Catherine Dong is a summer associate at Bloomberg Beta and will be working at Facebook as a machine learning engineer. Major tech companies have actively reoriented themselves around AI and machine learning: Google is now "AI-first," Uber has ML running through its veins, and internal AI research labs keep popping up. They're pouring resources and attention into convincing the world that the machine intelligence revolution is arriving now. They tout deep learning, in particular, as the breakthrough driving this transformation and powering new self-driving cars, virtual assistants, and more. Despite this hype around the state of the art, the state of the practice is less futuristic.

Andrew Ng is a soft-spoken AI researcher whose online postings talk loudly. A March blog post in which the Stanford professor announced he was leaving Chinese search engine Baidu temporarily wiped more than a billion dollars off the company's value. A June tweet about a new Ng website, Deeplearning.ai, Today that speculation is over. Deeplearning.ai is home to a series of online courses Ng says will help spread the benefits of recent advances in machine learning far beyond big tech companies such as Google and Baidu.

Amazon Lex is a service for building conversational interfaces into any application using voice and text. Amazon 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").

The race to make computers smarter and more human-like continued this week with IBM IBM claiming it has developed technology that dramatically cuts the time it takes to crunch massive amounts of data and then come up with useful insights. Deep learning, the technique used by IBM, is a subset of artificial intelligence (AI) that mimics how the human brain works. IBM's stated goal is to reduce the time it takes for deep learning systems to digest data from days to hours. The improvements could help radiologists get faster, more accurate reads of anomalies and masses on medical images, according to Hillery Hunter, an IBM Fellow and director of systems acceleration and memory at IBM Research. Until now, deep learning has largely run on single server because of the complexity of moving huge amounts of data between different computers.

Custom accelerators for neural network training have garnered plenty of attention in the last couple of years, but without significant software footwork, many are still difficult to program and could leave efficiencies on the table. This can be addressed through various model optimizations, but as some argue, the efficiency and utilization gaps can also be addressed with a tailored compiler. Eugenio Culurciello, an electrical engineer at Purdue University, argues that getting full computational efficiency out of custom deep learning accelerators is difficult. This prompted his team at Purdue to build an FPGA based accelerator that could be agnostic to CNN workloads and could eek maximum utilization and efficiencies on a range of deep learning tasks, including ResNet and AlexNet. Snowflake is a scalable and programmable, low-power accelerator for deep learning with a RISC based custom instruction set.

IBM unveiled a new technique today that's supposed to drastically reduce how much time it takes to train distributed deep learning (DDL) systems by applying a ton of powerful hardware to the task. It works by optimizing data transfer between hardware components that run a deep neural network. The key issue IBM is trying to solve is that of networking bottlenecks in distributed deep learning systems. While it's possible to spread the computational load for training a deep neural network out over many computers, that process becomes less and less efficient because of high-latency connections between the hardware doing the actual computation. PowerAI DDL, a new communication library released in conjunction with an explanatory research paper, aims to improve efficiency by making sure that the systems at play take advantage of all the high-performance connections available.

Even though I did not follow his older courses, they seem really appreciated, at least on this subreddit. I hope these new ones will set an even higher standard. That way, newcomers may share an identical set of notations, principles and methodologies so we can all focus on other tasks, such as visualization. You will practice all these ideas in Python and in TensorFlow. What do you guys think of this choice?

Deep Learning-powered image recognition is now performing better than human vision on many tasks. We examine how human and computer vision extracts features from raw pixels, and explain how deep convolutional neural networks work so well.

Chatbots are "computer programs which conduct conversation through auditory or textual methods". Apple's Siri, Microsoft's Cortana, Google Assistant, and Amazon's Alexa are four of the most popular conversational agents today. They can help you get directions, check the scores of sports games, call people in your address book, and can accidently make you order a $170 dollhouse. These products all have auditory interfaces where the agent converses with you through audio messages. In this post, we'll be looking more at chatbots that operate solely on the textual front.

One problem with AI right now is exactly what we tried to address above i.e. plethora of jargon words that seem indistinguishable and often hard to digest. Here is what you need to remember. Machine Learning is what's making AI possible. The way to achieve machine learning is via techniques and the most known of those is Deep Learning. Will DL make all other techniques irrelevant?