#Office – Acronyms pane in Word, another amazing example of #AI embedded in our day to day tools – Powered by Microsoft Graph!

Hi!

Today’s post is, one more time, related to some amazing Artificial Intelligence features embedded in Microsoft Office. And this is very helpful if you work in an organization with tons of Acronyms. I’m sure, you have your own set of acronyms at different levels: Team, Group and Organization.

When you are new to this Acronyms, is very hard to get up to date with all of them. That’s why the Acronyms feature in Word is very important, it may help us and save us lot of time!

The Acronyms page is the [References] tab in the Ribbon, or you can just search for it.

Search for Acronyms Pane in Word

Once, you enabled the pane, it will analyze the text of your Word document and also analyze the definitions mostly used on your organization to get a sense of “what can be an Acronym“. It will leverage the Microsoft Graph to surface definitions of terms that have been previously defined across emails and documents.

The results are amazing:

Word Acronyms page results

Another amazing example of AI in our day to day use.

Happy coding!

Greetings @ Burlington

El Bruno

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#AI – MineRL, play #Minecraft to benefit science!

Hi !

I’ve write a couple of time about project Malmo and Minecraft, so if you like Minecraft and Artificial Intelligence, MineRL will make your day. Let’s start with some basis:

MineRL is a large-scale dataset on Minecraft of seven different tasks, which highlight a variety of research challenges including open-world multi-agent interactions, long-term planning, vision, control, navigation, and explicit and implicit subtask hierarchies.

There are 2 main ways to be involved with MineRL, entering the AI (DL) competition, or playing Minecraft (to create more source data to train and test models!)

In the play more, MineRL want to solve Minecraft using state-of-the-art Machine Learning! To do so, MineRL is creating one of the largest datasets of recorded human player data. The dataset includes a set of tasks which highlights many of the hardest problems in modern-day Reinforcement Learning: sparse rewards and hierarchical policies.

There is plenty of information and details on the main website, and as soon as I finish some of my current work and personal projects, I’ll for sure spend more time here!

More information http://minerl.io/about/

Happy coding!

Greetings @ Toronto

El Bruno

#AI – #Translatotron is not dot a dorky name, it’s maybe the best translator ever #GoogleResearch

Hi !

A couple of days ago, Google presented Translatotron. The name is not the best name, however the idea is amazing:

Google researchers trained a neural network to map audio “voiceprints” from one language to another. After the tool translates an original audio, Translatotron retains the voice and tone of the original speaker. It converts audio input directly to audio output without any intermediary steps.

Model architecture of Translatotron.

As usual, the best way to understand this, is to see Translatotron in action. Let’s take a look at the following audios.

Input (Spanish)
Reference translation (English)
Baseline cascade translation
Translatotron translation (canonical voice)
Translatotron translation (original speaker’s voice )

There is a full set of sample audios here: https://google-research.github.io/lingvo-lab/translatotron/#fisher_1

This is an amazing technology, and also a great starting point for scenarios where it’s important to keep original speaker vocal characteristics. And let me be honest, it’s also scary if you think on Fake Voice scenarios.

Happy coding!

Greetings @ Toronto

El Bruno

Source: Introducing Translatotron: An End-to-End Speech-to-Speech Translation Model

#Event – Resources for the sessions about #DeepLearning and #CustomVision at the @ChicagoCodeCamp

Hi!

Another post-event post, this time with a big thanks to the team behind one of the most amazing event I’ve been this year: Chicago CodeCamp.

I had the chance to meet a lot of amazing people, to learn a lot during the sessions and also to visit the great city of Chicago.

As usual, now it’s time to share slides, code and more.

Deep Learning for Everyone? Challenge Accepted!

