@JustSayin24
That real-time kernel activation map was life-changing. If, whilst editing these videos, you've ever questioned whether the vast amounts of effort are worth what amounts to a brief, 10s clip, just know that it's these moments which have truly stuck with me.
@somnvm37
"one way to think about this vector, is as a point in 4096 dimentional space" give me a minute, I now gotta visualise a 4096 dimentional space in my head.
@2hcy
I've been in the field for 10 years and never had anyone describe this so clearly and visually. Brilliant, thank you!
@kellymoses8566
Computers not being fast enough to make a correct algorithm practically usable reminds me of Reed–Solomon error correcting codes. They were developed in 1960 but computers were too slow for them to be practical. They went unused until 1982 when they were used in Compact Discs after computers had become fast enough.
@4.0.4
The visualization is what takes this video from good to fantastic. It's very evident you put a lot of effort into making this visually engaging, which is very didactic!
@millanferende6723
2:17 - THIS symbolizes the difference between "AI generated content YouTube channels" and real human-beings YouTube channels, like yours.
@optiphonic_
Your visualisations helped a few concepts click for me around the layers and activations Ive struggled to understand for years. Thanks!
@frostebyte
I really appreciate how well you communicate non-verbally despite using very little A-roll. You're expressions are clear yet natural even while reading, enunciating and employing tone, and there's no fluff; you have a neutral point for your hands to signal that there's no gesture to pay attention to. I couldn't find anything to critique in your vids if I tried and this seems particularly easy to overlook. Thanks for every absolute banger!
@michaelala4924
Awesome video! Funny how the moment we stopped understanding AI also appears to be the moment it started working lol
@ernestuz
I was working with deep neural networks at the university during the late 90s, the main issue that stopped all progress was the use of a kind of functions between layers (the sigmoid as activation function), this effectively stopped the learning backpropagating from the output layers and limiting how many layers you can use (the problem is called the vanishing gradient). Once people rediscovered ReLU (it was invented in the early 70s, I believe, but I think the inventor published it in Japanese, so it went unnoticed) deep neural networks became possible. High computation needs were only a problem if you wanted real time or low latency, those days we used to leaving the computer calculating during nighttime to get something next day.
@siddharth-gandhi
Stellar video, you’re gifted at communication. Keep at it!
@zhoudan4387
It is not that the neural networks magically “understands” what is important. It is that the information is not random, so can be synthesized into smaller chunks. The synthesis process is what creates patterns, thus understanding.
@krismicinski
This is seriously one of the most excellent technical explainer videos I have ever seen. It mixes an abstract, cartoon-level understanding with a near-totally-precise explanation and the visualizations are seriously well developed. Some individual scenes and shots clearly took a ton of effort and unlike the vast majority of work in this space there is no hype.
@AlvingGarcia
I've been studying AI for the past year and the first 2 minutes was the best explanation I have see of how Transformers and ChatGPT works so far. Ive studied everything from Andrew Ngs Coursera courses, to Andrej Karpathy and more. Thank you for this great video!
@phlogiston-aether
An absolute gem of deeply resonant explanations coring complex topics presented flawlessly, kudos to you.
@ben9089
This was an incredible introduction in just 18 minutes. I continue to be blown away by this channel.
@talkysassis
My dad graduated around 2009. One of his teachers (that was my teacher at Computer Science too) said at the time neural networks would thrive if they find their place in practical applications, but at the time most computer work as analogue to human work, but we've been learning how to abstract everything and work from there. That was when programs designed for computers instead of digital versions of physical media got very very popular. As he said: the tools were already there, we just needed to know how to use them, and we would shift from designing computer programs from real world to design the real world around computers. This teacher is still one of the smartest people I've ever seen so far. To put that into perspective, digital document control until a few years ago was very tied to how we handled them with paper and programs for dedicated digital control were a massive change of paradigm when they got adopted. We now tie small databases with documents, link them to project files, communicate projects with attributes... What a good place for something like NNs.
@totheknee
17:02 - Which mostly forgotten AI algorithms are there? We have Expert Systems, Neural Networks, Backtracking, Semantic Nets, Decision Theory, Fuzzy Logic, Temporal Difference, and that's about it.
@manic-pixie
I was literally talking to my roommate about this last night!! Thanks for the excellent video
@WelchLabsVideo