Understanding Digital Computers
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This is because brains and computers work very differently. Both compute, but only one understands--and there are some very compelling reasons to believe that this is not going to change. It appears that there is a more technical obstacle that stands in the way of Strong A. All digital computers are binary systems. This means that they store and process information exclusively in terms of two states, which are represented by different symbols--in this case 1s and 0s.
It is an interesting fact of nature that binary digits can be used to represent most things; like numbers, letters, colors, shapes, images, and even audio with near perfect accuracy.
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This two-symbol system is the foundational principle that all of digital computing is based upon. Everything a computer does involves manipulating two symbols in some way. As such, they can be thought of as a practical type of Turing machine --an abstract, hypothetical machine that computes by manipulating symbols. A Turing machine's operations are said to be "syntactical", meaning they only recognize symbols and not the meaning of those symbols--i. Even the word "recognize" is misleading because it implies a subjective experience, so perhaps it is better to simply say that computers are sensitive to symbols, whereas the brain is capable of semantic understanding.
It does not matter how fast the computer is, how much memory it has, or how complex and high-level the programming language. The Jeopardy and Chess playing champs Watson and Deep Blue fundamentally work the same as your microwave. Put simply, a strict symbol-processing machine can never be a symbol-understanding machine. The influential philosopher John Searle has cleverly depicted this fact by analogy in his famous and highly controversial " Chinese Room Argument ", which has been convincing minds that "syntax is not sufficient for semantics" since it was published in And although some esoteric rebuttals have been put forth the most common being the "Systems Reply" , none successfully bridge the gap between syntax and semantics.
But even if one is not fully convinced based on the Chinese Room Argument alone, it does not change the fact that Turing machines are symbol manipulating machines and not thinking machines, a position taken by the great physicist Richard Feynman over a decade earlier. Feynman described the computer as "A glorified, high-class, very fast but stupid filing system," managed by an infinitely stupid file clerk the central processing unit who blindly follows instructions the software program.
Here the clerk has no concept of anything--not even single letters or numbers. In a famous lecture on computer heuristics , Feynman expressed his grave doubts regarding the possibility of truly intelligent machines, stating that, "Nobody knows what we do or how to define a series of steps which correspond to something abstract like thinking. These points present very compelling reasons to believe that we may never achieve Strong A.
Perhaps even the most accurate of brain simulations will not yield minds, nor will software programs produce consciousness. It just might not be in the cards for a strict binary processor. There is nothing about processing symbols or computation that generates subjective experience or psychological phenomena like qualitative sensations. Upon hearing this, one might be inclined to ask, "If a computer can't be conscious, then how can a brain? It even uses electrical activity to process information, just like a computer. Yet somehow we experience the world subjectively--from a first person perspective where inner, qualitative and ineffable sensations occur that are only accessible to us.
Take for example the way it feels when you see a pretty girl, drink a beer, step on a nail, or hear a moody orchestra. The truth is, scientists are still trying to figure all this out. Even neuroscientist and popular author Sam Harris--who shares Musk's robot-rebellion concerns--acknowledges the hard problem when stating that whether a machine could be conscious is "an open question".
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Unfortunately he doesn't seem to fully realize that for machines to pose an existential threat arising from their own self-interests, conscious is required. Yet although the problem of consciousness is admittedly hard, there is no reason to believe that it is not solvable by science. So what kind of progress have we made so far? Consciousness Is A Biological Phenomenon Much like a computer, neurons communicate with one another through exchanging electrical signals in a binary fashion.
Either a neuron fires or it doesn't, and this is how neural computations are carried out. But unlike digital computers, brains contain a host of analogue cellular and molecular processes, biochemical reactions, electrostatic forces, global synchronized neuron firing at specific frequencies, and unique structural and functional connections with countless feedback loops. Even if a computer could accurately create a digital representation of all these features, which in itself involves many serious obstacles, a simulation of a brain is still not a physical brain. There is a fundamental difference between the simulation of a physical process and the physical process itself.
This may seem like a moot point to many machine learning researchers, but when considered at length it appears anything but trivial.
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In other words, the hardware of the "machine" matters, and mere digital representations of biological mechanisms have no power to cause anything to happen in the real world. Let's consider another biological phenomenon, like photosynthesis. Photosynthesis refers to the process by which plants convert light into energy. This process requires specific biochemical reactions only viable given a material that has specific molecular and atomic properties.
