#machine language

[soillodge]

[techaedu]

Machine learning is an exciting field that has been growing rapidly over the past few years. With the increasing demand for machine learning skills, there has never been a better time to start learning about this field. In this article, we'll provide some tips on how to get started with machine learning.

[philearning]

MACHINE LEARNING: A PRACTITIONER'S APPROACH

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[jackoshadows]

The official website of Nand to Tetris courses

Came across this website and it’s such a helpful learning aid. I wish such websites existed back in the day when I was learning microprocessors and logic gates.

[feyosha]

FORBIDDEN TONGUE

Every now and then I remember that Malbolge exists and I get to spend the better part of an hour cry-laughing at the world’s worst programming language

already starting off strong, but it gets worse

Wow! Sounds easy and intuitive to use! What’s the “crazy operation” you ask? We’ll get to that later. For now let’s see what a program in this language looks like :)

Thanks! I hate it!

it’s so difficult to work with that the first program was written by another brute force search program

mmmmm delicious base-3 arithmetic, what could go wrong? (For reference, that means this program forgoes the usual “0/1″ values of binary code in favor of a much more fun “0/1/2″ set of values)

ah.

Here’s how the language actually figures out what to do. It’s got 8 “simple” commands that can be executed easily by *checks notes* running the code itself through the modulo operation and taking the result.

As a bonus, on top of all that every single character in your code will now alter what every single other character does. So I hope you’re alright with cracking a cipher every time you add a new letter to your program!

oh god oh fuck.

behold, Malbolge’s primary arithmetic operation and what you’ll be using for most of your math while programming with it :)

This looks specifically designed to be the least logical math operation you could make, and knowing what the rest of Malbolge is I’d wager that’s precisely what happened. I never want to ever use this and it’s my favorite thing I’ve ever seen.

https://en.wikipedia.org/wiki/Malbolge

Anyways here’s the wiki page if you wanna read through it more deeply, I’m gonna sit here holding in my laughter staring at the hello world program again.

[business901-blog]

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[topdigitalmarketingtrends]

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[r2consulting]

Unveiling the Power of Text Embeddings: A Pillar in Natural Language Processing

Introduction

In the vast realm of Natural Language Processing (NLP), text embeddings have emerged as a fundamental and powerful tool, revolutionizing the way computers understand and process human language. Text embeddings represent a breakthrough in transforming raw textual data into a format that machines can comprehend, facilitating various applications across industries. This article delves into the significance of text embeddings and explores their diverse uses in the ever-evolving landscape of NLP.

Understanding Text Embeddings

Text embedding is a technique used in natural language processing (NLP) to represent words or sentences as numerical vectors in a high-dimensional space. The goal of text embedding is to capture semantic relationships between words or phrases, enabling machines to understand and process textual information more effectively. One popular method for text embedding is word embedding, which represents individual words as vectors.

Here’s a brief explanation of how text embedding works, focusing on word embedding:

Word Embedding:

Word embedding techniques map words to continuous vector spaces where semantically similar words are located closer to each other.

One common method is to use pre-trained word embeddings, such as Word2Vec, GloVe, or FastText. These embeddings are learned from large text corpora and capture semantic relationships.

Neural Network Architecture:

Word embeddings are often learned using neural network architectures, such as shallow neural networks, recurrent neural networks (RNNs), or more commonly, deep neural networks like the word2vec Skip-Gram and Continuous Bag of Words (CBOW) models.

Training:

During training, the neural network learns to predict the context of words in a given sentence. In the Skip-Gram model, the objective is to predict the context words given a target word, while in CBOW, the goal is to predict the target word based on its context.

Vector Space Representation:

After training, each word is represented as a dense vector in a high-dimensional space. The dimensionality of these vectors is determined by the chosen embedding model but is typically in the range of a few hundred to a few thousand dimensions.

Importance of Text Embeddings

Semantic Understanding: Text embeddings empower machines to grasp the semantic meaning of words and sentences. By representing words in a continuous vector space, embeddings capture the relationships between words based on context and usage. This semantic understanding is crucial for various NLP tasks, such as sentiment analysis, text classification, and named entity recognition.

Efficient Information Retrieval: In information retrieval systems, text embeddings enhance search accuracy and relevance. By transforming documents or queries into vector representations, similarity metrics can be used to measure the closeness of these vectors, allowing for more efficient and precise retrieval of information. This is particularly valuable in search engines, recommendation systems, and content clustering.

Transfer Learning: Text embeddings facilitate transfer learning, where pre-trained models can be leveraged for downstream tasks. Pre-trained embeddings, such as Word2Vec, GloVe, or BERT, capture general language patterns from vast corpora, enabling them to be fine-tuned for specific tasks with limited labeled data. This accelerates model training and improves performance across various NLP applications.

Text Summarization and Generation: Embeddings play a pivotal role in text summarization and generation tasks. By encoding the essence of a document into a vector representation, machines can generate concise summaries or even create coherent sentences. This is invaluable in applications like automated content creation, chatbot responses, and news summarization.

