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energyscholar
New Member

USA
38 Posts

Posted - 11/20/2012 :  17:11:10  Show Profile Send energyscholar a Private Message  Reply with Quote
My thesis is that there was a secret government science project in the 1990s, which I call ULTRA II, that got a lucky breakthrough at the intersection of the scientific disciplines of Solid State Physics, Complex System Biology, Information Systems, and Neuroscience. ULTRA II was intended to produce a cryptographic tool for breaking Public Key Cryptography. In the event, the project's results were much more significant. This topic remains quasi-secret. I say 'quasi-secret' because the multiple new technologies developed by ULTRA II are so useful that the results of using them are becoming harder to hide. It seems project ULTRA II invented a new General Purpose Technology (e.g. fire, agriculture, metallurgy, wheel, writing, chemistry, combustion engine, electricity, radio, computer, atomic power, internet, ...). This new general technology does not yet have an official name. I call it 'Quantum Neural Network' technology. The physical implementation seems to be a 'teleportation/entanglement based winner-take-all style recurrent topological quantum neural network'. The genius project scientists, and everyone who learned about it through official channels, seem to be bound by some sort of Non Disclosure Agreement. I am not so bound, and I learned about this topic mostly through open source research. It's quite important, for the general best interests of humanity, that this topic not remain secret for too many more years (18 Nov 2012). It is a sensitive topic that threatens certain sacred cows. Several major world religions may take offense, as one notably took offense at Galileo. It is best if this knowledge diffuses gradually, starting with scientific skeptics and atheists. A challenging and subtle ethical predicament was involved, and remains relevant today.

*************************************************************

Above is a summary of my my thesis. I came by it due to information from two sources. Some of this I'm writing up real time, some of it I'm pulling from stuff I already wrote.

* Hey people, I have this content in formatted documents, please speak up if you would find it easier to read in, say, a Google Doc published to the web. Unless people request it, I'll post info here with formatting stripped away. I'm posting what I have pretty raw, because I hope the benefit of having more people involved is worth the trade off of additional weeks and months writing, editing, and formatting. *

My two sources of information are:

1. For several years I was close personal friends with one of the supposed project scientists, whom I shall call 'David'. He's definitely the strangest human being I have ever known, by a large margin. We were housemates, so we chatted almost every day for several years. He outright told me just enough about ULTRA II to pique my interest. He was very careful about what he said, and was obviously under a Non Disclosure Agreement. He didn't so much tell me about the science behind project ULTRA II, as leave a trail of intellectual breadcrumbs, which it took me years to follow. He would speak in great detail on certain technical topics (not covered by NDA), but would clam up completely on other topics which he obviously knew equally well. I don't know where this person is now. He's probably dead, as he suffered from a debilitating and probably terminal disease during the years I knew him. He seemed to be concerned about 'plausible deniability', but having a terminal illness probably loosened his tongue quite a bit.

David was the classic 'warrior-scholar' archetype. He had high rank in the United Kingdom military forces (He showed me his passport, probably the real one), and was clearly affiliated with a military intelligence organization. He was a very experienced combat veteran. His body was covered with scars from bullets and shrapnel. 9 mm pistol slugs leave a different scar than .762 rifle slugs. He was very skilled at information warfare, and openly admitted to me the week we met that his oaths and history would sometimes require him to lie to me. He had VERY strong Operational Security (OPSEC) skills, such that he left very little evidence he even existed, and NO electronic or money trail.

David was utterly crazy brilliant. I confirmed experimentally that he had both eidetic memory and astounding mathematical ability. He also spoke many languages, including English, Mandarin, Cantonese, Russian, Serbian/Croation, Spanish, German, Japanese, and a few others. I tested his fluency by finding speakers of most of those languages and having them engage him in conversation. He was an expert hacker of the very highest grade (I'm no slouch myself, but his skills make mine look like those of a stupid child in comparison). He spent endless hours quietly working on his 'projects', which mostly involved making and using advanced neural network artificial intelligence applications. I'm a professional software engineer and he liked to chat about his current project, so I was able to follow most of what he did. One of his hobbies involved grafting neural network based AI into MMO computer games to make high grade bots, which he would then train to mimic particular human players.

When David departed he left no physical evidence he had ever been there. However, we had several housemates who also got to know him pretty well, and some of them are still in the neighborhood. One well established longtime member of this discussion group lives a few blocks away, and some of the housemates might be willing to give interviews, if it seems important. In other words, there ARE other people who can attest to David's presence , brilliance, and weirdness.

I have a lot more I could say about David, but that seems enough for now.

2. I mostly learned about ULTRA II through many years of open source research. I followed quite the trail of breadcrumbs. While ULTRA II itself, and its immediate results, are secret, a lot of related information, especially of a technical nature, has gradually leaked out. The scientists and mathematicians who work with this new technology don't publish classified details, but they DO publish non-classified papers, and sometimes the mental compartmentalization between classified and non-classified leaks a little.

Also, in the intervening 20 years, the 'official' science has advanced quite a bit. For example, in 1993 only a few specialized scientists and cryptographers knew about quantum teleportation. Now a QT array is standard in any good quantum optics lab, current max official distance is 143 km (sufficient to reach low earth orbit, or LEO), and the technology is being openly considered as a way to provide better security for high value financial transactions.

In the process of following all those breadcrumbs, and trying to assemble a coherent picture of what seems to have occurred, I mostly studied a linked set of about five scientific disciplines. Since scientific disciplines branch and combine, and the boundaries are sometimes fuzzy, you could really call the count anywhere between three and seven. I pick five as a nice round number. By that count, they are: Solid State Physics, Quantum Computation, Neuroscience, Complex Systems Biology, Wolfram's 'New Kind of Science'

Five Scientific Disciplines

In order to understand the rudiments of Quantum Neural Network technology one must know the basic vocabulary and principles of five different scientific disciplines. These are the scientific disciplines of the scientists recruited by DARPA for ULTRA II. A good working knowledge of the History of Science and Technology is also very helpful.

1. Solid State Physics
2. Quantum Computation, another branch of Physics
3. Artificial and Biological Neural Networks, a branch of Neuroscience
4. Complex System Biology, a branch of Mathematical Biology
5. Wolfram’s New Kind of Science (NKS).

One need not learn all the jargon, only a few of the most important words and ideas of each.

Solid-state physics is the study of rigid matter, or solids, through methods such as quantum mechanics, crystallography, electromagnetism, and metallurgy. It is the largest branch of condensed matter physics. Solid-state physics studies how the large-scale properties of solid materials result from their atomic-scale properties. Thus, solid-state physics forms the theoretical basis of materials science. It also has direct applications, for example in the technology of transistors and semiconductors. [footnote: lifted verbatim from Wikipedia http://en.wikipedia.org/wiki/Solid_state_physics ]

Quantum Computation is the study and practice of building quantum computers. A Quantum Computer (QC) is a device that makes direct use of quantum mechanical phenomena, such as superposition and entanglement, to perform operations on data. Physicists who study Quantum Computation sometimes specialize in Quantum Information Systems (QIS).

Artificial and Biological Neural Networks are branches of Neuroscience. Biological neural networks are known to be the basis for biological intelligence. Artificial neural networks have been around for about sixty years, and are one basis for artificial intelligence.

Complex System Biology (CSB) is a relatively new cross-disciplinary scientific field. CSB combines study of Complex Systems with principles of Mathematical Biology. CSB seeks to understand, and possibly replicate, the origin and nature of both life and intelligence. CSB gets its magic by cleverly exploiting certain known emergent properties.

A New Kind of Science (NKS) is a best-selling, award-winning, controversial book by Stephen Wolfram, published in 2002. It contains an empirical and systematic study of computational systems such as cellular automata. Wolfram calls these systems simple programs and argues that the scientific philosophy and methods appropriate for the study of simple programs are relevant to other fields of science. [wikipedia - http://en.wikipedia.org/wiki/A_New_Kind_of_Science]

******************************************************

I'm working on a reading list, with page references. It will be a combination of science books, some of them written by supposed ULTRA II project scientists, and scientific papers. If a reader is curious where a given factoid came from and it is neither footnoted nor obvious, please just ask for clarification,

I'll stop there for now, and allow my new found Skeptic Friends to consider what format works best. I also don't yet know what ORDER is best. Do I lay out great steaming gobs of science? Do I tell the story of what I think the humans involved actually did? Do I delve into the ethical predicament they encountered? I have chapters in progress, or already written, on each of those topics. DO I post EVERYTHING I've written and gathered, all at once, however incomplete and incoherent? That last doesn't seem like a good idea, but I'm open to whatever seems like it will work best.

The total background reading I estimate at a few thousand pages of dense scientific literature.

My total content, to date, is about 200 pages of rough first draft.

