AT,

I suspect that there is some sort of new logic at work in Google, machine
vision and other areas of pre-AGI, specifically...

Google FINALLY made their search engine look for all synonyms and
variations of each word (unless you surround it with "quotes"). Machine
vision often becomes much more useful when a "recognition" is redefined as
an INability to eliminate the prospect of an object being present. This
handily solves the partial obscurement challenges, unusual orientations,
etc.

I suspect that AGI-logic will become the process of manipulating
POSSIbilities rather than manipulating Bayesian PROBAbilities. There is
100% probability of whatever is present being there, and 0% probability of
objects that are not present being there. Only in VERY familiar
circumstances are Chi Square computed conditional probabilities, Bayesian
computations, etc. worth anything at all.

Then, the process of learning becomes the discovery of which possibilities
*might* be relevant, and which possibilities are clearly irrelevant. This
has the possibility of sidestepping the "probabilities of probabilities of
probabilities" conundrum, where you are trying to manipulate probabilities
through a haphazard process that itself is full of probabilities of being
grossly wrong. This has apparently sunk the implementation of practical
learning of Bayesian computations.

When you have eliminated the impossible, whatever remains, *however
improbable*, must be the truth.
Sherlock Holmes

This approach runs into problems when there is insufficient information to
eliminate multiple "truths" **AND** those truths lead to different actions
that have different-valued outcomes. Perhaps we **MUST** be as complex as
we are, just to be able to gather enough data to reduce the
possibility-space down to a tractable size?!!! This could bode poorly for
small implementations.

Any thoughts?

Steve
=======================
On Wed, Dec 18, 2013 at 1:54 PM, Anastasios Tsiolakidis <
[email protected]> wrote:

> I am in favour of all kinds of Products on top of the current AGI
> codebases, as opposed to Ben who has OKed only some kinds of Products. But
> I see no place for a Minimum Viable Product as scalability is they key
> unknown of search space algorithms, if I may call it that. Perhaps Siri is
> a brilliant NLP assistant or Siri 3 will be a brilliant NLP assistant,
> mastering 1000 words, but as I have pointed out before, by no means being
> the first one to say so, full-blown language depends on a constant
> goertzelification, constantly redefining words and occasionally creating
> new ones, trying to produce a tight fitting film over a much more expansive
> reality, a bit like the rubber case for your smartphone. Reaching out to
> the millions of words and meanings and uses and deciding which ones will do
> the trick or which ones to modify to do the trick, well, even in terms of
> parallel programming and complexity metrics is a bit of a nightmare. I do
> believe however  that the products will show the way, just like I expect
> robotics with their cumbersome dancing routines and other human-inspired
> but limited and primitive repertoires to slowly but inexorably advance
> towards MVP-like states and beyond. Especially by focusing on the
> *transitions* between repertories or domains etc, for example imagine how
> well a chatbot would do if it could appropriately and seamlessly transition
> between certain dialogue modes like "me", "wikipedia", "joke",
> "arithmetic", "common sense" - it would probably pass the Turing test
> already.
>
> I was going to jump the gun and say I know what is not an MVP for AGI,
> "machine vision would not be an MVP", but then I had to remind myself that
> in my very own analysis a human level vision system would probably need to
> ask itself questions such as "could it be that there is an irregularly
> shaped black and white table partially obstructing a black and white cat?"
> in order to do visual object recognition. I cannot prove that the
> compositionality inherent in machine vision is of the same order as the
> compositionality/productivity of natural language or that of "physical
> existence" (meaning the compositionality offered by the material world
> itself once you actually interact with it, with its tremendous potential
> for emergence/surprise, such as getting an electric shock by touching the
> millionth piece of metal you encountered while all the previous touches
> gave no such shock). I think it would be OK if machine vision is an order
> magnitude below the complexity of language too, it would still be an MVP
> and quite a formidable one!
>
> Although my terminology is a bit more standard than the acrobatics of our
> usual suspects on this list, I am perfectly aware the intuitions will not
> be clear to the majority of AGIers, and certainly they are intuitions that
> relate weakly to a lot of academic but "unreal" AGI like Hutter's. I will
> try to see if watching Hofstadter can equip us with a few updated, powerful
> shared terms/metaphors, but at the end of the day the intuitions are for
> those who understand them and are willing and able to do three things:
> build, build, build!
>
> AT
>
>
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