Satellites are best known for helping smartphones map driving routes or televisions deliver programs. But now, data from some of the thousands of satellites orbiting Earth are helping track things like crop conditions on rural farms, illegal deforestation, and increasingly, poverty in the hard-to-reach places around the globe. As much as that data has the potential to provide invaluable information to humanitarian organizations, watchdog groups, and policymakers, there is too much of it to sift through in order to draw insights that could influence important decisions. A team of researchers from Stanford University, however, says it has developed an efficient way. By creating a deep-learning algorithm that can recognize signs of poverty in satellite images – such as condition of roads – the team sorted through a million images to accurately identify economic conditions in five African countries, reported the scientists in the journal Science on Thursday.

This article was posted by SmileJet on Dev Battles. She paints and writes poetry. She's also an artificial intelligence from the movie Her, which imagines how a juiced-up Siri will change our lives. Now, tech companies large and small are racing to make this a reality. You've heard the jargon: AI, machine learning, deep learning, neural networks, natural language processing.

There is universal proverb that roughly translates into "Empty vessels make the most noise." Some ascribe it to Plato, but having come from Punjab (India), I can safely confirm that the Punjabi translation means exactly that, and Punjab has as much to do with Plato as chicken tikka masala has to do with French cuisine. One of my favourite version of this proverb comes from Polish, which translated into English means, "The cow which moos a lot gives little milk." This pretty much reflects a certain philosopher from Oxford who without having any foundation in principles of engineering, let alone (proper) machine learning, seems to think himself as the leading authority to warn the world against the perils of machine learning. And for the last few years has busied himself making outrageous, pseudoscientific and shamanic claims to sell his book.

So how do we program a computer to translate human language? The simplest approach is to replace every word in a sentence with the translated word in the target language. This is easy to implement because all you need is a dictionary to look up each word's translation. But the results are bad because it ignores grammar and context. So the next thing you might do is start adding language-specific rules to improve the results.

Leading professionals and the top minds in deep learning from industry, entrepreneurship and academia will come together for an evening of networking and keynote talks. Join us for the evening dinner to discover the rapidly evolving landscape of deep learning startups and industry as well as the latest research advancements and investment trends in AI.

An Elon Musk-backed artificial intelligence research group just got a brand new toy from chip maker Nvidia. Nvidia nvda said on Monday that it had donated one of its new supercomputers to the OpenAI non-profit artificial intelligence research project. OpenAI debuted in December with financial backing from Tesla and SpaceX CEO Musk along with money from other high-profile technology luminaries like LinkedIn lnkd co-founder Reid Hoffman and PayPal pypl co-founder Peter Thiel. OpenAI's goal is partly to create a non-profit outside the corporate sector that could research artificial intelligence technologies without a financial incentive. The concern is that many companies like Google and Facebook that are researching artificial intelligence technologies would horde talent and only work on projects beneficial to their financial interests.

Deep Reinforcement Learning has recently gained a lot of traction in the machine learning community due to the significant amount of progress that has been made in the past few years. Traditionally, reinforcement learning algorithms were constrained to tiny, discretized grid worlds, which seriously inhibited them from gaining credibility as being viable machine learning tools. Here's a classic example from Richard Sutton's book, which I will be referencing a lot. After Deep Q-Networks [4] became a hit, people realized that deep learning methods could be used to solve high-dimensional problems. One of the subsequent challenges that the reinforcement learning community faced was figuring out how to deal with continuous action spaces.

Neuroscience is experiencing a data revolution in which many hundreds or thousands of neurons are recorded simultaneously. Currently, there is little consensus on how such data should be analyzed. Here we introduce LFADS (Latent Factor Analysis via Dynamical Systems), a method to infer latent dynamics from simultaneously recorded, single-trial, high-dimensional neural spiking data. LFADS is a sequential model based on a variational auto-encoder. By making a dynamical systems hypothesis regarding the generation of the observed data, LFADS reduces observed spiking to a set of low-dimensional temporal factors, per-trial initial conditions, and inferred inputs. We compare LFADS to existing methods on synthetic data and show that it significantly out-performs them in inferring neural firing rates and latent dynamics.

In the last few months, I have had several people contact me about their enthusiasm for venturing into the world of data science and using Machine Learning (ML) techniques to probe statistical regularities and build impeccable data-driven products. However, I've observed that some actually lack the necessary mathematical intuition and framework to get useful results. This is the main reason I decided to write this blog post. Recently, there has been an upsurge in the availability of many easy-to-use machine and deep learning packages such as scikit-learn, Weka, Tensorflow etc. Machine Learning theory is a field that intersects statistical, probabilistic, computer science and algorithmic aspects arising from learning iteratively from data and finding hidden insights which can be used to build intelligent applications. Despite the immense possibilities of Machine and Deep Learning, a thorough mathematical understanding of many of these techniques is necessary for a good grasp of the inner workings of the algorithms and getting good results.

In a new automotive application, we have used convolutional neural networks (CNNs) to map the raw pixels from a front-facing camera to the steering commands for a self-driving car. This powerful end-to-end approach means that with minimum training data from humans, the system learns to steer, with or without lane markings, on both local roads and highways. The system can also operate in areas with unclear visual guidance such as parking lots or unpaved roads. We designed the end-to-end learning system using an NVIDIA DevBox running Torch 7 for training. An NVIDIA DRIVETM PX self-driving car computer, also with Torch 7, was used to determine where to drive--while operating at 30 frames per second (FPS).