Faster forward and backward convolutions using the Winograd convolution algorithm; Improved performance and reduced memory usage with FP16 routines on Pascal GPUs; Support for LSTM recurrent neural networks for sequence learning that deliver up to 6x speedup. Support for LSTM recurrent neural networks for sequence learning that deliver up to 6x speedup. One of the new features we've added in cuDNN 5 is support for Recurrent Neural Networks (RNN). RNNs are a powerful tool used for sequence learning in a number of fields, from speech recognition to image captioning. For a brief high-level introduction to RNNs, LSTM and sequence learning, I recommend you check out Tim Dettmers recent post Deep Learning in a Nutshell: Sequence Learning, and for more depth, Soumith Chintala's post Understanding Natural Language with Deep Neural Networks Using Torch.

Amazon Lex is a service for building conversational interfaces into any application using voice and text. Lex provides the advanced deep learning functionalities of automatic speech recognition (ASR) for converting speech to text, and natural language understanding (NLU) to recognize the intent of the text, to enable you to build applications with highly engaging user experiences and lifelike conversational interactions. With Amazon Lex, the same deep learning technologies that power Amazon Alexa are now available to any developer, enabling you to quickly and easily build sophisticated, natural language, conversational bots ("chatbots"). Speech recognition and natural language understanding are some of the most challenging problems to solve in computer science, requiring sophisticated deep learning algorithms to be trained on massive amounts of data and infrastructure. Harnessing these technologies, Lex enables you to define entirely new categories of products made possible through conversational interfaces.

One of the basic features of artificial intelligence (AI) is the ability to recognize images and process them. Companies like Microsoft and Google have debuted tools to show how accurate their image recognition platforms are. Now it seems that Amazon wants in as well as they have announced Amazon Rekognition. This is an image recognition service that is part of a suite of deep-learning services that Amazon has recently announced for developers. For the most part it does what most image recognition services do, which is to identify human faces, identify emotions, and label objects just by looking at it.

Big data collected from customer behaviour from all strata of the Internet has added valuable information to improve sales force and customer services. With the analytical power of Machine Learning, the mass of data can be transformed into thin causalities. For example, the Artificial Intelligence algorithms are able to review the complexity of Big Data and simplify the client's information through accurate analysis of the buying journey. Since its first public use in the late 1990s, Machine Learning continues to be discussed. AlphaGo, a computer program developed by Google DeepMind in London to play the board game Go, represents one of the most notable examples of deep learning; that is to say a machine now is able to independently analyze the amounts of data with extremely high performance.

Artificial intelligence is getting its teeth into lip reading. A project by Google's DeepMind and the University of Oxford applied deep learning to a huge data set of BBC programmes to create a lip-reading system that leaves professionals in the dust. The AI system was trained using some 5000 hours from six different TV programmes, including Newsnight, BBC Breakfast and Question Time. In total, the videos contained 118,000 sentences. First the University of Oxford and DeepMind researchers trained the AI on shows that aired between January 2010 and December 2015. Then they tested its performance on programmes broadcast between March and September 2016.

Apache Spark MLib – MLlib fits into Spark's APIs and interoperates with NumPy in Python (as of Spark 0.9) and R libraries (as of Spark 1.5). You can use any Hadoop data source (e.g. HDFS, HBase, or local files), making it easy to plug into Hadoop workflows. Singa, recently accepted into the Apache Incubator, is an open source framework intended to make it easy to train deep-learning models on large volumes of data.Singa provides a simple programming model for training deep-learning networks across a cluster of machines, and it supports many common types of training jobs: convolutional neural networks, restricted Boltzmann machines, and recurrent neural networks. Models can be trained synchronously (one after the other) or asynchronously (side by side), depending on whatever works best for the given problem.

Amazon today announced three new artificial intelligence-related toolkits for developers building apps on Amazon Web Services. At the company's AWS re:invent conference in Las Vegas, Amazon showed how developers can use three new services -- Amazon Lex, Amazon Polly, Amazon Rekognition -- to build artificial intelligence features into apps for platforms like Slack, Facebook Messenger, ZenDesk, and others. The idea is to let developers utilize the machine learning algorithms and technology that Amazon has already created for its own processes and services like Alexa. Instead of developing their own AI software, AWS customers can simply use an API call or the AWS Management Console to incorporate AI features into their own apps. AWS CEO Andy Jassy noted that Amazon has been building AI and machine learning technology for 20 years and said that there are now thousands of people "dedicated to AI in our business."

Soon, hundreds of billions of devices will be infused with intelligence. AI will revolutionize every industry. READ MORE 7. 7 The global ecosystem for NVIDIA GPU Deep Learning has scaled out rapidly. Breakthrough results triggered a race to adopt AI for consumer internet services: TRANSLATION RECOGNITION SEARCH RECOMMENDATIONS 8. 8 Cloud service providers, from Alibaba and Amazon to IBM and Microsoft, make the NVIDIA GPU deep learning platform available to companies large and small. Pinterest is Changing Online Retail with GPUs 12. 12 AI can solve problems that seemed well beyond our reach just a few years back.

Under, behind and inside many of the apps we use every day, a revolution is underway. It's a revolution that started decades ago but today is empowering companies to deliver better, smarter services with greater ease and on broader scales than ever before. At Singularity University's inaugural Global Summit, Neil Jacobstein, chair of Artificial Intelligence and Robotics, provided a primer showing how artificial intelligence literally transforms everything it touches. First of all, it's critical to define the scope of artificial intelligence (AI), which can be categorized into four areas: techniques in pattern recognition, software agency (that is, software that acts like real users), an exponential technology that is accelerating other exponential technologies, and a vision of a future superhuman intelligence (that fortunately hasn't happened yet). Anyone who has seen a science fiction film is likely familiar with this last area, but it's the other three areas where AI is making huge strides at a revolutionary pace.

The meeting format typically alternates between lecture/paper discussions and lab sessions where we review code. In our lecture sessions we discuss and gain a better understanding of course lectures. In our lab sessions, we walk methodically through code from course assignments. We intend to expand our projects beyond the course material, based on the interests of the group. We welcome all new members and participants, regardless of experience level, who are excited about rolling up their sleeves to dig into Deep Learning.