By Peter Macdiarmid/Science New Scientist article New Scientist is publishing articles on the most recent advances in scientific research and computing technologies.

This week we’re publishing on new technology news in particular.

First up is new hardware development, and the news we’re most excited about is from an organisation called Enablence Technologies.

Enablance is a company working on software that can use artificial intelligence to read, analyse and generate images for images.

It’s been described by its founder as a new kind of digital humanities tool that aims to create digital images that are “more human than they are digital”.

Enablense is currently working on a machine vision tool called Enblazer that can automatically read, annotate and annotate images of people, places and objects.

This is the first time the company has actually demonstrated this in practice, says CEO and co-founder Dan O’Brien.

He says this type of image recognition will eventually be used in “real time” to make maps of people or places.

And what exactly will be done with the images generated by Enblazers machine vision is yet to be determined, but Enablace is keen to put it to the test.

Enablances work has already been reported in the New Scientist. “

We want to build it so that our cameras can be used for a range of applications in different fields, like healthcare, robotics and education.”

Enablances work has already been reported in the New Scientist.

The company has already shown it can recognise people in a crowd, and it can create a 3D reconstruction of an image by analysing a range to understand how the object is made.

And the company is also looking to use Enblaze to make an image of a human face, and then extract facial information from the image.

“Enblaze is a machine learning algorithm which is able to extract features from an image that have already been captured in a camera,” says O’Briens.

“This is very much like the way we work in our labs, where we can extract features like a person’s age or skin tone from a photo of someone in the lab, and from that, we can create an image from the captured image.

We are interested in learning more about the algorithms behind Enblazes technology.”

What you need to know about the Enblazi machine learning system: Enblance’s project is not just about the machine vision system that it’s built on, though, and its work has been published in a number of academic journals.

It has been awarded a research grant from the Natural Sciences and Engineering Research Council of Canada (NSERC), and a research award from the Australian National University (ANU).

Enablity has been working on this type to help understand the human visual system for some time.

“What we have developed is a system that is able, on a small scale, to identify features that are present in images,” says Enblase co-director and cofounder Chris McConchie.

“For example, we’ve been able to identify people by their faces.

This type of detection system is similar to the way our facial recognition system works, but it is also able to be able to recognise people based on their faces in the same way.

So, we are very excited to be working on the first-ever machine vision based detection system.”

Enblake also has a range the Enflaze project is developing to build an image reconstruction system, says McConnie.

The Enblazed software system works with an Enblazing image library that includes images from various types of cameras, and has a system to generate the images.

“So, the system can be trained to recognize images of human faces, but you can also train it to identify objects that have different attributes that you might expect from human faces or objects,” says McConnie.

This could help the Enablaze system to learn from images that have a variety of attributes, like human faces.

And it could help Enablase to use these images in its software to build its machine vision software.

What we’re learning about Enblue is that it is not going to be an image-based system, it’s going to have to be based on a database.

Enbleeze says its system can read, process and apply images from an array of cameras.

It can also analyse images of a range from a range camera, and apply these images to a map of people based upon the attributes of the face.

McConcha says the system will also be able generate a 3-D reconstruction, and use this to make 3D maps of a person or object.

EnBLaze is already developing software to help it with the image analysis, and to generate a database of images of faces.

The project is in its early stages, but O’Connor says EnBLance is working