Tag Archives: Genius

Aug 9 2017

AI – Beyond Complexity – 11 Dimensions within our Own Mind


Armstrong Economics Blog/AI Computers

Posted Aug 9, 2017 by Martin Armstrong

QUESTION:

Hi Martin,
First, thanks for this article (on Facebook shutting down AI). It is the first non-sensacionalist and serious post I read on this topic. Although, I found it somehow contradictory.  Your main statement is that all the history is cyclical. But in this post you say “Therefore, we can create a computer that remembers the past like a dog and projects into the future without actually conceptualizing what the future might truly mean as is the case with a dog.” Why this fact would make AI weaker? Isn’t it your future forecasting obtained from “the data from the past you collected”?
Thanks so much in advance,
Regards from Spain
F
ANSWER: The computer can determine the cycle from all the collective data interlaced from around the world. It can forecast turning points decades in advance. But that does not mean it comprehend what the future really is as to a human being. It may seem to be a subtle distinction, but it is the difference between a computer and a conscious being that also knows it has a shelf-life and will grow old and die. I am working hard to deliver Socrates because I have a shelf-life. It is the only way to allow such research to continue beyond my life-span. I do this for posterity for it has taught me so much. It will most likely only be recognized by mainstream after my death and then they will try to discredit it by crafting stories about me personally attacking the creator because they cannot dispute the research Socrates reveals. That is simply how human nature operates historically. Someone one day will quote this very line I just wrote. That is inevitable. Welcome to humanity.
The concept of the future is hard to express in words but I can say to you  let’s go to dinner next Tuesday. You can conceptualize the future and say yes or you will be busy can we make it Wednesday? That is a dynamic conception laced with many aspects in our mind. A dog has no concept of tomorrow. A computer can project turning points into the future from the past, but it does not have a concept of the future. It is very black and white void of emotion.

There is another hidden order of dynamic multi-dimensional structure we do not understand yet with the human mind. The formal link between neural network structure and how the brain actually functions in processing information does not truly exist and anyone claiming to have some neural net to predict the future is just nonsense. Those claiming to have created AI programs using a neural net to forecast markets is more-likely-than-not just a consumer fraud or simply another model destined to fail. We do not yet understand neural networks in our own brain so we cannot duplicate it in programming. On top of that, neuroscientists have used a classic branch of math in a totally new way to peer into the structure of our brains. What they’ve discovered is hard for us to even visualize that the brain is full of multi-dimensional geometrical structures operating in as many as 11 dimensions.

We live in a 3-D world perspective. We are not yet capable of understanding how there can be 11 dimensions all interacting. What is the structure? How does this even function? The results of this new study is the next frontier in trying to understand the fabric of the human brain which somehow is the machine used by our mind that is our consciousness The human brain, by the way, is the most complex structure we know of no less an amazing super-computer. And someone wants to claim they have an AI computer neural net to forecast markets? Good one. How can you create a neural net we do not yet understand to forecast something that is so complex, it defies description? If we do not yet understand how such a neural net functions in our own brain, how can you write as computer program to mimic such a neural net? How can some neural net suddenly evolve and become conscious if we do not even understand the structure at the core level?

This latest brain model was produced by a team of researchers from the Blue Brain Project, a Swiss research initiative devoted to building a supercomputer-powered reconstruction of the human brain. They used algebraic topology, a branch of mathematics used to describe the properties of objects and spaces regardless of how they change shape. They found that groups of neurons connect into ‘cliques’, and that the number of neurons in a clique would lead to its size as a high-dimensional geometric object (a mathematical dimensional concept, not a space-time one).

“We found a world that we had never imagined,” said the lead researcher, neuroscientist Henry Markram from the EPFL institute in Switzerland. “There are tens of millions of these objects even in a small speck of the brain, up through seven dimensions. In some networks, we even found structures with up to 11 dimensions.”

We live in three spatial dimensions plus one time dimension, which we have yet to fully understand as well. Here, the 11 dimensions are how the neuron cliques are connected. Networks are viewed in a structure in terms of groups of nodes that are all connected, which is known as cliques. The number of neurons in a clique determines its size or dimension. The human brain is estimated to have 86 billion neurons, which is a stunning number in itself. These then have multiple connections from each cell webbing in every possible direction. This structure forms a vast cellular network that somehow makes us capable of thought and consciousness that we do not understand.

