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छूटे कोई दीन दुखी ना, सबको करो शुमार लिखो, दबी हुई चीखों को बुन कर, कर्कश इक ललकार लिखो। धरम जो पूछे शरणागत का, जात जो पूछे साधो की, हिंदुस्तानी दिल ऐसा भी हुआ नहीं लाचार लिखो। लिखने दो उनको BSE, NSE और GDP, तुम आम आदमी की जेबों में बची चवन्नी चार लिखो। इस शोर शराबे में सहमा सा सच जो तुमको मिल जाए, एक बार लिखो, दस बार लिखो, तुम उसको बारम्बार लिखो। है वक़्त अभी कुछ कहने का, है वक़्त नहीं चुप रहने का, नफरत के तूफानों में घिरते, इंसानों को प्यार लिखो। सुनो अभागा बदल गए हैं यहाँ मायने शब्दों के, देशभक्त ऐसे हैं गर तो, खुद को तुम गद्दार लिखो।

Like fashion, even scientific research is subjected to cyclical fads. Topics, techniques come into limelight and then fall out of favor when the gains plateau.  In machine learning, NNs were in vogue in early 90s when SVM came onto the scene and blew them out of water on almost every problem and then got superseded in turn by the log-linear models, CRFs and others. Then in beginning of this decade, NNs have made a tremendous come back, this time becoming the tool of choice for NLP as well. Due to their name and a very crude similarity to how the biological brain is organized, NNs tend to invite comparisons with human brain and every improvement achieved with their help tends to be seen as an step towards Artificial General Intelligence, at least in popular media and common people.  Now, what would happen if tomorrow a different method, a different paradigm of Machine Learning were start performing better than NNs? There is no reason why it could not happen AFAIK. W...