In this episode of the O'Reilly Data Show, I spoke with Yishay Carmiel, president of Spoken Labs. As voice becomes a common user interface, the need for accurate and intelligent speech technologies has grown. And although computer vision is a common entry point for deep learning, some of the most interesting commercial applications of deep neural networks are in speech recognition. Carmiel has spent several years building commercial speech applications, and along the way he has witnessed (and helped architect) massive improvements in speech technologies.

You can think of deep learning, machine learning and artificial intelligence as a set of Russian dolls nested within each other, beginning with the smallest and working out. Deep learning is a subset of machine learning, which is a subset of AI. AI is any computer program that does something smart, broadly speaking. It can be a pile of if-then statements or a complex statistical model. Usually, when a computer program designed by AI researchers actually succeeds at something – like winning at chess – many people say it's "not really intelligent", because the algorithm's internals are well understood.

Big data in healthcare can now be measured in exabytes, and every day more data is being thrown into the mix in the form of patient-generated information, wearables and EHR systems. Traditional methods of analysis are no longer enough to handle, let alone take proper advantage of, the potential that healthcare data holds. This is where deep machine learning (or simply, "deep learning") comes in. However, its greatest power lies in its ability to extract value from data in ways that humans and traditional machine learning methods cannot. Deep machine learning has applications in a number of healthcare areas.

Technology and data made possible advances in...technology and data. JL The Economist reports: New techniques have made training deep networks feasible. This takes a lot of number-crunching power, which became available when several AI research groups realised that graphical processing units (GPUs), the specialised chips used in PCs and video-games consoles to generate fancy graphics, were also well suited to running deep-learning algorithms. HOW HAS ARTIFICIAL intelligence, associated with hubris and disappointment since its earliest days, suddenly become the hottest field in technology? The term was coined in a research proposal written in 1956 which suggested that significant progress could be made in getting machines to "solve the kinds of problems now reserved for humans…if a carefully selected group of scientists work on it together for a summer". That proved to be wildly overoptimistic, to say the least, and despite occasional bursts of progress, AI became known for promising much more than it could deliver.

Understanding how a nervous system computes requires determining the input, the output, and the transformations necessary to convert the input into the desired output [1]. Artificial neural networks are a conceptual framework that provide insight into how these transformations are carried out, and have also played a crucial factor in the success of many pattern recognition tasks such as for handwriting [2] and object [3] detection. An important feature of neural networks is their ability to capture the underlying regularities in a task domain by representing the input with multiple layers of active neurons. This distributed representation of the input is based on the hierarchal processing and information flow of biological systems [4,5]. In a multi-layered network, complex internal representations can also be constructed by repeatedly adjusting the weights of the connections in order to ensure that the output is close to the desired output [6].

Facebook has unveiled DeepText, a deep learning-based text comprehension engine that uses neural networks to understand the context of posts in over 20 languages. DeepText uses several deep learning neural network architectures, as well as its artificial intelligence (AI) backbone FBLearner Flow and the Torch open source machine learning library, to perform word-level and character-based learning. The system can understand slang and make sense of potentially ambiguous phrases. For example, if a Facebook user posts the phrase'I like apple' DeepText can work out whether it refers to the fruit or Apple. Facebook had to go beyond normal neuro-linguistic programming (NLP) techniques with DeepText, as the extensive pre-processing logic built on top of intricate software engineering and language knowledge is ineffective at picking up variations in languages and spelling when people post on the same topic.

In this capstone project, you will leverage what you've learned throughout the Nanodegree program to solve a problem of your choice by applying machine learning algorithms and techniques. You will first define the problem you want to solve and investigate potential solutions and performance metrics. Next, you will analyze the problem through visualizations and data exploration to have a better understanding of what algorithms and features are appropriate for solving it. You will then implement your algorithms and metrics of choice, documenting the preprocessing, refinement, and postprocessing steps along the way. Afterwards, you will collect results about the performance of the models used, visualize significant quantities, and validate/justify these values.

According to the American Cancer Society, cancer kills more than 8 million people each year. Early detection can boost survival rates. Researchers and clinicians are feverishly exploring avenues to provide early and accurate diagnoses, as well as more targeted treatments. Blood screenings are used to detect many types of cancers, including liver, ovarian, colon and lung cancers. Current blood screening methods typically rely on affixing biochemical labels to cells or biomolecules.

OK, let's drop autonomous vehicles for a second. This post is about creating a machine that writes its own code. More specifically, we are going to train a character level Long Short Term Memory neural network to write code itself by feeding it Python source code. The training will run on a GPU instance on EC2, using Theano and Lasagne. If some of the words here sound obscure to you, I will do my best to explain what is happening. This experiment is greatly inspired by this awesome blog post that I highly recommend reading.

First was the Monte Carlo tree search, an algorithm that rather than attempting to examine all possible future moves instead tests a sparse selection of them, combining their value in a sophisticated way to get a better estimate of a move's quality. The second was the (re)discovery of deep networks, a contemporary incarnation of neural networks that had been experimented with since the 1960s, but which was now cheaper, more powerful, and equipped with huge amounts of data with which to train the learning algorithms. The combination of these techniques saw a drastic improvement in Go-playing programs, and ultimately Google DeepMind's AlphaGo program beat Go world champion Lee Sedol in March 2016. Now that Go has fallen, where do we go from here? Following Kasparov's defeat in 1997, scientists considered that the challenge for AI was not to conquer some cerebral game.