Multimodal Understanding: In the era of multimodal data, where text is often accompanied by images, audio, or video, text embeddings provide a bridge for understanding different modalities. Integrating text embeddings with embeddings from other modalities enables more comprehensive analysis, facilitating tasks like image captioning, sentiment analysis in videos, and cross-modal retrieval.

Final Thoughts

Text embeddings have emerged as a linchpin in advancing the capabilities of Natural Language Processing, fostering a deeper understanding of language semantics and enhancing the efficiency of various applications. As technology continues to evolve, text embeddings will likely play a pivotal role in shaping the future of human-machine interactions, enabling machines to navigate the complexities of language with unprecedented accuracy and sophistication.

[joborientedcourses]

The Ultimate Guide to Machine Learning Certification.

The certification offers a well-structured curriculum covering foundational concepts such as linear regression and classification to advanced topics like natural language processing and computer vision.

https://medium.com/@hi.aasmohammad/the-ultimate-guide-to-machine-learning-certification-f77e862569b2

[wizardnuke]

hozier songs are all "i wouldn't be here without black artists" "you should kill your boss" "i love ireland :(" "DO Y OU NEED A DOG. I CAN BARK"

[river-taxbird]

AI hasn't improved in 18 months. It's likely that this is it. There is currently no evidence the capabilities of ChatGPT will ever improve. It's time for AI companies to put up or shut up.

I'm just re-iterating this excellent post from Ed Zitron, but it's not left my head since I read it and I want to share it. I'm also taking some talking points from Ed's other posts. So basically:

We keep hearing AI is going to get better and better, but these promises seem to be coming from a mix of companies engaging in wild speculation and lying.

Chatgpt, the industry leading large language model, has not materially improved in 18 months. For something that claims to be getting exponentially better, it sure is the same shit.

Hallucinations appear to be an inherent aspect of the technology. Since it's based on statistics and ai doesn't know anything, it can never know what is true. How could I possibly trust it to get any real work done if I can't rely on it's output? If I have to fact check everything it says I might as well do the work myself.

For "real" ai that does know what is true to exist, it would require us to discover new concepts in psychology, math, and computing, which open ai is not working on, and seemingly no other ai companies are either.

Open ai has already seemingly slurped up all the data from the open web already. Chatgpt 5 would take 5x more training data than chatgpt 4 to train. Where is this data coming from, exactly?

Since improvement appears to have ground to a halt, what if this is it? What if Chatgpt 4 is as good as LLMs can ever be? What use is it?

As Jim Covello, a leading semiconductor analyst at Goldman Sachs said (on page 10, and that's big finance so you know they only care about money): if tech companies are spending a trillion dollars to build up the infrastructure to support ai, what trillion dollar problem is it meant to solve? AI companies have a unique talent for burning venture capital and it's unclear if Open AI will be able to survive more than a few years unless everyone suddenly adopts it all at once. (Hey, didn't crypto and the metaverse also require spontaneous mass adoption to make sense?)

There is no problem that current ai is a solution to. Consumer tech is basically solved, normal people don't need more tech than a laptop and a smartphone. Big tech have run out of innovations, and they are desperately looking for the next thing to sell. It happened with the metaverse and it's happening again.

In summary:

Ai hasn't materially improved since the launch of Chatgpt4, which wasn't that big of an upgrade to 3.

There is currently no technological roadmap for ai to become better than it is. (As Jim Covello said on the Goldman Sachs report, the evolution of smartphones was openly planned years ahead of time.) The current problems are inherent to the current technology and nobody has indicated there is any way to solve them in the pipeline. We have likely reached the limits of what LLMs can do, and they still can't do much.

Don't believe AI companies when they say things are going to improve from where they are now before they provide evidence. It's time for the AI shills to put up, or shut up.

[intelvueofficial]

ChatGPT Integration Services:

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[lingocurio]

‘Ukuhumusha’—A New Way to Hack OpenAI's ChatGPT - Decrypt

[techcommgeekmom]

Tech Comm is safe from AI

#TechComm need not worry about AI. Here's why: (Yes, a new TechCommGeekMom blog entry!)

Hoshi Sato, the 1st Comms Officer for the Enterprise. She did not rely on AI alone, and was Uhura’s idol. (I also named my car “Hoshi” in her honor.) I know, I know. I definitely don’t write here as often as I used to–not by a longshot. But, that’s actually a good thing. It means that I’m doing a lot to keep busy. Between work, STC volunteering, and dealing with an ornery young autistic adult,…

[business901-blog]

Implicit guidance and control (IGC) is a crucial component of the OODA loop process that often goes unnoticed. It refers to the subconscious processing and intuitive decision-making based on past experiences, cultural norms, and internalized knowledge. This hidden driver shapes individuals’ observations, orientation, decisions, and actions in complex environments. 

The IGC process allows the brain to process vast amounts of information without conscious awareness, enabling rapid and effective decision-making. It draws upon previous experience and subconscious processing to guide real-time actions, especially in high-pressure situations. This aspect of the OODA loop is particularly important for experienced professionals, as it allows them to make quick and informed decisions based on their intuition and past experiences.