Artificial intelligence applications have gone head-to-head with humans in a lot of scenarios over the past few years and the latest application has seen esports players take the latest loss. An artificial intelligence application beat a top player in the multiplayer online game Dota 2. At The International championship in Seattle earlier this week, a program developed by firm OpenAI managed to beat top Dota 2 player Danil Ishutin, better known as Dendi, in a series of 1 vs. 1 matches. During the first match, the bot took the victory after Ishutin and it traded kills and blows. But after taking an early lead in the next round, Ishutin forfeited the match and gave the bot the final win. For OpenAI, Dendi was also the latest major player the bot managed to take down.

Let start at the beginning; back in the 50's we dreamt about robots making our tea (AI) and what we could do with our spare time, fuelled by the space race and science fiction it was a heady dream. Until that is the 80's, where we started to'teach' machines simple tasks (Machine Learning), the more times a machine completed a task the more it learnt and improved. We've now reached Deep Learning, where through the improvement of data storage, processing power and network speed algorithms can be used so that software can train itself through multiple iterations and vast amounts of data.

Facial recognition is a biometric solution that measures unique characteristics about one's face. Applications available today include flight checkin, tagging friends and family members in photos, and "tailored" advertising. To perform facial recognition, you'll need a way to uniquely represent a face. In 1960, Woodrow Bledsoe used a technique involving marking the coordinates of prominent features of a face. Among these features were the location of hairline, eyes and nose.

Deep Learning algorithms consist of a different set of models due to the flexibility that neural network allows while building a full fledged end-to-end model. Advanced architecture can be stated as one that has a demonstrated track record of being an efficient and successful model but the problem arises while dealing with typical tasks related to images. Computer vision is basically based on the theoretical and technological aspect for building artificial systems which have the ability to gather automatic visual information from images or multi-dimensional data. It is focussed on the self-executing extraction, analysis and studying about useful information from a particular image or a sequence of images. Broadly the computer vision consists of tasks like Object Recognition, Identification, Detection, Content-based image retrieval, Image Segmentation and much more. After getting an insight of what basically advanced architecture is and computer vision we move towards the study of some important deep learning advanced architecture.

Let me say a few words about datasets export capabilities before we start. When we design neural network we think about it in terms of computational graph. This is the core abstraction behind popular deep learning frameworks. Computational graph consists of math operations and variables. We developed the powerful dataset export tool that opens up the possibility to configure export with computational graphs.

That's the vision of Andrew Ng, a founder of the Google Brain deep learning project, and former head of AI at Baidu–a position he left in March–who is today announcing a set of five interconnected online courses on the subject. Participants in the "Deep Learning Specialization," available only through Coursera, will be steeped in neural networks, backpropagation, convolutional networks, recurrent networks, computer vision, natural language processing, and more. They'll get hands-on experience using the technology in healthcare, visual object recognition, music generation, language understanding, and other applications. "Today, if you want to learn deep learning, there are lots of people searching online, reading [dozens of] research papers, reading blog posts, and watching YouTube videos," Ng tells Fast Company. "I admire that, but I want to give people that want to break into AI a clear path of how to get there." Today, the major breakthroughs in the field are coming from the world's largest tech companies, which have in-house AI departments and are investing significantly in the field.

See the Hacker News Discussion for additional context. When I read today's news about OpenAI's DotA 2 bot beating human players at The International, an eSports tournament with a prize pool of over $24M, I was jumping with excitement. For one, I am a big eSports fan. I have never played DotA 2, but I regularly watch other eSports competitions on Twitch and even played semi-professionally when I was in high school. But more importantly, multiplayer online battle arena (MOBA) games like DotA and real-time strategy (RTS) games like Starcraft 2, are seen as being way beyond the capabilities of current Artificial Intelligence techniques.

Convolutional neural networks are fantastic for visual recognition tasks. Good ConvNets are beasts with millions of parameters and many hidden layers. In fact, a bad rule of thumb is: 'higher the number of hidden layers, better the network'. AlexNet, VGG, Inception, ResNet are some of the popular networks. Why do these networks work so well?

Waring against the misuse of artificial intelligence (AI) Tesla and SpaceX CEO Elon Musk has said that people should be more concerned with it than the risk posed by escalating tensions with North Korea, the media reported. "If you're not concerned about AI safety, you should be. If you're not concerned about AI safety, you should be. Musk's comments were in reference to his non-profit start-up, OpenAI, defeating several of the world's best players at a video game, reports The Hill magazine. OpenAI first ever to defeat world's best players in competitive eSports.

DEEPMIND, the Alphabet Inc-owned artificial intelligence (AI) company best known for creating software capable of beating the world's best players at the strategy game Go, has targeted the science fiction video game Starcraft II as its next big research milestone.