OpenAI and the Dota 2 Bot

OpenAI – a non-profit AI research company, founded by Elon Musk and Sam Altman, which focuses on developing friendly AI – unveiled their Dota 2 AI Bot in August 2017, which is capable of defeating top Dota professional players in 1v1 matches.

OpenAI Logo

Dota 2 is a multiplayer online battle arena (MOBA) game developed by the Valve Corporation. First released on July 2013, the game is a sequel to the community game Defence of the Ancients (DotA) which was released back in 2003 as a mod for the game Warcraft III.

The Dota 2 Game Logo

A typical match of Dota 2 is played by five-verses-five (5v5), although other variations of the game exists, such as 1v1. The players each choose a ‘hero’ from 115 playable characters, each with its strengths and weaknesses, and various abilities and powers. The game is played in a real-time strategy manner, where each ream battles the other and attempts to destroy the ‘Ancient’ (the large structure on their base) of the opposing team while defending their own.

What is AlphaGo, AlphaGo Zero, and AlphaZero

AlphaGo – developed by the DeepMind team of Google – is an AI program which plays the board game Go.

A Go board (By Donarreiskoffer - Self-photographed, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=43383)

The Go board game is an abstract strategy game, which has been invented in China over 2500 years ago. Despite its simple set of rules, Go is considered to be much more complex than Chess, and is one of the most studied strategy game of all time.

The AlphaGo Logo

The AlphaGo uses a Monte Carlo tree search algorithm to find moves using the trained deep neural network which works as its knowledge core. AlphaGo was initially trained on a training set of over 30 million moves data from human Go matches. It was then further trained by letting it compete against copies of itself using reinforcement learning.

TensorFlow Lite Developer Preview Announced

TensorFlow yesterday (14th Nov) announced the developer preview of TensorFlow Lite, a lightweight solution of TensorFlow for mobile and embedded devices, targeted for low-latency inference of on-device machine learning models.

TensorFlow Lite Logo

TensorFlow Lite is an evolution of TensorFlow Mobile, and designed to be lightweight, cross-platform (Android and iOS for a start), and fast.

Through the Android Neural Networks API, TensorFlow Lite would be capable of utilizing purpose-built machine learning hardware in the devices as they become available.

A trained TensorFlow model can be converted to the TensorFlow Lite format (.tflite) using the provided converter, and deployed to the mobile app (Android or iOS), where the converted model gets executed using the TF Lite Interpreter.

TensorFlow Lite contains a C++ API with a Java API wrapper on Android.

It has out-of-the-box support for MobileNet, Inception V3, and Smart Reply Models.

Read more about TensorFlow Lite on the following links,

• Announcing TensorFlow Lite - Google Developers
• Introduction to TensorFlow Lite 

Related Links,

https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite

https://developer.android.com/ndk/guides/neuralnetworks/index.html

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Machine UI : An IDE for Machine Learning, currently in Alpha

Machine UI, or just "Machine" as it's commonly referred, is an IDE for Machine Learning, which is currently in its Alpha stage. It has been designed to work with TensorFlow, and aims at simplifying setting up machine Learning experiments so that you spend more time experimenting, and less time configuring.

The interface of Machine UI (Note: This is a screenshot from their announcement video)

As per their announcement video, the machine learning experiments are set up visually. The input data, convolutions, and the outputs are placed as nodes on a graph. You can think of it as a more interactive version of the Tensor Board which comes with TensorFlow.

TensorFlow 1.0 Released!

TensorFlow 1.0 has been released!

Just a week back I posted about the announcement of TensorFlow 1.0 and the new features coming. Now, it's finally here. It was released at the first TensorFlow Dev Summit held yesterday (15th Feb) at Mountain View, California.

The official TensorFlow website is now updated for the 1.0 release, which also now has a section for TensorFlow Fold which allows to work with input data of different shapes and sizes, and which has now been made open-source. The installation instructions have also been updated for the latest version, and they have added a guide on how to upgrade your application to TensorFlow 1.0.

The release notes for 1.0 suggests that all the anticipated features are here: The XLA compiler, Debugger, The Java API, Mobile support improvements, and much more. There's also news reports coming in which states that more machine learning models, such as SVMs, will come to TensorFlow. Better support for Keras is also hinted. I'm eagerly waiting for Keras to upgrade to support new features in TensorFlow 1.0.

Related posts:

• http://www.codesofinterest.com/2017/02/tensorflow-1-mobile-and-java-support.html

Related links:

• https://www.tensorflow.org/
• https://research.googleblog.com/2017/02/announcing-tensorflow-fold-deep.html
• http://venturebeat.com/2017/02/15/google-releases-tensorflow-1-0-with-new-machine-learning-tools/
• https://github.com/tensorflow/tensorflow/releases/tag/v1.0.0

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TensorFlow 1.0 announced with better Mobile support and Java API

TensorFlow, with its r0.12 version gave the (much awaited) compatibility to Windows (check How to setup TensorFlow on Windows), and an Experimental API for the GO language. Now, they have announced the first major version - version r1.0 - of TensorFlow, with several exciting features.

While Python would still be the primary API - and would be the most complete API for TensorFlow - version r1.0 will introduce a new, experimental Java API. While it could be far from complete, and may take a few releases to stabilize, having the Java API may open TensorFlow to new possibilities. Also note that in the current release candidates of r1.0, you will need to compile TensorFlow from source in order to get the Java interface (instructions from TensorFlow GitHub page), and it's still only available for Linux and Mac OS.