Neural Networks and Learning Machines Amazon

Hype, “Big Data”, and Towards a More Pragmatic Analytics
My team at Amazon couldn't

According to Industry Pundits™, IT departments worldwide will be spending an amount of money equal to the GDP of several midsized countries on Big Data. As someone who has been around the block a few times, I have seen many hype cycles come and go. But there is something truly staggering about the hype around Big Data these days. It is enough to make you question the whole thing – or at least wonder just how deep the inevitable trough of disillusionment is going to be!

Is It Big Data, or Big Hype?

There is, of course, a kernel of truth to the hype. Much of the most interesting work in tech in general – and the startup world in particular – is occurring in what can vaguely be called the “Big Data” space. Amazon, Facebook, and Google, three of the so called “four horsemen of technology” (the fourth being Apple) drive their profitability via Big Data. As these companies lead, it isn’t just other startups following, but corporate IT departments are suddenly are looking at their long running “Business Intelligence” initiatives and wondering why they are not seeing the same kinds of return on investment. They are thinking…  if only we tweaked that “BI” initiative and somehow mix in some “Big Data”, maybe *we* could become the next Amazon.

Source: The Nomadic Developer

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Stock market analysts on trial

2002-02-09 14:24:25 by on-trial

The amount of poor and self-interested advice that is being issued by brokerages and their analysts. To this day, the majority of stockbrokers are compensated on the number of trades their customers make, not on the returns they generate for them or on the quality of the advice they provide. We believe that the price targets and analyst ratings are made with several masters in mind, none of whom are the individual investor. In a similar fashion, sell-side stock analysts are generally compensated based upon the overall profitability of their firms, not the quality or accuracy of their analysis. In the end, analysts have minimal structural incentive to be accurate in their predictions; rather their built-in incentive is to be as favorable to their corporate clients as possible. It is a...

Gurus' Results Stay Consistently Bad  — Forbes
Investment gurus make their money selling market predictions, not following them. Their overall performance has been historically and consistently dismal. Why people pay for market predictions is a one of Wall Street's biggest mysteries.

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