Who invented green computing

What are two major techniques involved in green computing? What are the three goals of green computing? What are examples of green technology? What is green computing and its advantages? What are some examples of green computing? Is Green Computing good for business? Where is green computing used? What are 5 ideas of green computing? What is power consumption in green computing? What is green coding? What are the objectives of green computing?

What are the initiatives taken in India for green computing? When was green computing invented? Who use supercomputers? Are supercomputers still used? Does NASA use supercomputers?

Which field Supercomputers are used? What are the advantages of supercomputers? Which is fastest supercomputer in the world? Which is the most powerful computer? The view that machines cannot give rise to surprises is due, I believe, to a fallacy to which philosophers and mathematicians are particularly subject. This is the assumption that as soon as a fact is presented to a mind all consequences of that fact spring into the mind simultaneously with it.

It is a very useful assumption under many circumstances, but one too easily forgets that it is false. A natural consequence of doing so is that one then assumes that there is no virtue in the mere working out of consequences from data and general principles. The nervous system is certainly not a discrete-state machine.

A small error in the information about the size of a nervous impulse impinging on a neuron, may make a large difference to the size of the outgoing impulse. It may be argued that, this being so, one cannot expect to be able to mimic the behaviour of the nervous system with a discrete-state system.

It is true that a discrete-state machine must be different from a continuous machine. But if we adhere to the conditions of the imitation game, the interrogator will not be able to take any advantage of this difference.

The situation can be made clearer if we consider some other simpler continuous machine. A differential analyser will do very well. A differential analyser is a certain kind of machine not of the discrete-state type used for some kinds of calculation. Some of these provide their answers in a typed form, and so are suitable for taking part in the game. It would not be possible for a digital computer to predict exactly what answers the differential analyser would give to a problem, but it would be quite capable of giving the right sort of answer.

Under these circumstances it would be very difficult for the interrogator to distinguish the differential analyser from the digital computer. It is not possible to produce a set of rules purporting to describe what a man should do in every conceivable set of circumstances. One might for instance have a rule that one is to stop when one sees a red traffic light, and to go if one sees a green one, but what if by some fault both appear together?

One may perhaps decide that it is safest to stop. But some further difficulty may well arise from this decision later. To attempt to provide rules of conduct to cover every eventuality, even those arising from traffic lights, appears to be impossible.

With all this I agree. From this it is argued that we cannot be machines. I shall try to reproduce the argument, but I fear I shall hardly do it justice. It seems to run something like this. But there are no such rules, so men cannot be machines.

I do not think the argument is ever put quite like this, bat I believe this is the argument used nevertheless. For we believe that it is not only true that being regulated by laws of behaviour implies being some sort of machine though not necessarily a discrete-state machine , but that conversely being such a machine implies being regulated by such laws.

However, we cannot so easily convince ourselves of the absence of complete laws of behaviour as of complete rules of conduct. There are no such laws. We can demonstrate more forcibly that any such statement would be unjustified. For suppose we could be sure of finding such laws if they existed. Then given a discrete-state machine it should certainly be possible to discover by observation sufficent about it to predict its future behaviour, and this within a reasonable time, say a thousand years.

But this does not seem to be the case. I have set up on the Manchester computer a small programme using only units of storage, whereby the machine supplied with one sixteen figure number replies with another within two seconds.

I would defy anyone to learn from these replies sufficient about the programme to be able to predict any replies to untried values.

I assume that the reader is familiar with the idea of extra-sensory perception, and the meaning of the four items of it, viz. These disturbing phenomena seem to deny all our usual scientific ideas.

How we should like to discredit them! Unfortunately the statistical evidence, at least for telepathy, is overwhelming. It is very difficult to rearrange one's ideas so as to fit these new facts in. Once one has accepted them it does not seem a very big step to believe in ghosts and bogies. The idea that our bodies move simply according to the known laws of physics, together with some others not yet discovered but somewhat similar, would be one of the first to go.

This argument is to my mind quite a strong one. One can say in reply that many scientific theories seem to remain workable in practice, in spite of clashing with E. This is rather cold comfort, and one fears that thinking is just the kind of phenomenon where E. A more specific argument based on E. The machine can only guess at random, and perhaps gets right, so the interrogator makes the right identification.

Suppose the digital computer contains a random number generator. Then it will be natural to use this to decide what answer to give. But then the random number generator will be subject to the psycho-kinetic powers of the interrogator. Perhaps this psycho-kinesis might cause the machine to guess right more often than would be expected on a probability calculation, so that the interrogator might still be unable to make the right identification.

