An AGI can learn to decompress data where the compressor is unknown or
partially known to it. IMO a conscious AGI can learn to decompress more
efficiently data that was compressed by another similarly conscious entity.
Compression and consciousness effect communication protocol. IOW compressed
On Thursday, April 22, 2021, at 3:59 PM, Matt Mahoney wrote:
> Sort of. A FFT requires dividing up the audio samples into blocks. The
> cochlea instead applies bandpass filters to produce an array of continuous
> signals.
Is there a standard algorithm for doing the same thing in place of an FFT?
On Thu, Apr 22, 2021 at 8:54 AM Matt Mahoney
wrote:
> ... You can't test for consciousness.
>
That depends on how you define it. Who has pulled a Shane Legg on
"definitions of consciousness" the way he did on definitions of
intelligence?
> It is common to expand data to compress better.
>
Ve
A 2nd use of the word compress exists here though - yes brains do compress data
into a brain, lookup Trie Tree. This allows the brain to make new predictions
that are correct. It also allows you to compress a file by storing the errors
in your predictions as a long code so upon decompression you
On Thu, Apr 22, 2021, 9:46 AM stefan.reich.maker.of.eye via AGI <
agi@agi.topicbox.com> wrote:
> I always thought that an FFT is the most likely first step in speech
> recognition, seeing as you can almost recognize it visually from a 2D
> frequency/time plot as a human.
>
Sort of. A FFT requires
Just a few comments on this thread. The difference between predicting and
understanding is that one requires consciousness. In other words, there is
no difference. We use prediction to test understanding. You can't test for
consciousness.
Brains don't compress, they predict. You can only compress
I always thought that an FFT is the most likely first step in speech
recognition, seeing as you can almost recognize it visually from a 2D
frequency/time plot as a human.
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There are no vectors in the use of CNNs to detect spoken keywords that I
mentioned in the first post. I only mentioned vector spaces after
immortal.discoveries mentioned something about recognition of various sizes. I
guess bringing vector spaces into the discussion was a mistake on my part. I
All the vectors in a vector space that contain a magnitude could be multiplied
by a constant in order to scale it. Similarly, the directions of all the
vectors could be rotated by a constant in order to rotate the image or objects
defined by the vectors. If the vector space is in more than 2 d
This sounds like just any algorithm though lol, Word2vec essentially learns dog
is similar to cat by X amount, it stores or at least in my view stores their
shared contexts in a tree, hence bloating up RAM like weee, ok not that bad but
does expand, then this helps tons for compression. AI does
To put in a more abstract form, some useful data that can be sensed from nature
is effectively compressed and therefore would have to be decompressed
(expanded) in order to extract it so that it could then be compressed in
another form.
AI is not just an entry in a compressor contest, it is abou
But yes a brain is what does the compression, whether it is using the brain or
using its hands to group the similar articles in a group, it's all pattern
finding! It's all brain intelligence that works faster than slow brute force.
--
Artificial General Int
Oh and furthermore I believe AGI IS the bext compressor because it can do a
BETTER JOB than me at trying to find out how to do tricks n flicks on the file
to compress it more! HAHAHA!!! :P
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I explain it fully in my AGI handbook though :D.
earth
e a r t h
e a r t h
htrae
e
a
r
t
h
are all Earth
In vision, a tiny boat is recognized as a big boat if you see a tiny boat for
the first time and only know in memory a big boat. And you can fill in the rest
of the image if only see ha
Not all 'integral' objects can be recognized from visual edge filters. Some
years ago I gave myself a few days to make an edge detector myself and I got
amazing results where a person or an solid object was a different color and
contrasted against the background. But when the pixels were mixed u
I am not familiar with what you are talking about. Can it be used for simple
speech recognition?
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You're going to have to explain this better.I'm not 'seein it'.Look how
easy I can explain image recognition - convert an image into lines using a
sobel line filter, predict the next pixels, but with a distance pattern
recognizer i.e. you know the memory ex. 'boat' and see 'b--o--?' and
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