RoboSumo, one of the latest Open AI experiments in machine learning, involves a pair of'robots' dropped into a virtual arena without even the knowledge necessary to walk, and forced to learn the tricks of sumo wrestling purely by trial and error. The video posted on YouTube shows how the bots initially clash without employing any tactics or strategy, but after a number of bouts their movements start to resemble those of human wrestlers, as they learn to dodge and attack. According to the Wired, OpenAI researchers created RoboSumo because the competition apparently generated extra complexity which "could allow faster progress than just giving reinforcement learning software more complex problems to solve alone." "When you interact with other agents you have to adapt; if you don't you'll lose," Maruan Al-Shedivat, one of the RoboSumo creators, said.

I've been working on several natural language processing tasks for a long time. One day, I felt like to draw a map of the NLP field where I earn a living. I'm sure I'm not the only person who wants to see at a glance which tasks are in NLP. I did my best to cover as many as possible tasks in NLP, but admittedly this is far from exhaustive purely due to my lack of knowledge. And selected references are biased towards recent deep learning accomplishments.

Cambridge Consultants showed off an deep-learning driven application this week at GTC Europe in Munich that lets you pick up a stylus and sketch out a few lines, and watch, in real time, as the application turns your squiggles into art in one of seven styles resembling everything from moody J.M.W Turner oil paintings to neon-hued pop art. It's a demo that stunned the more than 3,000 attendees during NVIDIA CEO Jensen Huang's keynote speech Tuesday at the show. Huang even climbed down from the stage to pick up a stylus and sketch a stylized NVIDIA logo and a profile of a man -- which the application transformed into a Picasso-esque painting as he worked -- grinning as the audience applauded. The story behind the story: a finely tuned generative adversarial network that sampled 8,000 great works of art -- a tiny sample size in the data-intensive world of deep learning -- and in just 14 hours of training on an NVIDIA DGX system created an application that takes human input and turns it into something stunning. Building on thousands of hours of research undertaken by Cambridge Consultants' AI research lab, the Digital Greenhouse, a team of five built the Vincent demo in just two months.

I have been actively focusing on specialising Deep Learning for the last 2 years. My personal interest towards Deep learning started around 2015 when Google open sourced Tensorflow. Tried quickly couple of examples from the Tensorflow documentation and left with a feeling that Deep learning is difficult, partly because the framework was new and required better hardware and tons of patience. Fast forward to 2017 I have spent 100's of hours working on Deep learning projects and the technology has become more and more accessible due to several advancements in software (ease of usage -- Keras, PyTorch), hardware(GPU becoming commercially viable for someone like me sitting in India - Not still cheap), availability of data, good books and MOOCs. After completing the 3 most popular MOOCS in deep learning from Fast.ai, deeplearning.ai/Coursera

We set up competitions between multiple simulated 3D robots on a range of basic games, trained each agent with simple goals (push the opponent out of the sumo ring, reach the other side of the ring while preventing the other agent from doing the same, kick the ball into the net or prevent the other agent from doing so, and so on), then analyzed the different strategies that emerged. Agents initially receive dense rewards for behaviours that aid exploration like standing and moving forward, which are eventually annealed to zero in favor of being rewarded for just winning and losing. Despite the simple rewards, the agents learn subtle behaviors like tackling, ducking, faking, kicking and catching, and diving for the ball. Each agent's neural network policy is independently trained with Proximal Policy Optimization. To understand how complex behaviors can emerge through a combination of simple goals and competitive pressure, let's analyze the sumo wrestling task.

Anjuli Kannan is a senior software engineer at Google. She is a member of the Brain Team, which works to advance the field of machine intelligence through a combination of basic research, software (TensorFlow), and applications that improve people's lives. Recently she was a core member of the team that brought the Smart Reply feature to Inbox by Gmail. Software is changing the world. QCon empowers software development by facilitating the spread of knowledge and innovation in the developer community.

Within the silos of incumbent financial services, so-called fintech companies are good at picking off one thing only and doing it well. This approach is also taken within data science, where a lot of the properly intelligent work is about understanding the domain (problem) and how best to use the information/data for the problem you have. In doing so, a fintech approach – collaboration, open-sourcing code – is helping to gradually change the culture of finance, even in some hitherto heavily guarded domains. Newsweek is hosting an AI and Data Science in Capital Markets conference on December 6-7 in New York. Dr Tristan Fletcher, research director, Thought Machine, said: "Without this specialisation and domain knowledge, it's very hard to rise above the noise. However, the algorithms themselves are often applicable in many areas or problems, and we are probably seeing decreasing specialisation here. "Fintech lends itself particularly to specialisation because there are many well-packaged problems that need to be solved and can be clearly delineated – KYC, AML, credit checking etc.

AI Music, a British startup, uses AI to make smart adjustments to music, such as syncing the tempo with the movements of the video it's being played on. Image generated by deep neural networks.

Machines have been there for a long time and have been helping us with everything, from commonplace things like cleaning the house to detecting threats. Artificial intelligence is the technology that allows machines to perform tasks that normally require human intelligence. According to Gartner, Artificial Intelligence to be the most disruptive technology of our era, particularly machine learning (ML) and Deep Learning (DL), which are subsets of AI. Machine Learning (ML)--the machine's ability to keep improving its performance without humans having to explain exactly how to accomplish all the tasks it's given. Within just the past few years machine learning has become far more effective and widely available. Deep Learning (DL)--the machine's ability to sift through massive amounts of data to find patterns and get smarter over time. It involves the construction of artificial neural networks, using software and complex algorithms to recreate the capacity of the human brain to learn. The science of deep learning, a sub-discipline of ML, is only a recent development in the grand scheme of things, but during its short existence, it has been producing some impressive technological achievements. Using machine learning and deep learning techniques, we can now build systems that learn how to perform tasks on their own as well as find patterns from millions of data.

This third video in the Deep Learning Fundamentals playlist describes what an artificial neural net is, how it works, and shows how one is built using Keras.