*****************************************************

"It is Easier to get Forgiveness than Permission" - Rear Admiral Grace Hopper

Edited by - energyscholar on 11/24/2012 01:36:20

Dave W.
Info Junkie

USA
24972 Posts

Posted - 11/20/2012 :  17:43:02   [Permalink]  Show Profile  Visit Dave W.'s Homepage Send Dave W. a Private Message  Reply with Quote
The information about your sources was worthless. What is this alleged new General Purpose Technology (anGPT)? You put it in the same category as "fire, agriculture, metallurgy, wheel, writing, chemistry, combustion engine, electricity, radio, computer, atomic power, internet," but describe it not at all. (I'll forgive, for the moment, that chemistry and metallurgy aren't technologies.)

Even without knowing the name or the science behind it, people can describe what a wheel does, for example. In twenty-five words or less, what does this anGPT do?

Edited: in other words, why should I spend time reading your 200 pages or the thousand pages of background material?

- Dave W. (Private Msg, EMail)
Evidently, I rock!
Why not question something for a change?
Visit Dave's Psoriasis Info, too.
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Boron10
Religion Moderator

USA
1243 Posts

Posted - 11/20/2012 :  17:43:39   [Permalink]  Show Profile Send Boron10 a Private Message  Reply with Quote
You still have not yet told us about this "lucky breakthrough." Care to enlighten us?
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Dave W.
Info Junkie

USA
24972 Posts

Posted - 11/20/2012 :  17:45:48   [Permalink]  Show Profile  Visit Dave W.'s Homepage Send Dave W. a Private Message  Reply with Quote
37 seconds, Boron. "Razor tight" as the conservatives would say.

- Dave W. (Private Msg, EMail)
Evidently, I rock!
Why not question something for a change?
Visit Dave's Psoriasis Info, too.
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energyscholar
New Member

USA
38 Posts

Posted - 11/20/2012 :  19:11:26   [Permalink]  Show Profile Send energyscholar a Private Message  Reply with Quote
In twenty-five words or less, what does this GPT do?


When Electricity was invented, what did it do?

When first two ARPANET nodes were connected in 1969, what did the internet do?

When it was first conceived, what did a Turing machine do?

In the roughly twenty years since it was invented, what does topological Quantum Neural Network Technology do?

A partial answer, and the best I can do (in 67+ words):

efficiently emulate a quantum Turing machine
find the prime factors of large numbers in polynomial time
rapidly search large databases given partial data (e.g. associative memory)
sequence DNA (e.g. solve the Jones polynomial)
face recognition
automated language translation and recognition
self piloting vehicles
personal digital assistant
computational knowledge engine
adiabatic topological quantum neural computation
cognitive signature recognition and emulation
very low current room temperature superconductivity
teleport information
strong artificial intelligence
emit coherent phonon bursts for sensing purposes

"It is Easier to get Forgiveness than Permission" - Rear Admiral Grace Hopper
Edited by - energyscholar on 11/20/2012 19:26:45
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Dave W.
Info Junkie

USA
24972 Posts

Posted - 11/20/2012 :  20:21:52   [Permalink]  Show Profile  Visit Dave W.'s Homepage Send Dave W. a Private Message  Reply with Quote
Originally posted by energyscholar

When Electricity was invented, what did it do?
Discovered, not invented. Which aspect? Until the 1800s, electrical phenomenon were curiosities (like picking up feathers with static-charged amber rods) with no practical applications at all (ignoring attempts at using electric eels to cure diseases, of course).

When electrical applications (technologies are applications of scientific knowledge) took off (Westinghouse, Tesla, etc.), the technologies could be summarized as new ways to transmit and/or store energy to do work.
When first two ARPANET nodes were connected in 1969, what did the internet do?
It interconnected two different networks, allowing communications between/across them.
When it was first conceived, what did a Turing machine do?
It represented a mathematical model of a hypothetical computing machine, useful for discussing the act of computation.
In the roughly twenty years since it was invented, what does topological Quantum Neural Network Technology do?
Since the only place I can find using that exact phrase ("topological Quantum Neural Network Technology") is your blog, and you don't explain it, I really couldn't say. I'm going to guess that you mean a quantum neural net implemented on a topological quantum computer. If that's the case, then because TQCs effectively have the same computing power as any other quantum computing model, you're asking a question which conflates the implementation of a computing device with the implementation of a form of computation. "What does a neural network running on an IBM PC do?" Anything any other neural net does.
A partial answer, and the best I can do (in 66+ words):

emulate a quantum Turing machine
Any QC should be able to do that.
find the prime factors of large numbers in polynomial time
rapidly search large databases given partial data (e.g. associative memory)
sequence DNA (e.g. solve the Jones polynomial)
face recognition
automated language translation and recognition
All proposed benefits of quantum computing in general.
self piloting vehicles
You mean ones that are easier to make or better than what we currently have?
personal digital assistant
28-year-old technology.
computational knowledge engine
Wolfram|Alpha. I haven't been tremendously impressed.
adiabatic topological quantum neural computation
A term for which Google provides zero results. It sounds like another confusion of models.
cognitive signature recognition and emulation
Google provides exactly one result, perhaps you'll remember it:
Altos is correct. The back-end system that does this is based on quantum neural network technology developed in the 1990s, which has gradually worked its way into non-spy use. This technology is NOT computer-based and, instead, is an independent physical system of anyons interacting within the 2DEG environment of HEMTs. This system has been transcribing phone calls to text, suitable for data mining analysis, for over a decade. This function is part of ECHELON, and is operated by the Five Eyes (AUS CAN NZ UK US). The same AI also handles a bunch of other tasks, such as Face Recognition, Cognitive Signature Recognition and Emulation, et cetera. Apologies for the use of so much jargon, but no simpler terms to describe this stuff exist yet. Look it up in Wikipedia if you are interested, or seek out a Post Quantum Historical Retrospective.
Really, the "jargon" that Google says comes from you alone doesn't speak well about your thesis.
very low current room temperature superconductivity
A dream for decades.
teleport information
Known for decades.
strong artificial intelligence
Kinda vague.
emit coherent phonon bursts for sensing purposes
Done for centuries. This describes, for instance, tapping on pipes at collapsed buildings, hoping to hear a trapped survivor tap back.

- Dave W. (Private Msg, EMail)
Evidently, I rock!
Why not question something for a change?
Visit Dave's Psoriasis Info, too.
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HalfMooner
Dingaling

Philippines
15821 Posts

Posted - 11/20/2012 :  20:43:11   [Permalink]  Show Profile Send HalfMooner a Private Message  Reply with Quote
I can't wait till Marvel's writers and artists get to work on your thesis, energyscholar. This stuff's dynamite, and right up Stan Lee's alley.

Biology is just physics that has begun to smell bad.” —HalfMooner
Here's a link to Moonscape News, and one to its Archive.
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energyscholar
New Member

USA
38 Posts

Posted - 11/21/2012 :  01:33:33   [Permalink]  Show Profile Send energyscholar a Private Message  Reply with Quote
I'm going to guess that you mean a quantum neural net implemented on a topological quantum computer.


No. Not at all. Just the opposite.

A Turing machine, a neural network, certain cellular automata, and various other systems are all capable of Universal Computation. Each of them can emulate the behavior of any other, with greater or lesser efficiency. This is called Universal Computation (UC). For example, a classical neural network can emulate an XOR gate, and many XOR gates can be assembled into a Turing machine.

Similarly, systems capable of Universal Quantum Computation (UQC) can all emulate each other. This is the power of quantum computation, first described in 1980 by Richard Feinman. UQC can efficiently emulate UC, but the reverse is not true.

A topological quantum neural network is the base physical system. A quantum neural network can emulate a CNOT gate, and many CNOT gates can be assembled into a quantum Turing machine. This is one (rather limited, but it works) way a QNN performs quantum computation.


Since the only place I can find using that exact phrase ...


Someone puts way too much faith in Google search. I already TOLD you this is a secret technology with no official name. I just said that I coined the term. Why would you expect anyone else to use it yet?

Also, you should already be aware that there is an entire art and science associated with manipulating Google search. GULAG is one such (rather primitive) tool. When it's being done by a major government, for the purpose of concealing inconvenient or classified topics, they generally have Google's cooperation.

This item I mention:

rapidly search large databases given partial data (e.g. quantum associative memory)

IS Google search. We've all been using it for years. Look at the historical timing. Actually, this technology was first leased to Alta Vista (remember them), but their board of directors got greedy (dare I say 'evil'), so the lease was yanked and Alta Vista folded. Google was leased this technology next, and they have been somewhat better stewards.

sequence DNA (e.g. solve the Jones polynomial) - Check out the history of the Human Genome Project. Unexpected access to QNN technology, and it's ability to solve the Jones polynomial, is why that project succeeded so much sooner and faster than expected, and with considerable controversy.

face recognition & automated language translation and recognition - both can be DONE, somewhat, with classical neural networks, but they both work a lot better running on QNN hardware. Access to QNN hardware is why these technologies are now routine.

self piloting vehicles
You mean ones that are easier to make or better than what we currently have?