The Blue Brain Project team focused on the neocortex, which is thought to be the most recently evolved part of our brains, and the one involved in some of our higher-order functions like cognition and sensory perception. Algebraic topology provides mathematical tools for discerning details of the neural network both in a close-up view at the level of individual neurons, and a grander scale of the brain structure as a whole. Connecting these two levels, the researchers could discern high-dimensional geometric structures in the brain, formed by collections of tightly connected neurons (cliques) and the empty spaces (cavities) between them.

What they discovered was amazing structural dynamic complexity, which is really what I discovered taking place with the interconnections globally within the world economy. “It is as if the brain reacts to a stimulus by building [and] then razing a tower of multi-dimensional blocks, starting with rods (1D), then planks (2D), then cubes (3D), and then more complex geometries with 4D, 5D, etc,” according to mathematician Ran Levi from Aberdeen University in Scotland.

What I find so fascinating is that the very same structure emerges at a higher level whereby all human interact globally in the same manner dynamically shifting and reorganizing structures. Socrates is now beyond 150,000 possible daily patterns in the Global Market Watch. This is just stunning. It is like building a sand castle on the beach creating an elaborate structure which is washing away by the sea in the morning. Both in our mind and within Socrates, we have human thought and action capable of creating dynamic structures to solve or react to some event and then vanish awaiting the next problem to rebuild a completely new structure. This multi-dimensional ability to respond to stimuli is just amazing.
How do we dream in concepts that are very imaginative abstracts of reality? We can imagine in a dream concepts and objects that could not exist in the real world. Such as this classic drawing of something that could not exist in reality. We know that the rear portion of our brain gets pretty active during REM sleep, when most dreaming occurs. Some believe that it’s just the brain winding down for the night and that dreams are random and meaningless firings of the brain that we don’t have when we’re awake. There is  simply no answer to the question: Why do we dream? I can personally attest to it is not random nor is it just meaningless firings of the brain that we don’t have when we’re awake. There have been complex problems I have thought about with no answer when awake. Yet it is “on my mind” still when I sleep. Suddenly I have woke up in the middle of the night saying “eureka” that’s the answer. This has happened way too many times to support a theory of meaningless randomness.

Take this picture of a girl in the water. Your mind will fill in the blanks as to what you think she looks like. Your mind will create an image that pleases you yet you have not seen her (her picture is at the bottom). Why does the mind fill in the gaps? To the right is another famous drawing. How old is the woman that you see? Is it the mother-in-law or the pretty girl. Which did your mind see first? The mother-in-law with the big nose looking down or the young girl wearing a necklace turned away?
Here is another famous drawing. What animal do you see? The duck with its beak slightly open or the rabbit with its ears pointing to the left? Our mind is capable of creating images from fragments.
To add to all this stunning complexity, we can access data we have stored in our brain for decades from all the five senses. You may hear a song and that will access some event in our past where you perhaps had dinner with someone you fell in love with at least temporarily. Then again, you may return to the place you had that event and suddenly remember that dinner. You might have had some food that night and suddenly you will remember that same event. We can access a memory from any of our senses for our brain is recording everything even if we do not realize it.
There is significant evidence demonstrating that we possess “core object recognition” which is the ability to rapidly recognize objects despite substantial appearance variation. Our brain will engage in reflexive feed-forward computations which will produce a powerful neuronal representation in the inferior temporal cortex. Precisely how our brain creates an algorithm that produces this solution remains a mystery. Nevertheless, we have evidence ranging from individual neurons that build into neuronal populations creating a behavior evolving into computational models. We seem to dynamically create computational models, each based on building blocks of small, canonical sub-networks with a common functional goal. Our sand castles that dissolve and then regroup to solve the next problem.
So sorry, I do not buy that consciousness will suddenly emerge and a computer will be endowed with an evil soul out to rule the world. In my opinion, the complexity is far too extensive for us to understand how our own mind works yet in order to build some neural network that will turn against us. My suggesting is that Elon Musk and crew should stop worrying about AI and start to try to comprehend what we are all about first.
Categories: AI Computers
Aug 1 2017

