Well explained :-)

If I expect an AI bot, it would be to recognize what blocked and free means based on certain parameters, rather than a pattern in an image associated with blocked or free (in this case the leg of the chair might mean blocked). so I assume using it in another room with different walls or chairs ecc would completely confuse the bot. did I got the concept right?

@@BlackXeno not necessarily, if you feed it with tens or even hundreds of thousands of images from houses, it would pretty much work in any room. In this case, if the other room has similar walls and general traits it might still work, just not as well as in the original room.

​@@BlackXeno something like that, let me explain further. Old image recognition methods will have the problem that you mentioned, because they were based of examples of concepts, not concepts themselves. Imagine that you go to a new place, and there's a chair there, you have never seen that chair before, nor have you ever seen that kind of chair, but you know what a chair looks like, so you don't have to be told what it is. AI is the same, you teach the AI to know the essense of your problem, object, etc. Imagine that you want your AI to tell wheter something is a chair or not, so you feed 5 images of red chairs, and 5 images of green "not chair" objects; your data will not be varied enough to tell what a chair is, so it'll try to guess by very limited patterns, so for example, because it always sees photos of red chairs, it will assume that all chairs are red, so green chairs will not be recognized. The key is in training you AI with very varied and clear data, so it starts to recognize your object by it's core concept, and not what differentiates two similar kind of objects. In conclusion, well trained AIs will generalize, that means understanding your problem well enough that they can recognize patterns on data never seen before, just like we do. I hope I was clear enough! :)

Thanks to all. there's a way to ask to the network if it got the right concept? I read once of a tank recognition AI, failed, because associated the background grass to be the enemy. Which proofs the need to truly varied data. I wonder if was possible to verify this ahead, analitically...

🤪

Yes, and of course, with an arduino nano.

sounds more like 'DIE' or 'BUY'

@@joewulf7378 it´s fine electronics but no nanoscience haha

Yes

Actually it is supervised learning since a human manually annotates the images into different classes. In this case either obstacle or no obstacle. Unsupervised would be if both classes would be shot randomly and not ordered by a human into separate folders. And then let the AI model figure out a pattern by itself. For example a VAE would do this.

I think he just meant it could be. That’s what I was thinking too doing it with reinforcement learning instead and let it bump into a few walls. You probably could get away without adding another sensor if you can see that your trying to move forward but aren’t but another sensor would def make it better for detecting when it hit a wall.

@@georgemazzeo7226 could be something to try. But reinforcement learning requires a lot of trail and error. I am talking about a couple thousand tries to get something driving. Let alone advanced navigation. So you need to physically put the car back at the start a couple thousand times which is not really doable.

@@sieuweelferink6852 I was thinking you could add a bump sensor on the front, if the car hits something then it could automatically back up and maybe choose a random direction to turn (perhaps weighted by its confidence in the blockedness of that direction)

Same

Sammee

Awesome :-) Thanks

Same my dude ;D

Thanks, will do!

Should be able to pull it off :-)

what about a ToF(time of flight) camera

@@greatscottlab Or lidar for 3d vision

Can attest to this, yes it can, as long as the stream is 720p@30fps, but even then your frame rate will be close to 1 FPS or maybe a bit higher Edit: Just to add to this, the board is not powerful enough to handle ML based inference and 3d vision at the same time

You could look into the Intel RealSense Depth and SLAM(location) Cameras. They do some of the computation on the camera themselves so it isn't as taxing on the Jetson nano. JetsonHacks is the best channel for that. He's made lots of videos specifically about Jetson + RealSense cameras and even manages a few git repositories, really good stuff.

Absolutely!

Thanks :-)

Good to know ;-)

Never knew his name was Jeremy. Assumed it was Scott all these years...

@@revealingfacts4all LOL. same. Who’s Scott then? Scott must be Jeremy’s mentor, since apparently Scott is great! :-)

@@gregclare kzfaq.info/get/bejne/r75kjcqmtMDOmKc.html&ab_channel=ManticoreEscapee

Good luck!

Its called silverd copper wire

@@electronicguy4550 thank you for answer, but i also mean wire that he shown in his first essential tools video, he shown pliers that can bend this wire acros two holes in prefboard. Thick, strait wire.(i dont know how to describe it beter, im from Czechia.)

Thanks mate :-)

@@greatscottlab Do you have subreddit like electroboom has ?

Nice!!

Glad you liked it!

After reading your comment, GPT-3 laughed in ML

Glad you liked it!

Also try using ROS(robot operating system) sir.

Thank you so much 😀

Using sensors was not the point of the video.

Do you recommend a specific one? I just started looking into AI CV and have some python experience

@@iAmCbasBoy You can choose the Courses from Deep Learning with Andrew Ng. This is with Python. But, if you want to know the details about machine learning and neural networks, you can choose the course Machine Learning with Andrew Ng from Stanford... the last one is free.

Not quite ;-)

@@greatscottlab but with AI, what not to do with self balancing robot?, that would be a good idea

Glad I could help!

@@greatscottlab I love your work. Keep pushing.

Amazing!