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By the time you read this, 30 acts of will have been committed. Experian can help you reduce fraud and improve your customer experience. See how at DMA_USA DMA 360:

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Een geslaagd nieuwjaarsdiner met het CMI-team! Ook dit jaar helpen wij je graag met jouw data-uitdagingen!

datathon organised this WE by is now live ! is proud to participate with participer avec my Data Science Starter Program colleagues. May the be with us !

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Weather station report: Temperature: 15.34000015258789°C, 59.61°F Rain: , January 20, 2018 at 10:24AM
Big Data Is Overrated Compared To Human Ingenuity
Those who are experts in what big data can do aren’t going to be as successful as those who truly know how to show what it can do.
By Diego Fischer

We live in an era in which top executives can’t survive without breaking into song about the wonders of big data. They have to be able to create beautiful correlations of data, or at least hire a business analyst to handle it for them. In other words, you need to understand on an intricate level the math behind what you’re doing or you won’t be at the top.

Or on some level, you have to at least come off that way. We’re obsessed with finding new ways to mention new big data – in our cars, our homes, our cities – and investors even love the idea of building big data software for big data. We’re transitioning to a point where influential CEOs are given a great deal of credit for “big data initiatives.” Machine learning can spot patterns, it can tell you things, it can help create amazing products. The brightest CEOs are “seeing the Matrix” and discerning brilliant thoughts thanks to the mysterious power of a machine to learn and discern meaning.

Barron’s Tiernan Ray applauded Nvidia CEO Jen-Hsun Huang for giving “a shout out for deep learning” shortly before Ray categorized it as the “use of massive clusters of GPUs to teach computers new skills.” Except that’s not the definition of deep learning according to several other sources, with some calling it “A subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks,” and Nvidia itself referring to it as a field of machine learning that “uses many-layered Deep Neural Networks (DNNs) to learn levels of representation and abstraction that make sense of data such as images, sound and text.”

Nvidia’s Huang is one of the few people who actually understand what deep learning is, if only because he’s turned his own hardware company into one that is prepared for its commoditization. Graphics processing units – which were usually reserved for high-tech gaming and visual design – now can number-crunch at an incredible speed.

Huang will succeed by powering the future. Those who simply know how to look at it and say, “Yep, machine learning is gonna change the world, and here’s an algorithm to spit out data” won’t be the kings of it.

Let me be clear: Big data, machine learning and deep learning aren’t overrated on their own. It’s simply going to be a very different world soon – one where the extremely analytical, overly lauded big data experts of the world won’t be quite as popular.

I have a thesis: In five years, it’s not going to be possible to build a big data startup, as the Oracles, Amazons and Apples of the world will have commoditized it. Apple’s acquisition of a “dark data” (a ridiculous term referring to unstructured data) company for $200 million, Amazon’s $20 million purchase of machine learning and artificial intelligence security company and a whole host of other AI M&A’s is the sign not of a boom but of a coming consolidation.

We are not far from the natural endpoint, where Amazon Web Services’ algorithms can inherently build other algorithms to make sense of your big data in a click. In the same way that few stray from Amazon, Oracle, Microsoft or Google for their cloud storage, few will stray from the cloud-based AI number-crunchers that will discern meaning for a fraction of the price that SaaS companies can.

Don’t believe me? Amazon just opened up an entire consultancy service. TechCrunch’s Ingrid Lunden noted that this machine learning lab will pair “Amazon machine learning experts with customers looking to build solutions using the AI tech. And it’s releasing new features within Amazon Rekognition, Amazon’s deep learning-based image recognition platform: real-time face recognition and the ability to recognize text in images.”

So, it’s already begun. Those who are experts in what big data can do – which is where the future truly lies – aren’t going to be as successful as those who truly know how to show what it can do.

And though an algorithm may be able to cover sports, you cannot clone or generate whimsy or humor or the essence of what makes writing enjoyable to read. We are not (at least not yet) at a point where computers are able to have full conversations, let alone exude the creativity to come up with ideas. The creative geniuses of the future may, in fact, be aided by big data, but they will simply use it (as one would use Google to search the giant database known as the internet) to ask the right questions to solve the world’s problems.

A machine can bring together disparate pieces of information into a consumable, even actionable piece of information (sadly, glitches happen, with a “computer glitch” costing Knight Capital $440 million). However, it inherently is there to answer a question or create a series of potential questions for you to ask it. Interpreting the results of big data will soon become far more important than what your big data startup’s algorithm can do.

What I’m saying is that while an algorithm has the capacity to ask questions on a high level, only a human being is able to write those questions. The CEOs of the future will understand how to interpret results well and ask the next question, and the question after that, and take that answer forward.

And in the end, our gut will always be part of decision making. That gut instinct that speaks to you – that tells you to try something, to ask a question, to do something differently – it cannot necessarily correlate to a piece of data. It can absolutely correlate with your future success.