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😂

The presenter is fake. She is a robot because no one with human feelings and a soul can move their arms and hands like that. Someone who controls the robot thought it was a good idea to give the AI ​​10,000 hours of a conductor's time, so that it could learn to move its arms. Unreal. It's like watching a 90s robot dance, perfect movements, zero soul. She is so histrionic that it does not help in the explanation, it simply, ..., distracts. It's like a child's performance at school.

Loved this

Precise explanation. Thank you very much

Thank you for the video. Somehow I’m a bit disappointed, but it’s my fault. From the title, I expected to hear how deep learning is complementing convolutional networks, but deep learning is mentioned just superficially. Maybe a next video?

Deep learning seems like a perfect match for hyperspectral imaging.

yyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyy

Which software can provide satelite digitization with AI?

Great!

Great Channel. Great Idea. Her accent sounds great for American Ears. Very attractive rhythm to listen too.

Nice Explanation, worth watching, very informative

النَّظِيرُ فِي عِلْمِ الْفَلَكِ : نُقْطَةٌ فِي السَّمَاءِ عَلَى خَطٍّ عَمُودِيٍّ مِنَ الْمُرَاقِبِ تَحْتَ قَدَمَيْهِ مُبَاشَرَةً

What is this website

This reminds me of the way Johny Srouji presents a new Apple Silicon architecture in the Apple keynotes. Very precise, focused and polished.

Really good content, but please change the host/speaker

situations complexes

Great work

We love you Julie

Fun Fact: She's AI

When deep learning, meets satellite imagery, what do you get ? Fires like Hawaii that can burn steel and take 90 degree corners like a street racer. Thank you I needed to see that to truly have the pieces snap into focus, no stupid puns or jokes at all. Serious as those fires.

very nice video and the articulation is very good.

Holy mf raw audio

Il aurait fallu nommer cette vidéo autrement

Wonderful 👏

I really don't understand the value of this video series. It's pretty obvious what the challenges are, but this doesn't teach how to do anything or give great examples. It doesn't showcase any software. It doesn't really do anything. What is the point of this video?

Le vocal est très bien réussi ainsi que la présentation. J'ai beaucoup aimé

Nice video ! Feel free to check if you have requests for internships, maybe people are waiting for answers... 😄

Et dans les faits aujourd'hui, on ne peut pas dire que ces grandes manœuvres ont permis à ses troupes d'être efficaces.

"l'objet est difficilement identifiable à l'œil humain". Vous en faites un peu trop pour vendre votre outil là 😄 Professionnellement, un œil entraîné

Good job, guys

Thanks Julie and Earthcube Team!

Multiple images for same trajectory to superimpose will give accuracy

This is basic, non-specific drivel ! Any shcool-kid with an interest will have thought of this. And start with an AI system that makes her "English" understandable !!

The source from witch to attract satellite imagery is limited or limited to just, say one satellite? To have a off-Nadir is sometimes better than Nadir, for instance knowing height on a building. Also is there any consideration of privacy...

I keep looking at the hand movement and forgot to listen. It is weird.

Are u French?

From baking cupcakes, to this.... hmm.

i speak fluent English, but i bet it doesn't sound this smooth and beautiful!!!

When will video #4 be available?

Thanks for the video!

Does this have to do anything with geomatics?

hello

This was fascinating

Espectacular Video. Muchas gracias por compartir el conocimiento y la metodologia para la realizacion de deteccion. Muy interesante.

So concise and clear.

Hi Julie, I listened to your RUclips video with great interest and found it extremely professional and informative...really outstanding job! I was hoping you may be able to answer a couple of questions I have that are inspired by your presentation: Given: - NVIDIA's plans to build its "Eos" supercomputer, a machine expected to be the world’s fastest AI system after it begins operations later this year, and its advertised specifications: -- 18.4 exaflops of AI computing performance -- 275 petaflops of traditional scientific computing performance - the current state of the art deep learning algorithms as they apply to satellite images; be they Earthcube's, Preligens' or a widely accepted performance standard - the following Worldview-3's Specs: -- 0.31m Pan Sensor resolution (aka GSD, Ground Sample Distance; off-nadir is geometric mean) -- 680,000 sq km capacity per day 1. How many small tiles do you estimate would have to be created in order to be processed by the current neural networks? 2. How long do you estimate it take NVIDIA's Eos to process the number of tiles estimated in question one? 3. On average, how many distinct objects are classified by the current neural networks used in commercial or laboratory settings? 4. Looking ten years out, how many distinct objects do you think neural networks, coupled with forecasted improvements in conventional (non-quantum) supercomputering, will be able to simultaneously classify? I sincerely appreciate your time and please don't spend too much of it figuring out highly informed answers. I'm just looking for "ballpark" figures from a recognized expert in the field. If you have a Patreon or a PayPal account, I am more than willing to entertain compensation for the time you spend answering my questions. I wish you and yours the best, and I hope all are safe, happy, and healthy. :-) Thank you and kind regards, Rob Reference: nvidianews.nvidia.com/news/nvidia-announces-dgx-h100-systems-worlds-most-advanced-enterprise-ai-infrastructure

great presentation.. i was looking this

Degaussing dock?

What is a degaussing... Port?

Y aurait-il une place piur les cryptologues?