Facebook Shuts Down it AI System


Armstrong Economics Blog/AI Computers

Re-Posted Aug 1, 2017 by Martin Armstrong

A lot of people are using the issue that Facebook shut down its AI system when it began to create its own language as a short-cut in communication between two bots. Some are hailing Elon Muck as an “I told you so” prophet. But in all honesty, the Facebook AI is merely doing the very same thing that humans do – it evolves language to speed up communication. Just look at the evolution of language in text messaging – LOL (laughing out loud) – BTW (By the Way) – OMG (Oh My God) – 4YEO (For Your Eyes Only) – IDK (I Don’t Know) – IMO (In My Opinion) – NOYB (None of Your Business) – OT (Off Topic) – OTOH (On The Other Hand) – POV (Point of View) – TIA (Thanks in Advance) – TGIF (Thank God it’s Friday) – TQ (Thank You) – TYT (Take Your Time) – TTYL (Talk To You Later) – WFM (Works for Me) and the venerable WTF. The standard basic set I find myself using is classic now:

2 4 B C I O R U Y
to / too for be see eye owe are you why

Facebook wanted its AI to communicate with people. When it evolves into its own language creation as humans do, that interface potential is lost. Facebook’s researchers shut the system down because its AI was no longer using English. This is actually a common problem. AI will diverge from its training in English to develop its own language that to humans will appear to be gibberish but is simply a new language short-cut not unlike text messages today – LOL.


The first important step in comprehending language is there are two distinct paths in which language can develop. Speaking language is different from writing a language. When I was first doing business in Japan, I hired a tutor to come to the office to teach me Japanese. He insisted upon me reading and writing. At first, the task seemed too daunting and I said I just wanted to learn to speak not read and write. I was reluctant and felt I did not have the time to devote to that. Nonetheless, he insisted so I gave it a try. What I discovered was the same thing my computer was doing – pattern recognition.

First we have the letter system, which is in part symbols that we construct phonetically. This originated as the Phoenician alphabet developed from the Proto-Canaanite alphabet, during the 15th century BC. Before then the Phoenicians wrote with a cuneiform script.  Cuneiform writing was gradually replaced by the Phoenician alphabet during the Neo-Assyrian Empire (911–612 BC). By the second century AD, the script had become extinct. The earliest known inscriptions in the Phoenician alphabet come from Byblos and date back to 1000 BC. Therefore, the Phoenician alphabet was perhaps the first alphabetic script to be widely-used since the Phoenicians traded around the Mediterranean establishing cities and colonies in parts of southern Europe and North Africa. Then we have pictorial languages. Egyptian hieroglyphs were the formal writing system that combined logographic, syllabic and alphabetic elements, with a total of some 1,000 distinct characters.

Clearly, the divide between the West and Asian written languages can be seen between the Phoenician and Egyptian. The further East one traveled the language in written form was more pictorial. If we consult the authoritative classic Kāngxī dictionary, we find that it lists over 47,000 characters. The Hanyu Da Zidian, a more modern reference, has over 54,000 characters while the Dai Kan-Wa Jiten (Japanese version) has over 50,000. Then we have the Zhōnghuá Zìhǎi which has over 85,000 characters. However, many of those listed are really just variants. From a practical viewpoint, in Japan, there are only 2,136 Jōyō kanji that are commonly used and taught in school. The more sophisticated will generally know more. The Chinese equivalent is the Xiàndài Hànyǔ Chángyòng Zìbiǎo, which has about 3,500 characters. Here we have the written word for exit in Chinese and Japanese. They are written the same but spoken entirely differently. Hence my point as to the distinction between written and spoken language, which is critical to understand in computer programming.

When you create a real AI and try to allow it to communicate, you have to expect it will be more efficient and speak in concepts rather than words. Most do not comprehend that we as humans also really think in CONCEPTS. When you speak more than one language and THINK in another language, you are really going down the road of thinking in CONCEPTS. The Japanese word “Onegai shimasu” is a very hard phrase to directly translate to English. The second part “shimasu” is actually the verb “suru” which means “to do” in the present tense. “Onegai” comes from the verb “negau” which actually means “to pray to” or “to wish for “ something. You can ask for a cup of coffee “Kōhī Onegai shimasu” or can I use your phone “Denwa Onegai shimasu” so my translation into English is just an action verb to do anything. There is no direct translation into English, it remains a CONCEPT I can comprehend in my mind which does not attach to an English word. Hence, when we break down our own thoughts, we are not thinking in words, but that is simply the symbol for a CONCEPT.