Let’s start with the Deep Learning resources


Demos Source Code: https://github.com/elbruno/events/tree/master/2019%2005%2011%20Chicago%20CodeCamp%20Deep%20Learning

Session: How a PoC at home can scale to Enterprise Level using Custom Vision APIs

And also the [How a PoC at home can scale to Enterprise Level using Custom Vision APIs] resources

Demos Source Code: https://github.com/elbruno/events/tree/master/2019%2005%2011%20Chicago%20CodeCamp%20CustomVision

And finally, some Machine Learning.Net, Deep Learning and Custom Vision resources:

My posts on Custom Vision and ONNX

  1. Object recognition with Custom Vision and ONNX in Windows applications using WinML
  2. Object recognition with Custom Vision and ONNX in Windows applications using WinML
  3. Object recognition with Custom Vision and ONNX in Windows applications using Windows ML, drawing frames
  4. Object recognition with Custom Vision and ONNX in Windows applications using Windows ML, calculate FPS
  5. Can’t install Docker on Windows 10 Home, need Pro or Enterprise
  6. Running a Custom Vision project in a local Docker Container
  7. Analyzing images in a Console App using a Custom Vision project in a Docker Container
  8. Analyzing images using PostMan from a Custom Vision project hosted in a Docker Container
  9. Building the CustomVision.ai project in Docker in a RaspberryPi
  10. Container dies immediately upon successful start in a RaspberryPi. Of course, it’s all about TensorFlow dependencies
  11. About ports, IPs and more to access a container hosted in a Raspberry Pi
  12. Average response times using a CustomVision.ai docker container in a RaspberryPi and a PC

Windows 10 and YOLOV2 for Object Detection Series

See you next one in Chicago for some Deep Learning fun!

Happy coding!

Greetings @ Toronto

El Bruno

#Event – See you @ChicagoCodeCamp on May 11, 2019 for some Deep Learning and Custom Vision experiences

Hi !

I’m very lucky to be at the next Chicago CodeCamp with another session around Custom Vision:

How a PoC at home can scale to Enterprise Level using Custom Vision APIs

It all started with a DIY project to use Computer Vision for security cameras at home. A custom Machine Learning model is the core component used to analyze pictures to detect people, animals and more in a house environment. The AI processing is performed at the edge, in dedicated hardware and the collected information is stored in the cloud. The same idea can be applied to several CCTV scenarios, like parking lots, train stations, malls and more. However, moving this into enterprise scale brings a set of challenges, which are going to be described and explained in this session.

More Information: https://www.chicagocodecamp.com/ and remember that we will be also talking about Deep Learning.

Greetings @ Toronto

El Bruno

#Windows10 – Windows Vision Skills (Preview), an amazing set of AI APIs to run in the edge!

Hi!

Today’s announcement is a big one if you are interested on move AI capabilities to the Edge. The Windows team make public the preview of Windows Vision Skills framework:

Windows Vision Skills framework is meant to standardize the way AI and CV is put to use within a WinRT application running on the edge. It aims to abstract away the complexity of AI and CV techniques by simply defining the concept of skills which are modular pieces of code that process input and produce output. The implementation that contains the complex details is encapsulated by an extensible WinRT API that inherits the base class present in this namespace, which leverages built-in Windows primitives which in-turn eases interop with built-in acceleration frameworks or external 3rd party ones.

The official blog explain the basic features of the framework and describes a set of scenarios like Object Detector, Skeletal Detector, and Emotion Recognizer.

We have UWP Apps in the repo samples, and it only took 1 min to setup everything to get the App up and running. In the following image, it smoothly detects a person and a chair.

The next image is the sample for Skeletal detector (as a old Kinect dev, this really makes me happy!)

This is an big announcement, because all of this APIs are native , and that means we can easily use them in

Greetings @ Toronto

El Bruno

References


#AI – AI for Earth, AI tools in the hands of those working to solve global environmental challenges

Hi !

When I was in Ohio @CodeMash, I was lucky enough to meet Jennifer Marsman, Principal Engineer & speaker on the AI for Earth team at Microsoft (@jennifermarsman). She hosted an amazing session where she shared details about some projects on AI for Earth.

