Re: Is a Madmax outcome coming before 2020? Thus do we need anonymity?
You seem to be a highly skilled software engineer. How does this strike you?
http://armstrongeconomics.com/2013/06/20/self-aware-artificial-intelligence/
http://armstrongeconomics.com/2013/06/20/self-aware-artificial-intelligence/
Seems everyone is using that statement about Armstrong's computer being self-aware to discredit him. So let me clarify the difference between sentient and self-aware. I have argued that Kurzweil's Singularity can not occur for as long as computers are not integrated into reproduction and evolution because the entropy added by the humans is not contained within the entropy of the self-learning computers.
Here is an excerpt:
Quote from: me
Algorithm ≠ Entropy
Proponents of the technological singularity theory cite the exponential increase in computing hardware power such as Moore's Law and recent software advances such the sophisticated Spaun artificial brain which can pass simple IQ tests and interact with its environment; also IBM's Watson computer which defeated Jeopardy and chess masters, subsequently was recently programmed to do lung cancer diagnosis more accurately than human doctors.
However, the speed of the computing hardware and the sophistication of the software has no relevance because creativity can't be expressed in an algorithm. Every possible model of the brain will lack the fundamental cause of human creativity every human brain is unique. Thus each of billions of brains is able to contemplate possibilities and scenarios differently enough so that it is more likely at least one brain will contemplate some unique idea that fits each set of possibilities at each point in time.
An algorithm or model can describe what and how to do and even be generalized to respond to unknown future scenarios by observing patterns and deducing rules about its environment, but it can't vary its imperfections nondeterministically, because the input entropy (to the algorithm) is known a priori and is finite. Whereas, for the collection of all human brains, the entropy is unbounded and thus the future can't be predetermined, i.e. isn't deterministic.
Imagine if life was perfect and without chance. Life would be deterministic and could be modeled with an algorithm, then failure couldn't exist, everything would be known in advance, and thus there could be no change that wasn't predictable, i.e. real change wouldn't exist and the universe would be static. Life requires imperfection and unbounded diversity, else life doesn't exist and isn't alive. Equality and perfection are the ambition of the insane who probably don't realize they must destroy life to reach their goal.
Thus the theory that it would be impossible to predict what computers would contemplate is nonsense because the input entropy of the models of the brain will always be finite and deterministic from the time the input entropy is varied.
Pseudo-random number generators are deterministic from the time the seed is changed. Even dynamically capturing entropy from the changing content of the internet would be deterministic from each moment of capture to the next, and the model of capture would be lacking diversity and static (only modified by a human).
Thought Isn't Fungible
To make the computers as creative as the humans would require inputting the entropy from all the human brains. Yet there is no plausible way to extract the future uniqueness of human brains other than to allow them interact with the environment over unbounded time, because the occurrence of creativity is probablistic (by chance) as the dynamic diversity of human minds interact with the changing environment. The term unbounded means there is no way to observe or capture that uniqueness a priori other than through the future of life as it unfolds.
Inmates can be forced to do manual labor because it is possible to observe the performance of the menial tasks. However, it is impossible (or at least very inefficient and imprecise) to determine whether a human is feigning inability or giving best effort at a knowledge task. Manual labor is fungible, i.e. nearly any person with average IQ and dexterous limbs can be substituted to do the task. Whereas, knowledge production such as programming the computer, authoring content or developing marketing plans, requires diversity of thought.
Proponents of the technological singularity theory cite the exponential increase in computing hardware power such as Moore's Law and recent software advances such the sophisticated Spaun artificial brain which can pass simple IQ tests and interact with its environment; also IBM's Watson computer which defeated Jeopardy and chess masters, subsequently was recently programmed to do lung cancer diagnosis more accurately than human doctors.
However, the speed of the computing hardware and the sophistication of the software has no relevance because creativity can't be expressed in an algorithm. Every possible model of the brain will lack the fundamental cause of human creativity every human brain is unique. Thus each of billions of brains is able to contemplate possibilities and scenarios differently enough so that it is more likely at least one brain will contemplate some unique idea that fits each set of possibilities at each point in time.
An algorithm or model can describe what and how to do and even be generalized to respond to unknown future scenarios by observing patterns and deducing rules about its environment, but it can't vary its imperfections nondeterministically, because the input entropy (to the algorithm) is known a priori and is finite. Whereas, for the collection of all human brains, the entropy is unbounded and thus the future can't be predetermined, i.e. isn't deterministic.
Imagine if life was perfect and without chance. Life would be deterministic and could be modeled with an algorithm, then failure couldn't exist, everything would be known in advance, and thus there could be no change that wasn't predictable, i.e. real change wouldn't exist and the universe would be static. Life requires imperfection and unbounded diversity, else life doesn't exist and isn't alive. Equality and perfection are the ambition of the insane who probably don't realize they must destroy life to reach their goal.
Thus the theory that it would be impossible to predict what computers would contemplate is nonsense because the input entropy of the models of the brain will always be finite and deterministic from the time the input entropy is varied.
