> but how many are actually doing AI programming in Snap?
Remember, I am still waiting for your definition of what "_really_ programming" is, that classifies Scheme as "real" programming language, but not Snap!.
Clue: it's impossible to make such a definition, because Snap! is fully as powerful as Scheme (and not in the trivial sense of "Turing Completeness"), just easier for kids to use.
Again, you're only proving that you didn't read or understand a thing I wrote. I already gave you links to Ken Kahn's eCraft2Learn project and Jen Mönig's demos, but obviously you didn't look at any of it.
Since you refuse to look at or acknowledge anything I link to, I will walk you through some of it here.
TL;DR: Ken and Jens and others are successfully using pure Snap! to teach AI and machine learning to kids. This directly contradicts all of your accusations. Here is the proof.
Ken arrived at the MIT Artificial Intelligence Laboratory in the 1970s, where Marvin Minsky and Seymour Papert served on his PhD committee. His thesis "Creation of Computer Animation from Story Descriptions" was a very early example of generative AI.
During the 1970s (specifically between 1973 and 1979), Ken taught artificial intelligence to elementary school children as a graduate researcher in the MIT AI Laboratory's Logo Group.
He developed a distributed concurrent constraint visual programming language called Pictorial Janus in 1990-1991.
https://en.wikipedia.org/wiki/Janus_(concurrent_constraint_p...
From 1995-present, he developed a visual programming language called ToonTalk, that looks and feels like a 3D video game, for teaching programming and high level CS concepts like concurrency and message passing to kids.
https://toontalk.com/
https://toontalk.com/English/kenkahn.htm
https://en.wikipedia.org/wiki/ToonTalk
53 years after he started teaching Logo to kids with Seymour Papert at the MIT AI Lab Logo Group, he's still teaching AI Programming and Computer Science to kids, now with Snap! and his eCraft2Learn project. Here are his 172 citations on google scholar:
https://scholar.google.com/citations?user=9hQiyqcAAAAJ&hl=en
Here's a video of Jens Mönig demonstrating the data extensions to Snap! that enable programming neural nets in pure Snap! block code.
No JavaScript. No WASM. No WebGPU or WebGL. Pure Snap! blocks.
Again, disproving your accusations that Snap! is bullshit, a toy, has no connection to the real world, and is not useful for teaching AI or advanced computer science concepts.
What's new in Snap! 11 - Data - Extensions - Neural Networks
https://www.youtube.com/watch?v=U9W04TEMBUk
My (Jens Mönig's) closing keynote address at SnapCon25 in Heidelberg, introducing deep machine learning capabilities to Snap as an alternative way to create custom predicate blocks.
The projects created "live" during this talk can be found here:
Backpropagation ANN Sprite Layers:
https://snap.berkeley.edu/snap/snap.html#present:Username=je...
Instruments Recognition:
https://snap.berkeley.edu/snap/snap.html#present:Username=je...
Gesture Recognition:
https://snap.berkeley.edu/snap/snap.html#present:Username=je...
Transcript excerpts about how Snap! teaches AI programming and CS concepts to kids. All quotes are from Jens Mönig's closing keynote address at SnapCon25 (lightly edited for clarity), with links to the video timestamps so you can verify each quote's source of truth:
NOT A TOY: IT TEACHES THE HIGH-LEVEL IDEAS
Abstraction has nothing to do with technology -- it is philosophy
https://www.youtube.com/watch?v=U9W04TEMBUk&t=554s
> This is not about ones and zeros. This is not about how a processor works. This is not about how technology works. This has nothing to do with technology. This is all about philosophy. This is all about how we humans think about things.
If you think teaching merge sort is teaching algorithms, you are not doing your job
https://www.youtube.com/watch?v=U9W04TEMBUk&t=4518s
> If you're teaching merge sort and you think you're teaching algorithms, that's fine for teaching an algorithm, but you're not doing your job.
