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LATEST POSTS
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Skill Foundry – ARTIFICIAL INTELLIGENCE WITH PYTHON
INTRODUCTION………………………………………………………………………………………….. 3
Setting Up AI Development Environment with Python……………………………….… 7
Understanding Machine Learning — The Heart of AI…………………………………… 11
Supervised Learning Deep Dive — Regression and Classification Models………. 16
Unsupervised Learning Deep Dive — Discovering Hidden Patterns………………. 21
Neural Networks Fundamentals — Building Brains for AI ……………………………. 26
Project — Build a Neural Network to Classify Handwritten Digits ………………. 30
Deep Learning for Image Classification — CNNs Explained………………………… 33
Advanced Image Classification — Transfer Learning………………………………….. 37
Natural Language Processing (NLP) Basics with Python…………………………….. 41
Spam Detection Using Machine Learning …………………………………………………. 45
Deep Learning for Text Classification (with NLP) …………………………………….. 48
Computer Vision Basics and Image Classification ……………………………………. 51
AI for Automation: Files, Web, and Emails ………………………………………………. 56
AI Chatbots and Virtual Assistants …………………………………………………………… 61 -
Eyeline Labs VChain – Chain-of-Visual-Thought for Reasoning in Video Generation for better AI physics
https://eyeline-labs.github.io/VChain/
https://github.com/Eyeline-Labs/VChain
Recent video generation models can produce smooth and visually appealing clips, but they often struggle to synthesize complex dynamics with a coherent chain of consequences. Accurately modeling visual outcomes and state transitions over time remains a core challenge. In contrast, large language and multimodal models (e.g., GPT-4o) exhibit strong visual state reasoning and future prediction capabilities. To bridge these strengths, we introduce VChain, a novel inference-time chain-of-visual-thought framework that injects visual reasoning signals from multimodal models into video generation. Specifically, VChain contains a dedicated pipeline that leverages large multimodal models to generate a sparse set of critical keyframes as snapshots, which are then used to guide the sparse inference-time tuning of a pre-trained video generator only at these key moments. Our approach is tuning-efficient, introduces minimal overhead and avoids dense supervision. Extensive experiments on complex, multi-step scenarios show that VChain significantly enhances the quality of generated videos.
FEATURED POSTS
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Louis Rosenberg – The profound danger of conversational AI
https://venturebeat.com/ai/the-profound-danger-of-conversational-ai/
We are about to enter a new age of Conversational Computing and despite the many benefits, it will unleash a new level of danger for targeted #manipulation, #persuasion, and #coercion.
Instead of firing buckshot into broad populations, these new AI methods will function more like “heat-seeking missiles” that mark users as individual targets and adapt their conversational tactics in real time, adjusting to each individual personally as they work to maximize their persuasive impact.
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ComfyDock – The Easiest (Free) Way to Safely Run ComfyUI Sessions in a Boxed Container
https://www.reddit.com/r/comfyui/comments/1j2x4qv/comfydock_the_easiest_free_way_to_run_comfyui_in/
ComfyDock is a tool that allows you to easily manage your ComfyUI environments via Docker.
Common Challenges with ComfyUI
- Custom Node Installation Issues: Installing new custom nodes can inadvertently change settings across the whole installation, potentially breaking the environment.
- Workflow Compatibility: Workflows are often tested with specific custom nodes and ComfyUI versions. Running these workflows on different setups can lead to errors and frustration.
- Security Risks: Installing custom nodes directly on your host machine increases the risk of malicious code execution.
How ComfyDock Helps
- Environment Duplication: Easily duplicate your current environment before installing custom nodes. If something breaks, revert to the original environment effortlessly.
- Deployment and Sharing: Workflow developers can commit their environments to a Docker image, which can be shared with others and run on cloud GPUs to ensure compatibility.
- Enhanced Security: Containers help to isolate the environment, reducing the risk of malicious code impacting your host machine.
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What the Boeing 737 MAX’s crashes can teach us about production business – the effects of commoditisation
Airplane manufacturing is no different from mortgage lending or insulin distribution or make-believe blood analyzing software (or VFX?) —another cash cow for the one percent, bound inexorably for the slaughterhouse.
The beginning of the end was “Boeing’s 1997 acquisition of McDonnell Douglas, a dysfunctional firm with a dilapidated aircraft plant in Long Beach and a CEO (Harry Stonecipher) who liked to use what he called the “Hollywood model” for dealing with engineers: Hire them for a few months when project deadlines are nigh, fire them when you need to make numbers.” And all that came with it. “Stonecipher’s team had driven the last nail in the coffin of McDonnell’s flailing commercial jet business by trying to outsource everything but design, final assembly, and flight testing and sales.”
It is understood, now more than ever, that capitalism does half-assed things like that, especially in concert with computer software and oblivious regulators.
There was something unsettlingly familiar when the world first learned of MCAS in November, about two weeks after the system’s unthinkable stupidity drove the two-month-old plane and all 189 people on it to a horrific death. It smacked of the sort of screwup a 23-year-old intern might have made—and indeed, much of the software on the MAX had been engineered by recent grads of Indian software-coding academies making as little as $9 an hour, part of Boeing management’s endless war on the unions that once represented more than half its employees.
Down in South Carolina, a nonunion Boeing assembly line that opened in 2011 had for years churned out scores of whistle-blower complaints and wrongful termination lawsuits packed with scenes wherein quality-control documents were regularly forged, employees who enforced standards were sabotaged, and planes were routinely delivered to airlines with loose screws, scratched windows, and random debris everywhere.
Shockingly, another piece of the quality failure is Boeing securing investments from all airliners, starting with SouthWest above all, to guarantee Boeing’s production lines support in exchange for fair market prices and favorite treatments. Basically giving Boeing financial stability independently on the quality of their product. “Those partnerships were but one numbers-smoothing mechanism in a diversified tool kit Boeing had assembled over the previous generation for making its complex and volatile business more palatable to Wall Street.”