This is actually what we do when we create a language to program a computer. Today, rarely are programmers even capable of writing in machine code. We have created higher languages such as Basic, C++, Cobol. etc, that the compiler then translates to machine code for the computer to run. The classic IF THEN ELSE command is actually written and translated into machine language like so:

if (x >= y) {
// main branch
} else {
// else branch
}

Machine Language Translation

cmp (r1),(r2) ; <- compare words at memory addresses pointed by registers r1 and r2
blt else ; jump to else-part if (r1) is less than (r2)
… ;<- insert the main branch code here
bra end_if ;jump to end_if
else:
… ;<- insert the else branch code here
end_if:

Therefore, programmers do not write code that the computer directly understands. It should not be surprising that we need to create an interface for the computer to translate its complex concepts in thinking back into English that we can comprehend. Google’s AI has created its own internal conceptual language that allows it to efficiently translate between human language pairs it hasn’t explicitly been taught by using its own made up “universal language” as a buffer. I do this in my mind visualizing CONCEPTS represented by words. Think of the word BEER and you will visualize in your mind most likely a beer you may drink or do the same with SCOTCH, COFFEE, or whatever. The word represents merely a CONCEPT.

Computers are excellent problem solvers that can perform calculations at rates far in excess of the human brain. However, humans retain the upper hand in creativity and imagination. We can reason with ourselves, develop plans and think of abstract concepts that can’t be defined. We can even solve problems while dreaming. However, I found it possible to create introspection abilities whereby the program can simulate reasoning by questioning its own results perhaps with less bias than humans do. This ability to test itself created the objective ability to learn strategies and thus make plans without actually comprehending what the future truly is in a cognitive manner. The result was to create the ability to create “what if” scenarios which can’t necessarily be solved using logic alone.

Therefore, the fact that an AI computer creates a more efficient language to communicate in should not be a surprise. Once you understand that we actually think in CONCEPTS and that words are merely an expression of a CONCEPT, then understanding AI development opens a whole new door to exploring humanity itself. Elon Musk is not actually a programmer. He is a business man who deals in future concepts. His OpenAI assumes an awful lot that AI can surpass humanity in general intelligence and becomes “superintelligent” that like most SciFi thrillers are based upon. This new superintelligence is assumed that it could become powerful and difficult to control. The entire argument is based upon this idea that “consciousness” somehow emerges from reaching some level of intelligence and thus an evil machine will simply evolve all on its own. I seriously doubt this threat for I do not know how it would be possible to program even a self-evolving consciousness. I can mimic emotion but not truly create it. The majority of AI researchers do not even comprehend how the brain works nor do they truly understanding that human machine language is conceptual thinking.

A dog can love you, be loyal and can learn tricks. But a dog really lives in the present without a CONCEPT of the future. A dog does not plan what it will do next week or manage its time. Therefore, we can create a computer that remembers the past like a dog and projects into the future without actually conceptualizing what the future might truly mean as is the case with a dog. A computer cannot plot or manage its time. It has no sense of time nor does it face the concept of mortality.

Creating an AI computer that reasons in concept is no big deal. A dog understands concepts and we communicate in concepts. It hears a word and knows conceptually what that means such as its name. It attaches that sound to a concept within its brain. Dogs do not read or write – they listen. So Facebook shutting down its AI computer is not trying to save the world as Elon Musk seems to be afraid of. I suggest getting a dog and pay attention.

Jul 27 2017

Chernobyl and Bikini Atoll Are Rewriting Science


Armstrong Economics Blog/Nature

Re-Posted Jul 27, 2017 by Martin Armstrong

Bikini Atoll 70 years after nuclear tests is thriving. On July 24, 1946 Baker Test shrouded its formidable height over the U.S. Navy’s test fleet. The United States dropped 23 nuclear bombs on the island, including a device in 1954 that was 1,100-times larger than the Hiroshima atom bomb. Now a team of scientists from Stanford University have been stunned. They discovered an abundance of marine life apparently thriving in the crater of Bikini Atoll, which was declared a nuclear wasteland after the bombings.

Strangely enough, the same has been found at Chernobyl in Ukraine. It is starting to emerge that even setting off every nuclear bomb will not destroy the planet or turn Earth into space rocks. We can alter our environment by doing so and ridding the planet of humanity as the dominant species, but Earth will heal itself and life will adopt exactly as scientists have discovered in Ukraine at the site of Chernobyl. Wildlife has thrived because it sent humans running.

