Bill Vorhies, editor-in-chief of Data Science Central and has many years of experience in data science and business analysis models, wrote that the most important development of artificial intelligence and deep learning in the past year was not technology, but the transformation of business models - all the giants will Its deep learning IP is open source. Undoubtedly, the "open source wave" is a major trend that can not be ignored in the field of artificial intelligence in 2016, and the most popular project is Google's deep learning platform TensorFlow. Let's start with TensorFlow, take stock of the 2016 AI open source project, and finally count the Top 50 of Github's most popular deep learning open source project. 1. Google's second generation deep learning engine TensorFlow open source In November 2015, Google's open source deep learning platform TensorFlow. In April 2016, Google launched the distributed TensorFlow. Now, TensorFlow has become one of the most popular deep learning platforms in the industry. 2. Google open source global most accurate language parser SnytaxNet On May 13, 2016, Google Research announced that SyntaxNet, the world's most accurate natural language parser, is open source. Google open source goes further. According to reports, the language understanding accuracy of Google's models trained on the platform is over 90%. SyntaxNet is an open source neural network framework running in TensoFlow that provides a natural language understanding system based on Google's disclosure of all the code needed to train a new SyntaxNet model with its own data, and Google has trained it to analyze English text. Model Paesey McParseface. Paesey McParseface is based on powerful machine learning algorithms that learn to analyze the linguistic structure of sentences and explain the function of each word in a particular sentence. Among such models, Paesey McParseface is the most accurate in the world, and Google hopes it will help researchers and developers interested in automatically extracting information, translation and other applications in natural language understanding (NLU). 3. Google launched Deep&Wide Learning, an open source deep learning API On June 29, 2016, Google launched Wide & Deep Learning and opened up the TensorFlow API. Developers are welcome to use this latest tool. At the same time open source is also the implementation of Wide & Deep Learning, as part of the TF.Learn application interface, allowing developers to train the model themselves. 4. Google open source TensorFlow automatic text summary generation model On August 25th, 2016, Google opened up a model for extracting text information and automatically generating abstracts in TensorFlow, especially for long text processing, which is very useful for automatically processing massive amounts of information. The most typical example of an automatic text summary is that the title of the news report is automatically generated. In order to make a summary, the machine learning model needs to be able to understand the document and extract important information. These tasks are very challenging for the computer, especially in the document. In case of increased length. 5. Google open source image classification tool TF-Slim, defining TensorFlow complex model On August 31, 2016, Google announced the open source TensorFlow advanced software package TF-Slim, which enables users to quickly and accurately define complex models, especially image classification tasks. Since its release, TF-Slim has grown considerably. There are many types of network layers, cost functions, and evaluation criteria. Training and evaluation models also have many convenient routines. These tools allow you to worry about the details when you are working on a large scale such as reading data in parallel or deploying models on multiple machines. In addition, Google researchers have produced the TF-Slim image model library, which provides definitions and training scripts for many widely used image classification models, all written using standard databases. TF-Slim and its components have been widely used within Google, and many upgrades have been integrated into tf.contrib.slim. 6. Google open source large-scale database, 1 billion + data, explore the limits of RNN On September 13, 2016, Google announced the open source large-scale language modeling model library. The study titled "Exploring the Limits of RNN" was launched in February this year. The open source is now more eye-catching. . Research and testing have achieved excellent results, and the open source database contains about 1 billion English words and 800,000 words, most of which are news data. This is a typical industrial study, and only a big company like Google can do it. This time open source should also play a role in the field of machine translation, speech recognition, etc. as the author hopes. 7. Google open source TensorFlow diagram to generate a model that can truly understand the image On September 23, 2016, Google announced the model of the latest version of the open source map generation system Show and Tell on TensorFlow. The system uses an encoder-decoder neural network architecture with a classification accuracy rate of 93.9%, which can generate accurate new graphs when encountering new scenes. Google said that this shows that the system can really understand the image. 8. Google open source large database, including 8 million + video On September 28, 2016, Google announced on the official blog that it would open up a video database containing 8 million Youtube video URLs, with a total video duration of 500,000 hours. Also released are video-level tags extracted from a collection of 4,800 knowledge maps. This database has a significant increase in size and coverage compared to existing video databases. For example, the more famous Sports-1M database consists of only 1 million Youtube videos and 500 sports categories. Google’s official blog says that Youtube-8M represents almost exponential growth in the number and variety of videos. 