
Inside an AI 'brain' – What does machine learning look like? - breck
https://www.graphcore.ai/blog/what-does-machine-learning-look-like
======
Smerity
The consensus in the machine learning / deep learning research communities is
that this is a colourful "nothing" visualization. Little to no information is
conveyed, it doesn't represent the architectures in any sane way that
researchers or the broader non-technical community can use / understand, and
it primarily seems a vehicle to use the hype surrounding AI to advertise
Graphcore.

I'm not the only one[1] within the research community to think this.

If you find any insights from this, I'd honestly first be surprised and then
second be interested to know what insights you gleamed from it.

Background: researcher who publishes papers in deep learning.

[1]:
[https://twitter.com/jackclarkSF/status/834461913262157824](https://twitter.com/jackclarkSF/status/834461913262157824)
(thread containing a member of OpenAI who specializes in communicating complex
machine learning topics to the media and a primary developer of PyTorch /
member of Facebook's AI Research lab)

~~~
chestervonwinch
> The consensus ... is ...

> I'm not the only one within the research community to think this.

I should hope not if it's indeed the consensus :) Anyhow, I agree with your
sentiment.

Comparisons with organic intelligence? Check.

Vague descriptions of new technologies? Check.

Flashy, uninformative graphics? Check.

Maybe I'm being too cynical.

~~~
ythn
"IEEE Spectrum: We read about Deep Learning in the news a lot these days.
What’s your least favorite definition of the term that you see in these
stories?

Yann LeCun: My least favorite description is, “It works just like the brain.”
I don’t like people saying this because, while Deep Learning gets an
inspiration from biology, it’s very, very far from what the brain actually
does. And describing it like the brain gives a bit of the aura of magic to it,
which is dangerous. It leads to hype; people claim things that are not true.
AI has gone through a number of AI winters because people claimed things they
couldn’t deliver."

[1] [http://spectrum.ieee.org/automaton/robotics/artificial-
intel...](http://spectrum.ieee.org/automaton/robotics/artificial-
intelligence/facebook-ai-director-yann-lecun-on-deep-learning)

~~~
unityByFreedom
I wish this practical understanding of AI were understood by the public.

It seems that, when educating the public about AI in order to advance the
field, we must first go through this phase where people do not understand and
we risk taking a step back.

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partycoder
I wonder what is the motivation behind rendering the network inside a circle,
and how to interpret from these charts... like: which neural ensemble is
connected to which, etc.

~~~
isoprophlex
Beautiful images for sure, but what do we learn from them?

Without any explanation of the questions you raise, this page is 99% marketing
speak, and to me, next to useless.

~~~
partycoder
Didn't want to sound as severe but yes... it's hard to decipher why they want
it to make like a petri dish under a microscope.

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YeGoblynQueenne
Historically, purpose-built processor architectures were always surpassed by
general-purpose architectures. Think of Lisp Machines and x86, for a very
relevant example of an architecture specifically designed for AI applications,
that was quickly rendered obsolete by a general-purpose architecture.

Why graphcore is going to be any different is anybody's guess. Although, I
admit the concept sounds cool on paper and the graph plots look pretty- I'd
hang one on my wall for sure.

~~~
sdenton4
We're​ in a different historical moment, though. GPUs are specialized hardware
for linear algebra, and have kind of taken over. TPUs are similarly
specialized. The big difference is that Moore's law seems to be stalled out
for standard, general purpose processing; as a result there's a lot to be
gained in specializing.

Now whether these particular professors are worth anything is a different
question...

~~~
posterboy
*processors

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zebrafish
Can somebody explain this to me? _Unlike a scalar CPU or a vector GPU, the
Graphcore Intelligent Processing Unit (IPU) is a graph processor._

Is this just marketing mumbo-jumbo? I don't understand how a "graph processor"
would look any different than a vector processor.

~~~
deepnotderp
Founder of a different deep learning chip startup here. They're talking about
deep learning's computational graphs. The only differences between that and a
general purpose program graph is that a) the (general) absence of control flow
and b)the operations are all arithmetic and generally predictably so.

So basically yeah, it's marketing.

------
fxj
Here is some explanation: [https://www.graphcore.ai/blog/graph-computing-for-
machine-in...](https://www.graphcore.ai/blog/graph-computing-for-machine-
intelligence-with-poplar)

------
gnipgnip
Seems like PR bait. Tim Davis' page is much much nicer.

[http://www.cise.ufl.edu/research/sparse/matrices/synopsis/](http://www.cise.ufl.edu/research/sparse/matrices/synopsis/)

I think Trefethen too has nice visualization like this (or maybe that was the
spectral thing).

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jlebrech
Wow that looks organic, it looks like a bacteria learning and adapting to its
surroundings.