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A perfect computer simulation--an emulation--of photosynthesis will never be able to convert light into energy no matter how accurate, and no matter what type of hardware you provide the computer with. However, there are in fact artificial photosynthesis machines. These machines do not merely simulate the physical mechanisms underlying photosynthesis in plants, but instead duplicate, the biochemical and electrochemical forces using photoelectrochemical cells that do photocatalytic water splitting.
All these processes have a name. Taking in information is called input , storing information is better known as memory or storage , chewing information is also known as processing , and spitting out results is called output. Imagine if a computer were a person. Suppose you have a friend who's really good at math. She is so good that everyone she knows posts their math problems to her. Each morning, she goes to her letterbox and finds a pile of new math problems waiting for her attention. She piles them up on her desk until she gets around to looking at them.
Each afternoon, she takes a letter off the top of the pile, studies the problem, works out the solution, and scribbles the answer on the back.
She puts this in an envelope addressed to the person who sent her the original problem and sticks it in her out tray, ready to post. Then she moves to the next letter in the pile. You can see that your friend is working just like a computer. Her letterbox is her input; the pile on her desk is her memory; her brain is the processor that works out the solutions to the problems; and the out tray on her desk is her output.
Once you understand that computers are about input, memory, processing, and output, all the junk on your desk makes a lot more sense:. Artwork: A computer works by combining input, storage, processing, and output. All the main parts of a computer system are involved in one of these four processes.
As you can read in our long article on computer history , the first computers were gigantic calculating machines and all they ever really did was "crunch numbers": solve lengthy, difficult, or tedious mathematical problems. Today, computers work on a much wider variety of problems—but they are all still, essentially, calculations.
Everything a computer does, from helping you to edit a photograph you've taken with a digital camera to displaying a web page, involves manipulating numbers in one way or another. Photo: Calculators and computers are very similar, because both work by processing numbers.
However, a calculator simply figures out the results of calculations; and that's all it ever does. A computer stores complex sets of instructions called programs and uses them to do much more interesting things. Suppose you're looking at a digital photo you just taken in a paint or photo-editing program and you decide you want a mirror image of it in other words, flip it from left to right. You probably know that the photo is made up of millions of individual pixels colored squares arranged in a grid pattern.
The computer stores each pixel as a number, so taking a digital photo is really like an instant, orderly exercise in painting by numbers! To flip a digital photo, the computer simply reverses the sequence of numbers so they run from right to left instead of left to right. Or suppose you want to make the photograph brighter. All you have to do is slide the little "brightness" icon. The computer then works through all the pixels, increasing the brightness value for each one by, say, 10 percent to make the entire image brighter.
So, once again, the problem boils down to numbers and calculations.
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What makes a computer different from a calculator is that it can work all by itself. You just give it your instructions called a program and off it goes, performing a long and complex series of operations all by itself. Back in the s and s, if you wanted a home computer to do almost anything at all, you had to write your own little program to do it. For example, before you could write a letter on a computer, you had to write a program that would read the letters you typed on the keyboard, store them in the memory, and display them on the screen.
Writing the program usually took more time than doing whatever it was that you had originally wanted to do writing the letter. Pretty soon, people started selling programs like word processors to save you the need to write programs yourself. Today, most computer users rely on prewritten programs like Microsoft Word and Excel or download apps for their tablets and smartphones without caring much how they got there. Hardly anyone writes programs any more, which is a shame, because it's great fun and a really useful skill.
Most people see their computers as tools that help them do jobs, rather than complex electronic machines they have to pre-program. Some would say that's just as well, because most of us have better things to do than computer programming. Then again, if we all rely on computer programs and apps, someone has to write them, and those skills need to survive. Thankfully, there's been a recent resurgence of interest in computer programming. There's a growing hobbyist movement, linked to build-it yourself gadgets like the Raspberry Pi and Arduino.
And Code Clubs , where volunteers teach kids programming, are springing up all over the world. The beauty of a computer is that it can run a word-processing program one minute—and then a photo-editing program five seconds later. In other words, although we don't really think of it this way, the computer can be reprogrammed as many times as you like. This is why programs are also called software. They're "soft" in the sense that they are not fixed: they can be changed easily. By contrast, a computer's hardware —the bits and pieces from which it is made and the peripherals , like the mouse and printer, you plug into it —is pretty much fixed when you buy it off the shelf.
The hardware is what makes your computer powerful; the ability to run different software is what makes it flexible. That computers can do so many different jobs is what makes them so useful —and that's why millions of us can no longer live without them!