Today in "computer beats human at thing computers could not previously beat humans at" news: Google Deepmind has bested StarCraft 2 pros at their own game. "AlphaStar" was unveiled on a livestream last night, in a show revolving around matches against top StarCraft pros Grzegorz "MaNa" Komincz and Dario "TLO" Wünsch. All the games AlphaStar won were actually prerecorded, because GOOGLE ARE COWARDS COME FIGHT ME. Notably, the AI was playing the same version of StarCraft that you or I could boot up right now – unlike OpenAI's Dota bots, who failed to beat Dota 2 pros last year at a cutback version of the game. That's 2-0 to Google, who also beat the GO world champion back in 2016.

Summary: Quantum computing is already being used in deep learning and promises dramatic reductions in processing time and resource utilization to train even the most complex models. Here are a few things you need to know. So far in this series of articles on Quantum computing we showed that Quantum is in fact commercially available today and being used operationally. We talked about what's available in the market now and whether it's a good idea to get started now or wait a year, but not too long because it's coming fast. We also talked about some of the pragmatic issues such as how do you actually program these devices and how faster they really are.

Artificial intelligence is poised to break out of the hype cycle and become an important element in strategic business planning. A lot has been written about AI over the last year, but much of what has been offered for public consumption has been superficial and, in some cases, misleading. AI is neither a simple panacea poised to solve all our business problems, nor is it a looming evil ready to usher in a dystopian future. It is a powerful tool that can aid decision making in a fast-moving, digital business climate--and there's ample evidence that it's already happening. As machine learning practitioners who have spent the better part of their careers involved with AI and machine learning, here is a look into the future of AI.

There is an impedance mismatch between model development using Python and its Machine Learning tool stack and a scalable, reliable data platform. The former is what you need for quick and easy prototyping to build analytic models. The latter is what you need to use for data ingestion, preprocessing, model deployment and monitoring at scale. It requires low latency, high throughput, zero data loss and 24/7 availability requirements. This is the main reason I see in the field why companies struggle to bring analytic models into production to add business value.

In 2012, Krizhevsky et al. [1] rolled out the red carpet for the Deep Convolutional Neural Network. This was the first time this architecture was more successful that traditional, hand-crafted feature learning on the ImageNet. Their DCNN, named AlexNet, contained 8 neural network layers, 5 convolutional and 3 fully-connected. This laid the foundational for the traditional CNN, a convolutional layer followed by an activation function followed by a max pooling operation, (sometimes the pooling operation is omitted to preserve the spatial resolution of the image). Much of the success of Deep Neural Networks has been accredited to these additional layers.

Are you guys interested in making your theoretical foundations of Deep Learning, Machine Learning, and Reinforcement Learning strong enough so that you can do ML with confidence? I have compiled a list of awesome lectures starting from 2012-till date, and the list is continuously growing. Please find the courses in my GitHub repo Deep Learning Drizzle. You're welcome to share it with anyone who might be curious to know these techniques in depth. Feel free to star or fork it & also please send a PR if you have some suggestions!

Intel has released a new platform dubbed Nauta that lets data scientists and developers use Kubernetes and Docker to conduct distributed deep learning (DL) at scale. Its client software, open sourced today under an Apache 2.0 licence, can run on Ubuntu 16.04, Red Hat 7.5, MacOS High Sierra and Windows 10. It supports both batch and streaming inference, its GitHub repo shows. Streaming inference entails deploying a model on a system and processing singular data as it is received). Hugely popular orchestration engine Kubernetes, originally developed by Google, makes it easier to build and deploy container-based applications.

There is the popular saying, "Practice makes perfect". The principle behind it is that the more you work on something, in theory you should get better at it. This principle was put to the test when Google's DeepMind AI trained to beat some of the world's top StarCraft 2 pros, which they successfully did by winning 10-1. The games were played against StarCraft 2 pros TLO and MaNa, where each pro played five games against the AI. We had previously heard that DeepMind wanted to test their AI by playing against StarCraft 2 pros, and these results seem to prove that the AI has what it takes. However as noted by Engadget, DeepMind did not have the benefits you might expect from AI.

DeepMind, a subsidiary of Alphabet that's focused on cracking artificial intelligence, has announced a new landmark in that grand quest: beating humans at galactic warfare. The news: AlphaStar, the company's latest learning algorithm, defeated professional Starcraft II players for the first time, scoring 10 wins and one loss against the pros, called TLO and MaNa. The popular real-time strategy game involves players competing as one of three races to building structures and engaging in combat across a sprawling battlefield. Practice, practice: AlphaStar learned to play within an environment called the AlphaStar League. A large neural network first observed replays of expert human games.

Deep learning has been successfully applied to a variety of image classification tasks. There has been keen interest to apply deep learning in the medical domain, particularly specialties that heavily utilize imaging, such as ophthalmology. One issue that may hinder application of deep learning to the medical domain is the vast amount of data necessary to train deep neural networks (DNNs). Because of regulatory and privacy issues associated with medicine, and the generally proprietary nature of data in medical domains, obtaining large datasets to train DNNs is a challenge, particularly in the ophthalmology domain. Transfer learning is a technique developed to address the issue of applying DNNs for domains with limited data. Prior reports on transfer learning have examined custom networks to fully train or used a particular DNN for transfer learning. However, to the best of my knowledge, no work has systematically examined a suite of DNNs for transfer learning for classification of diabetic retinopathy, diabetic macular edema, and two key features of age-related macular degeneration. This work attempts to investigate transfer learning for classification of these ophthalmic conditions. Part I gives a condensed overview of neural networks and the DNNs under evaluation. Part II gives the reader the necessary background concerning diabetic retinopathy and prior work on classification using retinal fundus photographs. The methodology and results of transfer learning for diabetic retinopathy classification are presented, showing that transfer learning towards this domain is feasible, with promising accuracy. Part III gives an overview of diabetic macular edema, choroidal neovascularization and drusen (features associated with age-related macular degeneration), and presents results for transfer learning evaluation using optical coherence tomography to classify these entities.