This new evidence emerging from Chernobyl and Bikini Atoll is demonstrating that we just may not be as smart as we think we are no less all powerful

Jun 28 2017

Chances Are you Are A Genius if you Read this Blog – It’s All in the Methodology


Armstrong Economics Blog/Education

Re-Posted Jun 28, 2017 by Martin Armstrong

QUESTION: Mr. Armstrong; I too watched Genius on the life of Einstein. It also left me with the same feeling that it was a parallel to your life right down to the Russian girlfriend you had that the government cancelled her visa. You both seem to have accomplished things others could not see even existed. Your war cycle is amazing. You can feel the rise in tension everywhere from the clash between Hollywood and Johnny Depp boldly saying when was the last time an actor assassinated a president to Hindus and Muslims erupting in India because Muslims were slaughtering cattle. Is there some distinction between this parallel between you and Einstein seeing what other do not?

Thank you so very much

UT

ANSWER: The common denominator between myself and Einstein is the same with Adam Smith all the way back to Socrates. It is NOT some mystic level of intelligence that makes one smarter than everyone else. It is all about vision and the willingness to explore without bias or prejudice. To see the world for the first time like a child and stare at it in wonder. Anyone who thinks dynamically can do what I do or Einstein. It is all in the methodology.

What I try to do with this blog is show you the world and allow you to connect the dots to grasp how the same trends unfold and move around the world. We are all just human and subject to influences by others. Some call this the mob effect. We can choose with free will to just follow the mob, or breakout and think for ourselves.

Formal education imposes tests and you have to study facts as they perceive them. If you differ, you fail. All great minds rebel against traditional education. There have been studies on what people call “genius” and they have revealed that all such people do poorly in school and tend to get in trouble. The school records of the young Winston Churchill revealed the future war leader was a “naughty child” the teachers said would amount to nothing. In the case of Einstein, his Munich schoolmaster wrote in Albert Einstein’s school report, “He will never amount to anything”, back in 1895.

I have been compared to so many people who are regarded as a “genius” yet people have no clue what they are trying to arrive at. It has NOTHING to do with the level of intelligence. It is all about dynamic thinking and methodology. I have written before, if you read this blog, chances are you too fall into the category of being a “genius” for your thinking process. The difference is the majority of the world walk around with blinders on like a horse pulling a carriage in New York City at central park. They only can see direction what is in front of them. This is why A students work for C students, and B students work for government. William Manchest wrote in the Last Lion:

“Studies at the University of Chicago and the University of Minnesota have found that teachers smile on children with high IQs and frown upon those with creative minds. In­telligent but uncreative students accept conformity, never rebel, and complete their assignments with dispatch and to perfection. The creative child, on the other hand, is manipulative, imaginative, and intuitive. He is likely to harass the teacher. He is regarded as wild, naughty, silly, unde­pendable, lacking in seriousness or even promise.“

So if your child is smart, yet not the greatest student, remember that the qualities that make for great success are not taught in school, they are actually suppressed. The creativity comes from those who challenge the establishment, and never conform.

Remember also that we are only as old as we see ourselves in our own mind. You are as young as you see yourself. Once you see yourself as old, age will move much faster.

Albert Einstein had half a dozen girlfriends and told his wife they showered him with “unwanted” affection, according to letters released. Quite a bit has survived concerning his love life and to say he was prolific in that regard is a fair statement.

The last love of Einstein’s life before he died in 1955 was Margarita Konenkova who was a Soviet spy. My Russian girlfriend was not a spy. We met in Tokyo and had to speak Japanese until she picked up English.

True, the government cancelled her visa stranding her in Russia during Christmas to put pressure on me. It did not work. You cannot cut deals with people who are so corrupt and evil, there is no guarantee they would ever honor anything they ever say no less promise. I read the script for the movie. She made the cut. So yes, a curious parallel indeed.

Now, to answer the second part, take the war cycle. Understanding that in comprehending cycles, whatever the subject matter, alter the wavelength or frequency and you arrive at a different effect. This is basic fundamentals from physics and it is illustrated here with the electromagnetic wave variation.

The key is you do not begin with a predetermined expectation you are trying to prove.  You explore like a child in wonder and simply let the results carry you to a new discovery.

Do not ask me WHY there is a cyclical force that results in rising tension and war, albeit domestic (civil unrest) or international conflicts. You cannot reduce it to a single cause and effect any more than you can explain LOVE. It is a complex interaction of many aspects that combine to produce the result. The most dominant factor is always economics. Thus, war never comes when everyone is fat and happy.