Fornux
20 Poirier J8V 1A6 Gatineau, QC, Canada

th Update In this update we added more accuracy and quality of different object detection by changing the core engine. Now we also added our software which is combined with temperature detection for Thermal Infrared cameras. This feature adds to the complexity of the "All seeing" AI. In the next few weeks, we will be talking about a novel technique that will incorporate depth recognition. This feature will bring about price reduction in the industry of auto manufacturer in regards to technology for example.

9th Update Welcome to another update where we wanted to show you this video. In this video we try to point out the accuracy and quality compared to other engines in the works, at detecting objects. This is only a sample as more objects will be detected overtime for a more complete result. This results in a higher number of object detected overall. Next, consists of developping a more accurate system.

8th Update First off, we would like to welcome everyone in the new year! 2020 has been full of challenges, and 2021 doesn't feel like giving anyone a break so we'll push through with everything we got. Secondly, we've been doing just that, working during the holidays. We've surprisingly made good progress! We're currently integrating and optimizing core functionality that will vectorialize each frame of a live video from the start. This "object recognition" and the as...sociation between frames will be done on a much higher level of accuracy, as you can compare the two pictures below. The next step consists of testing live feed, and adding some context. The next time you'll hear form us, we'll post a video to demonstrate the difference.

We all know how difficult of a year it's been, more then ever. Sometimes the holidays are just the break that we need to find the strength, and to refill our energy levels to keep pushing in the next year. In hoping that the magic of Christmas fills every corner of your heart and home with joy, now and always. Wishing everyone a Merry Christmas!

7th Update Last update we started working on "integrating regressions to trace back objects and to predict their positions." Although it's relatively easy to operationalize, it's another challenge to have the right algorithm for the right tasks. To understand, regression concerns modeling the relationship between variables that are iteratively refined using a measure of error in the predictions made by the model.... Regression methods are a workhorse of statistics and have been co-opted into statistical machine learning. This may be confusing because we can use regression to designate the class of problem, and the class of algorithm as well. Thus, regression is a process. One that is very peculiar, since the objectives are the likes of which we have never seen before. Here's a look at some of the most popular regression algorithms that are considered: Ordinary Least Squares Regression (OLSR) Linear Regression Logistic Regression Stepwise Regression Multivariate Adaptive Regression Splines (MARS) Locally Estimated Scatterplot Smoothing (LOESS)

Welcome to the 6th update Here's a demonstration of the state of Artificial Narrow Intelligence (A.N.I.) in 2020 using the popular OpenCV, based on TensorFlow. We are planning to integrate regressions to trace back objects and to predict their positions. We would also like the public to be aware that we are targeting a new paradigm called Artificial Collective Intelligence (A.C.I). It will complement the existing Artificial General Intelligence (A.G.I.) and Artificial Super Intelligence (A.S.I.). https://youtu.be/2c4ow2eqEfo

5th update Nov 1st - Nov 15th We're now implementing movement detection by linking the continuity of the objects across different frames of a video. This will allow to extrapolate the position of the object in any point in time.... Stay tuned for a video on the latest step from Philippe Bouchard on November 15th!

4th update Oct 19th - Nov 1st Having the skeleton built, we can now implement the inference of particular instances by reference to vectorization of static images.... So in other words, we'll be able to deduce object types present in images, by converting the pixel-based ones into vector-based versions with every facet treated as a line or shape. (i.e. image shown on the bottom which on the right represents a pixel-based image, versus the left representing a vector-based version) You might think this only applies to images, but this also include any sort of text, symbols, and other tiny details. So it doesn't only apply to object recognition.