I already write about some cool features embedded in Office to help us to be more productive. These features are mostly based on Artificial Intelligence. And one great example is the Microsoft Word Editor feature.
Today I was reading an article “New AI tools help writers be more clear, concise and inclusive in Office and across the web” (see references) , and I realize that the Microsoft Word Editor focus not only on productivity, it also help us to fight bias and to be more inclusive while we are writing.
Important: this feature is not enabled by default. In order to enable this, we must follow these steps “
Open Word Options
Select Proofing section
Go to [When correcting spelling …. / Writing Style] option
Enable the [Inclusiveness] options
Once we enable these features, the editor will start to analyze for age bias, cultural bias, and more.
I’ve tested this, and it’s nice to get suggestions based on gender bias, like use firefighter instead of fireman.
It also suggest other options if we are using “whitelist”
In the references sections, I shared 2 amazing articles where Microsoft explains the details about this feature.
And, in the personal side, this is also a great tool for non-english speakers like myself, to learn Inclusiveness and also to do a better work.
I already write about some cool features embedded in Office to help us to be more productive. Some of them are AI based, and others are so simple and useful, like this one, that they deserve a post.
This is a classic one: we are trying to avoid big attachment in emails, if you are still attaching files to your emails, please STOP. Instead of attaching files, we store these files in OneDrive or Sharepoint, and we share the link.
One extra step here, is validate the permissions for the file or folder. You don’t want to share a file, and forget to grant permissions to the desired audience.
That’s why, now Outlook will check the links that you embed in your email body and the recipients of your email and show the following message if some recipients don’t have access to links in the message.
Cambios de ultimo momento. Mañana estaré como uno de los ponentes virtuales en el Global AI on Tour para Argentina. Estaré hablando de drones, y más que hablando más bien programando un poco un drone para pasarlo bien. Y utilizando un poco de AI para hacer esto más divertido.
Para subir el nivel, en la agenda pueden ver que los demás speakers van a tocar temas mucho más serios e interesantes.
In my previous post, I shared an example where I analyzed the camera feed using a Image Recognition model created using Custom Vision. Today I’ll expand the sample, and show in real time the detected MVPs logos with a frame in the drone camera feed.
Let’s take a look at the demo working in the following image.
In the top of the image, we can see the app console log, with the information received for each analyzed frame. When an image is detected, we can see the tag, the probability and the bounding box coordinates.
In order to position the frames in the correct location, I need to make some math using the current camera and image size and the returned bounding box values for, height, left, top and width. Lines 87-110.