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Bind AI Review: AI-Generated Code for Landing Pages & More!                                                                                                        What is Bind AI? Bind AI is an advanced AI-powered tool that generates code, creates landing pages, and writes technical content efficiently. Feature or Why Needed? Advanced AI models for coding and content creation. Generate code in 70+ programming languages. Built-in editor for clean code generation and debugging. Seamless integration with GitHub and Google Drive. Real-time HTML email and web page previews. Support for custom GPTs with AI studio. Unlimited queries using personal API keys. Feature-packed document editor for technical content. Lifetime deal available on AppSumo. Access to all future model and feature updates. Affordable one-time payment options. Best For?  C-suite executives Developers Web design agencies Technical content creators Software engineers To know more, Click: https://lnkd.in/g5uADVSa #BindAIReview #BindAI #CodeGeneration #LifetimeDeal #AIModels #GPT4 #Claude35 #GitHubIntegration #DocumentEditor #CustomAIBots #ContentCreation

Reinforcement Learning from Human Feedback (RLHF) for LLMs https://ift.tt/UZYcXjA LLMs An ultimate guide to the crucial technique behind Large Language Models Reinforcement Learning from Human Feedback (RLHF) has turned out to be the key to unlocking the full potential of today’s large language models (LLMs). There is arguably no better evidence for this than OpenAI’s GPT-3 model. It was released back in 2020, but it was only its RLHF-trained version dubbed ChatGPT that became an overnight sensation, capturing the attention of millions and setting a new standard for conversational AI. Before RLHF, the LLM training process typically consisted of a pre-training stage in which the model learned the general structure of the language and a fine-tuning stage in which it learned to perform a specific task. By integrating human judgment as a third training stage, RLHF ensures that models not only produce coherent and useful outputs but also align more closely with human values, preferences, and expectations. It achieves this through a feedback loop where human evaluators rate or rank the model’s outputs, which is then used to adjust the model’s behavior. This article explores the intricacies of RLHF. We will look at its importance for language modeling, analyze its inner workings in detail, and discuss the best practices for implementation. Importance of RLHF in LLMs When analyzing the importance of RLHF to language modeling, one could approach it from two different perspectives. On the one hand, this technique has emerged as a response to the limitations of traditional supervised fine-tuning, such as reliance on static datasets often limited in scope, context, and diversity, as well as broader human values, ethics, or social norms. Additionally, traditional fine-tuning often struggles with tasks that involve subjective judgment or ambiguity, where there may be multiple valid answers. In such cases, a model might favor one answer over another based on the training data, even if the alternative might be more appropriate in a given context. RLHF provides a way to lift some of these limitations. On the other hand, however, RLHF represents a paradigm shift in the fine-tuning of LLMs. It forms a standalone, transformative change in the evolution of AI rather than a mere incremental improvement over existing methods. RLHF represents a paradigm shift in the fine-tuning of LLMs. It forms a standalone, transformative change in the evolution of AI. Let’s look at it from the latter perspective first. The paradigm shift brought about by RLHF lies in the integration of human feedback directly into the training loop, enabling models to better align with human values and preferences. This approach prioritizes dynamic model-human interactions over static training datasets. By incorporating human insights throughout the training process, RLHF ensures that models are more context-aware and capable of handling the complexities of natural language. I...

Revisiting the question, “Will AI take our jobs?” Here’s an example to consider: while browsing the official Filament Discord for content and project ideas, I noticed that half the questions aren’t even about Filament itself. Many people lack foundational skills in the underlying tech stack—things like Eloquent relationships, Tailwind, Livewire, and Alpine. This highlights why AI won’t fully replace developer jobs anytime soon. While AI (and tools like Filament) can create a “rough draft” or a basic “version 1” of what’s needed, developers still need significant skills to customize the final product to meet exact client requirements. This is why I think we’ll see two kinds of developers emerging: 1. Those who use AI or no-code tools to build simple projects that don’t need much customization. 2. Those who can fully customize projects, adding the polish and functionality that clients expect. It’s similar to the WordPress ecosystem, where there are: • Installers and theme/plugin configurators • Real developers, the ones who write custom code. #AI #tech #futuretech

You’ve likely heard the buzz about AI website builders. 🚀 These tools can create websites in minutes—layouts, content, images, and all—ready to publish at the click of a button. But here’s the burning question: Could advanced AI start impacting jobs in web development? 🧠 Probably not. Read more: https://lnkd.in/eWH7qPRb #AIWebsiteBuilders #WebDevelopment #TechTalk #WebDesign #TechDebate

You’ve likely heard the buzz about AI website builders. 🚀 These tools can create websites in minutes—layouts, content, images, and all—ready to publish at the click of a button. But here’s the burning question: Could advanced AI start impacting jobs in web development? 🧠 Probably not. Read more: https://lnkd.in/em3R6sip #AIWebsiteBuilders #WebDevelopment #TechTalk #WebDesign #TechDebate

How can AI and machine learning enhance modern web development? Read More- https://lnkd.in/dc3B4-ic Follow Code To Career #AIinWebDevelopment #MachineLearning #WebDevelopment #AIandML #SmartWebsites #TechInnovation #WebDesign #UserExperience #Automation #MachineLearningInWebDev #AIWebTools #VoiceSearchOptimization #PersonalizedContent #WebSecurity #AIandMLInTech #SEO #AIForWebDesign #AIChatbots #SmartWebDevelopment

