
Brains Cling to Old Habits When Learning New Tricks - digital55
https://www.quantamagazine.org/brain-computer-interfaces-show-that-neural-networks-learn-by-recycling-20180327/
======
rabidrat
The designers must have put that in as a way to avoid "catastrophic
forgetting"[0].

[0]
[https://en.wikipedia.org/wiki/Catastrophic_interference](https://en.wikipedia.org/wiki/Catastrophic_interference)

~~~
oddity
Who would have thought that a little bit of close-mindedness might be good for
something?

Imagine trying to convince an AI of the future to give up deeply held, almost
religious beliefs because the designers wanted to mitigate this problem.
Sounds like the premise of a sci-fi plot. It might even exist already.

~~~
dade_
It reminds me of the adage, "Keep an open mind, but not so open that your
brains fall out."

Though I like the more eloquent 1886 speech by Sir Edward Clarke in the U.K.
House of Commons:

But what did that speech amount to? It came to this ingenuous confession of an
open mind. The mind was indeed so open that it had nothing in it at all.

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subroutine
Degree of (and physio mechanisms regulating) plasticity are brain-region
specific.

Motor cortex is using a plasticity framework that supports 'procedural' memory
formation. This type of learning is relatively slow and relies on small
incremental changes (for ML people, maybe this is similar to having your nn
regularization parameter cranked way up).

Other areas of cortex can support 'declarative' memory formation. For example
you can learn things like the definition of a new word (lang), features of a
novel object (vis), where you parked your car outside your new apartment
(spatial/episodic), within just a few seconds.

For the control of prosthetic limbs, there is perhaps no better brain-computer
interface than motor cortex. Despite its relatively laborious procedural-type
learning framework, limb movement is precisely what PMC has been optimized
for. Since we've been digging around PMC for a while and have managed to make
these electronic prosthetics work well, it's only natural to see what other
tasks we can perform with those PMC-embedded electrodes. We should however
keep in mind, though, the human brain has a wondrous variety of subregions
optimized via evolution to perform all manner of computations, to support some
subtype of behavior or mental capacity, with varying degrees of task-
specificity. All I'm really saying is that... we will likely find out that a
PMC-computer interface is garbage at doing some of the things we imagine
should be possible, like typing words into a text box using thoughts. While
it's likely possible (motor cortex could think about moving virtual fingers
typing on a keyboard, and the computer interface could pick up on these signal
patterns), there is another set of brain regions better suited for this task
that could be tapped-into. But yes, " _Some_ Brain _Regions_ Cling to Old
Habits When Learning New Tricks".

~~~
goldenkey
So essentially, the holy grail of a purely "abstraction of an abstraction of
an abstraction ad infinitum" AI is mostly a pipe dream? Everyone loves to hope
that everything that matters can be built from [insert object here].
Physicists want strings. Programmers want s-expressions or combinators or
[choose your poison]. And AI researchers want the post-TLU artificial neuron
model to learn it all..

I have always thought that machine learning is the less likely path to
artificial human-level intelligence. It makes more sense to create a universe.
I know your laughing now - but please hear me out. A digital universe can be
merely a bunch of cells on a grid with specified rules for state-space
updates. Cellular automata are the most well known example ie. Game of Life.

If we look at the requirements for evolution and the requirements for
information stability and movement, we can then proceed to create a 2d
universe, tinker enough until we get life. Information stability and movement
is mostly just the entities of matter and energy, and their conversion and
total conservation. The life may not be all that remarkable at first but we
run the universe more cycles, throw more compute at it, and we can push the
clock forward quite a large amount. The trick of course, is not to try to make
OUR universe. Too much information. Too much compute power required. Too much
space...likely without life -- so wasted resources for our goal. And lastly,
too many layers - even going from bosons and fermions we have:
Protons/Neutrons/Electrons/Atoms/Molecules/Amino Acids/Proteins/RNA/DNA/Single
Cells/Multicellular Life Forms/Mammals/Primates/Humans. The last 3 could be
considered as evolution - so not all have to be present at the same time.
However, all the other abstractions are a real dimensionality crusher. Are
they totally necessary for an intelligent being? I doubt it.

Once we find the local cluster of life we are interested in, in our digital
universe, we could even do procedural generation instead of full state-space
updates. So we can speed up the evolution of our target even quicker.

There's nothing I haven't said here but here's my blog post about this idea if
you're interested in reading more: [https://scrollto.com/life-a-universe-
simulation/](https://scrollto.com/life-a-universe-simulation/)

~~~
isseu
Maybe AGI is just a problem of computation power and complexity.

Can infinite monkeys write a brain?

