Information theory
A mathematics of information itself. Claude Shannon showed that any message can be measured in bits, that a communication channel corrupted by noise still has an exact maximum error-free rate — its capacity — and that with the right coding you can transmit right up to that limit with an arbitrarily small error rate.
गहरा अभिलेखागार फ़िलहाल अंग्रेज़ी में लिखा है — सत्यापित अनुवाद रोडमैप का हिस्सा हैं। आपके ब्राउज़र की अनुवाद सुविधा इस पृष्ठ पर अच्छी तरह काम करती है।
✦ अरे, सच में?
Shannon's 1948 paper put the word "bit" — binary digit, coined by his Bell Labs colleague John Tukey — into print for the first time, and proved something that sounds impossible: over a channel scrambled by random noise, you can send data with an error rate as close to zero as you like, provided you stay below the channel's capacity. Every error-correcting code since — in deep-space probes, hard drives, QR codes and mobile phones — lives inside that one theorem.
यह क्या है
Information theory is the study of how much information a message carries and how reliably it can be sent. Its central quantity is entropy: a message is informative to the degree that it is surprising, and Shannon measured that surprise in bits. Content that you could have predicted — a redundant letter, a repeated word — carries little information; content you could not predict carries a lot. From this one idea Shannon derived hard limits: the smallest number of bits a source can be compressed to, and the greatest rate at which those bits can be pushed through a noisy channel without error. Crucially, information theory ignores meaning entirely — it counts only the statistics of the symbols, which is exactly why it applies equally to speech, images, DNA, and machine code.
यह क्यों महत्वपूर्ण था
Before 1948, engineers built communication systems by intuition and rule of thumb, with no way to say what was even possible. Shannon gave the field its physics: exact, provable bounds on compression and on error-free transmission, and the shocking result that noise sets a *ceiling on rate, not on accuracy* — below capacity, errors can be driven arbitrarily low. That reframed communication and storage as coding problems with known optimal answers, and it supplied the common currency, the bit, in which all information could be measured and traded regardless of its form.
इसने क्या संभव किया
Information theory is the shared root of data compression (ZIP, JPEG, MP3), error correction (from Voyager's signals to every hard drive and Wi-Fi link), cryptography, and the modern understanding of channel capacity that governs how fast any wire, fibre, or radio band can carry data. It gave computing and communication a unified quantitative foundation — the bit as a universal unit — and its concept of entropy reaches into physics, statistics, biology, and machine learning. Together with Boolean logic and the stored-program computer, it completes the theoretical trio underneath the digital age.
न्यूनतम कार्यक्षम संस्करण
Entropy: the average number of yes/no questions you need to pin down a message. A fair coin flip carries exactly one bit; a heavily biased coin carries less, because you can usually guess it. This one measure — bits of surprise — is the foundation the rest is built on.
इस प्रविष्टि का पूरा वृत्तांत अभी प्रतीक्षित है — मानचित्रकार काम पर हैं। ग्राफ़ में इसका स्थान पहले ही सत्यापित है।
इसके लिए चाहिए
स्रोत
- — Claude E. Shannon, 'A Mathematical Theory of Communication,' *Bell System Technical Journal* (1948)
- — Claude E. Shannon & Warren Weaver, *The Mathematical Theory of Communication* (1949)
- — James Gleick, *The Information: A History, a Theory, a Flood* (2011)
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