I mean the ones we have right now. The ones whose public appearance a few years ago startled almost everyone, especially the computer and AI specialists who did not see it coming. While these AI routines can run on classical neural network hardware (usually emulated by Turing machines), QNN-based hardware is why they work well enough to be street legal in California.

Wolfram|Alpha. I haven't been tremendously impressed.


So you've been somewhat impressed? WolframAlpha is now a standard tool in many scientific fields. Considering how young it is, that's already pretty impressive. It's better at Physics than I am, which maybe is not saying a lot, but still sounds like it's pushing the envelope towards Strong AI.

Check out Stephen Wolfram's biography and life work. His 1200 page book, "A New Kind of Science", is near the top of my recommended reading list. His ideas are both deeply counter-intuitive and demonstrably true. Although he does not publicly admit to having worked for DARPA, Wolfram's work is highly applicable to mathematical cryptography. Wolfram's CA Rule 30 would have been an ideal basis technology for certain 80s and 90s era NATO cryptographic systems. If Wolfram did a successful cryptography project for DARPA in the 80s, and considerable circumstantial evidence suggests he did, then he would have been a good choice to lead ULTRA II in 1991.

I can't prove it, and I'm not 100% certain, but I think Steven Wolfram was probably the lead project scientist for ULTRA II.

adiabatic topological quantum neural computation
A term for which Google provides zero results. It sounds like another confusion of models.


It sounds like someone trusts online search too much, and does not read enough actual paper books. Lots of cutting edge science is not adequately indexed in Google. You should not expect to find useful information about secret classified technology on Google

Try this one, titled "Quantum Neural Computation":

http://www.springer.com/computer/ai/book/978-90-481-3349-9

It's expensive and hard to get via Inter-library loan, but it's a good light read. Two papers compiled in this book explicitly discuss adiabatic quantum neural computation. A different paper discusses the topological approach to quantum neural computation.

I'm strongly biased against video, but try listening to Dr. Suzanne Gilbert's 6 part series about Adiabatic Quantum Computing on Youtube

http://www.youtube.com/watch?v=wLY0lBwUHWw (warning: She works for a Seattle company that sells this tech). If you like those, watch her other videos about the basics of quantum computing. Personally, I prefer written info and dislike video, but video has its uses.

Google provides exactly one result, perhaps you'll remember it:
Altos is correct. The back-end system that does this is based on quantum neural network technology developed in the 1990s, which has gradually worked its way into non-spy use. This technology is NOT computer-based and, instead, is an independent physical system of anyons interacting within the 2DEG environment of HEMTs. This system has been transcribing phone calls to text, suitable for data mining analysis, for over a decade. This function is part of ECHELON, and is operated by the Five Eyes (AUS CAN NZ UK US). The same AI also handles a bunch of other tasks, such as Face Recognition, Cognitive Signature Recognition and Emulation, et cetera. Apologies for the use of so much jargon, but no simpler terms to describe this stuff exist yet. Look it up in Wikipedia if you are interested, or seek out a Post Quantum Historical Retrospective.
Really, the "jargon" that Google says comes from you alone doesn't speak well about your thesis.


I told you, right in the thesis, that I coined the term. Yes, those are my words. They were in response to DARPA announcing that it was hiring project scientists to do a "cognitive signature recognition" project:

http://www.schneier.com/blog/archives/2012/01/authentication_1.html

I do apologize for all the jargon. I'm posting HERE, to skeptic friends, because I'm trying to find ways to explain the same ideas with a bare minimum of jargon. If one's audience does not understand the words one uses then one is doomed to be not understood. The above jargon is encompassed by the various sciences I introduce above.

Spy terms like ECHELON, UK/USA, and Five Eyes, while not well known, have very precise meanings. I think I made it pretty clear that I've flirted with that world. Upon request I'm even willing to provide my 12 page autobiography, titled "Autobiography of an Accidental Conspirator". My life story is pretty boring, and I don't recommend it, but it is available. I wrote it up because who tells the tale is relevant to the ULTRA II story.

teleport information
Known for decades.


Yes, known in theory since the 1935 EPR paper, but now we routinely do it. It was first discussed experimentally in 1993 by a group of scientists and cryptographers affiliated with DARPA.

Given that we now know that quantum teleportation actually works, and that DARPA knew this by 1993, it's not a huge stretch to imagine that DARPA might have tried to harness this new technology, back then, for cryptographic purposes.

This VERY RELEVANT scientific article

http://rspa.royalsocietypublishing.org/content/464/2089/1.full

clearly explains why TOPOLOGICAL quantum computation is a better bet than the various other approaches. I'm 99.9% certain that Gavin Brennan, one of the two authors, is in the QNN Non Disclosure Club. One can confirm via Google that Brennan has done consulting work for WolframAlpha.



"It is Easier to get Forgiveness than Permission" - Rear Admiral Grace Hopper
Edited by - energyscholar on 11/21/2012 02:43:46
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On fire for Christ
SFN Regular

United Kingdom
1123 Posts

Posted - 11/21/2012 :  03:50:39   [Permalink]  Show Profile Send On fire for Christ a Private Message  Reply with Quote
What's an example of something that has leaked into mainstream high technology? None of the stuff you mention has any mysterious origins, it's all pretty well understood and evolved gradually. Face recognition (or any kind of pattern recognition), data mining, decision making etc can be done using standard non-quantum neural networks.

Also your story about "David" is kind of hard to believe. A skilled combat veteran, multilingual, eidetic memory, mathemagician, master hacker? Anything else? Did he play Jazz flute in his spare time?
Why would a guy like that be living with housemates anyway? Does James Bond have a room-mate?

Sounds like he's delusional and/or a con-man to me. If he even existed.

For we walk by faith, not by sight.
Edited by - On fire for Christ on 11/21/2012 03:57:22
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Dave W.
Info Junkie

USA
24972 Posts

Posted - 11/21/2012 :  05:30:25   [Permalink]  Show Profile  Visit Dave W.'s Homepage Send Dave W. a Private Message  Reply with Quote
Quick note, I'll address more later:
Originally posted by energyscholar

I told you, right in the thesis, that I coined the term. Yes, those are my words. They were in response to DARPA announcing that it was hiring project scientists to do a "cognitive signature recognition" project:

http://www.schneier.com/blog/archives/2012/01/authentication_1.html

I do apologize for all the jargon. I'm posting HERE, to skeptic friends, because I'm trying to find ways to explain the same ideas with a bare minimum of jargon. If one's audience does not understand the words one uses then one is doomed to be not understood. The above jargon is encompassed by the various sciences I introduce above.
No. In this case, you coined the term "cognitive signature" when "cognitive footprint" would have sufficed (and provides plenty of results in Google). The only reason I can see to coin new terms when old ones exist is to be purposefully obfuscatory.

- Dave W. (Private Msg, EMail)
Evidently, I rock!
Why not question something for a change?
Visit Dave's Psoriasis Info, too.
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Jussi
New Member

1 Post

Posted - 11/21/2012 :  07:50:46   [Permalink]  Show Profile Send Jussi a Private Message  Reply with Quote
energyscholar,
you use awfully many words to tell almost nothing.

In short, you have two sources for your extraordinary claims.
1. Friend.
2. Internet?

And apparently your main claim is that QNNs exist already,
but you don't back that up with any evidence.

So far it seems you have nothing to show, right?
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Dave W.
Info Junkie

USA
24972 Posts

Posted - 11/21/2012 :  13:27:27   [Permalink]  Show Profile  Visit Dave W.'s Homepage Send Dave W. a Private Message  Reply with Quote
Originally posted by energyscholar

I'm going to guess that you mean a quantum neural net implemented on a topological quantum computer.
No. Not at all. Just the opposite.

A Turing machine, a neural network, certain cellular automata, and various other systems are all capable of Universal Computation. Each of them can emulate the behavior of any other, with greater or lesser efficiency. This is called Universal Computation (UC). For example, a classical neural network can emulate an XOR gate, and many XOR gates can be assembled into a Turing machine.

Similarly, systems capable of Universal Quantum Computation (UQC) can all emulate each other. This is the power of quantum computation, first described in 1980 by Richard Feinman. UQC can efficiently emulate UC, but the reverse is not true.

A topological quantum neural network is the base physical system. A quantum neural network can emulate a CNOT gate, and many CNOT gates can be assembled into a quantum Turing machine. This is one (rather limited, but it works) way a QNN performs quantum computation.
But why bother? Why not just build a topological quantum computer? Why go through the trouble of building a some-other-model quantum computer, then implementing a QNN on top of it, and then implement a topological quantum computer within the QNN? If we can't currently build a TQC, but can make an effective QTM, then simulate the TQC directly with the QTM. What need is there for an intermediate QNN?

