Why can not we be us robots who have take over?
(Robots based on hydrocarbon technology instead of silicon and metal) :scratchin:
Ether way will be posible but short lived an becom extinct.!!!
Why can not we be us robots who have take over?
(Robots based on hydrocarbon technology instead of silicon and metal) :scratchin:
Connection machines (often called neural networks but I strongly dislike that misleading term) LEARN how to solve problems similar to those the were taught with. For example set of old bank loan applications (data about the person applying) and the subsequent late payment and default data on each. Then they were more accurate at predicting which new loan application to grant than the bank's human loan officers were.Computers and any type of AI is only capable of doing predetermined actions. And I believe that a human writes in the code it will do nothing more. AI is never will exist.
Connection machines (often called neural networks but I strongly dislike that misleading term) LEARN how to solve problems similar to those the were taught with. For example set of old bank loan applications (data about the person applying) and the subsequent late payment and default data on each. Then they were more accurate at predicting which new loan application to grant than the bank's human loan officers were.
They can learn to solve other problems and the weight of the various internal connections they have developed in learning phase can be examined by humans. Many times the human can not understand how the connection machine "thinks" better than he can to solve the problems.
To give a second example, many continuous fluid flow production process, such as paper or modern beer making have about a thousand variables, like the pH at 50 points along the flow, 50 temperature points in it, the flow rate, the drying rate, the air flow used to drying, how much bleach or hops to add and where, etc., etc. that determine the final quality of the paper or beer.
Typically some old timer, a master paper maker or brewer, had a "feel" for what needed to be adjusted as the inputs varied, but he could not tell you how he knew or what he knew and when he died, the company was in big trouble. Thus they instrumented everything they could and feed the data into a connection machine and told it the quality of the paper or beer that set of variables had produced. After a year or two of this training, the connections machine began to control these thousand or so variables but did not use exactly the same inputs the master brewer or paper maker did. Both, for example, tasted the flow at various points, but taste is hard to describe and instrument. None the less the output quality became high and more consistent. No human can control 1000 variables as well as the connection machines can and certainly can not understand how it is solving the problem.
SUMMARY: Some forms of AI are programmed by human to learn from examples, not programmed to solve some specific problems the human knows how to solve and could write code to do so. When the human does not know how to create code that will solve the problem, this is a good approach.
YOU ignorantly assume AI must be programed to solve problems, but that ceased to be the case at least 25 years ago.
100 Billion Neurons with each having 7000 synaptic connections. Think of those connections as program breaks or steps where the logic can be branched out depending on the logic. So, now you have 100 Billion arm processors or smaller each doing 7000 steps. What is so difficult about it?
Why do you say that, when in post 84 there are several examples of them learning to solve problems and control complex processes even better than humans can?The fundamental problem isn't that computers CANNOT learn, its that they cannot do it a fraction as well as we can.
Why do you say that, when in post 84 there are several examples of them learning to solve problems and control complex processes even better than humans can?
What you say is true ONLY of computers that humans supply with their processing algorithm. Perhaps you do not realize there is a whole class of computer which are supplied only with a learning algorithm (and some sample data to learn from). Did you read post 84? It seems not or your understanding is very limited.
An AI cannot have an original thought, all it's responses have to be pre-programed into it.
And something many fail to see is that a great deal of human innovation was driven by emotion - like hunger, for example - and a machine can never be given true emotions.
Your assumption about size, weight and power needs is very wrong.... A machine whose size, weight, energy consumption, and waste is orders of magnitude greater then the human brain fails to impress me. ...
... Machines work in mathmatics. Humans work in reality. And considering that the whole idea of mathmatics is to quantify, communicate, and describe reality in a similar way to how language is meant to communicate thoughts and ideas. Of course machines would be better to do something mathmatically then human beings. ...
To Fedr808 and others who know nothing about connection computers (called “neural networks” by most) here is a greatly over simplified example:
Assume a three layer machine. (It has been proven that it can do anything a machine with more layers can.) and that:
The input layer and the internal layer each have 26 nodes. Input modes 1 thru 26 and internal layer nodes A thru Z. (Why I chose 26, but it is good to have one input node for each fact known, even if it seems to humans not to be of much importance. (If that is true the connection machine will learn that and give near zero weight to the connections between the input node corresponding to that fact and all the internal nodes.)
Let’s assume there are only two output nodes correspond to a binary decision to be made. For example grant or not to grant a loan application.
Some of the inputs will be continuous variables (for example applicants age, perhaps coded a voltage between -1 and 1 Volts) and others binary (Male vs Female, Married or not, etc. perhaps coded as either -0.5V or + 5V) There can be strong negative feedback between the two output nodes. This forces one to be nearly +1 and the other to be -1. Or the human user can just take the more positive one as the result.
Initially all connection weights are random strengths between -1 and 1. For example initially the weight on input node 12 to inter mediate node M may be -0.7452 and from 12 to K may be +0.3867 etc.
Then a simple learning rule could be: If output on a learning trial is correct, then all of the positive connections are slightly increased and negative ones made slightly more negative. The next learning trial may further increase the connection strength of some connection and reduce some of the weights that were increased by the prior trial. After many learning trial have been made (all with different data samples) the connection weight rarely change much, if at all with the next dozen or so trials. Then the connection machine computer can be used to make judgment calls on cases it has never before seen.
I.e. it can take the available data about a new loan applicant and decide if he should get the loan he is requesting. If connection machines, trained up on prior loan payment results, had be used one could be nearly sure that the current financial crisis would not exist as they would never grant loans to people very unlikely to pay back the loan. They don’t suffer from greed for loan placement fee or the smile of a pretty girl, etc. They just use loan repayment histories to decide. Note no human programed the machine to make these decisions - It learned how to decide. The very same machine (with different inputs and historic data) could learn to give advice to person 1 about marrying person 2. etc. THE MACHINE LEARNS, how to decide.