9. Google released Open Images image dataset, containing 9 million annotation images On October 1, 2016, following the release of 8 million video datasets the day before yesterday, Google released the image database Open Images, which contains 9 million annotation data, with more than 6,000 labels. Google wrote in an official blog that it is closer to real life than ImageNet, which has only 1,000 categories. For those who want to train computer vision models from scratch, this data is far enough. Just in December, Google also opened up the script for the Open Images parallel download tool, which is faster than 200 M in 5 days. 10.DeepMind open source AI core platform DeepMind Lab (with papers) On December 5, 2016, DeepMind announced the open source of its AI core platform DeepMind Lab. DeepMind Labs uploaded all the code to Github for research and research by researchers and developers. The DeepMind Lab platform integrates several different AI research areas into one environment, allowing researchers to test AI agent navigation, memory and 3D imaging capabilities. It is worth mentioning that this code also includes AlphaGO code, Google hopes to increase the openness of AI capabilities, let more developers participate in AI research, and observe whether other developers can challenge and break the record of DeepMind. 1.Facebook open source Go engine DarkForest Six months ago, Facebook opened up its Go game, DarkForest. The training code is now fully released. Github link: https://github.com/facebookresearch/darkforestGo. 2. Facebook open source text classification tool fastText, can be fast and accurate without deep learning On August 19, 2016, Facebook AI Labs (FAIR) announced the openText text analysis tool fastText. fastText can be used for both text categorization and vocabulary vector representation. The accuracy of text categorization is comparable to some commonly used deep learning tools, but it is much faster in time - the model training time is reduced from a few days to a few seconds. In addition to text categorization, fastText can also be used to learn the vector representation of words. Facebook says fastText performs much better than the most advanced morphological characterization tools such as Word2vec. 3. Facebook open source computer vision system deepmask, understanding images from pixel level (with papers and code) On August 26, 2016, Facebook announced the openmask computer vision system deepmask, which said the system can "understand objects from the pixel level." Facebook hopes that open source can accelerate the development of computer vision. However, Facebook does not use these tools in its own products. It is open source before it is implemented in specific applications. It is somewhat different from the so-called "open source". In this regard, Yann LeCun, the head of Facebook's artificial intelligence team FAIR, said that it is because FAIR is based on research that is not subject to the short-term benefits of the company, and can really promote the development of artificial intelligence technology. 4.Facebook open source AI training and test environment CommAI-env On September 27, 2016, Facebook announced the opening of the AI ​​training and test environment CommAI-env, which can be used to set up the agent in any programming language. According to reports, the CommAI-env platform is used to train and evaluate AI systems, especially AI systems that focus on communication and learning. Unlike OpenAI Gym, which can be done from intensive learning to playing games, Facebook's CommAI-env focuses on communication-based training and testing, which is to encourage developers to better create artificial intelligence that can communicate and learn. Should be the company's ten-year plan. Facebook also said that CommAI-env will continue to be updated and will compete to promote the development of AI. In terms of the AI ​​test environment, Facebook also open sourced CommNet, a model that allows neural network-based agents to interact and collaborate to develop and collaborate with CommAI-env. In December, Facebook also opened up TorchCraft, which bridges the gap between deep learning environment Torch and StarCraft, allowing researchers to use controllers to write intelligent agents that can play StarCraft games. 5.Facebook Jia Yangqing sends a message to Caffe2go, the phone can run the neural network On November 8, 2016, Caffe author and Facebook researcher Jayan Yang published a new machine learning framework, Caffe2go, on the official website, and said that it will be partially open source in the next few months. Caffe2go is smaller, faster to train, less computationally demanding, and runs on a mobile phone, making it the core technology for Facebook machine learning. OpenAI 1.OpenAI launches the agent training environment OpenAI Gym The establishment of the non-profit organization OpenAI, which was founded at the end of 2015, broke the pattern of giants such as Google and Facebook occupying the AI ​​field, but its founder, Tesla CEO Musk, repeatedly published the artificial intelligence threat theory. What is the purpose of creating an OpenAI in Musk? On May 4, 2016, OpenAI released OpenAI Gym, an artificial intelligence research tool set, for developing and comparing intensive learning algorithms, analyzing OpenAI Gym or finding out the real motives of Musk. 2. Another open source: OpenAI introduces the deep learning infrastructure
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