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.
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
page is the [References] tab in the Ribbon, or you can just search for it.
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.
amazing example of AI in our day to day use.
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!
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.
As usual, the best way to understand this, is to see Translatotronin action. Let’s take a look at the following audios.
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.
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.
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:
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
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
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
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).
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%
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
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.
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.
Una herramienta increíble dentro de Excel para obtener rápidamente información sobre nuestros datos.