Fair enough question. I'm involved directly in designing pseudo code (systems 
models, policies, and logic in a computational format). Second, I was a 4GL 
developer and worked directly as a professional in systems dev and systems 
engineering for 22 years (my own R&D excluded). I currently employ a small 
development team to set up the dev environment for my practical case. I'm more 
hands-on than my time permits, but that means I'm learning a lot about google 
and specialized plugins and how all works together.  As such, I've identified 
the need to develop a custom-encryption system to protect the data with. This 
is possible via a semantic application of my NLU.  In this sense, semantic 
means something else.

The next step would be to start coding the actual NLU - already being deployed 
for many years - as well as other, mature frameworks, which would form the 
layered, reasoning/unreasoning backbone of the eventual system. All these 
frameworks are expressed as systems models in the NLU format. First level = 
collecting, translating, and normalization to knowledge-maturity level 5 (my 
own hierarchies). Second level has application for evolutionary systems.

In the 1st and 2nd level events, I would employ the best I (and hopefully my 
co-funders) would be able to afford to start implementing the series of designs 
and algorithms, which already exist in design format. I might even team up with 
a university and their postgrad programs. Except for not yet having been able 
to resolve IP issues, this has been explored over a number of years and it 
seems highly feasible.

Robert Benjamin





________________________________
From: Stefan Reich via AGI <[email protected]>
Sent: Monday, 04 February 2019 6:48 PM
To: AGI
Subject: Re: [agi] The future of AGI

Thanks for your input, it's interesting. Are you involved in any code 
production? (Sorry if I should know already...)

Stefan

Am Mo., 4. Feb. 2019 16:58 hat Nanograte Knowledge Technologies 
<[email protected]<mailto:[email protected]>> geschrieben:
Hi Stefan

I meant that there seems to be a popular view emerging, which nudges in the 
direction of rethinking the prevailing architectural approach towards enabling 
agi. It further means I'm recognizing how the pattern might be shifting, and 
that I'm in support of such a view. In my opinion - and with respect to the 
incredible effort that has gone into such ventures - attempting to duplicate 
the human brain was never a sound-enough approach. Such a fallible organ.

Modern-day, real-time language translators offer sufficient advancement in NLU, 
does it not?  I like your suggestion about converging around image/audio 
recognition and learning logic as a single unit of cognition (perhaps). The 
latest AI can accurately read lips at a distance. Furthermore, apps now perform 
facial recognition from among crowds and track those faces. Some AI apps 
monitor and analyze bio-metric forces (electo-magnetic forces) around the body 
and other visible human characteristics as tell-tale indicators of inner intent 
and emotional states. It helps to identify potential criminals and deceivers. 
In addition, many computer games have shown a reactive-learning capability 
based on cause-effect scenarios. And then you go and casually plonk in the 
mother lode - evolutionary algorithms.

This is the exact point at which I restate the likely need of a radical new 
approach. If we cannot express computational evolution in terms of 
recombination and diversification, we may have not yet managed to cross our 
own, intellectual Abyss.

As some suggested here (in my own words); we are inherently restricted by our 
own human-reasoning universe. Is constructive reasoning about an unreasoning 
universe the required level of super-positional madness designers should 
attain, or should we rather entice the machine to indulge itself accordingly? 
Maybe then, a bit of both.

I think, first, we should ourselves evolve via recombination, not adaptation. 
Morphing, not mimicking. If researchers and designers voluntarily became agi, 
perhaps we would understand it a little better. Sure, the world would probably 
reject us and call us nuts (as was done with Tesla), but they would still 
appropriate our output.

Such a radical approach. How to do our damndest not to try and make any sense 
of it at all, purely relying on our collective ken and instinct. Some say 
ancient-astronautical mindsets, merely following in the footprints that were 
already laid down for those who would follow after and read the signs.

Only time would tell. I'm enjoying the journey. The destination is not my 
concern. There is no more right, or wrong.  Only to be correct in every 
instance of a moment presented to our manifestation (in the sense of a physical 
artifact with identity). In my lifetime I'd love to synergize with fellow 
pilgrims though. I see a think tank of the quality that Alexander Graham Bell 
founded and where scientists and intellectuals and inventors and passionate 
others flocked to. I think, this is how humankind might get closer to 
manifesting agi.

Robert Benjamin

________________________________
From: Stefan Reich via AGI <[email protected]<mailto:[email protected]>>
Sent: Monday, 04 February 2019 2:01 PM
To: AGI
Subject: Re: [agi] The future of AGI

> Many commentators here agreed (over time) how agi development requires a 
> radically-different approach to all other computational endeavors to date.

Not sure what that means. A really good NLU will go a very long way, and then 
we'll have to find a new "magic learner" module that replaces neural networks, 
both for image/audio recognition and learning logic. I suggest evolutionary 
algorithms.

On Mon, 4 Feb 2019 at 05:45, Nanograte Knowledge Technologies 
<[email protected]<mailto:[email protected]>> wrote:
Perhaps it's because, for its exponential complexity, agi defies theoretical 
science. If no executable, framework of computational intelligence exists, 
what's the use of being able to run at the speed of light?