(An aside, it was Feynmann, in 1981, who described a basic quantum computer. A universal quantum computer wasn't described until 1985, by Deutsch.)
Since the only place I can find using that exact phrase ...
Someone puts way too much faith in Google search. I already TOLD you this is a secret technology with no official name.
But you're spitting out names of stuff you think the secret tech can do, as if we should have heard of the stuff you're naming. The terms with which I was unfamiliar I Googled, to see what those things are, not what your anGPT is.
I just said that I coined the term. Why would you expect anyone else to use it yet?
But you didn't coin "personal digital assistant" or "computational knowledge engine" or even "cognitive footprint." Why would you, in that list, provide many terms you haven't coined along with a few you have?
Also, you should already be aware that there is an entire art and science associated with manipulating Google search. GULAG is one such (rather primitive) tool. When it's being done by a major government, for the purpose of concealing inconvenient or classified topics, they generally have Google's cooperation.
We definitely won't have a productive discussion if you continue to imply that you are sole gate-keeper of the truth here on these pages.
This item I mention:

rapidly search large databases given partial data (e.g. quantum associative memory)

IS Google search. We've all been using it for years. Look at the historical timing.
If that's the basis for your whole thesis, I can only remind you that post hoc ergo propter hoc is a fallacy.
sequence DNA (e.g. solve the Jones polynomial) - Check out the history of the Human Genome Project. Unexpected access to QNN technology, and it's ability to solve the Jones polynomial, is why that project succeeded so much sooner and faster than expected, and with considerable controversy.
They finished a whole two years earlier than the expected 15. Isn't it more reasonable to think that the initial estimates didn't take into account Moore's Law, or unexpected advances in genomics or sequence analysis, or international help, than to think that some super-dooper secret technology sped things up by a measely 13%?
face recognition & automated language translation and recognition - both can be DONE, somewhat, with classical neural networks, but they both work a lot better running on QNN hardware. Access to QNN hardware is why these technologies are now routine.
So is your anGPT nothing more than quantum neural networks? Seems that way, and it seems they've had a perfectly good name since 1998 (at the latest).
self piloting vehicles
You mean ones that are easier to make or better than what we currently have?
I mean the ones we have right now. The ones whose public appearance a few years ago startled almost everyone, especially the computer and AI specialists who did not see it coming. While these AI routines can run on classical neural network hardware (usually emulated by Turing machines)...
You seem to have quite the fixation on neural networks and Turing machines. Nobody writing serious code these days is emulating neural networks on Turing machines. They're using PCs and/or Macs and/or supercomputers and/or specialized embedded processors. When people make actual Turing machines, they do so by emulating a Turing machine on a non-Turing (and only maybe Turing-complete) processor, and they do it as art or a joke, because there's no need to actually build a Turing machine. Just like no sane person has ever implemented Schroedinger's Cat thought experiment.
...QNN-based hardware is why they work well enough to be street legal in California.
Again, it seems like you're rejecting the idea that street-legalness is due to Moore's Law working for umpty-ump years and combining in awesome ways with super-cheap (compared to just ten years ago) cameras and GPS. There doesn't seem to be any need to conclude that a QNN is involved. Do you have any evidence that one is at the heart of the recently-in-the-news self-driving vehicles other than the fact that some futurists weren't 100% correct prognosticators?
Wolfram|Alpha. I haven't been tremendously impressed.
So you've been somewhat impressed? WolframAlpha is now a standard tool in many scientific fields. Considering how young it is, that's already pretty impressive. It's better at Physics than I am, which maybe is not saying a lot, but still sounds like it's pushing the envelope towards Strong AI.
Pretty good at math and physics, but not so good outside those realms.
adiabatic topological quantum neural computation
A term for which Google provides zero results. It sounds like another confusion of models.
It sounds like someone trusts online search too much, and does not read enough actual paper books. Lots of cutting edge science is not adequately indexed in Google. You should not expect to find useful information about secret classified technology on Google

Try this one, titled "Quantum Neural Computation":

http://www.springer.com/computer/ai/book/978-90-481-3349-9

It's expensive and hard to get via Inter-library loan, but it's a good light read. Two papers compiled in this book explicitly discuss adiabatic quantum neural computation. A different paper discusses the topological approach to quantum neural computation.
And here you're basically admitting that you mixed the two terms together to come up with "adiabatic topological quantum neural computation."
I'm strongly biased against video, but try listening to Dr. Suzanne Gilbert's 6 part series about Adiabatic Quantum Computing on Youtube

http://www.youtube.com/watch?v=wLY0lBwUHWw
But does she talk about adiabatic topological quantum neural computing?

Look, my point is about your use of terms, not whether particular quantum technologies exist. "Adiabatic" and "topological" refer to how computations are performed, and "neural" really just refers to data flow. Since neural nets can be implemented on any quantum computer, be it adiabatic or non, topological or non, the "neural" adjective really refers to a sort of software, while the first two adjectives refer to hardware. You've combined the terms in a way that nobody else is doing, resulting in a huge descriptor that prevents seekers of knowledge from continuing on their own, rather than enlightening them.
teleport information
Known for decades.
Yes, known in theory since the 1935 EPR paper, but now we routinely do it. It was first discussed experimentally in 1993 by a group of scientists and cryptographers affiliated with DARPA.

Given that we now know that quantum teleportation actually works, and that DARPA knew this by 1993, it's not a huge stretch to imagine that DARPA might have tried to harness this new technology, back then, for cryptographic purposes.
But what does it have to do with your anGPT?
This VERY RELEVANT scientific article

http://rspa.royalsocietypublishing.org/content/464/2089/1.full

clearly explains why TOPOLOGICAL quantum computation is a better bet than the various other approaches.
Only for particular prolem sets.
I'm 99.9% certain that Gavin Brennan, one of the two authors, is in the QNN Non Disclosure Club. One can confirm via Google that Brennan has done consulting work for WolframAlpha.
But why should I care who is doing the work? What is the anGPT? What does it do that allegedly paved the way for all the other "breakthroughs" you listed?

Oh, wait. I see in your OP you wrote:
I call it 'Quantum Neural Network' technology.
Again, "quantum neural network" has a meaning going back at least 14 years now, publicly. If that's not what you're talking about, then you need to change what you call it.

- Dave W. (Private Msg, EMail)
Evidently, I rock!
Why not question something for a change?
Visit Dave's Psoriasis Info, too.
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energyscholar
New Member

USA
38 Posts

Posted - 11/23/2012 :  22:14:43   [Permalink]  Show Profile Send energyscholar a Private Message  Reply with Quote
What's an example of something that has leaked into mainstream high technology? None of the stuff you mention has any mysterious origins, it's all pretty well understood and evolved gradually. Face recognition (or any kind of pattern recognition), data mining, decision making etc can be done using standard non-quantum neural networks.


The things I named are NOT publicly well understood. They work, so obviously SOMEONE understands them, but they are ALL proprietary technologies. I'll walk through a few of them:

1. Google Search

Never has Google, or anyone else, explained the technical details of how Google search works. We do know there is a 'Google OS' that runs on generic hardware, but we don't know much more about it. Somehow Google search is able to reach into a multi-Petabyte database and find something in a few milliseconds. How does this work?

Google tells us that they use neural network technology quite extensively. But what kind? How does it work?

My answer, and it's pretty obvious to anyone who studies artificial neural networks, is that Google Search is a form of Associative Memory.

Associative Memory - also known as Autoassociative memory, Associative memory is a generic term that refers to all memories that enable one to retrieve a piece of data from only a tiny sample of itself. Some classes of neural network have associative memory.


But what sort of neural network architecture can hold petabytes in associative memory? I assert that the HUGE database size, and the SPEED with which it operates, is characteristic of Quantum Associative Memory [ http://arxiv.org/abs/quant-ph/9807053 - Scientific Article on Quantum Associative Memory ]. I can't prove that Google's performance is beyond the limits possible to classical neural network architecture, but this sure seems to be the case. [I would be pleased if someone else who has extensive practical experience with artificial neural networks would state their opinion on this topic ]

Applied QNN technology is one way to explain this minor mystery. The timing also fits the proposed ULTRA II timeline.

