"I'm not sure I'd drop so many f-bombs"

That's because you're not black or kool like me ;).


On Thu, Apr 3, 2014 at 3:51 PM, Sergio Donal <[email protected]> wrote:

> I also read it several years ago and do not remember pretty much either,
> but the thought that comes to my mind is that he was speaking that we are
> basically predicting the environment, either spatially, temporally or any
> other feature. So prediction is a form of "completion the scene" and there
> is where creativity comes up, we predict the current scene based in our
> past observations (and completions) but since the scene may be new, we are
> applying past experience to solve new problems. Or something like that...
>
> Now I realize, doesn't this sound kind of Bayesian?
>
>
>
>
> On Thu, Apr 3, 2014 at 11:28 PM, Mike Archbold <[email protected]>wrote:
>
>> I'm not sure I'd drop so many f-bombs, but I had the feeling he's got
>> a good compression/pattern match overall command scheme, and not
>> math-heavy to boot, but not sure if he's got the ability to solve
>> general problems.  In other words, what is the processing structure
>> for solving problems?  I've read On Intelligence, 10 years ago, but
>> have at present only a superficial understanding of his approach.
>>
>> On 4/3/14, Azn A <[email protected]> wrote:
>> > He doesn't have is a fully temporal semantic database structure with a
>> > generative normalization pattern matcher/vectorized scale free
>> calculation
>> > minimizer... I.e. the G in AGI. In other words, he's missing the minimal
>> > concept basis for processing that has an optimal scalability while
>> > minimizing complexity, without that it's a crap shoot if someone will
>> ever
>> > succeed in making a better than human level AGI that isn't just brute
>> > forcing human intelligence by putting too much human knowledge in a
>> system
>> > and claiming it is better than human level intelligence while it is
>> > actually less than human level due to a complete lack of creativity.
>> >
>> > Even worse, the deepest problem that nobody ever thinks about is the
>> > knowledge representation system... they all have static/fragile designs,
>> > Hawkins' products are really bad even in the sorta neural-net form he
>> has
>> > is fucking self-crippling in the number of associations that can be
>> built
>> > up. I've never seen a fucking neural-network capable of self-reflection
>> and
>> > differentiation, among many other conceptual paradoxical forms humans
>> have
>> > no problems thinking about..
>> >
>> >
>> >
>> >
>> > On Thu, Apr 3, 2014 at 1:44 PM, Mike Archbold <[email protected]>
>> wrote:
>> >
>> >> Long article, but interesting.  It sounds like he's built the input
>> >> but not the output, the motor control.  I don't think there is any
>> >> convincing argument about what paradigms to use for strong AI / AGI
>> >> since there isn't a working such thing yet.  Some math-first
>> >> approaches seem like they want to lose you with the formula, otherwise
>> >> I like math....
>> >>
>> >> On 4/3/14, John Rose <[email protected]> wrote:
>> >> > You don't have enough math here.
>> >> >
>> >> >
>> >> >
>> >> > People are now looking more at the mathematical formalisms of
>> software
>> >> > systems. There are so many and of such variety.
>> >> >
>> >> >
>> >> >
>> >> > Hawkins has one approach not the only approach.
>> >> >
>> >> >
>> >> >
>> >> > He's hardcoding the components mimicked from biological intelligence.
>> >> > Are
>> >> > there more efficient and easier to build components and are there
>> >> > components
>> >> > that morph? His don't morph. It looks to me like hardcoded AI BUT I
>> >> haven't
>> >> > studied the system.
>> >> >
>> >> >
>> >> >
>> >> > John
>> >> >
>> >> >
>> >> >
>> >> >
>> >> >
>> >> > From: Azn A [mailto:[email protected]]
>> >> >
>> >> >
>> >> >
>> >> > http://www.theregister.co.uk/2014/03/29/hawkins_ai_feature
>> >> >
>> >> >
>> >> >
>> >> > "These are complex biological systems that were not designed by
>> >> > mathematical
>> >> > principles [that are] very difficult to formalize completely," he
>> told
>> >> us.
>> >> >
>> >> >
>> >> >
>> >> > "This reminds me a bit of the beginning of the computer era," he
>> said.
>> >> "If
>> >> > you go back to the 1930s and early 1940s, when people first started
>> >> > thinking
>> >> > about computers they were really interested in whether an algorithm
>> >> > would
>> >> > complete, and they were looking for mathematical completeness, a
>> >> > mathematical proof. If you today build a computer, no one sits around
>> >> > saying
>> >> > 'let's look at the mathematical formalism of this computer.' It
>> reminds
>> >> me
>> >> > a
>> >> > little about that. We still have people saying 'You don't have enough
>> >> math
>> >> > here!'
>> >> >
>> >> >
>> >> >
>> >> >
>> >> >
>> >> >
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