#computer networking program

[spac3ducky]

re-downloaded Tux Paint ... Dexter says hi !     

[lxde-official]

hey i need some help

I'm developing a video compression algorithm and I'm trying to figure out how to encode it in a way that actually looks good.

basically, each frame is made up of a grid of 5x8 pixel tiles, each cell being one of 16 tiles. 8 of these tiles can be anything, while the other 8 are hard-coded.

so far, my algorithm simply compares each tile of the input frame to each hard-coded tile, and the 8 tiles that match the least are set to the "custom" tiles, and the other ones that match the hard-coded ones more are set to those hard-coded ones.

this works okay, but doesn't account for if two input frame tiles are the same thing or similar, it would be better to re-use custom tiles (eg, if the whole screen is black — due to the limitations of the screen I'm using, a solid black tile must be a custom tile, but a solid white one can be hard coded).

speed isn't that important, as each frame is only 80x16 pixels at the most, with one bit per pixel, and each tile is 5x8 pixels, for a grid of 16x2 tiles.

TL;DR: I need help writing an algorithm that can arrange 16 tiles into a 16x2 grid, while also determining the best pattern to set 8 of those tiles to, while leaving the other 8 constant.

[unwelcome-ozian]

Beta intelligence military esque

Alice in wonderland

Alters, files, jpegs, bugs, closed systems, open networks

brain chip with memory / data

Infomation processing updates and reboots

'Uploading' / installing / creating a system of information that can behave as a central infomation processing unit accessible to large portions of the consciousness. Necessarily in order to function as so with sufficient data. The unit is bugged with instructions, "error correction", regarding infomation processing.

It can also behave like a guardian between sensory and extra physical experience.

"Was very buggy at first". Has potential to casues unwanted glitches or leaks, unpredictability and could malfunction entirely, especially during the initial accessing / updating. I think the large amount of information being synthesized can reroute experiences, motivations, feelings and knowledge to other areas of consciousness, which can cause a domino effect of "disobedience", and or reprogramming.

I think this volatility is most pronounced during the initial stages of operation because the error correcting and rerouting sequences have not been 'perfected' yet and are in their least effective states, trail and error learning as it operates, graded by whatever instructions or result seeking input that called for the "error correction".

I read the ask about programming people like a computer. Whoever wrote that is not alone. Walter Pitts and Warren McCulloch, do you have anymore information about them and what they did to people?

Here is some information for you. Walter Pitts and Warren McCulloch weren't directly involved in the programming of individuals. Their work was dual-use.

Exploring the Mind of Walter Pitts: The Father of Neural Networks

McCulloch-Pitts Neuron — Mankind’s First Mathematical Model Of A Biological Neuron

Introduction To Cognitive Computing And Its Various Applications

Cognitive Electronic Warfare: Conceptual Design and Architecture

Security and Military Implications of Neurotechnology and Artificial Intelligence

Oz

[sminny-wew]

I've always been Team NetIris but ever since my friends made me aware of Saito x Meiru it feels like my third eye's been opened (feat. @dunedragon's Saito design)

Also golly gee I wonder why the text is those colors.....Hmmmm 💚🤍🩶🖤

🔫 GO LISTEN TO THE HIT SONG "A HUMAN'S TOUCH" BY TWRP AND MCKENNA RAE ON YOUTUBE OR SPOTIFY I DON'T CARE JUST DO IT 🔫

[arctic-hands]

I got my little Zerowriter epaper typing machine working the other day and I'm having fun writing on it and since I actually remembered to install Samba on it so I can actually transfer the txt documents I bang out on it to my laptop to actually edit and further save it.

The problem being that I actually have no idea what Samba or SMB is, I have no idea what will happen to my documents if wifi is down or otherwise off, I don't know how it will save if I make this ultra portable and am away from my laptop and home wifi, and I have no idea what Google Play apps are actually trustworthy to put on my phone to access the network on my Samsung android if that's a thing I need to do if I want to write away from my wifi

Halp I'm the most tech-inept millennial and don't understand shit it took seven hours to get the raspi and Samba working and i may have cried q but bc I didn't know what I was doing

[frank-olivier]

The Mathematical Foundations of Machine Learning

In the world of artificial intelligence, machine learning is a crucial component that enables computers to learn from data and improve their performance over time. However, the math behind machine learning is often shrouded in mystery, even for those who work with it every day. Anil Ananthaswami, author of the book "Why Machines Learn," sheds light on the elegant mathematics that underlies modern AI, and his journey is a fascinating one.