Pseudo-random number generators are deterministic from the time the seed is changed. Even dynamically capturing entropy from the changing content of the internet would be deterministic from each moment of capture to the next, and the model of capture would be lacking diversity and static (only modified by a human).
Thought Isn't Fungible
To make the computers as creative as the humans would require inputting the entropy from all the human brains. Yet there is no plausible way to extract the future uniqueness of human brains other than to allow them interact with the environment over unbounded time, because the occurrence of creativity is probablistic (by chance) as the dynamic diversity of human minds interact with the changing environment. The term unbounded means there is no way to observe or capture that uniqueness a priori other than through the future of life as it unfolds.
Inmates can be forced to do manual labor because it is possible to observe the performance of the menial tasks. However, it is impossible (or at least very inefficient and imprecise) to determine whether a human is feigning inability or giving best effort at a knowledge task. Manual labor is fungible, i.e. nearly any person with average IQ and dexterous limbs can be substituted to do the task. Whereas, knowledge production such as programming the computer, authoring content or developing marketing plans, requires diversity of thought.
Armstrong claims his computer was capable of learning from and be aware of its environment and took evasive action when its security was threatened. This is entirely possible as we know for example that A.I. neural networks can distill patterns from a series of inputs. However what is not possible is for the computer to have feelings about its relatives, nor be able to come up with an innovative idea that wasn't contained within its input entropy. The input entropy of the set of all humans is not only the environmental input since birth, rather it also includes the entire evolutionary history of that particular ancestry path via the DNA. One thing I forget to put in my linked essay is that the known genome is not finite. There resolution of the DNA is not limited to the finite genes, instead there are finer grained elements within the genome which we are either already aware of or will discover.
When we think about the interaction of the environment with the collective genome, we need to pay attention to my point about path dependencies and why no two events can ever be exactly identical (although cycles can have general resemblance).
So computers will do amazing things that seem almost sentient, but they will never replace humans unless they are able to reproduce biologically with the randomness (entropy) of the infinite encoding in the genome.
http://unenumerated.blogspot.com/2011/01/singularity.html
Quote from: Nick Szabo
The Singularitarian notion of an all-encompassing or "general" intelligence flies in the face of how our modern economy, with its extreme specialization, works. We have been implementing human intelligence in computers little bits and pieces at a time, and this has been going on for centuries. First arithmetic (first with mechanical calculators), then bitwise Boolean logic (from the early parts of the 20th century with vacuum tubes), then accounting formulae and linear algebra (big mainframes of the 1950s and 60s), typesetting (Xerox PARC, Apple, Adobe, etc.), etc. etc. have each gone through their own periods of exponential and even super-exponential growth. But it's these particular operations, not intelligence in general, that exhibits such growth.
...
Another way to look at the limits of this hypothetical general AI is to look at the limits of machine learning. I've worked extensively with evolutionary algorithms and other machine learning techniques. These are very promising but are also extremely limited without accurate and complete simulations of an environment in which to learn. So for example in evolutionary techniques the "fitness function" involves, critically, a simulation of electric circuits (if evolving electric circuits), of some mechanical physics (if evolving simple mechanical devices or discovering mechanical laws), and so on.
These techniques only can learn things about the real world to the extent such simulations accurately simulate the real world, but except for extremely simple situations (e.g. rediscovering the formulae for Kepler's laws based on orbital data, which a modern computer with the appropriate learning algorithm can now do in seconds) the simulations are usually very woefully incomplete, rendering the results usually useless. For example John Koza after about 20 years of working on genetic programming has discovered about that many useful inventions with it, largely involving easily simulable aspects of electronic circults. And "meta GP", genetic programming that is supposed to evolve its own GP-implementing code, is useless because we can't simulate future runs of GP without actually running them. So these evolutionary techniques, and other machine learning techniques, are often interesting and useful, but the severely limited ability of computers to simulate most real-world phenomena means that no runaway is in store, just potentially much more incremental improvements which will be much greater in simulable arenas and much smaller in others, and will slowly improve as the accuracy and completeness of our simulations slowly improves.
Nor does the human mind, as flexible as it is, exhibit much in the way of some universal general intelligence. Many machines and many other creatures are capable of sensory, information-processing, and output feats that the human mind is quite incapable of. So even if we in some sense had a full understanding of the human mind (and it is information theoretically impossible for one human mind to fully understand even one other human mind), or could somehow faithfully "upload" a human mind to a computer (another entirely conjectural operation, which may require infeasible simulations of chemistry), we would still not have "general" intelligence, again if such a thing even exists.
That's not to say that many of the wide variety of techniques that go under the rubric "AI" are not or will not be highly useful, and may even lead to accelerated economic growth as computers help make themselves smarter. But these will turn into S-curves as they approach physical limits and the idea that this growth or these many and varied intelligences are in any nontrivial way "singular" is very wrong.
...