AI for Earth puts Microsoft cloud and AI tools in the hands of those working to solve global environmental challenges

See references

The work that the AI for Earth teams are doing are amazing, and I was really impressed by the “Mexican whale story”. The team uses image analysis to identify individual animals in regular persons photos or videos, and using meta data like date and location of a photo or a video, they can generate paths of animal migration. And yes, the photos came from public social media spaces like Facebook, Instagram or YouTube.

So, I got this information as a draft for a while, and now I get some more details and it makes sense to share it. The project name is Wild Me:

Wild Me is using computer vision and deep learning algorithms to power Wildbook, a platform that can identify individual animals within a species.  They also augment their data with an intelligent agent that can mine social media. 

And as usual, a video is the best way to explain this:

Besides Wild Me, there are other amazing projects like SilviaTerra or FarmBeats. You can find the complete list of projects and challenges here (link).

Happy Coding!

Greetings @ Burlington

El Bruno

References

#Office – #Clippy is back in Office for Windows and Mac (powered by #AI)

Hi !

So, after my post yesterday, about the suggestions in Outlook based on an email content, somebody asked me why they don’t see this in Outlook.

I was sharing my experience in Outlook for Mac, in Windows we can find a [Suggested Meeting] option on top of the email content.

And then, when we expand the section, we may find the related meeting information

Happy coding!

Greetings @ Toronto

El Bruno

My posts

#Office – Clippy is back in Office (powered by #AI)

Hi !

I’ve already share some of the new AI features in Office, like Excel Ideas or real-time captions / subtitles in PowerPoint.

Today I’ll switch to Office editor, to share something amazing. I added Clippy to this post title and the idea is similar to the Clippy intents a long time ago:

An AI assistant will analyze the text while you are writing it or reading it, and it will suggest actions related to this text.

A single image is the best way to show this

I received this email last week and I notice that part of the text was automatically highlighted and show an amazing suggestion. It seems that the Word, Outlook editor is becoming more and more intelligent.

And I’m really looking forward to some extensibility SDK, so someone can really bring back Clippy at 100%

Happy coding!

Greetings @ Toronto

El Bruno

My posts

#Office – Ideas Tool para Microsoft Excel basado en AI (#Excel, #AI)

Buenas !

Hoy voy a cambiar a Excel para compartir algunas de las otras increíbles características de Inteligencia Artificial en Microsoft Office: Excel Ideas.

Excel Ideas es esta nueva herramienta que tenemos en Excel que nos permitirá obtener algunas ideas sobre información seleccionada en una hoja de trabajo. Para utilizar esta herramienta, necesitamos seleccionar un conjunto de datos y hacer clic en el botón [ideas] en la cinta de opciones

Microsoft Excel Ideas.jpg

Para escribir este post, he descargado un conjunto de muestra de datos espaciales de SpatialKey (ver referencias). El archivo de seguro de muestra contiene 36.634 registros en Florida para 2012 de una compañía de muestra que implementó un plan de crecimiento agresivo en 2012. Hay columnas de valor asegurado total (TIV) que contienen TIV de 2011 y 2012, por lo que este conjunto de datos es ideal para probar la característica de comparación. Este archivo tiene información de dirección que puede elegir para geocodificar, o puede usar la latitud/longitud existente en el archivo.

Una vez que seleccioné el conjunto completo de datos, es bueno ver las siguientes ideas

Si usted es un usuario de PowerBI, este tipo de información y análisis será familiar. Para mí es bueno ver que tenemos estas capacidades en un solo clic en una herramienta popular como Excel.

Hay más de + 40 Insights y es agradable para navegar por ellos y encontrar que en el Condado de Miami Dade, el deducible del sitio es mayor para las construcciones de marco de acero y hormigón armado.

Microsoft Excel Ideas Sample 03

Con 2 clic podemos analizar algunos datos abiertos relacionados con el tráfico en Ontario y ver cómo el tráfico aumenta lentamente en Hwy 69 desde 1988 hasta 2010.

Microsoft Excel Ideas Sample 04

Una herramienta increíble dentro de Excel para obtener rápidamente información sobre nuestros datos.

Happy coding!

Saludos @ Toronto

El Bruno

References

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