[https://en.wikipedia.org/wiki/Infinite_monkey_theorem](https://en.wikipedia.org/wiki/Infinite_monkey_theorem)

~~~
goldenkey
It almost certainly is. But we can at least say that our universe is a
solution. Still isn't guaranteed to help remove the infinite though since we
don't know if our universe is just one flavor of an infinite class of
parameters - some which may be unobservable in full precision.

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sporkologist
This might also explain why learning a 2nd language is usually harder than the
1st, and why getting rid of, or changing, an accent is so hard. All that time
invested in those old neural circuits, better make use of those first.

~~~
bornonline1
I'm still not convinced learning a 2 language is harder, even as an adult. I
now know 3 and if you put some effort and time (or even better get immersed)
you will learn it. Children have YEARS of amazing immersion, adult patience
and care. Who wouldn't learn a language in such fertiile conditions.

As for the accent which i have, i don't know yet. I will try to correct it and
see how it goes.

~~~
uoaei
The step from one language to two is much, much larger than the step from two
to three or beyond. I did this seriously for the first time when I was 21/22,
and it was initially difficult to get used to the concept of thinking not in
terms of the temporal order in which words are introduced (i.e. the syntax of
the language in which you are speaking) or at least shifting your process to
generate sentences on the fly in a new order. In my experience (native
English, learned German) I had a relatively smooth transition but even so it
was difficult not to construct sentences as I spoke in the typical English
construction. Once you get comfortable with shutting off that reflex to
formulate thoughts according to the syntax and vocabulary of your own
language, learning language becomes a lot smoother. This reflex is of course
more strongly burned-in the older one gets due to the decrease in
neuroplasticity as we age.

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jfoutz
I know very little about learning, but it sounds sorta like multi armed
bandit. Put a lot of emphasis on past success, rather than exploring. I would
guess a really good tennis player would have a hard time getting good at
squash, as opposed to a rank beginner. There is nothing to unlearn for the
beginner.

~~~
gboudrias
Indeed this is a well-known phenomenon:
[https://en.wikipedia.org/wiki/Negative_transfer_(memory)](https://en.wikipedia.org/wiki/Negative_transfer_\(memory\))

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drawkbox
Skills should be leaned on when needed but we should always approach new
things, and revisit things you know, with a _beginners mind_ zen.

I believe a large part of being an engineer or problem solver is to take
complex things and make them simple as can be but no simpler as Einstein
stated, that way others can also come into that with a _beginners mind_.

Basically be Feynman and learn how things really work and when you find out,
explain them as basic/simple as possible for understanding [1]. If you can’t
explain something in simple terms, you don’t understand it.

[1] [https://kottke.org/17/06/if-you-cant-explain-something-in-
si...](https://kottke.org/17/06/if-you-cant-explain-something-in-simple-terms-
you-dont-understand-it)

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doomlaser
The most interesting thing I read recently about brain plasticity is that, the
brain recycles connections formed in earlier memories when you learn something
new. So you will lose the ability to easily recall old memories if tasked to
learn new tricks.

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riazrizvi
So neurons don’t disassemble their current patterns to find the optimal
pattern for the latest training goal? That sounds like a good neural algorithm
because how do you know that the old should be fully replaced with the new? If
I were God, my creatures’ brains would adapt older patterns so they could do
double duty, especially in the short term. Because there is still a high
Bayesian likelihood that the older behaviors might need to be recalled. This
neural model creates creatures that can adapt to an expanding set of training
goals.

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nabla9
If you are learning something that requires high level control and conscious
decision making and correction, what is typical rate of full feedback loop?
Maybe 1-2 times per second?

If you train nonstop for an hour, that's just 3600 - 7200 times. That's
relatively small batch size to learn something. Even 10 or 100 hours of
training is not that much. It's not surprise if brains reuses old stuff as
much as possible.

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CannisterFlux
"""Yu [...] also noted that their experiments only tracked the brain’s
activity for one or two hours. The researchers can’t yet rule out the
possibility that reassociation is a fast interim way for the brain to learn
new tasks; over a longer time period, realignment or rescaling might still
show up."""

I know that for musical instrument practice the common advice I've read is
that it is better to practice 30 minutes every day rather than a 3½-hour
session at the weekend in part because what you learn actually sinks in when
you are resting. Maybe the realignment or rescaling happened when the monkeys
slept?

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Graziano_M
Probably the cause of the "Einstellung Effect" [0]

[0]
[https://en.wikipedia.org/wiki/Einstellung_effect](https://en.wikipedia.org/wiki/Einstellung_effect)

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ram_rar
No wonder, its hard to teach async programming to old time programmers :)

~~~
segmondy
Is it? Or perhaps it's hard to teach to stubborn programmers.

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randyrand
A defense for ageism?

