CITO Research. He has written more than 20 books about the strategic intersection of business and technology. Dan writes about data science, cloud computing, mobility, and IT management in articles, books, and blogs, as well as in his popular column on Forbes.com.

Topics: Eureqa, Golden Age of Analytics, Machine Intelligence

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