In financial services, data science may be a promising investment


Posted by John Houston on May 12, 2017

In the financial services arena, data scientists are taking a seat next to traditional business analysts, tasked with finding value in an ever-expanding quantity of data. By delivering smarter insights using analytics and cognitive technologies, data scientists help address problems in investing and trading strategies, portfolio management, regulatory reporting, client service, and more.

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Four things robots can do for your distribution center


Posted by Brenna Sniderman on December 09, 2016.

As we think about advanced, smart technologies, the thing that comes to mind is: robots. Well, maybe just for me. But I think of smart robots that can learn from their surroundings, adjust and figure things out on their own. Robots that can learn from each other, move objects, and work relatively more safely alongside humans, each augmenting the other.

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Minds and machines: experts + algorithms > experts


Posted by Jim Guszcza on October 31, 2016

The relationship between human and artificial intelligence (AI) is becoming one of the major issues of the day. A recent World Economic Forum report predicted that more than five million jobs will be lost to AI-fueled automation and robotics over the next four years1. It’s interesting to consider the relative abilities of human and machine intelligence in a specific arena: making predictions and forecasts. When is AI better at predicting outcomes, and when are humans? What happens when you combine forces? And what role–if any–will human judgment play as algorithms continue to evolve? It turns out that algorithmic forecasting has limits that machine learning-based AI methods cannot surpass; human judgment will not be automated away anytime soon.

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Can we all agree to avoid these “I wish I would haves” about cognitive computing?


Posted by Paul Roma, on October 10, 2016

Cognitive computing is already huge, and it’s likely only going to get bigger. And so far I’ve already observed a few seriously risky views on cognitive computing. Why are they risky? Because if they take hold, they’re likely to lead many to say “I wish I would have” in the not-so-distant future. In this case, the implications of getting it wrong, or simply not getting on board fast enough, are serious. Don’t let yourself get caught saying these things a year from now.

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