On the other hand, he might be able to guess right without any questioning, by clairvoyance. With E. If telepathy is admitted it will be necessary to tighten our test up. The situation could be regarded as analogous to that which would occur if the interrogator were talking to himself and one of the competitors was listening with his ear to the wall. The reader will have anticipated that I have no very convincing arguments of a positive nature to support my views.

If I had I should not have taken such pains to point out the fallacies in contrary views. Such evidence as I have I shall now give. Let us return for a moment to Lady Lovelace's objection, which stated that the machine can only do what we tell it to do. Another simile would be an atomic pile of less than critical size: an injected idea is to correspond to a neutron entering the pile from without. Each such neutron will cause a certain disturbance which eventually dies away.

If, however, the size of the pile is sufficiently increased, the disturbance caused by such an incoming neutron will very likely go on and on increasing until the whole pile is destroyed. Is there a corresponding phenomenon for minds, and is there one for machines? There does seem to be one for the human mind. An idea presented to such a mind will on average give rise to less than one idea in reply.

A smallish proportion are super-critical. Animals minds seem to be very definitely sub-critical. In considering the functions of the mind or the brain we find certain operations which we can explain in purely mechanical terms. This we say does not correspond to the real mind: it is a sort of skin which we must strip off if we are to find the real mind.

But then in what remains we find a further skin to be stripped off, and so on. In the latter case the whole mind is mechanical. It would not be a discrete-state machine however. We have discussed this. These last two paragraphs do not claim to be convincing arguments. But what can we say in the meantime? What steps should be taken now if the experiment is to be successful? As I have explained, the problem is mainly one of programming.

Advances in engineering will have to be made too, but it seems unlikely that these will not be adequate for the requirements. Estimates of the storage capacity of the brain vary from 10 10 to 10 15 binary digits. I incline to the lower values and believe that only a very small fraction is used for the higher types of thinking.

Most of it is probably used for the retention of visual impressions. I should be surprised if more than 10 9 was required for satisfactory playing of the imitation game, at any rate against a blind man. A storage capacity of 10 7 would be a very practicable possibility even by present techniques. It is probably not necessary to increase the speed of operations of the machines at all. Parts of modem machines which can be regarded as analogues of nerve cells work about a thousand times faster than the latter.

Our problem then is to find out how to programme these machines to play the game. At my present rate of working I produce about a thousand digits of programme a day, so that about sixty workers, working steadily through the fifty years might accomplish the job, if nothing went into the waste-paper basket.

Some more expeditious method seems desirable. In the process of trying to imitate an adult human mind we are bound to think a good deal about the process which has brought it to the state that it is in. We may notice three components,. Other experience, not to be described as education, to which it has been subjected.

Instead of trying to produce a programme to simulate the adult mind, why not rather try to produce one which simulates the child's? If this were then subjected to an appropriate course of education one would obtain the adult brain. Presumably the child-brain is something like a note-book as one buys it from the stationers.

Rather little mechanism, and lots of blank sheets. Mechanism and writing are from our point of view almost synonymous. Our hope is that there is so little mechanism in the child-brain that something like it can be easily programmed. The amount of work in the education we can assume, as a first approximation, to be much the same as for the human child. We have thus divided our problem into two parts. The child-programme and the education process.

These two remain very closely connected. We cannot expect to find a good child-machine at the first attempt. One must experiment with teaching one such machine and see how well it learns. One can then try another and see if it is better or worse. There is an obvious connection between this process and evolution, by the identifications. One may hope, however, that this process will be more expeditious than evolution.

The survival of the fittest is a slow method for measuring advantages. The experimenter, by the exercise of intelligence, should be able to speed it up. Equally important is the fact that he is not restricted to random mutations. If he can trace a cause for some weakness he can probably think of the kind of mutation which will improve it. It will not be possible to apply exactly the same teaching process to the machine as to a normal child.

It will not, for instance, be provided with legs, so that it could not be asked to go out and fill the coal scuttle. Possibly it might not have eyes. But however well these deficiencies might be overcome by clever engineering, one could not send the creature to school without the other children making excessive fun of it. It must be given some tuition.

We need not be too concerned about the legs, eyes, etc. The example of Miss Helen Keller shows that education can take place provided that communication in both directions between teacher and pupil can take place by some means or other. We normally associate punishments and rewards with the teaching process. Some simple child-machines can be constructed or programmed on this sort of principle. The machine has to be so constructed that events which shortly preceded the occurrence of a punishment-signal are unlikely to be repeated, whereas a reward-signal increased the probability of repetition of the events which led up to it.

