=========================================
Like Don, I agree strongly with the main points John is raising here.
How to achieve a better understanding of intelligence and the mind is a central goal of this community,
and it's good that he has raised these points.
Is it good to get into fine points and refinements? Maybe... at least for some of us....
-----------------------------------------------------------
John said:
>.......I'd like to cite the following item that came up recently:
How to achieve a better understanding of intelligence and the mind is a central goal of this community,
and it's good that he has raised these points.
Is it good to get into fine points and refinements? Maybe... at least for some of us....
-----------------------------------------------------------
John said:
>.......I'd like to cite the following item that came up recently:
Fly's Brain -- A High-Speed Computer: Neurobiologists Use State-of-the-Art Methods to Decode the Basics of Motion Detection
>This research illustrates some interesting points:
>1. Many theories and models that were formulated a half century or more ago could be and have been quite accurate:
"Back in 1956, a mathematical model was developed that predicts how movements in the brain of the fly are recognized and processed. Countless experiments have since endorsed all of the assumptions of this model."
------------------------
Me:
There have been many examples in the past few centuries of mathematics
and mathematical insights developed many decades before it was used,
understood or appreciated by domain experts. This is certainly an important point
these days...
---------------
John:
and mathematical insights developed many decades before it was used,
understood or appreciated by domain experts. This is certainly an important point
these days...
---------------
John:
2. But many features of such models could not be tested against the available neural evidence: "We simply did not have the technical tools to examine the responses of each and every cell in the fly's tiny, but high-powered brain."
--------------------------------------------
Me:
The specific example of the fly is interesting in many ways.
It is hard to refrain from telling several very amusing old stories...
But more relevant: in 2008, when we had discussions all over the Engineering Directorate
at NSF on the new initiative on Cognitive Optimization and Prediction (COPN)... we decided to
focus on .. learning... in VERTEBRATE brain. The real target was to understand how
the brains of the smallest mammal, the mouse, could be as powerful as they are in learning to predict
and to make effective decisions (or control). But lower vertebrates were very much included, because they offer a kind
of logical progression of design important to understanding or replicating the mouse eventually. Understanding
the progression of design is really important to understanding the design principles.
So why didn't we include invertebrates?
This is an important question which has been debated at VERY great length.
At a big workshop across ALL NSF, people studying really simple organisms like aplysia and nematode
argued that we should master them first before even trying to do anything at all with vertebrates.
There were a few typical appeals in the spirit of "give all the money to us and none to them."
There were especially interesting appeals to study taste and digestion in the context of lobsters...
(Am glad that Senator Grassley wasn't there...)
The aplysia people made the good and serious point that a lot of the biochemical mechanisms used
in vertebrates can be found in aplysia as well, and that aplysia might be a good place to learn about them.
This is an important and reasonable point. For the biology directorate, which has a responsibility to
understand these biochemical mechanisms, it is certainly justified to invest some money in these areas,
as one part of their portfolio, in order to develop understanding which could be used later in the more
important task of understanding the biochemical aspects of what we see in vertebrate brains. But for
us... the full challenge or end target of understanding the mouse is in sight clearly enough now that there
is no excuse for ignoring it. The sad fact is that there is a good amount of money out there today (as there should
be) for aplysia and nematodes, but, now that there is no money for NEW COPN projects, there is essentially no funding for directly addressing the bigger target. Aplysia simply aren't intelligent enough to be good testbeds for
the fundamental issues in prediction and optimization that we could and should be pursuing more directly and effectively.
We are in a situation of gross unmet opportunity.
As we are in many other areas, like energy.
Flies, however, are not aplysia. They present a very different set of questions.
Those of us who really want to achieve a crossdisciplinary understanding of mind and intelligence
should include the classic work of Bitterman on the list of must-read foundations. At least, his
Scientific American article from the 60's, which is easy enough. Bitterman did a great first cut at
explicating the qualitative progression in types of intelligence from fish (and lamprey?)
up to mammal. Curiously enough -- in his later work (some in Science in the 70's), Bitterman found that honeybees
scored as high as mammals in some experiments that reptiles couldn't quite handle. Whatever we make of this...
it raises the question: "Is there a SECOND whole progression here that we could learn from?"
