

Why "Build to last" is bullcrap consultants suck and how Digg failed to innovate - joop
http://www.joopdorresteijn.com/Archive/why-%E2%80%98build-to-last%E2%80%99-is-bullcrap-consultants-repeat-and-startups-suck/
Its not competition between companies but business-models. It’s not about product lifecycles but about shorter strategy life cycles
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swombat
Not a great article. Very poor english ("Today we live in a world that
exponentially innovating world." << first sentence? C'mon!), and the point is
very poorly made and, on the whole, incorrect!

I think there's probably some interesting point hidden somewhere in this
article, but I can't put my finger on it - it's certainly nowhere near the
surface.

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josefresco
THe three main points I was able to extract were reflected in the title:

Innovate or die. Consultants suck. Digg sucks because they didn't cash out ..
and er tried to innovate but failed?

I'd agree with 2/3

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davi
I get the point of the article, but I stubbed my toe on this quote within it:

"By 2023, a $1,000 computer will exceed the computation capability of the
Human Brain."

How to quantify the 'computation capability' of the human brain? If we knew
the transformations the brain makes to the information it receives, we would
_understand_ the brain, which we do not.

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troystribling
The transformations performed by some regions of the brain have been modeled.

The estimates of the brains equivalent computational capacity I have read use
functional models of regions of the brain, (i.e. audio, visual, ...) to obtain
an estimate per neuron and extrapolate that to the entire brain. Estimates are
10e14 to 10e15 operations per second.

See [http://books.google.com/books?id=88U6hdUi6D0C&pg=PA122&#...</a>

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davi
That was interesting, thanks for the link.

However -- the neural functions cited by Kurzweil for which 'operations per
second' have been estimated are:

1) edge and motion detection in the retina

2) computation of time delay between a sound arriving at one of our two ears
versus the other

3) silicon computations required to model 10e4 cerebellar neurons

Let's throw out (3), since it's hard to know from the quick mention he gives
what 'model' means in this context.

Now consider (1) and (2): they represent specialized, if experimentally
accessible computations. The relationship of operations in the retina to
operations in the cortex is unknown. (It seems, actually, that cortex
_reimplements_ certain retinal functions, e.g. directional selectivity, for
unknown reasons.) Similarly, IIRC, the time difference between a sound
arriving at the two ears is thought to be computed by the olivary nucleus
(<http://en.wikipedia.org/wiki/Superior_olivary_nucleus>), a specialized
brainstem structure.

So what do the retina and the brainstem have to do with the cortex? Even if
you accept (3), you can ask the same question about cerebellum.

In the link you give, Kurzweil says that these studies show that "it is clear
that we can emulate the functionality of brain regions with less computation
than would be required to simulate the precise nonlinear operation of each
neuron and all of the neural components (that is, all of the complex
interactions that take place inside each neuron)."

I hope he's right; but the evidence he gives is pretty thin.

[ edit to make each item on the list have its own line ]

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anthonyrubin
What is meant by this:

"We are currently preparing students for jobs that don’t yet exist using
technologies that haven’t been invented"