These definitions do not presuppose any feelings on the part of the machine. I have done some experiments with one such child-machine, and succeeded in teaching it a few things, but the teaching method was too unorthodox for the experiment to be considered really successful.

The use of punishments and rewards can at best be a part of the teaching process. Roughly speaking, if the teacher has no other means of communicating to the pupil, the amount of information which can reach him does not exceed the total number of rewards and punishments applied. If these are available it is possible to teach a machine by punishments and rewards to obey orders given in some language, e. The use of this language will diminish greatly the number of punishments and rewards required.

Opinions may vary as to the complexity which is suitable in the child machine. One might try to make it as simple as possible consistently with the general principles. The propositions would have various kinds of status, e. Another such fact might be,. The processes of inference used by the machine need not be such as would satisfy the most exacting logicians.

There might for instance be no hierarchy of types. But this need not mean that type fallacies will occur, any more than we are bound to fall over unfenced cliffs.

The imperatives that can be obeyed by a machine that has no limbs are bound to be of a rather intellectual character, as in the example doing homework given above. Important amongst such imperatives will be ones which regulate the order in which the rules of the logical system concerned are to be applied. For at each stage when one is using a logical system, there is a very large number of alternative steps, any of which one is permitted to apply, so far as obedience to the rules of the logical system is concerned.

These choices make the difference between a brilliant and a footling reasoner, not the difference between a sound and a fallacious one. The idea of a learning machine may appear paradoxical to some readers. How can the rules of operation of the machine change? They should describe completely how the machine will react whatever its history might be, whatever changes it might undergo.

The rules are thus quite time-invariant. This is quite true. The explanation of the paradox is that the rules which get changed in the learning process are of a rather less pretentious kind, claiming only an ephemeral validity.

The reader may draw a parallel with the Constitution of the United States. An important feature of a learning machine is that its teacher will often be very largely ignorant of quite what is going on inside, although he may still be able to some extent to predict his pupil's behaviour.

This should apply most strongly to the later education of a machine arising from a child-machine of well-tried design or programme. This is in clear contrast with normal procedure when using a machine to do computations: one's object is then to have a clear mental picture of the state of the machine at each moment in the computation. This object can only be achieved with a struggle.

Most of the programmes which we can put into the machine will result in its doing something that we cannot make sense of at all, or which we regard as completely random behaviour. Intelligent behaviour presumably consists in a departure from the completely disciplined behaviour involved in computation, but a rather slight one, which does not give rise to random behaviour, or to pointless repetitive loops.

The reader should reconcile this with the point of view on pp. Processes that are learnt do not produce a hundred per cent. It is probably wise to include a random element in a learning machine see p.

A random element is rather useful when we are searching for a solution of some, problem. Suppose for instance we wanted to find a number between 50 and which was equal to the square of the sum of its digits, we might start at 51 then try 52 and go on until we got a number that worked.

Alternatively we might choose numbers at random until we got a a good one. This method has the advantage that it is unnecessary to keep track of the values that have been tried, but the disadvantage that one may try the same one twice, but this is not very important if there are several solutions.

The systematic method has the disadvantage that there may be an enormous block without any solutions in the region which has to be investigated first. Now the learning process may be regarded as a search for a form of behaviour which will satisfy the teacher or some other criterion. Since there is probably a very large number of satisfactory solutions the random method seems to be better than the systematic. It should be noticed that it is used in the analogous process of evolution.

But there the systematic method is not possible. How could one keep track of the different genetical combinations that had been tried, so as to avoid trying them again? We may hope that machines will eventually compete with men in all purely intellectual fields. But which are the best ones to start with? Even this is a difficult decision. Many people think that a very abstract activity, like the playing of chess, would be best.

It can also be maintained that it is best to provide the machine with the best sense organs that money can buy, and then teach it to understand and speak English. This process could follow the normal teaching of a child. Things would be pointed out and named, etc. Again I do not know what the right answer is, but I think both approaches should be tried. We can only see a short distance ahead, but we can see plenty there that needs to be done. But this may not be a real restriction on His powers, but only a result of the fact that men's souls are immortal, and therefore indestructible.

But the logical system will not have to be learnt. Samuel Butler , Erewhon , London , Apple has a rigorous program to ensure the safety of chemicals used in our products. Climate change is a defining issue of our time. Our goal is to achieve complete carbon neutrality by Below are detailed updates on our progress. We hold ourselves and our suppliers to the highest standards for labor and human rights protections, health and safety in the workplace, environmental practices, and the responsible sourcing of materials.

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