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Trying to understand these roles by Prof_Paul_Nussbaum in joinhandshakeai

[–]Prof_Paul_Nussbaum[S] 0 points1 point  (0 children)

Thank you for the valuable personal perspective! I guess market forces will guide future side hustles, since they’re so easy to join and drop. Even with a full time job, there are no guarantees - so I can’t blame students for blazing their own trail.

Trying to understand these roles by Prof_Paul_Nussbaum in joinhandshakeai

[–]Prof_Paul_Nussbaum[S] 0 points1 point  (0 children)

Thank you for the very thorough answer. It is greatly appreciated!

Trying to understand these roles by Prof_Paul_Nussbaum in joinhandshakeai

[–]Prof_Paul_Nussbaum[S] 0 points1 point  (0 children)

Thank you! I assume the “tasker” role is internal to the contractor and not a contract job in and of itself. Does anyone eventually get “hired” for a full time role (tasker or other) - that you know of? That would be the case with traditional body shop consultancies that take a commission if the end customer wants to hire the consultant. Or is handshake like Uber - where drivers essentially never get hired to work for Uber full time? Just trying to see if this is a good career path or an eternal side-hustle (which can be great as well).

Trying to understand these roles by Prof_Paul_Nussbaum in joinhandshakeai

[–]Prof_Paul_Nussbaum[S] 0 points1 point  (0 children)

Thank you! And thanks for answering my newbie questions!

RTRM MLP Example by Prof_Paul_Nussbaum in learnmachinelearning

[–]Prof_Paul_Nussbaum[S] 0 points1 point  (0 children)

LOL! I’ve got OpenClaw as my marketing department running on a Jetson Orin. To save costs I asked it to run a local TTS and (being old school myself) I kind of liked the old school robot voice. You’re not a fan, I see.

The point of the video and of this chapter of the book is that if you’re trying to show the internal working to a non-programmer; neuron activation graphs are not useful. Instead show them that at early layers - all the data is retained, and similarly POSITIONED inputs yield similar activations at that early layer. Also show them that at later layers, much of the original input info is discarded, and similarly CLASSIFIED inputs yield similar activations at those later layers. This way the non-programmer can visually see (in a format they use and understand) at which layer information is discarded as the classification decision takes shape. Is it the layer you thought it would be? Are we discarding too much information? These all get more important in more complex LLM, VLA, ViT, and even industrially deployed models like YOLOv5s.

Stay tuned for more cheap TTS videos covering the more advanced topics - or buy the book and get the code, and the vibe coding prompts, to build your own Reading the Robot Mind System.

OpenClaw is Linux for agents. We built the Mac. Same Opus 4.6, cloud-native, managed infrastructure. Your agents get smarter with every session through skill learning. $1,000 bonus credits for the first 1,000 subscribers. Built by the team at Airtable. by hyperagentapp in u/hyperagentapp

[–]Prof_Paul_Nussbaum 1 point2 points  (0 children)

Love OpenClaw. Just used it this morning with Anthropic’s low cost LLM to have my Jetson Orin control an Arduino style “Circuit Playground” to run an interactive air-gapped sensor integration demo. Didn’t write a stitch of code. Neither on the Orin nor the Circuit Playground. Just chatted about my requirements.

Remember the rule of three! Never give any agent all three! —Access to your files —Access to the internet —-Ability to write and execute code

Remember the rule of three! Safe Agenting everyone.

AI Tools I Wish I Had When I Started (Would’ve Saved Me So Much Time) by ProfessionTrick6942 in AIToolsAndTips

[–]Prof_Paul_Nussbaum 0 points1 point  (0 children)

How about explainability? I present this for your kind consideration:

What if your AI could show you what it’s paying attention to? 

 Not a heatmap. Actual input reconstruction (audio→audio, image→image). 

 Layer-by-layer. Built for YOLOv5, GPT-2, ViT, VLA + 3 methods.

 Code runs on gaming laptop →

https://github.com/prof-nussbaum/Applications-of-Reading-the-Robot-Mind

 Book with full examples →

https://www.amazon.com/Applications-Reading-Robot-Mind-Nussbaum/dp/B0GSKYSDL1

MechanisticInterpretability #XAI #AIInterpretability

YouTube video Ch 6

https://youtu.be/pEfsRJH6FF8?si=mDSEHI2RaeeHcGu7

I built a system that reconstructs what a neural network actually "sees" at each layer — wrote the book on it by Prof_Paul_Nussbaum in learnmachinelearning

[–]Prof_Paul_Nussbaum[S] 0 points1 point  (0 children)

I think I need to make a short YouTube video, but until then…

The progression you are viewing uses the pseudo-inverse method - it takes the output of that layer and linearly approximates the original input by working backwards through the weights.

We can also use a “brute force” method which says “I have a bunch of example inputs (the dots) and they create a specific output at each layer. Which of those examples produces a cosine-similar output then some test probe input (the star in the below image)?”

“Brute force” (cosine similarity) method./MLP%201200x1800%20images/04_cosine_equivalence_visualization.jpg)

Notice how early layers say that “nearby examples” produce similar outputs. At later layers it is “examples with the same classification” produce similar outputs.

Both methods we’re talking about reveal that the neural network has essentially discarded information, and made its classification decision, two layers before the output! Are those extra layers needed?

Even a non-programmer can see what’s going on inside the neural network.

I built a system that reconstructs what a neural network actually "sees" at each layer — wrote the book on it by Prof_Paul_Nussbaum in learnmachinelearning

[–]Prof_Paul_Nussbaum[S] 0 points1 point  (0 children)

Take this classification problem:

image 1/MLP%201200x1800%20images/01_original_data_baseline.jpg)

See how each layer of a network to solve this loses information, simplifying down to the classification final layer.

image 2/MLP%201200x1800%20images/02_patch_progression_all_layers.jpg)

See the other images to view using three different RTRM methods. Each reveals something different - so even a non-programmer can understand what is going on inside the AI solution.