If you indulge in the occasional television crime drama like most of us do, you've no doubt seen some high-tech investigative tools in action. Incredibly sophisticated video facial recognition is one that's commonly featured: Investigators watch live, grainy security footage, zoom in on a suspect's face, instantly snap it into high-resolution and immediately match the face to a criminal's photo in a massive database. But is that super-advanced level of technology realistic? While real-time video facial recognition remains in its infancy, the development of deep learning techniques -- part of the machine learning family -- is advancing the technology at a rapid pace. Deep learning has also fueled advances in a host of other artificial intelligence and cognitive computing applications for intelligence agencies.

The following was originally published on the CMS-Connected News Articles. Do you know that some of the content you read wasn't written by human beings? Automated Insights states, its software created one billion stories last year, many with no human intervention. This content you are reading was written by a real person, but just think, what are the chances you haven't consumed that type of content without knowing it? So what does it mean for content marketing?

To top this, many practical applications of Machine Learning are enabled by Deep Learning that extends the overall field of Artificial Intelligence. Deep Learning breaks down tasks in ways that makes all kinds of machine assists seem likely. At present, deep learning has moved beyond academic applications and is finding its way into our daily lives. Everything we discussed - Driverless cars, better preventive healthcare, even better movie recommendations, are all here today and will only improve given the rapid rate of advancement. Deep learning avoids the necessity of human-coded features and instead incorporates the feature engineering, feature selection, and model fitting into one step. Feature engineering & selection are fundamental to any application of Deep Learning that you can think off.

The Sparkling Water project is nearing its one-year anniversary, which means Michal Malohlava, our main contributor, has been very busy for the better part of this past year. The Sparkling Water project combines H2O machine-learning algorithms with the execution power of Apache Spark. This means that the project is heavily dependent on two of the fastest growing machine-learning open source projects out there. With every major release of Spark or H2O there are API changes and, less frequently, major data structure changes that affect Sparkling Water. Throw Cloudera releases into the mix, and you have a plethora of git commits dedicated to maintaining a few simple calls to move data between the different platforms.

So far, we have described the application of neural networks to supervised learning, in which we have labeled training examples. An autoencoder neural network is an unsupervised learning algorithm that applies backpropagation, setting the target values to be equal to the inputs. The autoencoder tries to learn a function \textstyle h_{W,b}(x) \approx x. In other words, it is trying to learn an approximation to the identity function, so as to output \textstyle \hat{x} that is similar to \textstyle x. The identity function seems a particularly trivial function to be trying to learn; but by placing constraints on the network, such as by limiting the number of hidden units, we can discover interesting structure about the data.

Get started with deep learning and neural networks with "Fundamentals of Deep Learning," by Nikhil Buduma. Security data science is booming--reports indicate that the security analytics market is set to reach $8 billion dollars by 2023, with a growth rate of 26%, thanks to relentless cyber attacks. If you want to stay ahead of emerging security threats in 2017, it is important to invest in the right areas. In March 2016, I wrote a piece on the 4 trends to be aware of for 2016; for my 2017 trends post, Cody Rioux from Netflix joins me, bringing his platform perspective. Our goal is to help you formulate a plan for every quarter of 2017 (i.e., 4 trends for 4 quarters).

How important is artificial intelligence (AI) to Baidu (NASDAQ: BIDU)? Gone is the era of PC, and soon will we say goodbye to the era of mobile internet ... We believe that coming is the era of artificial intelligence. Andrew Ng, Baidu's chief scientist, has some experience in this area. During his previous tenure at Google parent Alphabet, he led the Google Brain AI project. He is also an adjunct professor at Stanford University, where he taught AI.

December 27, 2016 --With excitement swirling around the possibility of a ninth planet, a rebound in the global tiger population for the first time in a century, and the DNA sequenced in space for the first time, 2016 has been a year full of scientific wonder. But as the year comes to a close, there are some breakthroughs particularly worth highlighting. In February, a century after Albert Einstein predicted their existence, an international team of researchers confirmed that they had actually detected a ripple in the fabric of spacetime for the first time. The detection of gravitational waves came across as a "chirp" across the detectors that make up the Laser Interferometer Gravitational-wave Observatory (LIGO), but the researchers say it was the result of two large celestial bodies, possibly black holes, colliding some 1.3 billion years ago. Then, in June, the scientists announced that the cosmos had chirped again.

A new artificial intelligence system can take still images and generate short videos that simulate what happens next similar to how humans can visually imagine how a scene will evolve, according to a new study. Humans intuitively understand how the world works, which makes it easier for people, as opposed to machines, to envision how a scene will play out. But objects in a still image could move and interact in a multitude of different ways, making it very hard for machines to accomplish this feat, the researchers said. But a new, so-called deep-learning system was able to trick humans 20 per cent of the time when compared to real footage. Researchers at the Massachusetts Institute of Technology (MIT) pitted twoneural networks against each other, with one trying to distinguish real videos from machine-generated ones, and the other trying to create videos that were realistic enough to trick the first system.

We are in the middle of a remarkable rise in the use and capability of artificial intelligence. Much of this growth has been fueled by the success of deep learning architectures: models that map from observables to outputs via multiple layers of latent representations. These deep learning algorithms are effective tools for unstructured prediction, and they can be combined in AI systems to solve complex automated reasoning problems. This paper provides a recipe for combining ML algorithms to solve for causal effects in the presence of instrumental variables - sources of treatment randomization that are conditionally independent from the response. We show that a flexible IV specification resolves into two prediction tasks that can be solved with deep neural nets: a first-stage network for treatment prediction and a second-stage network whose loss function involves integration over the conditional treatment distribution. This Deep IV framework imposes some specific structure on the stochastic gradient descent routine used for training, but it is general enough that we can take advantage of off-the-shelf ML capabilities and avoid extensive algorithm customization. We outline how to obtain out-of-sample causal validation in order to avoid over-fit. We also introduce schemes for both Bayesian and frequentist inference: the former via a novel adaptation of dropout training, and the latter via a data splitting routine. 1 Introduction Supervised machine learning (ML) provides a myriad of effective methods for solving prediction tasks. In these tasks, the learning algorithm is trained and validated to do a good job predicting the outcome for future examples from the same data generating process (DGP).