2. DNA Sequencing

As Dave W. already mentioned, the Human Genome was supposed to be a 15 year project, but actually finished in a mere 13 years. Here's one version of the History of that project, as described by Stanford University:

http://plato.stanford.edu/entries/human-genome/

Note this particular paragraph:

Suddenly, the HGP found itself challenged by sequencing plans from the private sector. In May 1998, TIGR's Venter announced he would partner with Michael Hunkapiller's company Applied Biosystems (ABI), a division of Perkin-Elmer Corporation which manufactured sequencing machines, to form a new company which would sequence the entire genome in three short years and for a fraction of the cost. The foreseen profits rested in the construction of a “definitive” database that would outdo Genbank by integrating medical and other information with the basic sequence and polymorphisms. The company, based in Rockville, MD and later named Celera Genomics, planned to use “whole-genome shotgun” (WGS) sequencing, an approach different from the HGP's. The HGP confined the shotgun method to cloned fragments already mapped to specific chromosomal regions: these are broken down into smaller bits then amplified by bacterial clones, sequences are generated randomly by automated machines, and computational resources are used to reassemble sequence using overlapping areas of bits. Shotgunning is followed by painstaking “finishing” to fill in gaps, correct mistakes, and resolve ambiguities. What Celera was proposing for the shotgun method was to break the organism's entire genome into millions of pieces of DNA with high-frequency sound waves, sequence these pieces using hundreds of ABI's new capillary model machines, and reassemble the sequences with one of the world's largest civilian supercomputers without the assistance provided by the preliminary mapping of clones to chromosomes. When WGS sequencing was considered as a possibility by the HGP, it was rejected because of the risk that repeat sequences would yield mistakes in reassembly.[10] But Venter by this time had successfully used the technique to sequence the 1.83 million nucleotide bases of the bacterium Hemophilus influenzae—the first free-living organism to be completely sequenced—in a year's time (Fleischmann et al. 1995).[11]


But how, exactly, does WGS sequencing, and the more sophisticated variants now in use, work? There's a is HUGE volume of technical research on this topic. However, it all comes down to proprietary computational technology that somehow 'fills in gaps, corrects mistakes, and resolves ambiguities'. The details of how this works are proprietary, and the tools that do it are based on proprietary technology.

Note how the WGS approach "was rejected because of the risk that repeat sequences would yield mistakes in reassembly". HGP scientists resisted this approach because they did not fully understand how it worked and therefore did not trust it. However, once the approach had actually been proven to work, doubting scientists backed off and accepted the approach advocated by Celera Genomics. You can't argue with success.

Detailed study of this topic will show that the computation process of sequencing shattered bits of DNA involves finding solutions to the Jones Polynomial. Without getting into too much math, the Jones Polynomial arises from the way that the two strands of DNA braid around each other, and is associated with that branch of mathematics called Braid Theory . It just so happens that topological quantum computation, but not other forms of quantum computation, ALSO relies heavily on Braid Theory, and is able to solve the Jones Polynomial rapidly and accurately.

None of this is proof that that topological quantum computation is involved in DNA sequencing. The details of actual DNA sequencing machines remains proprietary. Applied QNN technology is one way to explain this minor mystery. The timing also perfectly fits the proposed ULTRA II timeline.

3. Self-piloting vehicles

The AI for self-piloting vehicles is not controversial. There are many hints that military drones were using self-piloting vehicles long before Google introduced the self-driving Prius in 2007. Sebastian Thrun, the primary architect of Google self-driving cars and an AI professor at Stanford, explains how it works in considerable length. What Professor Thrun never mentions is what sort of underlying hardware it runs on, although he helpfully tells us that it is some sort of neural network.

These days, the standard way to make an artificial neural network is to simulate it on a computer. E.g. At an abstract level we use a Turing Machines to emulate a neural network, just as Alan Turing first wrote about in 1948. This approach works, but the performance of the neural network is limited by the speed of the computer emulating it. This process works, but it is not efficient. Modern computers are so fast that the neural networks they emulate are also quite fast. However, when even the best modern computers (or arrays of computers) emulate a large complicated recurrent neural network, the performance of the neural network is poor at best.

The best way to get high efficiency from a neural network is for the actual hardware to be a neural net, rather than to emulate one via computer. This is why biological neural nets generally outperform artificial neural nets, and why humans are good at things like face recognition, driving, word recognition, math & Physics, leaps of intuition, et cetera.

The requirements of piloting a car are quite rigorous. The system must be aware of its surroundings, and must react very rapidly. It must drive at least as well as an average human being. Current publicly-known artificial neural network technology, even the most advanced recurrent neural networks, seem to be not sufficient to the task. The great weight of evidence suggests that there is some sort of proprietary neural network technology at work here, nature unknown.

Does this prove that QNN technology is what enabled self piloting vehicles? No! Applied QNN technology is one way to explain this minor mystery. The timing also fits the proposed ULTRA II timeline, in that this more complicated technology was developed considerably later than Quantum Associative Memory and Jones Polynomial Solving.

*********************************
While we're at it, let's review the definition of Strong AI (from Wikipedia):

Strong AI is artificial intelligence that matches or exceeds human intelligence

Self piloting vehicles work. Clearly, then, for the specific problem of driving a car, Strong AI has arrived.


Why would a guy like that be living with housemates anyway?


For the first year of our association I frequently asked the same question. If you hear out my entire story you should be able to answer that question for yourself pretty well. For now, I decline to answer this question. I wish I already had this very complicated story assembled in a more coherent format. That's why I'm conducting this exercise on SkepticFriends.


No. In this case, you coined the term "cognitive signature" when "cognitive footprint" would have sufficed (and provides plenty of results in Google). The only reason I can see to coin new terms when old ones exist is to be purposefully obfuscatory.


Mea Culpa. Dave W. is correct, my wording is sloppy. Cognitive footprint is the correct term.

In short, you have two sources for your extraordinary claims.
1. Friend.
2. Internet?

And apparently your main claim is that QNNs exist already,
but you don't back that up with any evidence.

So far it seems you have nothing to show, right?


I should have been clear about this from the beginning. I can't PROVE that my thesis is correct. I can only suggest the thesis, and point out that it answers a whole slew of difficult questions that people mostly have not bothered to ask, but are there for the asking. In fact, my purpose is merely to INTRODUCE people to the ideas involved. In particular, I want people to understand the nature of the ethical predicament the project scientists faced.

I strongly suspect that, once there is a sufficient critical mass of people who understand the topic, proof will be forthcoming. I don't know in what form, or from whom it will come, but I have little doubt it will appear. Proof is pointless, however, until people understand the issues. If tomorrow someone were to leak details of the ULTRA II project, e.g. via Wikileaks or something similar, no one would know what to make of it. No one told me to do so, but I have taken on myself the task of teaching the world about QNN technology. I've chosen to start with scientific skeptics.

I'd also like to point out, again, that information on the internet is NOT sufficient to understand this topic. You have to read lots of paper books and study some real science. I recommend a University degree in Physics, followed by graduate degrees in Mathematical Biology (Complex System Biology) , another in Neuroscience (Artificial and Biological Networks), and a specialization in Topology (Braid Theory). I wish I'd had the time and money to get all those degrees. Because I've had to learn these topics mostly on my own, autodidact style, it's taken me longer, and I don't know them as well as I would like to. I saved myself at least $100,000 I didn't have, though.

I also wish to point out that, when any secret, censored, or sensitive topic is in question, one must be very suspicious of information one finds on the internet. A community of scientific skeptics already knows that. :-)


But why bother? Why not just build a topological quantum computer?


Because we still don't know how to build a Topological Quantum Computer (TQC)! TQC exploits topological entanglement/teleportation to perform quantum computation.

I wrote this little blurb to explain the rudiments of Topological Quantum Computation:

***********************************************************
A specific process must be followed to perform quantum computation. The qubit array must first be initialized to the proper superposition state. Then the qubit array is manipulated to perform the actual computation. Then certain measurements are made, and the results read. This all takes time. The more qubits are being manipulated, typically, the more unstable the system is, and the shorter the decoherence time. For systems of large qubits the decoherence time is many orders of magnitude smaller than the time required to perform the computation and read out the results. That will not work.

What is needed is a more robust form of entanglement. It must be resistant to environmental disturbance. It must have a quantum decoherence time much greater than the time required to perform computation and read the results.

Question: Do we know of any form of quantum entanglement that is less sensitive to disturbances, and that is either long lasting or persistent?

Answer: Yes! A particular exotic solid state system, called a FQHE fluid, manifests a type of quasiparticle called an anyon. Anyons are unique in that they are neither bosons nor fermions, but have characteristics of both. This is true because anyons exist only in two dimensions. When two anyons exchange places (rotate around each other) they become topologically entangled. This form of entanglement is not sensitive to disturbances in the environment, and can persist indefinitely.

The behavior of braided anyons in an FQHE liquid is described by a branch of Topology called ‘braid theory’. Braid theory is also associated with the Jones Polynomial, which is associated with DNA sequencing because a double helix is also a type of braid. Anyons have the innate quality that they can reliably maintain entanglement over a long time, making them highly suitable for experimental quantum teleportation. Topological entanglement of anyons shows that, if it is possible to manipulate a system of anyons in an organized fashion, such a system could theoretically perform quantum computation.
***************************************************************

A TQC requires precise manipulation of many anyons. An anyon-based TQC could also be described as a teleportation/entanglement based TQC. Such a system would qualify as a form of nanotechnology. Therefore, a working TQC is a form of quantum teleportation based nanotechnology! Later I will demonstrate why this strange new speculative technology is actually a new General Purpose technology, as it's not immediately obvious.