The fly brain was discussed in some detail in a workshop sponsored by Microsoft at the University of Washington,
around 1998, run by Chris Diorio.
But -- here is the problem. There are some very large neurons in the fly (and other arthropods) which are easy
to access, but do not provide the kind of throughput and complexity that could explain the kind
of things Bitterman was talking about. Whatever high-level intelligence is there seems to depend on things
called "mushroom bodies" which are much HARDER to access than neurons in the mouse. Huge numbers
of very tiny neurons....
Given a VERY limited budget for COPN, we felt it was better to "put all our eggs into the one basket,"
and focus on vertebrates. We said people could submit proposals using insects as testbeds, BUT ONLY
if they could convince reviewers interested in vertebrates that the work would really help more
than the competing proposals.
**IF** there had been NIH-style hundreds of millions available, it would have been rational to diversify the portfolio more,
and maybe even throw in an octopus or two...
(Stories I refrained from telling: the beautiful blond and the fly brain; the flyas an attack and evasive military vehicle; Microsoft meets the fly.)
=======================================
John:
It is hard to refrain from telling several very amusing old stories...
But more relevant: in 2008, when we had discussions all over the Engineering Directorate
at NSF on the new initiative on Cognitive Optimization and Prediction (COPN)... we decided to
focus on .. learning... in VERTEBRATE brain. The real target was to understand how
the brains of the smallest mammal, the mouse, could be as powerful as they are in learning to predict
and to make effective decisions (or control). But lower vertebrates were very much included, because they offer a kind
of logical progression of design important to understanding or replicating the mouse eventually. Understanding
the progression of design is really important to understanding the design principles.
So why didn't we include invertebrates?
This is an important question which has been debated at VERY great length.
At a big workshop across ALL NSF, people studying really simple organisms like aplysia and nematode
argued that we should master them first before even trying to do anything at all with vertebrates.
There were a few typical appeals in the spirit of "give all the money to us and none to them."
There were especially interesting appeals to study taste and digestion in the context of lobsters...
(Am glad that Senator Grassley wasn't there...)
The aplysia people made the good and serious point that a lot of the biochemical mechanisms used
in vertebrates can be found in aplysia as well, and that aplysia might be a good place to learn about them.
This is an important and reasonable point. For the biology directorate, which has a responsibility to
understand these biochemical mechanisms, it is certainly justified to invest some money in these areas,
as one part of their portfolio, in order to develop understanding which could be used later in the more
important task of understanding the biochemical aspects of what we see in vertebrate brains. But for
us... the full challenge or end target of understanding the mouse is in sight clearly enough now that there
is no excuse for ignoring it. The sad fact is that there is a good amount of money out there today (as there should
be) for aplysia and nematodes, but, now that there is no money for NEW COPN projects, there is essentially no funding for directly addressing the bigger target. Aplysia simply aren't intelligent enough to be good testbeds for
the fundamental issues in prediction and optimization that we could and should be pursuing more directly and effectively.
We are in a situation of gross unmet opportunity.
As we are in many other areas, like energy.
Flies, however, are not aplysia. They present a very different set of questions.
Those of us who really want to achieve a crossdisciplinary understanding of mind and intelligence
should include the classic work of Bitterman on the list of must-read foundations. At least, his
Scientific American article from the 60's, which is easy enough. Bitterman did a great first cut at
explicating the qualitative progression in types of intelligence from fish (and lamprey?)
up to mammal. Curiously enough -- in his later work (some in Science in the 70's), Bitterman found that honeybees
scored as high as mammals in some experiments that reptiles couldn't quite handle. Whatever we make of this...
it raises the question: "Is there a SECOND whole progression here that we could learn from?"
The fly brain was discussed in some detail in a workshop sponsored by Microsoft at the University of Washington,
around 1998, run by Chris Diorio.
But -- here is the problem. There are some very large neurons in the fly (and other arthropods) which are easy
to access, but do not provide the kind of throughput and complexity that could explain the kind
of things Bitterman was talking about. Whatever high-level intelligence is there seems to depend on things
called "mushroom bodies" which are much HARDER to access than neurons in the mouse. Huge numbers
of very tiny neurons....