Many commentators here agreed (over time) how agi development requires a 
radically-different approach to all other computational endeavors to date.  As 
evidenced, developing a feasible approach (in the sense of a platform) would 
require at least 10 years of R&D. In my opinion, that is correct. In my case it 
took more than 22 years - part-time. Towards an agi prototype then, with 
10-years' concentrated effort, perhaps another additional 5-7 years?

Perhaps we should start pooling our research and resources with those who offer 
the best 10-year result to date? I'm beginning to think this would be the best 
way forward. Imagine a safe, inclusive, collaborative environment where R&D 
parties could post real problems they needed solving and tangible credit was 
given to the authors of such solutions? We're talking sharing in the pot of 
gold at the end of the rainbow off course.

Except for those sticky-finger, big boys who do not play well with others at 
all. I'm quite certain they monitor this list trying to farm it yet never 
contributing one bit of usefulness to others.  Those we should weed out from 
any "collaborative" setup at every opportunity. They are only in it for 
themselves, not for the industry, or the benefit of the world. Yes, you know 
who you are!

This is the extent of my professional opinion.

Robert Benjamin

________________________________
From: Linas Vepstas <[email protected]<mailto:[email protected]>>
Sent: Monday, 04 February 2019 6:16 AM
To: AGI
Subject: Re: [agi] The future of AGI

I have no clue what Peter is actually thinking because he's coy and secretive. 
But I'm not pessimistic. I'm just perplexed why no one ever seems to try the 
obvious things. Or why I can never seem to explain obvious things  to anyone 
and have them understand it.  I am quite certain that one can do better than 
neural nets and more easily,  too, an have explained exactly how more times 
than I can count, but my words are not connecting with anyone who understands 
them. So, whatever. Day at a time.

--linas

On Sun, Feb 3, 2019 at 5:28 PM <[email protected]<mailto:[email protected]>> 
wrote:

I’m not that pessimistic at all.



Our own AGI project has made steady progress over the past 17 years in spite of 
only spending about $10 million – about 150 man-years of focused effort.  We’ve 
managed to successfully commercialize an early version of our proto-AGI engine 
in a company that now employs about 100 people 
www.smartaction.com<http://www.smartaction.com> . For the last 5 years my 
full-time team of about 10 people has been working on the next generation 
engine www.AGIinnovations.com<http://www.AGIinnovations.com> /  
www.Aigo.ai<http://www.Aigo.ai> . We are now ready to commercialize this more 
advanced platform.



Our focus has been limited to natural language comprehension/ learning, 
question answering/ inference, and conversation management.

I think that $100 million could go a long way towards functional, demonstrable 
proto AGI.  It seems to me that DeepMind hasn’t made good use of the $200 or 
$300million spend so far – they lack a proper theory of intelligence.  I don’t 
know why Vicarious, the other well-funded AGI company, hasn’t made better 
progress in perception/ action – my guess, for the same reason….

I think all of the theoretical calculations of processing power are widely off 
the mark – we’re not trying to reverse-engineer a bird – just need to build a 
flying machine.



My articles are here: https://medium.com/@petervoss/my-ai-articles-f154c5adfd37



Peter Voss



From: Linas Vepstas <[email protected]<mailto:[email protected]>>
Sent: Friday, February 1, 2019 10:26 PM
To: AGI <[email protected]<mailto:[email protected]>>
Subject: Re: [agi] The future of AGI



Thanks Matt, very nice post! We're on the same wavelength, it seems. -- Linas



On Thu, Jan 31, 2019 at 3:17 PM Matt Mahoney 
<[email protected]<mailto:[email protected]>> wrote:

When I asked Linas Vepstas, one of the original developers of OpenCog
led by Ben Goertzel, about its future, he responded with a blog post.
He compared research in AGI to astronomy. Anyone can do amateur
astronomy with a pair of binoculars. But to make important
discoveries, you need expensive equipment like the Hubble telescope.
https://blog.opencog.org/2019/01/27/the-status-of-agi-and-opencog/

Opencog began 10 years ago in 2009 with high hopes of solving AGI,
building on the lessons learned from the prior 12 years of experience
with WebMind and Novamente. At the time, its major components were
DeStin, a neural vision system that could recognize handwritten
digits, MOSES, an evolutionary learner that output simple programs to
fit its training data, RelEx, a rule based language model, and
AtomSpace, a hypergraph based knowledge representation for both
structured knowledge and neural networks, intended to tie together the
other components. Initial progress was rapid. There were chatbots,
virtual environments for training AI agents, and dabbling in robotics.
The timeline in 2011 had OpenCog progressing through a series of
developmental stages leading up to "full-on human level AGI" in
2019-2021, and consulting with the Singularity Institute for AI (now
MIRI) on the safety and ethics of recursive self improvement.

Of course this did not happen. DeStin and MOSES never ran on hardware
powerful enough to solve anything beyond toy problems. ReLex had all
the usual problems of rule based systems like brittleness, parse
ambiguity, and the lack of an effective learning mechanism from
unstructured text. AtomSpace scaled poorly across distributed systems
and was never integrated. There is no knowledge base. Investors and
developers lost interest….




--
cassette tapes - analog TV - film cameras - you


--
Stefan Reich
BotCompany.de // Java-based operating systems
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