{"id":35293,"date":"2019-10-31T22:03:28","date_gmt":"2019-10-31T19:03:28","guid":{"rendered":"https:\/\/prohoster.info\/blog\/lopnul-li-puzyr-mashinnogo-obucheniya-ili-nachalo-novoj-zari\/"},"modified":"2019-10-31T22:03:28","modified_gmt":"2019-10-31T19:03:28","slug":"lopnul-li-puzyr-mashinnogo-obucheniya-ili-nachalo-novoj-zari","status":"publish","type":"post","link":"https:\/\/prohoster.info\/en\/blog\/news\/lopnul-li-puzyr-mashinnogo-obucheniya-ili-nachalo-novoj-zari","title":{"rendered":"Has the machine learning bubble burst, or is it the dawn of a new era?","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Recently released article<\/a><\/noindex>, which shows the trends in machine learning of the last few years quite well. In short: the number of startups in the field of machine learning has sharply decreased in the last two years.<\/p>\n

\"Has
\nWell then. Let's discuss whether the bubble has burst, how to move forward, and talk about where this all came from.
\n<\/a><\/noindex>
\nTo begin with, let's discuss what was the booster for this curve. Where did it come from? Probably everyone remembers the victory<\/a><\/noindex> of machine learning in 2012 at the ImageNet competition. After all, this was the first global event! But in reality, it's not that simple. The growth of the curve actually began slightly earlier. I would break it down into several key moments.<\/p>\n

    \n
  1. The year 2008 marks the emergence of the term 'big data.' Real products started to appear<\/a><\/noindex> from 2010 onwards. Big data is directly related to machine learning. Without big data, stable operation of algorithms that existed at that time is impossible. And these were not neural networks. Until 2012, neural networks were the domain of a marginal minority. However, completely different algorithms that had existed for years, if not decades, began to be used: SVM<\/a><\/noindex>(1963, 1993), Random Forest<\/a><\/noindex> (1995), AdaBoost<\/noindex> (2003),... Startups of those years were primarily focused on the automatic processing of structured data: cash registers, users, advertising, and much more.\n

    The derivative of this first wave is a set of frameworks, such as XGBoost, CatBoost, LightGBM, etc.\n<\/li>\n

  2. In 2011-2012, convolutional neural networks<\/a><\/noindex> won several image recognition competitions. Their actual use took a while longer. I would say that mass-scale meaningful startups and solutions began to appear around 2014. It took two years to digest the fact that neural networks indeed work, to create convenient frameworks that could be installed and run in a reasonable time, and to develop methods that would stabilize and speed up convergence time.\n

    Convolutional networks allowed solving machine vision tasks: image classification and object detection, recognizing objects and people, improving images, etc.<\/li>\n

  3. 2015-2017. A boom of algorithms and projects tied to recurrent networks or their analogs (LSTM, GRU, TransformerNet, etc.). Effective speech-to-text algorithms and machine translation systems emerged. Some are based on convolutional networks for feature extraction, while others leverage the ability to collect large, high-quality datasets. <\/li>\n<\/ol>\n

    \n\"Has
    \n
    \n\"Has the bubble burst? Is the hype overheated? Did they die like blockchain?\"
    \nThat's right! Tomorrow Siri will stop working on your phone, and the day after, Tesla won't distinguish a turn from a kangaroo.<\/p>\n

    Neural networks are already functioning. They are present in dozens of devices. They genuinely enable monetization, changing markets and the surrounding world. The hype looks somewhat different:<\/p>\n

    \"Has
    \n
    \nSimply put, neural networks are no longer something new. Yes, many people have inflated expectations. But a significant number of companies have learned to apply neural networks and develop products based on them. Neural networks offer new functionalities, allow for job reductions, and decrease service costs:<\/p>\n