The techniques of Complex System Biology, as introduced by Stuart Kauffman, provide a plausible way to build an actual working topological QNN at the hardware level. This is because, as described in Kauffman's book "The Origins of Order: Self-Organization and Selection in Evolution" [needs a chapter and page reference!], certain 'poised' complex systems have the emergent property that they function like (are!) a neural network. This is part of the 'magic' of Complex System Biology. Anyons moving in an FQHE fluid seem to allow this sort of 'poised' complex system.

[This is another great topic for an original scientific paper! "Does the v = 5/2 FQHE State Support a Poised Complex System of Anyons?" I strongly suspect such a paper was written in 1991 by Kauffman, Tilden, and Hasslacher, but I doubt DARPA will allow it to be published until about 2068 CE. Yours might be, though! ]

Since a topological QNN can efficiently emulate a TQC, that is good enough! Working QNN hardware is sufficient to perform topological quantum computation! We'd still need to shape the QNN for the particular task, but techniques are already known to accomplish that. There are all sorts of details, but that is the basic principle. This also tells us that, as far as definitions are concerned, QNN technology is a form of quantum teleportation based nanotechnology.

All this could have been known to DARPA in 1991.


But you're spitting out names of stuff you think the secret tech can do, as if we should have heard of the stuff you're naming. The terms with which I was unfamiliar I Googled, to see what those things are, not what your GPT is.


Awkward, that. Also true. You asked the question, "In 25 words what does it do". I answered in 67+ words, but I had to take the shortcut of using technical jargon. That's why I said, right from the start, that these ideas are very difficult to describe without learning a lot of new vocabulary. Sorry about that, but I don't see a way around it. That's exactly the problem I'm learning how to overcome by posting here. That's one reason I need help. As I said at the beginning, this topic is too big for me!

But you didn't coin "personal digital assistant" or "computational knowledge engine" or even "cognitive footprint." Why would you, in that list, provide many terms you haven't coined along with a few you have?


I would do that because I don't have an editor. My writing was sloppy. I'll correct that on the next iteration.

We definitely won't have a productive discussion if you continue to imply that you are sole gate-keeper of the truth here on these pages.


I claim no such status. I'm a scientific skeptic, too. I was just warning people not to believe everything they see on the intertubes.

So is your GPT nothing more than quantum neural networks? Seems that way, and it seems they've had a perfectly good name since 1998 (at the latest).


Yes, that's all it is. Yes, the idea of QNN technology has had a perfectly good name in academia since 1998. No one has ever publicly claimed, or even acknowledged, the existence of a real one. My thesis is that actual working instances of topological Quantum Neural networks were developed by a DARPA project between 1991 and 1995. I claim that QNN technology is now quite sophisticated, and that it is responsible for many recent advancements in Artificial Intelligence. Furthermore, I claim that QNN is a new General Purpose Technology, like Electricity, Radio, Atomic Power, Computers, and the Internet.

You seem to have quite the fixation on neural networks and Turing machines.


For years I lived with a guy who seems to have helped complete the unfinished life work of Alan Turing. Turing invented both the Turing machine and the concept of artificial neural networks. Alan Turing is known as the Father of Computing and the Father of Artificial Intelligence.

The only way to learn about this topic was to go back to the basics. All modern computers have logical Turing machines at the heart of their CPU. Alan Turing was the first to show, in 1948, that a Turing Machine could emulate a neural network. Turing saw, however, that such an approach could never lead to Strong AI. Alan Turing spent the remainder of his life trying to implement an actual (not emulated!) artificial neural network. In the process, as a path to that goal, he defined the Morphogenesis problem in Mathematical Biology.

Morphogenesis Problem - The biological process that causes an organism to develop its shape, which includes brain and nervous system development. Understand this process, and you have the key to artificial brain technology.


I strongly suspect that, after ULTRA II was complete, some of the project scientists completely solved Morphogenesis in a practical way. I believe this is the origin of the WolframAlpha computational knowledge engine, and was also the solution to the ethical predicament they faced. That's another topic for another day.

Again, it seems like you're rejecting the idea that street-legalness is due to Moore's Law


Correct.

Pretty good at math and physics, but not so good outside those realms.


Yes, WolframAlpha is pretty good at math and science. Not so good, yet, outside those realms. It is, per the title, just an alpha release. Once again, let us review the definition of Strong AI:

Strong AI is artificial intelligence that matches or exceeds human intelligence

It's pretty clear that WolframAlpha matches or exceeds human intelligence in the fields of math and Physics. It's not clear how it works. It is a proprietary solution, and therefore also a mystery.

Applied QNN technology, combined with a full solution of the Morphogenesis Problem, is one way to explain this mystery. Note that Steven Wolfram's NKS especially applies to Morphogenesis, as is discussed at length in his book. In fact, a careful reading of Wolfram's NKS book actually has Wolfram explicitly discussing the type of artificial brain technology he probably used for WolframAlpha [needs a chapter and page reference].

OK, so we have clear proof of Strong AI in the fields of Driving, Math, and Physics. I am sure readers can think of quite a few other such fields getting close, like Face Recognition and Translation. How many such fields does it take before we officially acknowledge the existence of 'strong' artificial intelligence? The full meaning of strong AI seems to also imply consciousness, whatever that is. Will people worry about SkyNet starting a nuclear war? How will major global religions react to such an announcement? Does an artificial brain have a soul?

Look, my point is about your use of terms, not whether particular quantum technologies exist.


Thank you! Please point out problems in the terms I use! I just switched from research mode into discussion mode. This is almost my first time trying to explain this stuff. Please help me come up with better terms! This is way too big for me to handle alone.

But what does it have to do with your GPT?


This article, published by the Royal Society, explains why the topological approach to quantum computation is technically superior to other approaches:

http://rspa.royalsocietypublishing.org/content/464/2089/1.full

The authors explain, in language my mother understood, the exotic quantum mechanical properties of anyons that make them useful for quantum computing. Given that one author consults for WolframAlpha, and also had the same University doctoral thesis adviser as my friend 'David', I am 99.99% sure he is in the NDA club for QNN technology.

Only for particular problem sets.


True. But those problem sets apply to cracking public key cryptography, internet search, DNA sequencing, and a bunch of other useful topics.

Again, "quantum neural network" has a meaning going back at least 14 years now, publicly. If that's not what you're talking about, then you need to change what you call it.


That's what I'm talking about. My claim, again, is that QNN technology is not merely theoretical. I claim it is a practical applied technology, currently used by multiple governments and companies, that is nearly 20 years old.

At first blush QNN technology seems like it's only useful for AI. Not so! It can be applied to other endeavors, too. I've already described enough applications to qualify as a new General Technology. There are more. Some make me rather uncomfortable, and I am not eager to discuss them. I probably have not thought of the worst applications. So far, only those applications that can pass ethical muster have been allowed. Let us hope it stays that way!

We're now approaching the crux of the ethical predicament faced by the project scientists! I'll end this (very long) post with my description of when the project scientists realized they were facing an ethical predicament. They eventually realized that relinquishment was the only viable solution, as should have been done with Atomic Weapons.

The project scientists knew they would soon be able to deliver a working code-breaking machine. They were to also deliver detailed notes on everything they had done. They knew the spies would be happy with their new toy, and might not care all that much how it worked. This project would be closed. If successful, DARPA would surely do other projects based on similar technology.

The project scientists were smarter than the government spies who had hired them. They understood that QNN technology was much more important than the spy machine they had been asked to build. The code-breaking spy machine, or quantum computer, is one new technology. QNN technology creates other new technologies. The project scientists came to understand that QNN technology could be used to generate all manner of weird and wonderful new technologies. They also realized that QNN technology could very easily be perverted to serve evil ends. Some possible uses for QNN technology are almost too horrible to contemplate, and must never be attempted. The ULTRA II project scientists realized they had discovered a dangerous and powerful new technology. They did not want it for themselves, and they greatly feared the consequences of any human controlling it.

History is full of examples where new technology was used for perverse ends. The invention of dynamite produced high explosives and millions killed in war. Advances in biotechnology can cure diseases and heal injury, but also allow the invention of horrible weapons. Television is a terrific entertainment medium, but it also allows centralized mass mind control in the form of marketing, advertising, and televised news. Atomic Physics lead to control of atomic energy and the threat of global nuclear annihilation.

"The release of atom power has changed everything except our way of thinking...the solution to this problem lies in the heart of mankind. If only I had known, I should have become a watchmaker." - Albert Einstein, referring to how his early theoretical work lead to atomic weapons

The wise and worldly scientists of Project Ultra II understood the history of science and technology. They knew about unintended consequences. Some of them were also wise in the ways of politics, subterfuge, and espionage. They wanted to be the first inventors in history, ever, to maintain a degree of control over their own discovery, even after they were all dead. They had no ethical problem with making a code breaking spy machine. Their fear was that later, after they had turned over their work and notes, QNN technology might be used for terrible purposes.

