Re: GNU/Linux is the Empowerment of the PC. So What Do You Do?

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Sujet : Re: GNU/Linux is the Empowerment of the PC. So What Do You Do?
De : recscuba_google (at) *nospam* huntzinger.com (-hh)
Groupes : comp.os.linux.advocacy
Date : 10. Apr 2024, 18:12:57
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Organisation : A noiseless patient Spider
Message-ID : <uv6dq9$122g1$1@dont-email.me>
References : 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
User-Agent : Mozilla Thunderbird
On 4/9/24 9:09 PM, rbowman wrote:
On Tue, 9 Apr 2024 17:26:12 -0400, Chris Ahlstrom wrote:
 
I've seen some interesting books about neural logic circuits, but circa
1980.
 Rumelhart and McClelland's 'Parallel Distributed Processing' was the text
used in a seminar I took that summed up the state of the art. The
perceptron went back to McColloch and Pitts in 1944. It was a start but
had problems some of which were pointed out by Minsky. Hopfield came up
with the network named after him. Rumelhart and Hinton threw in back
propagation based on gradient descent in an '86 paper.
 Anyway, read the current literature and you'll see a lot of what was
discussed in the '80s. The problem was hardware. Nvidia is making big
bucks producing $40,000+ GPUs that can handle all the matrix operations
involved. That sort of power wasn't available in the '80s outside of a few
supercomputers and even they might have been breathing heavy.
 The other problem was the inevitable hype that oversold NNs.
 https://www.skynettoday.com/overviews/neural-net-history
 It may be tl;dr but if you're interested that covers the history very
well. It's telling that 'neural network' became a little toxic amid the
stumbles over the years and became 'machine learning'. Having watched the
technology waxing and waning for close to 40 years I don't know if the
current boom will become a bust.
My personal opinion on it that its currently over-hyped.
I've had some ML projects and my general conclusion is that it is essentially statistically-based rules, it is a good enough solution most of the time, but it depends on the nature of the task and can be quite quickly limited by its training set..especially if there's risks of over-training.  The ramifications are that its a good short/mid term fit for some activities, but really complex stuff (side eye at Tesla) are probably still a decade away, which also means that in the shortsighted vision of the Stock Market, they're going to lose interest in 2-3 years.
-hh

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