It amazes me how many people have heard about AlphaGo, but not about AlphaGo Zero.  In the future, I predict that we will look back on AlphaGo Zero as the watershed moment in AI development.

Here’s a great over of AlphaGo Zero and the techniques behind it.

AlphaGo Zero is able to achieve all this by employing a novel form of reinforcement learning, in which AlphaGo Zero becomes its own teacher. As explained previously, the system starts off with a single neural network that knows absolutely nothing about the game of Go. By combining this neural network with a powerful search algorithm, it then plays games against itself. As it plays more and more games, the neural network is updated and tuned to predict moves, and even the eventual winner of the games.

In this video, learn about the recently announced IoT Plug and Play, which is based on an open modeling language that allows IoT devices to declare their capabilities.  That declaration, which is called a Device Capability Model, is then presented when IoT devices connect to cloud solutions like Azure IoT Central and Partner Solutions which can then automatically understand the device and start interacting with it– all without writing any code.

   
Learn more about the Digital Twin Definition Language 
Find IoT Plug and Play devices in the Certified for Azure device catalog 
Try Azure IoT for free today: https://aka.ms/aft-iot

Here’s a great review of the most common machine learning algorithms by InfoWorld.

Recall that machine learning is a class of methods for automatically creating predictive models from data. Machine learning algorithms are the engines of machine learning, meaning it is the algorithms that turn a data set into a model. Which kind of algorithm works best (supervised, unsupervised, classification, regression, etc.) depends on the kind of problem you’re solving, the computing resources available, and the nature of the data.

ML.NET is a free, cross-platform and open source machine learning framework designed to bring the power of machine learning (ML) into .NET applications.

Live from Build 2019, we are joined by Cesar De La Torre Llorente who gives us a great overview of what the goals of ML.NET are, and shares with us some of the highlights of the 1.0 release.

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As businesses rush to become more data driven and leverage AI to better serve customers and be more competitive, enterprises are quickly learning that the way to AI readiness leads straight to the cloud. Here’s an interesting article on the state of AI-readiness, sometimes called digital transformation.

But how do companies step up their infrastructure to become “AI ready”? Are they deploying data science platforms and data infrastructure projects on premises or taking advantage of a hybrid, multi-cloud approach to their infrastructure? As more and more companies embrace the “write once, run anywhere” approach to data infrastructure, we can expect more enterprise developments in a combination of on-prem and cloud environments or even a combination of different cloud services for the same application. In a recent O’Reilly Media survey, more than 85% of respondents stated that they plan on using one (or multiple) of the seven major public cloud providers for their data infrastructure projects,

[…]

Enterprises across geographies expressed interest in shifting to a cloud data infrastructure as a means to leveraging AI and Machine Learning with more than 80% of respondents across North America, EMEA and Asia replying that this is their desired choice. A testament to the growing trend towards a hybrid, multi-cloud application development is the finding in the same survey that 1 out of 10 respondents uses all three major cloud providers for some part of their data infrastructure (Google Cloud Platform, AWS and Microsoft Azure).