UPDATE: Mar 20, 2016 - Added my new follow-up course on Deep Learning, which covers ways to speed up and improve vanilla backpropagation: momentum and Nesterov momentum, adaptive learning rate algorithms like AdaGrad and RMSProp, utilizing the GPU on AWS EC2, and stochastic batch gradient descent. We look at TensorFlow and Theano starting from the basics - variables, functions, expressions, and simple optimizations - from there, building a neural network seems simple! Deep learning is all the rage these days. What exactly is deep learning? Well, it all boils down to neural networks.

I understand where the article is coming from, but it sounds to me like a classic case of projecting their biases (i.e. the author has very strong feelings about a topic which is pretty marginal to the research papers themselves, and interprets the claims made in the papers through the lens of a worldview in which that topic is very important) For example, AlphaGo Zero makes claims about not using any human (Go) knowledge, which is, by human standards, pretty close to true. It mostly only uses general assumptions which would apply to most turn-based, perfect information board games. While that is certainly "knowledge about Go" in the strictest sense, such a distinction is pretty irrelevant in practice. The paper never claimed it had spawned an AGI that could solve any general problem without human intervention -- the context makes it pretty clear that the research applies to a narrow domain, and I don't believe any claims are made about not making any assumptions which rely on the properties of that narrow domain (indeed, such assumptions existing is pretty much a given -- whether they were implemented on purpose or by pure chance, the fact that it works in a domain and not in another is proof that this is the case)

Poachers are normally active at night. While tools such as infrared cameras are used to monitor living organisms, since poachers and animals they are hunting both give off heat, it is time-consuming and challenging to monitor infrared video streams for poachers all night. Thus a team of computer scientists led by USC Viterbi School of Engineering PhD student Elizabeth Bondi in Professor Milind Tambe's lab, labeled 180,000 humans and animals in infrared videos using a labeling tool they developed to expedite the process. The researchers used these labeled images and leveraged an existing deep learning algorithm known as Faster RCNN that they modified, to teach a computer to automatically distinguish infrared images of humans from those infrared images of animals. The challenge then was to deploy this algorithm to spot poachers in near real time using the laptop computers at base stations in the field, where footage is streamed from the drones that are being used to patrol national parks in Zimbabwe and Malawi.

Imagine what the world would be like if we could harness the multitude of data generated each day to catalyze positive change. What if we had the ability to predict and stop crimes before they happened, or could apply these same methodologies to save lives with better healthcare? With recent advances in artificial intelligence, these outcomes are not only possible, but an exciting reality! As we move swiftly into this new year, media, analysts and just about everyone is thinking about what will be'the next big thing' in technology. Looking back at 2017, this was a hallmark year for AI enthusiasm and awareness.

While it's safe to say that the future of medicine will likely never exclude doctors, their role in the future health care system may change dramatically. Earlier this year, Sebastian Thrun and colleagues published research in Nature, demonstrating that a deep learning neural network system was able to diagnose early-stage melanoma with comparable accuracy to that of human dermatologists. In terms of A.I. in health care, this is merely the tip of the iceberg.

No knowledge of machine learning is required for using the Signals model builder. Just choose from a variety of indicators, ranging from traditional technical analysis to deep learning or sentiment analysis based on media monitoring and combine them together. If you do happen to be a developer or a data scientist, you can develop new trading indicators from scratch and monetize your data science skills through the Signals Indicator Marketplace.

Music genre classification is one example of content-based analysis of music signals. Traditionally, human-engineered features were used to automatize this task and 61% accuracy has been achieved in the 10-genre classification. However, it's still below the 70% accuracy that humans could achieve in the same task. Here, we propose a new method that combines knowledge of human perception study in music genre classification and the neurophysiology of the auditory system. The method works by training a simple convolutional neural network (CNN) to classify a short segment of the music signal. Then, the genre of a music is determined by splitting it into short segments and then combining CNN's predictions from all short segments. After training, this method achieves human-level (70%) accuracy and the filters learned in the CNN resemble the spectrotemporal receptive field (STRF) in the auditory system.

Statistical methods protecting sensitive information or the identity of the data owner have become critical to ensure privacy of individuals as well as of organizations. This paper investigates anonymization methods based on representation learning and deep neural networks, and motivated by novel information theoretical bounds. We introduce a novel training objective for simultaneously training a predictor over target variables of interest (the regular labels) while preventing an intermediate representation to be predictive of the private labels. The architecture is based on three sub-networks: one going from input to representation, one from representation to predicted regular labels, and one from representation to predicted private labels. The training procedure aims at learning representations that preserve the relevant part of the information (about regular labels) while dismissing information about the private labels which correspond to the identity of a person. We demonstrate the success of this approach for two distinct classification versus anonymization tasks (handwritten digits and sentiment analysis).

Variational Bayesian neural nets combine the flexibility of deep learning with Bayesian uncertainty estimation. Unfortunately, there is a tradeoff between cheap but simple variational families (e.g.~fully factorized) or expensive and complicated inference procedures. We show that natural gradient ascent with adaptive weight noise implicitly fits a variational posterior to maximize the evidence lower bound (ELBO). This insight allows us to train full-covariance, fully factorized, or matrix-variate Gaussian variational posteriors using noisy versions of natural gradient, Adam, and K-FAC, respectively, making it possible to scale up to modern-size ConvNets. On standard regression benchmarks, our noisy K-FAC algorithm makes better predictions and matches Hamiltonian Monte Carlo's predictive variances better than existing methods. Its improved uncertainty estimates lead to more efficient exploration in active learning, and intrinsic motivation for reinforcement learning.

Abstract-- We propose to use neural networks for simultaneous detection and localization of multiple sound sources in human-robot interaction. In contrast to conventional signal processing techniques, neural network-based sound source localization methods require fewer strong assumptions about the environment. Previous neural network-based methods have been focusing on localizing a single sound source, which do not extend to multiple sources in terms of detection and localization. In this paper, we thus propose a likelihood-based encoding of the network output, which naturally allows the detection of an arbitrary number of sources. In addition, we investigate the use of sub-band cross-correlation information as features for better localization in sound mixtures, as well as three different network architectures based on different motivations. Experiments on real data recorded from a robot show that our proposed methods significantly outperform the popular spatial spectrum-based approaches.