Ananthaswami's interest in machine learning began when he started writing about it as a science journalist. His software engineering background sparked a desire to understand the technology from the ground up, leading him to teach himself coding and build simple machine learning systems. This exploration eventually led him to appreciate the mathematical principles that underlie modern AI. As Ananthaswami notes, "I was amazed by the beauty and elegance of the math behind machine learning."

Ananthaswami highlights the elegance of machine learning mathematics, which goes beyond the commonly known subfields of calculus, linear algebra, probability, and statistics. He points to specific theorems and proofs, such as the 1959 proof related to artificial neural networks, as examples of the beauty and elegance of machine learning mathematics. For instance, the concept of gradient descent, a fundamental algorithm used in machine learning, is a powerful example of how math can be used to optimize model parameters.

Ananthaswami emphasizes the need for a broader understanding of machine learning among non-experts, including science communicators, journalists, policymakers, and users of the technology. He believes that only when we understand the math behind machine learning can we critically evaluate its capabilities and limitations. This is crucial in today's world, where AI is increasingly being used in various applications, from healthcare to finance.

A deeper understanding of machine learning mathematics has significant implications for society. It can help us to evaluate AI systems more effectively, develop more transparent and explainable AI systems, and address AI bias and ensure fairness in decision-making. As Ananthaswami notes, "The math behind machine learning is not just a tool, but a way of thinking that can help us create more intelligent and more human-like machines."

The Elegant Math Behind Machine Learning (Machine Learning Street Talk, November 2024)

Matrices are used to organize and process complex data, such as images, text, and user interactions, making them a cornerstone in applications like Deep Learning (e.g., neural networks), Computer Vision (e.g., image recognition), Natural Language Processing (e.g., language translation), and Recommendation Systems (e.g., personalized suggestions). To leverage matrices effectively, AI relies on key mathematical concepts like Matrix Factorization (for dimension reduction), Eigendecomposition (for stability analysis), Orthogonality (for efficient transformations), and Sparse Matrices (for optimized computation).

The Applications of Matrices - What I wish my teachers told me way earlier (Zach Star, October 2019)

Transformers are a type of neural network architecture introduced in 2017 by Vaswani et al. in the paper “Attention Is All You Need”. They revolutionized the field of NLP by outperforming traditional recurrent neural network (RNN) and convolutional neural network (CNN) architectures in sequence-to-sequence tasks. The primary innovation of transformers is the self-attention mechanism, which allows the model to weigh the importance of different words in the input data irrespective of their positions in the sentence. This is particularly useful for capturing long-range dependencies in text, which was a challenge for RNNs due to vanishing gradients. Transformers have become the standard for machine translation tasks, offering state-of-the-art results in translating between languages. They are used for both abstractive and extractive summarization, generating concise summaries of long documents. Transformers help in understanding the context of questions and identifying relevant answers from a given text. By analyzing the context and nuances of language, transformers can accurately determine the sentiment behind text. While initially designed for sequential data, variants of transformers (e.g., Vision Transformers, ViT) have been successfully applied to image recognition tasks, treating images as sequences of patches. Transformers are used to improve the accuracy of speech-to-text systems by better modeling the sequential nature of audio data. The self-attention mechanism can be beneficial for understanding patterns in time series data, leading to more accurate forecasts.

Attention is all you need (Umar Hamil, May 2023)

Geometric deep learning is a subfield of deep learning that focuses on the study of geometric structures and their representation in data. This field has gained significant attention in recent years.

Michael Bronstein: Geometric Deep Learning (MLSS Kraków, December 2023)

Traditional Geometric Deep Learning, while powerful, often relies on the assumption of smooth geometric structures. However, real-world data frequently resides in non-manifold spaces where such assumptions are violated. Topology, with its focus on the preservation of proximity and connectivity, offers a more robust framework for analyzing these complex spaces. The inherent robustness of topological properties against noise further solidifies the rationale for integrating topology into deep learning paradigms.

