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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

Intelligent Partners: Man and the Machine

Posted by Guest Author

30.09.2015 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.

What’s important to understand is how collaboration between people and machine intelligence powers statistical creativity. However, this new paradigm first requires unlearning some of the patterns established by early forms of artificial intelligence.

Consider a game of chess. A chess master has a great memory and can evaluate a lot of positions, but that’s child’s play compared to Deep Blue. This chess-playing computer is known for being the first piece of artificial intelligence to win both a chess game and a chess match against a reigning master. Deep Blue can evaluate every possible move it can take at each turn, considering 200 million positions every second.

While Deep Blue’s power to play great chess is an awesome achievement, we need to put it into context. Deep Blue’s wins were the culmination of 12 years of development towards an extremely specialized task, and its potential moves were reliant on a static list of previous games. The computer can’t invent new moves that weren’t already in its database, and it would have to start back from square one if the rules of chess ever changed.

Imagine instead that the chess master and Deep Blue were on the same side of the table, working together. What if the two could communicate? Collaboratively creating and vetting potential strategies – one using his hard-earned expertise to handle new information and uncommon situations and the other using its vast database to discover optimal strategies and provide a sounding board? Wouldn’t this combination be more powerful?

The machine intelligence paradigm puts man and machine learning on the same team as equal partners. While Nutonian’s Eureqa automatically generates potential solutions through a powerful evolutionary search process, it communicates how it arrived at its results and flexibly accommodates outside guidance. This transparency allows anyone to incorporate their expertise into the system and seed the next round of discovery.

Nutonian believes that the best results happen when the user and the machines work together as partners in the process of invention. This productive partnership between man and machine heralds 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

Are Machines Partners or Foes?

Posted by Guest Author

22.09.2015 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?

Some science and technology experts have called AI “our greatest threat,” one which may spell “the end of the human race.” In its specific use with business data, many more have decried the perils of using “big data” to predict the future. The traditional data science approach is blind to unquantifiable factors and can be fooled by misleading correlations, but many businesses deal with sensitive subjects that require informed judgment and imprecise factors. On a more personal level, if even a skilled career like data science can be automated by a machine, what is there left for the rest of us?

What is still important to remember is that machines have their own strengths and weaknesses, just as humans do, and both sides have important roles to play in supporting each other. Machine algorithms have the capacity to churn through endless amounts of data but are subject to the biases of how they were programmed and limited by the inputs they are given. Humans can synthesize decades of experience into bursts of creativity but struggle to visualize data once it goes beyond three dimensions.

Here is where machine intelligence comes in. Machine intelligence allows its users, regardless of technical expertise, to harness and guide the power of today’s virtually unlimited compute power while encoding nuance and domain expertise from the user into the results. While automation allows machine intelligence to create new predictive models, the results are specifically designed to be transparent, interpretable and interactive. The end user can investigate how the system arrived at its conclusions and easily kick out false correlations, recognize mismatches with business realities, audit the robustness of potential models to assuage stakeholder concerns, and export the results into any number of other systems for further analysis.

Instead of perpetuating the machine vs. man rhetoric, Nutonian’s introduction of machine intelligence establishes a new machine-as-partner paradigm. Allowing the strengths of each side to bolster each other’s weaknesses empowers businesses to scale their data science initiatives across all levels and functional areas, exponentially increasing their analytical capacity to answer high-value questions. Augmentation, not replacement, is the key to 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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