AI is progressing at incredible speed!

Several people tend to think that all the new AI technologies like Convolutional neural networks, Recurrent Neural Networks, Generative adversarial networks,etc.. are used mainly in tech giants like Google , Microsoft , etc.. , in reality many enterprises are already leveraging deep learning in production like Zalando, Instacart and many others . Well known deep learning frameworks like Keras, Tensorflow, CNTK, Caffe2, etc.. are now finally reaching a larger audience.

Big data engines like Spark are finally able to pilot also deep learning workloads and also the first steps to make large deep neural networks models fit inside small cpu/low memory, occasionally connected IOT devices are coming.

Finally new hardware has been built specifically for deep learning :


But the AI space never sleeps and we are already seeing arriving new solutions/frameworks and architectures that are actually under development:

Ray: the new distributed execution framework that aims to replace the well known Spark!

Pytorch and framework that wants to compete and beat all the existing deep learning frameworks

To overcome one of the biggest problems on deep learning : amount of training data, snorkel a new framework has been designed to create brand new training data with little human interaction

Finally to help to create a better integration and performance of the deep learning models with the applications that want to consume those models a new prediction serving system Clipper has been designed .

The speed of AI evolution is incredible and be prepared to see much more than this in the near future!


My Top 2 Microsoft Build 2017 Sessions

Let’s start with Number 1 this is the Visionary Cloud that is arriving , compute nodes combined with FPGA neurons that act as Hardware Micro services communicating and changing their internal code directly attached to the Azure Network , like a global neural network. Do you want to know more? Click here and check directly with the new video index AI the content of this presentation jumping directly on the portions of the video that you like, searching words, concepts and people appearing in the video.

We can then look here at Number 2 (go to 1:01:54) :

Matt Velloso teaching to a Robot (Zenbo) how to recognize the images the robot sees using Microsoft Bot Framework and the new Custom Image Recognition Service.

Do you want to explore more?

Go here at channel9 and have fun exploring all the massive updates that has been released!

Image recognition for everyone

Warning : I’m NOT a data scientist, but I huge fan of cool technology !

Today I want to write of a new functionality that amazes me and that can help you to literally do “magic” things that you can think can be exclusive of super data scientists expert of deep learning and frameworks like TensorFlow, CNTK, Caffe,etc…

Imagine the following: someone  trains huge neural networks (imagine like mini brains) for weeks/months using a lot of GPUs on thousands and thousands of images.

These mini brains are then used to classify images and say something like: a car is present, a human is present, a cat etc… . Now one of “bad things” of neural networks is that usually you cannot understand how they really work internally and what is the “thinking process” of a neural network.


However latest studies on neural networks have found a way to “extract” this knowledge and Microsoft has delivered right now in April this knowledge or better these models.

Now I want to show you an example on how to do this.

Let’s grab some images of the Simpsons :


and some other images of the Flintstones:


For example 13 images of Simpson cartoon and 11 of Flintstones. And let’s build a program that can predict given a new image that is not part of the two image sets if it is a Simpson or Flintstone image. I’ve chosen cartoons but you can apply this to any image that you want to process (watches? consoles? vacation places? etc…).

The idea is the following: I take the images I have and give these images to the “model” that has been trained . Now the result of this process will be , for each image, a collection of “numbers” that are the representation of that image according to the neural network. An example to understand that: our DNA is a small fraction of ourselves but it can “represent” us, so these “numbers” are the DNA of the image.

Now that we have the image represented by a simple array of numbers, we can use a “normal” machine learning technique like linear regression to leverage this simplified representation of the image and learn how to classify them.

Applying the sample R code described in the article to only a small sample of images (13 and 11 respectively) using 80% for training and 20% for scoring we obtained the following result:


A good 75% on a very small amount of images !