This is not just obvious, but about 25 years ago was tested by some banks with the results that the connection machine's bad loan rate was lower than human evaluated loan applications but that was too hard on the egos of the human loan decision makers so was discontinued as they did other things for the bank, not just decide about loans.
PS to fedr808:This serves as a reply to your post 91, but I need to add that connection machine are typically very small compared to a lap top. In fact 20 years ago you could buy one on a chip. Your assumption about size, weight and power needs is very wrong.
True that they only can be used for the problem you trained them up to solve. I.e. they lack human flexibility, but are extremely fast. (Millions of times faster than any human at reaching a decision and that decision can change as more data is given just as fast.) There is no long set of steps to follow as common in programmed computers. The result is essentially immediately available - sort of like a resistor summing network. In fact after trained, they can be replaced by a simple resister summing network. For this, their very light weight and immunity to radiation jamming etc. they are used by the military.
Another extremely important point is they can learn to solve problems that no human knows how to create a solution algorithm for. I gave some examples of this in post 84 with complex industrial processes like paper making and beer brewing where some batches turn out good and others not so good and no one knows why.
Your idea that connection machines "work in mathematics" is also wrong. They work in volts and current flows much like the resister network that can replace any trained machine. For convenience they are almost always designed in a regular digital computer's simulation of the physical connection machine as then there is no soldering etc. to do. The changing connection weights while they are learning can be automated; no need to replace each resistor making the node-to-node connections with another on every learning trial.
I am sure they NEVER have been applied to a mathematical problem as you seem to be assuming is their use. What would be the "training set?"
No true. Here is your original post:...
But more to the point. Well actually, you haven't really made a real point. My original post was about what needs to happen to create a computer with actual intelligence, with higher level heuristic ability. ...
My "real point" as you say, in several posts, was just to show your post 85 claim is false or at least overstated. In many limited fields they learn to solve problems better than humans do,even some problem humans can not learn how to solve well enough to write an algorithm for conventional digitial computers.{post 85, in full}: The fundamental problem isn't that computers CANNOT learn, its that they cannot do it a fraction as well as we can.
{start of post 91}:"Several examples" can be considered as statistically relevent as a thousand person sample size for a statistic for a 300+ million person society.
Can they learn to do something BETTER, possibly. ...
I.e. it can take the available data about a new loan applicant and decide if he should get the loan he is requesting. If connection machines, trained up on prior loan payment results, had be used one could be nearly sure that the current financial crisis would not exist as they would never grant loans to people very unlikely to pay back the loan.
I agree that the current economic crisis was largely caused by greedy humans; but I said that it would not have been likely to occur IF connection machines, well trained on prior loan applications and the corresponding repayment records, had made the "lend" or "not lend" decisions.Of course it would still exist. It just takes in data sets (provided by humans) and produces data (looked at by humans.) If the goal is "make me lots of money in the next year so I can get a promotion" then that data might well be used to give loans to poor credit risks - because the money made in the next year would (for the person deciding how to use the data) outweigh the risks in the next ten years of defaults.
Neural networks are great tools to analyze fuzzy data. But they are just that - tools. They do not decide economic strategies or investment options.
No true. Here is your original post:
My "real point" as you say, in several posts, was just to show your post 85 claim is false or at least overstated. In many limited fields they learn to solve problems better than humans do,even some problem humans can not learn how to solve well enough to write an algorithm for conventional digitial computers.
You now understand that*, so you have been moving the goal posts - Now demanding flexibility, heuristic ability, creativity, etc.
I have several times agreed that connection machines lack these human abilities, but had to occasionally correct new errors you made, such refuting you claims about how large, heavy, and power hungry these machines were compared to a human brain** by point out that 20 years ago you could buy a connection machine on a chip! Also you falsely claimed they were only good for "mathematical problems" and I refuted that too with examples. I even went out on a limb to state that connection machines NEVER do mathematical problems as their is no training set data for them to learn with.
I am not seeking to argue with you - I am just doing what I often do - Correct nonsense when I read it in a post. For years, I have held the unofficial title as "the Sheriff of Nonsense" and I have "arrested" dozens of others posting nonsense by clear refutation of it.
* You even admitted they could learn to be better than humans is some applications a few post back.
** The human brain may use 1/3 of the body's total energy when you are sitting a desk thinking. A modern low-energy chip uses hundreds of times less energy. It also weight about 1000 times less and is at least 500 times smaller in volume. - Three strikes and you are OUT, is the normal rule.
Learning may occur as a result of habituation or classical conditioning, seen in many animal species, or as a result of more complex activities such as play, seen only in relatively intelligent animals.[1][2] Learning may occur consciously or without conscious awareness. There is evidence for human behavioral learning prenatally, in which habituation has been observed as early as 32 weeks into gestation, indicating that the central nervous system is sufficiently developed and primed for learning and memory to occur very early on in development.[3]
Play has been approached by several theorists as the first form of learning. Children play, experiment with the world, learn the rules, and learn to interact. Vygotsky agrees that play is pivotal for children's development, since they make meaning of their environment through play.
While this article describes the different environments in which learning happens, there seems as yet to be no description of how a collection of neurons learns nor where, exactly, in this collection of neurons "memory" is located.
I agree that the current economic crisis was largely caused by greedy humans; but I said that it would not have been likely to occur IF connection machines, well trained on prior loan applications and the corresponding repayment records, had made the "lend" or "not lend" decisions.
We agree connections machines did not make those critical decisions, humans did, so now we must live with troubles excessive human greed can cause.
To distort an expression about war: "Economics it too important to be left to humans."