Hyperblocks: if it takes one number, it takes any dimension of numbers
https://www.youtube.com/watch?v=U9W04TEMBUk&t=319s
> The cool thing about Snap! that we've told you all along, and that's been a theme of this conference, is that if it takes one number, it takes one number in any dimension, or it takes any dimension of numbers.
REAL NEURAL NETWORKS, IMPLEMENTED IN SNAP! ITSELF
There is no secret JavaScript block
https://www.youtube.com/watch?v=U9W04TEMBUk&t=3757s
> These neural networks are all implemented in Snap! itself. There is no secret JavaScript block in there. It's all implemented using this new object-oriented system that we have in there, where we just have prototypical inheritance with actual objects with a new request block.
The Nobel Prize in Physics, in one line of blocks
https://www.youtube.com/watch?v=U9W04TEMBUk&t=2097s
> This thing here is the current Nobel Prize of Physics. And I've seen more complicated code than that.
Asked on stage whether that script is the whole perceptron
https://www.youtube.com/watch?v=U9W04TEMBUk&t=4672s
> "Is that all there is to a perceptron?" -- "Yes." -- "So there's not any hidden magic?" -- "Nope. This is why we wanted to have it out in the open. There's no -- this is it."
KIDS CAN ACTUALLY DO THIS
A perceptron is simpler than the projects the kids in your classes already write
https://www.youtube.com/watch?v=U9W04TEMBUk&t=1234s
> This is a perceptron and this is a layer in a neural network. You've all written more complicated stuff than this, and the kids in your classes have written more complicated projects than this.
Using neural nets in Snap! is high school work
https://www.youtube.com/watch?v=U9W04TEMBUk&t=4490s
> I would expect that just using these neural nets to be something that you could probably do in high school.
Not teaching backpropagation in college is killing computer science
https://www.youtube.com/watch?v=U9W04TEMBUk&t=4502s
> Honestly, I think if you're not teaching backpropagation in college, you're killing computer science. This is the most relevant algorithm around.
CONNECTED TO THE REAL WORLD
Training a net live, on stage, on his own harmonica and recorder
https://www.youtube.com/watch?v=U9W04TEMBUk&t=2460s
> It'd be way cooler if we could use our own data. So let me use my own data.
Same algorithm: one instrument from another, one drawing from another, a sea mine from a rock
https://www.youtube.com/watch?v=U9W04TEMBUk&t=3723s
> It's the same neural network, the same algorithm, that can distinguish one instrument from another, one drawing from another, a sea mine from a rock. It's the same thing, folks.
Snap! 11 talks to Arduinos and speaks websockets
https://www.youtube.com/watch?v=U9W04TEMBUk&t=3800s
> You've seen [Jan?] show that now we can connect to Arduinos in Snap! 11, because he has ported Snap4Arduino -- he called it the S4A bridge, which is the most enormous understatement there is. Bernat has contributed websockets, which is immensely useful.
AND NOT ONE INCH OF AI HYPE
Anyone selling you "explainable AI" is lying to you
https://www.youtube.com/watch?v=U9W04TEMBUk&t=2272s
> If any company, if any mogul is telling you that they're working on explainable AI, they are lying to you. They're working on some chatbot that is going to come up with some gibberish that is going to be an explanation, but it's not going to be what's actually going on. There is no way to explain what's going on as soon as you have one hidden layer.
Would you take a life and death decision on an 80 percent chance?
https://www.youtube.com/watch?v=U9W04TEMBUk&t=1897s
> Think about 80%. This is a decision, friend or foe. Are you going to kill somebody with an 80% chance? Probably not. Are you going to take life and death decisions on an 80% chance? Probably not.
This might be the last algorithm we get to teach, so we had better teach it well
https://www.youtube.com/watch?v=U9W04TEMBUk&t=3739s
> This thing is eating up our lunch. We want to teach algorithms. This might be the last algorithm we get to teach. We better teach it well.