An artificial intelligence has beaten one of the world's top Dota 2 players in single combat today.

A lot of Andrew's recent stuff has been about evangelizing deep learning to new users or to semi-technical people (like project managers), so I would assume this new course is aimed at a similar audience. I'd be interested to hear from someone who has looked at it more closely though.

Digitizing plant specimens is opening up a whole new world for researchers looking to mine collections from around the world. Computer algorithms trained on the images of thousands of preserved plants have learned to automatically identify species that have been pressed, dried and mounted on herbarium sheets, researchers report. The work, published in BMC Evolutionary Biology on 11 August1, is the first attempt to use deep learning -- an artificial-intelligence technique that teaches neural networks using large, complex data sets -- to tackle the difficult taxonomic task of identifying species in natural-history collections. It's unlikely to be the last attempt, says palaeobotanist Peter Wilf of Pennsylvania State University in University Park. "This kind of work is the future; this is where we're going in natural history."

If the next era of human progress is built using AI, who gets to engineer it? Who will have the coding skills to use the software for creating AI products, or even more importantly, the skills to write that software? In an attempt to make the answer to those questions "anyone who wants to," Andrew Ng is releasing a new set of courses teaching deep learning on Coursera, the online learning platform he co-founded in 2012. Coursera was originally set up to offer an online class in machine learning; deep learning is a variety of that, involving exceptionally large datasets. The original machine learning course attracted more than 2 million students, Ng tells MIT Tech Review.

Tonight during Valve's yearly Dota 2 tournament, a surprise segment introduced what could be the best new player in the world -- a bot from Elon Musk-backed startup OpenAI. Engineers from the nonprofit say the bot learned enough to beat Dota 2 pros in just two weeks of real-time learning, though in that training period they say it amassed "lifetimes" of experience, likely using a neural network judging by the company's prior efforts. Musk is hailing the achievement as the first time artificial intelligence has been able to beat pros in competitive e-sports. OpenAI first ever to defeat world's best players in competitive eSports. Vastly more complex than traditional board games like chess & Go.

We are going to review the next chapter of the book: http://www.deeplearningbook.org/ For participants to gain the most experience and understanding of the material, having a volunteer presenter each week was an invaluable asset. So we have decided to continue this tradition and ask that one volunteer each week take on the challenge of presenting their findings from the material to the rest of the group. This presentation can be as short as 10 min or as long as an hour depending on the depth of the materials covered. It is also up to the presenter if they would like to prepare slides or give a free form talk on the subject.

Ok, I know a bit about dota (been playing it for 8 years now). I will try my best to put this into perspective. What: It beat players that many considered to be the absolute best at dota. The environment: 2 players move along a lane with the goal of destroying the other's defensive structure or killing the player 2 times for victory. Every 30 seconds weak npc minions enter the lane attack each other and players.

Google scientists have developed the first computer program capable of learning a wide variety of tasks independently, in what has been hailed as a significant step towards true artificial intelligence. The same program, or "agent" as its creators call it, learnt to play 49 different retro computer games, and came up with its own strategies for winning. In the future, the same approach could be used to power self-driving cars, personal assistants in smartphones or conduct scientific research in fields from climate change to cosmology. The research was carried out by DeepMind, the British company bought by Google last year for £400m, whose stated aim is to build "smart machines". Demis Hassabis, the company's founder said: "This is the first significant rung of the ladder towards proving a general learning system can work. It can work on a challenging task that even humans find difficult. The work is seen as a fundamental departure from previous attempts to create AI, such as the program Deep Blue, which famously beat Gary Kasparov at chess in 1997 or IBM's Watson, which won the quiz show Jeopardy! in 2011. In both these cases, computers were pre-programmed with the rules of the game and specific strategies and overcame human performance through sheer number-crunching power. "With Deep Blue, it was team of programmers and grand masters that distilled the knowledge into a program," said Hassabis. "We've built algorithms that learn from the ground up." The DeepMind agent is simply given a raw input, in this case the pixels making up the display on Atari games, and provided with a running score. When the agent begins to play, it simply watches the frames of the game and makes random button presses to see what happens. "A bit like a baby opening their eyes and seeing the world for the first time," said Hassabis. The agent uses a method called "deep learning" to turn the basic visual input into meaningful concepts, mirroring the way the human brain takes raw sensory information and transforms it into a rich understanding of the world. The agent is programmed to work out what is meaningful through "reinforcement learning", the basic notion that scoring points is good and losing them is bad. Tim Behrens, a professor of cognitive neuroscience at University College London, said: "What they've done is really impressive, there's no question.

The recent successes of deep learning have led to a wave of interest from non-experts. Gaining an understanding of this technology, however, is difficult. While the theory is important, it is also helpful for novices to develop an intuitive feel for the effect of different hyperparameters and structural variations. We describe TensorFlow Playground, an interactive, open sourced visualization that allows users to experiment via direct manipulation rather than coding, enabling them to quickly build an intuition about neural nets.

The main goal of this study is to extract a set of brain networks in multiple time-resolutions to analyze the connectivity patterns among the anatomic regions for a given cognitive task. We suggest a deep architecture which learns the natural groupings of the connectivity patterns of human brain in multiple time-resolutions. The suggested architecture is tested on task data set of Human Connectome Project (HCP) where we extract multi-resolution networks, each of which corresponds to a cognitive task. At the first level of this architecture, we decompose the fMRI signal into multiple sub-bands using wavelet decompositions. At the second level, for each sub-band, we estimate a brain network extracted from short time windows of the fMRI signal. At the third level, we feed the adjacency matrices of each mesh network at each time-resolution into an unsupervised deep learning algorithm, namely, a Stacked De- noising Auto-Encoder (SDAE). The outputs of the SDAE provide a compact connectivity representation for each time window at each sub-band of the fMRI signal. We concatenate the learned representations of all sub-bands at each window and cluster them by a hierarchical algorithm to find the natural groupings among the windows. We observe that each cluster represents a cognitive task with a performance of 93% Rand Index and 71% Adjusted Rand Index. We visualize the mean values and the precisions of the networks at each component of the cluster mixture. The mean brain networks at cluster centers show the variations among cognitive tasks and the precision of each cluster shows the within cluster variability of networks, across the subjects.