Given a VERY limited budget for COPN, we felt it was better to "put all our eggs into the one basket,"
and focus on vertebrates. We said people could submit proposals using insects as testbeds, BUT ONLY
if they could convince reviewers interested in vertebrates that the work would really help more
than the competing proposals.
**IF** there had been NIH-style hundreds of millions available, it would have been rational to diversify the portfolio more,
and maybe even throw in an octopus or two...
(Stories I refrained from telling: the beautiful blond and the fly brain; the flyas an attack and evasive military vehicle; Microsoft meets the fly.)
=======================================
John:
3. New technology can test the assumptions about previously unobservable features:
"Although it seems almost impossible to single out the reaction of a certain cell to any particular movement stimulus, this is precisely what the neurobiologists in Martinsried have now succeeded in doing."
----------------
Me:
This was certainly part of COPN as well. More could be said, but maybe not here and now.
John:
John:
4. But every new discovery opens up even more questions: "Just how much remains to be discovered was realized during the very first application of the new methods."
============================
Me:
Again - agree strongly.
There is a tendency for some folks to despair at some level, because it seems there
will be no end point... and hence no pot at the end of this rainbow.
But I would claim (and have various papers out there...)... the principles are in hand to
make it possible to understand and replicate that level of general purpose intelligence
we see in the brain of the smallest mouse. At a certain kind of philosophical level,
we are "already there"... but we certainly haven't proven we know enough to actually
provide a working model of this kind of general intelligence. But we can. How far we are from that target depends on us.
As it does with certain energy technologies
and space technologies, which we might have in 5 years or never, depending on what we do.
It's sad that the present trends are towards never. And, as Don has pointed out, one of the forces
pushing us towards "never" are the forces who pretend we are already there...
who overhype and oversell the widget they have plugged in this week...
----------------------
Again - agree strongly.
There is a tendency for some folks to despair at some level, because it seems there
will be no end point... and hence no pot at the end of this rainbow.
But I would claim (and have various papers out there...)... the principles are in hand to
make it possible to understand and replicate that level of general purpose intelligence
we see in the brain of the smallest mouse. At a certain kind of philosophical level,
we are "already there"... but we certainly haven't proven we know enough to actually
provide a working model of this kind of general intelligence. But we can. How far we are from that target depends on us.
As it does with certain energy technologies
and space technologies, which we might have in 5 years or never, depending on what we do.
It's sad that the present trends are towards never. And, as Don has pointed out, one of the forces
pushing us towards "never" are the forces who pretend we are already there...
who overhype and oversell the widget they have plugged in this week...
----------------------
John:
5. And the size of the problem remains immense: For the "fly's motion detection... one sixth of a cubic millimetre of brain matter contains more than 100,000 nerve cells -- each of which has multiple connections to its neighbouring cells."
----------------------------------------------
Me:
To physically build the equivalent of a mouse brain learning system requires both the
physical hardware and the architecture/algorithms. IBM (from work under Todd Hilton's area at DARPA and their Blue Brain stuff)
has claimed they are already up to the cat brain in terms of hardware. Even throwing some broad error bars
around that... I would claim that the understanding for the algorithm/architecture side is the real bottleneck.
What's more... from a VERY large viewpoint... many of us believe that the understanding is what's most important here
(along with some applications that could be done with brains much smaller than a mouse).
That's a value judgment. It's the kind of judgment which depends on us being able to act smarter than a mouse.
I certainly believe that such research is important. But note that the older theories and models enabled neuroscientists to focus on the critical issues and ask appropriate questions that could be answered with newer technology.
Older work other than mathematics -- we still have a lot to learn from
folks like Pribram and Freud, and there are a whole lot more.
--------
John:
What I want to emphasize is that the problems of cognitive science are so complex that no single branch can solve them by itself. The combined insights from different branches -- psychology, linguistics, philosophy, artificial intelligence, and neuroscience -- are all important, and they must be reconciled with one another.
------------------
Me:
I would add neural networks, engineering, mathematics, statistics and operations research to the list as well. (And of course "psychology" is not just one thing...)
Best of luck,
Paul
Best of luck,
Paul
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