Cristian Bodnar: Topological Message Passing (Michael Bronstein, August 2022)

Sunday, November 3, 2024

[ryan-is-a-god]

Learning about radio signals in class and I'm just over here like "ah, so AM radio would have been used in his time period, that's why his voice has static"

[vaguely-none]

Ask Game: List 5 things that make you happy, then put this in the askbox for the last 10 people who liked or reblogged something from you! get to know your mutuals and followers ;)

I won't spread this because I don't like that. But I will reply:

1. The bug I found after hours of work and false starts

2. The incidental side bug that resulted in "File not found" even though I thought it wouldn't load a file. Turns out it opened the file before determining it shouldn't be loaded.

3. Another unrelated bug in the poor woman's debugging build

4. Same bug in more places

5. The feature that I wanted to add to another program actually worked long since, it just happened to trip the major bug I listed first here

[nicknema]

The more that I learn how to program, the less I understand all the speed problems that affect modern software. I can write somewhat complex code that does long sequences of operations, and it gets executed instantly. I try to make a website and you can’t even see it loading. Math is free, memcpy is free. How are you taking 5 seconds to open a window with nothing in it, then 2 additional seconds to create an empty document? I don’t understand. What are you doing in all that time?

[horseracetests]

ive never drawn m3rv bcus trying to design a guy who wanted to be a digital gaia avatar so bad it put itself in a computer is a lot of work, but its out there in my mind palace

[blake447]

I will continue to uncontrollably dump the last 4 years of my life onto tumblr until I run out of things to post! Here's an old project of mine, GPU based boids that are designed to run in VRChat, shown in this lovely world right here https://vrchat.com/home/world/wrld_e9c2c3bd-fedc-4289-a8fd-c7151ffc3cdb They utilize SDF's that were developed alongside a raymarching shader for previewing bounds, and are more approximations of boids than anything. They don't have proper collision avoiding, but they are close enough. The complicated swarming behavior you can (kind of) see here comes from just 3 rules

1.> Dont crash 2.> Stay in groups 3.> Travel in the same direction as other

Being on VRChat a few concessions had to be made of course. VR performance is a little rough, its running on a Camera + Rendertexture loop instead of a proper compute shader, and vision and collision avoidance are approximated, but I'm happy with how they turned out.

The editor scripts I wrote are super fucking cursed, and just have bits of shaders straight up hard coded to past into .txt files to be read as shaders for generating variants of the SDF's, but eventually I'd like to return to this to get it polished up to the point where an actually sane individual could drop it into a world and get some nice fishies

[la-principessa-nuova]

A fun way to think about this, for people who don’t know what an algorithm really is, is that algorithm is basically a fancy word for recipe. It’s a set of instructions to take a set of inputs (ingredients) and produce a desired output.

When a site produces an algorithm to recommend content, the input comes from the information they have about you, other users, and the content they are ranking, and the output is an ordered list of top picks for you. It basically takes in everything and then the person writing the algorithm chooses which inputs to actually use.

Imagine that like as the entire grocery store is available to be used, so the person writing the recipe lists out the specific ones used within the steps of the recipe as ingredients to use.

Then they write all the steps in a way that another cook could understand, maybe with helpful notes (comments) along the way describing why they did certain things. And in the end they have a recipe that someone else could follow, make informed changes to, explain the reasoning for decisions, etc.

That’s a traditional algorithm. Sorting by a single field like kudos is the simplest form of this, like a recipe for toast.

Ingredients:

Bread, 1 slice

Instructions:

Put the bread in the toaster.

Pull down the lever.

Wait until it pops up.

Enjoy your toast!

Is that a recipe? Yes, clearly.

Now let’s consider what the equivalent of ML-based recommendation systems (the key differentiator of what people often refer to as “the algorithm”) is:

Ingredients:

The entire grocery store

Instructions:

Put the grocery store into THE MACHINE

THE MACHINE should be set to 0.135, 0.765, 0.474, 0.8833… (this list continues for hundreds of entries)

Consume your personalized Feed™️

Is that a recipe? Technically yes.

“Ao3 needs an algorithm” no it doesn’t, part of the ao3 experience is scrolling through pages of cursed content looking for the one fic you want to read until you get distracted by a summary so cursed that it completely derails your entire search

[simple-logic]

#Scrabble

Tech Word Challenge! 🔍

Can you unscramble today's mystery tech term? 🧠💻

Comments your answer below👇

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[magicaltimemachinedream]

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[suresh1407]

Learn About Computer and fundamental of computer..with the crazy dev.

find more articles about web development, programming languages, computer network and many more.

[quasistatic-motion]

Me speaking to my friends through the Computer