Move Over, Humans: The Next Evolution in Science?

It’s been 20 years since the late Superman actor Christopher Reeve damaged his spinal cord in a riding accident. Now, a potential breakthrough could solve the mystery of treating the injury.

But what’s most impressive is not what they discovered: a link between the long-term recovery of victims and high blood pressure. It’s how they discovered it.

They didn’t conduct a new study. They mined $60 million of basic research that had basically been discarded 20 years ago.

Using mathematical and machine learning techniques not previously available, researchers combed through the raw data of multiple studies on over 3,000 animals.

At the time these studies were originally conducted, there were too many variables for researchers to find successful results.

But with new computing techniques, this once unusable data now offers fresh insights.

Think about that. They didn’t discover anything new. They just found new ways to use old information. It’s more than a breakthrough in medical treatment.

It has the potential to transform medical science.

The difference between this and, say, the traditional human-based, hypothesis-driven science is a simply one: humans are prone to bias and error. Computers aren’t. And the software discovers patterns in large datasets – something humans just aren’t equipped to do. Then it tees up the results in a network diagram for further analysis.

It’s nothing short of remarkable!

The firm behind this software is Ayasdi. It uses a technique called topological data analysis, or TDA for short, developed my Stanford mathematician and Ayasdi co-founder Gunnar Carlsson.

There comes a point when the traditional mode of science can only go so far. As Ayasdi CEO Gurjeet Sigh puts it: “Traditionally, you have to be lucky, and then you have to have a stroke of insight. But the probability of being lucky is lower and lower over time, so you need these systems to do that work for you.”

That’s why I used my systems engineering background to make better trading decisions – using a system that sifts through social media chatter. It’s the same principle as what these doctors did by finding patterns in complex data.

And let’s face it – the stock market is one complex mine of data! That’s why we use systems to get a leg up, and how we bring strong profit opportunities to subscribers.

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