They decided that they must take drastic action to prevent their discovery from being turned into a terrible weapon or, possibly worse, an Orwellian spy machine. But how to stop this new technology from being abused? Even if they destroyed their notes and ceased all work on the project, it was only a matter of time until some other powerful country, like China or Russia, tried a similar approach. The scientific basis for QNN technology already existed, so someone else was bound to try it. A different approach was needed.


The solution they came up with is a topic for another day.

[Yes, that's a deliberate tease. Deal with it. Anything beyond here will require we pretend to accept that ULTRA II actually occurred. I was told how they handled the problem, and I've verified as best I can, but you guys are not going to like it. Before I tell you what I think they did, I'd like to have a general discussion of the nature of the ethical predicament, and some possible ways out. I'd like to see if anyone can come up with a better solution, even if it's too late for that now. ]

The title of my unfinished work in progress (totally unpublished, except for fragments above) is "The Layperson's Guide to Quantum Neural Network Technology". The subtitle, "It is Easier to get Forgiveness than Permission", refers to actions the project scientists took to escape the terrible ethical predicament they had unwittingly created.

I'm working in book form, but I see a video rendition later. As I've said, though, I'm rather out of my depth.


"It is Easier to get Forgiveness than Permission" - Rear Admiral Grace Hopper
Edited by - energyscholar on 11/30/2012 05:04:22
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HalfMooner
Dingaling

Philippines
15821 Posts

Posted - 11/24/2012 :  00:41:18   [Permalink]  Show Profile Send HalfMooner a Private Message  Reply with Quote
It all seems scamtastic to me.

Biology is just physics that has begun to smell bad.” —HalfMooner
Here's a link to Moonscape News, and one to its Archive.
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ThorGoLucky
Snuggle Wolf

USA
1115 Posts

Posted - 11/24/2012 :  08:44:46   [Permalink]  Show Profile  Visit ThorGoLucky's Homepage Send ThorGoLucky a Private Message  Reply with Quote
I see a recurrence of the fallacy of an argument from ignorance, specifically "it's proprietary and fast, and I don't understand how it could be non-quantum, therefor quantum". It looks like your thesis is indefensible, but an interesting story, thanks.

Is the following a fair summery? A crazy roommate prompts you to predict a grand government coverup of quantum neural networks, and your only evidence is hearsay and that detractors must be more educated than thou.
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Dave W.
Info Junkie

USA
24972 Posts

Posted - 11/24/2012 :  11:04:16   [Permalink]  Show Profile  Visit Dave W.'s Homepage Send Dave W. a Private Message  Reply with Quote
So let me get this straight: your hypothesis is that people have been secretly implementing and using a technology that has been publicly discussed for 14-plus years?
Originally posted by energyscholar

1. Google Search

Never has Google, or anyone else, explained the technical details of how Google search works. We do know there is a 'Google OS' that runs on generic hardware, but we don't know much more about it. Somehow Google search is able to reach into a multi-Petabyte database and find something in a few milliseconds. How does this work?

Google tells us that they use neural network technology quite extensively. But what kind? How does it work?

My answer, and it's pretty obvious to anyone who studies artificial neural networks, is that Google Search is a form of Associative Memory.
Associative Memory - also known as Autoassociative memory, Associative memory is a generic term that refers to all memories that enable one to retrieve a piece of data from only a tiny sample of itself. Some classes of neural network have associative memory.
But what sort of neural network architecture can hold petabytes in associative memory? I assert that the HUGE database size, and the SPEED with which it operates, is characteristic of Quantum Associative Memory [ http://arxiv.org/abs/quant-ph/9807053 - Scientific Article on Quantum Associative Memory ]. I can't prove that Google's performance is beyond the limits possible to classical neural network architecture, but this sure seems to be the case. [I would be pleased if someone else who has extensive practical experience with artificial neural networks would state their opinion on this topic ]

Applied QNN technology is one way to explain this minor mystery. The timing also fits the proposed ULTRA II timeline.
The above suggests a basic misunderstanding of what Google keeps secret. To avoid other people gaming Google's system and thus pushing their preferred pages to the top of the results when they're not strictly relevant to a searcher's query, Google keeps its algorithms for determining relevance and interlink weighting heavily under wraps. So much so that those of us who participate in the Google Adsense program can sometimes be stymied when Google informs us that there's a problem with one of our pages (and thus Google will refuse to display ads on it) without telling us what that problem is.

But the fact that they're doing keyword look-ups isn't secret at all. Nor is keyword searching a problem which becomes more difficult with an increase in the data set, not if one devolves it to a dictionary look-up of strongly hashed terms built from pre-processed data, efficiently stored. So, because the problem doesn't involve pattern-matching, and the time to completion rises with (at worst) logN proportionality, and it wouldn't benefit much from parallelism, I fail to see how any kind of neural net would increase performance, much less be required for Google to demonstrate the performance it actually has.

The one thing Google does that would benefit from a QNN (or even a CNN) is image matching, but they've offered that service only recently.
2. DNA Sequencing

As Dave W. already mentioned, the Human Genome was supposed to be a 15 year project, but actually finished in a mere 13 years. Here's one version of the History of that project, as described by Stanford University:

http://plato.stanford.edu/entries/human-genome/

Note this particular paragraph:
Suddenly, the HGP found itself challenged by sequencing plans from the private sector. In May 1998, TIGR's Venter announced he would partner with Michael Hunkapiller's company Applied Biosystems (ABI), a division of Perkin-Elmer Corporation which manufactured sequencing machines, to form a new company which would sequence the entire genome in three short years and for a fraction of the cost. The foreseen profits rested in the construction of a “definitive” database that would outdo Genbank by integrating medical and other information with the basic sequence and polymorphisms. The company, based in Rockville, MD and later named Celera Genomics, planned to use “whole-genome shotgun” (WGS) sequencing, an approach different from the HGP's. The HGP confined the shotgun method to cloned fragments already mapped to specific chromosomal regions: these are broken down into smaller bits then amplified by bacterial clones, sequences are generated randomly by automated machines, and computational resources are used to reassemble sequence using overlapping areas of bits. Shotgunning is followed by painstaking “finishing” to fill in gaps, correct mistakes, and resolve ambiguities. What Celera was proposing for the shotgun method was to break the organism's entire genome into millions of pieces of DNA with high-frequency sound waves, sequence these pieces using hundreds of ABI's new capillary model machines, and reassemble the sequences with one of the world's largest civilian supercomputers without the assistance provided by the preliminary mapping of clones to chromosomes. When WGS sequencing was considered as a possibility by the HGP, it was rejected because of the risk that repeat sequences would yield mistakes in reassembly.[10] But Venter by this time had successfully used the technique to sequence the 1.83 million nucleotide bases of the bacterium Hemophilus influenzae—the first free-living organism to be completely sequenced—in a year's time (Fleischmann et al. 1995).[11]
But how, exactly, does WGS sequencing, and the more sophisticated variants now in use, work? There's a is HUGE volume of technical research on this topic. However, it all comes down to proprietary computational technology that somehow 'fills in gaps, corrects mistakes, and resolves ambiguities'. The details of how this works are proprietary, and the tools that do it are based on proprietary technology.

Note how the WGS approach "was rejected because of the risk that repeat sequences would yield mistakes in reassembly". HGP scientists resisted this approach because they did not fully understand how it worked and therefore did not trust it. However, once the approach had actually been proven to work, doubting scientists backed off and accepted the approach advocated by Celera Genomics. You can't argue with success.
You can argue that that doesn't mean that HGP suddendly switched over to doing WGS.
Detailed study of this topic will show that the computation process of sequencing shattered bits of DNA involves finding solutions to the Jones Polynomial. Without getting into too much math, the Jones Polynomial arises from the way that the two strands of DNA braid around each other...
No, it does no such thing. The Jones Polynomial basically tells you whether two knots are topologically equivalent. It won't tell you whether two sequences of DNA bases are identical. And DNA sequencing doesn't rely in any way on how "two strands of DNA braid around each other." The notion is ludicrous.
...and is associated with that branch of mathematics called Braid Theory . It just so happens that topological quantum computation, but not other forms of quantum computation, ALSO relies heavily on Braid Theory, and is able to solve the Jones Polynomial rapidly and accurately.
No, all models of quantum computing are computationally equivalent with no simulation of one by another requiring exponential resources. Algorithms for evaluating the Jones Polynomial exist for quantum circuit computers. Yes, they work less efficiently then they would on a topological quantum computer, but since no topological quantum computers exist yet, less efficiency is still far better than evaluating the JP on classical architecture.
None of this is proof that that topological quantum computation is involved in DNA sequencing. The details of actual DNA sequencing machines remains proprietary. Applied QNN technology is one way to explain this minor mystery.
As James Randi noted, those who use only mental power to bend spoons are doing it the hard way. "Applied QNN technology" appears to require far more resources than needed to solve the problem at hand.
The timing also perfectly fits the proposed ULTRA II timeline.
Why don't you detail "the proposed ULTRA II timeline" so we can evaluate your use of the word "perfectly" ourselves?
3. Self-piloting vehicles

The AI for self-piloting vehicles is not controversial. There are many hints that military drones were using self-piloting vehicles long before Google introduced the self-driving Prius in 2007.
Hints? The military was showing off such vehicles publicly.
Sebastian Thrun, the primary architect of Google self-driving cars and an AI professor at Stanford, explains how it works in considerable length. What Professor Thrun never mentions is what sort of underlying hardware it runs on, although he helpfully tells us that it is some sort of neural network.

