Master The Data Science Game Through Machine Intelligence

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While being data driven, relying on data, understanding analytics has become cliche for organization, the underlying issue still holds water restricting many organization from becoming data/analytics literate. From process to machine, every aspect of your business should support the intelligence framework. Think of your organization as AI first. This requires a different mindset.

Josh Sullivan and Angela Zutavern, author of The Mathematical Corporation have a lot to say about this topic through their book. Let’s dive in.

What is a mathematical corporation?

Josh:

Machines are now learning to perceive and understand the world like humans do, in order to make their own decisions. As a result, the workforce of the future will be divided between creative people doing high-level cognitive tasks and intelligent machines that can do high-level work to complement human efforts. Corporations will have to rethink not only their employment models and the very nature of how work is done, but also how they lead, create competitive advantage, and evolve over time. The mathematical corporation is an organization that understands that, and is on a journey to operate and succeed in that world.

What’s machine intelligence?

Josh:

Unlike artificial intelligence and data science, machine intelligence is when machines, on their own, use the same type of learning approaches that humans do in order to understand what previously happened and make predictions. But in the future, machine intelligence and human intelligence will be blended together.

What’s the difference between how the mathematical corporation uses machine intelligence and how organizations today more commonly use data science and analytics?

Angela:

When most organizations today look at data, they’re looking at what happened in the past. For example, our whole economy depends on earnings reports from companies of what happened in the last quarter, or jobs growth for the last time period. Even organizations that are really adept at analytics rarely use them for predictive power. Among those that do, even fewer are taking advantage of what machines can do on their own. So the biggest gap right now in how organizations are operating is this: they’re not taking advantage of what machines are good at versus what people are good at.

Josh:

When machines think and reason—and there are some tasks they can perform as well or better than humans today—they have a mathematical context. That’s really important. How humans think and learn and how machines think and learn are quite different. And leaders have to understand that. We need to avoid thinking of machines as a panacea, that they’re going to do everything.

Because of the ways in which machines learn, it’s relatively easy to understand and identify the limitations of what they can do well as opposed to what we should always have humans doing.

Can any type of organization in any industry become a mathematical corporation, or is this just for tech companies?

Angela:

It can and will be every organization.

Josh:

It’s more about changing the nature of how people do work, so it’s applicable to business in general, not just technology companies. It’s about using technology to fundamentally change the way business works.

The future of work means differently to each one of us: some see it as more technology and less human, some expect a more humanized space and some others imagine it to be a no-workplace world. In our journey to unwrap FutureofWork, Work2.org invites leaders from various industries to help our global community to understand what the posterity holds for workers, leaders and organizations. While our team is busy at bringing this fresh ideas directly to you, we would appreciate our community help in making it possible. If you like what you’ve read, we would appreciate if you could spread the word within your circles and let us know if anything you want us to bring into this #FutureOfWork conversation.

Originally posted at AnalyticsWeek