Another way to look at the limits of this hypothetical general AI is to look at the limits of machine learning. I've worked extensively with evolutionary algorithms and other machine learning techniques. These are very promising but are also extremely limited without accurate and complete simulations of an environment in which to learn. So for example in evolutionary techniques the "fitness function" involves, critically, a simulation of electric circuits (if evolving electric circuits), of some mechanical physics (if evolving simple mechanical devices or discovering mechanical laws), and so on.
These techniques only can learn things about the real world to the extent such simulations accurately simulate the real world, but except for extremely simple situations (e.g. rediscovering the formulae for Kepler's laws based on orbital data, which a modern computer with the appropriate learning algorithm can now do in seconds) the simulations are usually very woefully incomplete, rendering the results usually useless. For example John Koza after about 20 years of working on genetic programming has discovered about that many useful inventions with it, largely involving easily simulable aspects of electronic circults. And "meta GP", genetic programming that is supposed to evolve its own GP-implementing code, is useless because we can't simulate future runs of GP without actually running them. So these evolutionary techniques, and other machine learning techniques, are often interesting and useful, but the severely limited ability of computers to simulate most real-world phenomena means that no runaway is in store, just potentially much more incremental improvements which will be much greater in simulable arenas and much smaller in others, and will slowly improve as the accuracy and completeness of our simulations slowly improves.
Nor does the human mind, as flexible as it is, exhibit much in the way of some universal general intelligence. Many machines and many other creatures are capable of sensory, information-processing, and output feats that the human mind is quite incapable of. So even if we in some sense had a full understanding of the human mind (and it is information theoretically impossible for one human mind to fully understand even one other human mind), or could somehow faithfully "upload" a human mind to a computer (another entirely conjectural operation, which may require infeasible simulations of chemistry), we would still not have "general" intelligence, again if such a thing even exists.
That's not to say that many of the wide variety of techniques that go under the rubric "AI" are not or will not be highly useful, and may even lead to accelerated economic growth as computers help make themselves smarter. But these will turn into S-curves as they approach physical limits and the idea that this growth or these many and varied intelligences are in any nontrivial way "singular" is very wrong.
On a separate topic, I see the national security agencies are now using the ISIS false flag operation (the west funded and armed them, e.g. to overthrow Syria) to justify their abrogation of our basic rights. These fuckers will never stop the encroaching Police state until we make them impotent technologically. Political solutions won't work until after society crashes and burns, because people are complacent until then.
Note the rise in the religious war was coming now due to the natural cycle. Nevertheless, our western governments are leveraging this (sowing chaos) to justify abrogating our human rights. The only way to take back what is ours, is to make the governments impotent with technology that empowers the individual. Political solutions will always end up owned by the elite, e.g. the coming IMF globalized monetary unit after 2024.
 | http://blog.mpettis.com/2014/11/china-europe-and-optimal-currency-zones/#comment-94248 Quote from: me Suvy, your assay of the problems is clairvoyant, but you wont get solutions politically in time, at least not on the national scope. Instead we will crash and burn first. Another much more severe global contagion will ensue in 2016. The near-term future of the youth is the internet and an internet monetary system controlled by no one. But Bitcoin has failed to be that unit. After 2032, you will see political solutions but your youthful demographic will get glossy-eyed and hand the power to globalized monetary fiat in return for idealistic slogans of end war which we will be very tired of by then because we will see a massive upswing of war from now until 2024 at least. For example, China and Japan are ramping up to fight in order to redirect public angst away from the failed national monetary policies. In short, the death of the nation-states and the rise of a global community. There will be a split between the first-class Knowledge workers who will go for individual sovereignty with an improved decentalized internet monetary unit and the rest who will side with a socialistic (political) fiat global reset. |
Armstrong correctly explains that the elite are not suppressing the precious metals. I had posted upthread a long explanation that the elite are not powerful enough to control the chaos in nature, rather what they do is to align themselves with creative destruction and experiment to find ways to achieve their goals of more globalized economies-of-scale for their multi-national corporations and political capture.
Armstrong is correct about the money center banks being very short-term fraud oriented. The elite who sit above the money center banks (Armstrong doesn't agree) could not further their aims for creative destruction symbiosis by trying to entirely suppress the precious metal prices. Fiat and futures markets on precious metals have sustained because the broad populace doesn't then want to use physical metal, but that is not really a manipulation rather it is what society has chosen.
The way the elite plan to obtain their goals for a global fiat is by pumping the world full of debt as they have done and then IMF will rush in with a new monetary system solution after 2024. The chaos from 2016 to 2024 will burn the confidence in the nation-states to the ground. This I argue was by-design of the long-term thinking elite such as Rothschild et al. They have aligned themselves with this natural cycle of chaos and the natural tendency of society towards socialism failure and debt.
Armstrong has apparently unlocked the math of the natural cycles. Apparently Pi (a.k.a. π = 3.1459) is the basis, which seems plausible since the circumference (= π2r, where r = radius) and area (= πr2, where r = radius) of a circle, as well all wave equations involve π.