These days, the standard way to make an artificial neural network is to simulate it on a computer. E.g. At an abstract level we use a Turing Machines to emulate a neural network, just as Alan Turing first wrote about in 1948.
You've got to quit throwing around the term "Turing Machine" like it's something special. That last sentence added nothing, especially when the type of neural net envisioned by Turing would be wildly inefficient and completely unable to take advantage of the properties of quantum computing that make QNNs interesting.
This approach works, but the performance of the neural network is limited by the speed of the computer emulating it. This process works, but it is not efficient. Modern computers are so fast that the neural networks they emulate are also quite fast. However, when even the best modern computers (or arrays of computers) emulate a large complicated recurrent neural network, the performance of the neural network is poor at best.

The best way to get high efficiency from a neural network is for the actual hardware to be a neural net, rather than to emulate one via computer. This is why biological neural nets generally outperform artificial neural nets, and why humans are good at things like face recognition, driving, word recognition, math & Physics, leaps of intuition, et cetera.
It's actually the massive parallelism of the brain that results in its efficiency over that of ANNs. We can't approach having a network of even just billions of artificial neurons running simultaneously, just as a practical matter.
The requirements of piloting a car are quite rigorous. The system must be aware of its surroundings, and must react very rapidly. It must drive at least as well as an average human being. Current publicly-known artificial neural network technology, even the most advanced recurrent neural networks, seem to be not sufficient to the task. The great weight of evidence suggests that there is some sort of proprietary neural network technology at work here, nature unknown.

Does this prove that QNN technology is what enabled self piloting vehicles? No! Applied QNN technology is one way to explain this minor mystery. The timing also fits the proposed ULTRA II timeline, in that this more complicated technology was developed considerably later than Quantum Associative Memory and Jones Polynomial Solving.
As Thor noted, this is again an argument from ignorance. You're proposing a "QNN of the gaps" argument, little different in style from the creationists' "god of the gaps."
While we're at it, let's review the definition of Strong AI (from Wikipedia):

Strong AI is artificial intelligence that matches or exceeds human intelligence

Self piloting vehicles work. Clearly, then, for the specific problem of driving a car, Strong AI has arrived.
No, the Wikipedia article clearly shows that outperforming a human at just a single task would be classified as "Weak AI":
Weak AI, in contrast to strong AI, does not attempt to simulate the full range of human cognitive abilities.
To be "Strong AI," a system must match or exceed a human's general intelligence.
I should have been clear about this from the beginning. I can't PROVE that my thesis is correct. I can only suggest the thesis, and point out that it answers a whole slew of difficult questions that people mostly have not bothered to ask, but are there for the asking.
"Aliens done it" explains the available evidence precisely as well.
In fact, my purpose is merely to INTRODUCE people to the ideas involved. In particular, I want people to understand the nature of the ethical predicament the project scientists faced.

I strongly suspect that, once there is a sufficient critical mass of people who understand the topic, proof will be forthcoming. I don't know in what form, or from whom it will come, but I have little doubt it will appear. Proof is pointless, however, until people understand the issues. If tomorrow someone were to leak details of the ULTRA II project, e.g. via Wikileaks or something similar, no one would know what to make of it. I mentioned in another post that I was peripherally involved in Wikileaks. The goal of the parent organization is global domination through media saturation (TM).
What parent organization would that be?
No one told me to do so, but I have taken on myself the task of teaching the world about QNN technology.
But you're not. You're just "teaching," based on apparent coincidences in timing and apparent leaps in technology (neither of which may be true, hence "apparent"), that some people are using QNNs. QNNs which, in reality, would have to be orders of magnitude larger than anything even the hype from D-Wave claims to exist.
I've chosen to start with scientific skeptics.
And I hope you're getting the sort of feedback you expected.
I'd also like to point out, again, that information on the internet is NOT sufficient to understand this topic.
The topic as you've presented it so far isn't actually QNNs, though, it's about the groups you claim are using QNNs. One need not understand the technology to see the logical flaws in your presentation.
I also wish to point out that, when any secret, censored, or sensitive topic is in question, one must be very suspicious of information one finds on the internet.
And thus you shoot your own argument in the foot, because you are presenting information on the Internet.
Because we still don't know how to build a Topological Quantum Computer (TQC)! TQC exploits topological entanglement/teleportation to perform quantum computation.

I wrote this little blurb to explain the rudiments of Topological Quantum Computation:

...The behavior of braided anyons in an FQHE liquid is described by a branch of Topology called ‘braid theory’. Braid theory is also associated with the Jones Polynomial, which is associated with DNA sequencing because a double helix is also a type of braid...
Again, that is complete nonsense. Not least because one of the very first steps in DNA sequencing is to unravel the helix.
A TQC requires precise manipulation of many anyons. An anyon-based TQC could also be described as a teleportation/entanglement based TQC. Such a system would qualify as a form of nanotechnology.
Only if all quantum computers would so qualify. You've got to stop tossing around buzzwords.
Later I will demonstrate why this strange new speculative technology is actually a new General Purpose technology, as it's not immediately obvious.
If it doesn't exist, then it's not a new GPT.
The techniques of Complex System Biology, as introduced by Stuart Kauffman, provide a plausible way to build an actual working topological QNN at the hardware level. This is because, as described in Kauffman's book "The Origins of Order: Self-Organization and Selection in Evolution" [needs a chapter and page reference!], certain 'poised' complex systems have the emergent property that they function like (are!) a neural network. This is part of the 'magic' of Complex System Biology. Anyons moving in an FQHE fluid seem to allow this sort of 'poised' complex system.
You don't seem to understand the technology very well. Or you're trying to dumb it down so much you've churned it into nonsense, like this:
Since a topological QNN can efficiently emulate a TQC...
If you have a "topological QNN," then you have a TQC by definition, no emulation requied.
Working QNN hardware is sufficient to perform topological quantum computation!
Any quantum computer can emulate a TQC, and thus perform topological quantum computation. It's merely a matter of efficiency.
We'd still need to shape the QNN for the particular task, but techniques are already known to accomplish that. There are all sorts of details, but that is the basic principle. This also tells us that, as far as definitions are concerned, QNN technology is a form of quantum teleportation based nanotechnology.
But the "definition" is nothing but buzzwords strung together.
All this could have been known to DARPA in 1991.
Because it was all public and uncensored in 1991.
But you're spitting out names of stuff you think the secret tech can do, as if we should have heard of the stuff you're naming. The terms with which I was unfamiliar I Googled, to see what those things are, not what your GPT is.
Awkward, that. Also true. You asked the question, "In 25 words what does it do". I answered in 67+ words, but I had to take the shortcut of using technical jargon. That's why I said, right from the start, that these ideas are very difficult to describe without learning a lot of new vocabulary. Sorry about that, but I don't see a way around it. That's exactly the problem I'm learning how to overcome by posting here. That's one reason I need help. As I said at the beginning, this topic is too big for me!
Again: the jargon isn't at all helpful if it doesn't lead anywhere. Perhaps you need to take another couple of years and teach yourself pedagogy.
OK, so we have clear proof of Strong AI in the fields of Driving, Math, and Physics.
No, we have no such thing according to the full Wikipedia definition you partially quoted.
How many such fields does it take before we officially acknowledge the existence of 'strong' artificial intelligence?
According to Wikipedia, all of them.
This article, published by the Royal Society, explains why the topological approach to quantum computation is technically superior to other approaches:

http://rspa.royalsocietypublishing.org/content/464/2089/1.full
Repeating yourself won't make your claim become true. You need to stop leaving off the qualifier, "for particular problems," as you later acknowledged to be true.
Given that one author consults for WolframAlpha, and also had the same University doctoral thesis adviser as my friend 'David', I am 99.99% sure he is in the NDA club for QNN technology.
Who is in or out of that alleged club is irrelevant. Since many scientists, mathematicians and engineers collaborate widely, playing Six Degrees of Steven Wolfram is unlikely to shed any light on anything. Its pointlessness is epitomized by the existence of Paul Erdös.
I've already described enough applications to qualify as a new General Technology.
Under what definition of "general technology?"

- Dave W. (Private Msg, EMail)
Evidently, I rock!
Why not question something for a change?
Visit Dave's Psoriasis Info, too.
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