The Art of Tokenization: Breaking Down Text for AI https://ift.tt/PvLIrgu Demystifying NLP: From Text to Embeddings Tokenization example generated by Llama-3-8B. Each colored subword represents a distinct token. What is tokenization? In computer science, we refer to human languages, like English and Mandarin, as “natural” languages. In contrast, languages designed to interact with computers, like Assembly and LISP, are called “machine” languages, following strict syntactic rules that leave little room for interpretation. While computers excel at processing their own highly structured languages, they struggle with the messiness of human language. Language — especially text — makes up most of our communication and knowledge storage. For example, the internet is mostly text. Large language models like ChatGPT, Claude, and Llama are trained on enormous amounts of text — essentially all the text available online — using sophisticated computational techniques. However, computers operate on numbers, not words or sentences. So, how do we bridge the gap between human language and machine understanding? This is where Natural Language Processing (NLP) comes into play. NLP is a field that combines linguistics, computer science, and artificial intelligence to enable computers to understand, interpret, and generate human language. Whether translating text from English to French, summarizing articles, or engaging in conversation, NLP allows machines to produce meaningful outputs from textual inputs. The first critical step in NLP is transforming raw text into a format that computers can work with effectively. This process is known as tokenization. Tokenization involves breaking down text into smaller, manageable units called tokens, which can be words, subwords, or even individual characters. Here’s how the process typically works: Standardization: Before tokenizing, the text is standardized to ensure consistency. This may include converting all letters to lowercase, removing punctuation, and applying other normalization techniques. Tokenization: The standardized text is then split into tokens. For example, the sentence “The quick brown fox jumps over the lazy dog” can be tokenized into words: ["the", "quick", "brown", "fox", "jumps", "over", "the", "lazy", "dog"] Numerical representation: Since computers operate on numerical data, each token is converted into a numerical representation. This can be as simple as assigning a unique identifier to each token or as complex as creating multi-dimensional vectors that capture the token’s meaning and context. Illustration inspired by “Figure 11.1 From text to vectors” from Deep Learning with Python by François Chollet Tokenization is more than just splitting text; it’s about preparing language data in a way that preserves meaning and context for computational models. Different tokenization methods can significantly impact how well a model understands and processes language. In this article, we...

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Devin is here, the world's first AI software developer (google it). This machine has already completed some gigs on Upwork. It will replace us all. Ok, calm down, I just wanted to get your attention. I don't think this AI or any other one will replace us for now. BUT at the same time, in the near future, AI might change the industry slowly, so that companies will need 9 software developers instead of 10. This will change the job market, and so on. So, in short, I don't think that we, software developers, have to panic now, but I think it's very important to keep an eye on this whole AI thing. We should be smart about it, and think how we should use AI in our own favour. #AI #software #softwaredevelopers #jobs #jobmarket #devin #chatgpt #copilot

#42 Teaching AI to “Think”, Fine-Tuning to SQL, Encoder-only models, and more! https://ift.tt/HlKwqhX And is AGI achievable? Good morning, AI enthusiasts! This is another resource-heavy issue with articles focusing on everything from early AI architectures to the latest developments in AI reasoning abilities. Enjoy the read! What’s AI Weekly One of the key issues with our current approach to AI reasoning can be summarized by the quote: “We teach the machines how we think we think.” It reflects a deeper flaw in training models based on human intuition, which isn’t necessarily how reasoning truly works (nobody knows). This opens up a broader discussion about how machines can independently develop reasoning skills rather than merely mimicking human approaches. Building on that foundation, this week, in the High Learning Rate newsletter, we are sharing some exciting developments reshaping how AI models might learn to reason. These advancements center around self-taught reasoning, where AI models enhance their capabilities by learning from their own reasoning processes. Read the complete article here! — Louis-François Bouchard, Towards AI Co-founder & Head of Community Learn AI Together Community section! Featured Community post from the Discord Mahvin_ built a chatbot using ChatGPT. The code imports various libraries like TensorFlow, PyTorch, Transformers, Tkinter, and CLIP to handle tasks related to neural networks, text classification, and image processing. You can try it on GitHub and share your feedback in the Discord thread! AI poll of the week! At the beginning of this year, AGI might have seemed a far-fetched idea, but it is surprising how much closer we have come to it. Is it the only obvious progression for AI? We think otherwise, but we would love to hear your thoughts! Collaboration Opportunities The Learn AI Together Discord community is flooding with collaboration opportunities. If you are excited to dive into applied AI, want a study partner, or even want to find a partner for your passion project, join the collaboration channel! Keep an eye on this section, too — we share cool opportunities every week! 1. Samyog_dhital is researching and exploring ways to enhance reasoning capabilities in LLMs. The goal is to solve this challenge, enabling LLMs to solve complex problems with logical, step-by-step planning similar to human reasoning. They are looking for someone to work on this and a potential co-founder. If you are interested, connect with them in the thread! 2. Dykyi_vladk is working on reimplementing and enhancing the PaLM model. If you are interested in NLP, contact him in the thread! 3. Knytfury is looking to work with someone on a new research paper or an existing paper’s implementation. If you are working on something and need some human resources to work on the paper, reach out in the thread! Meme of the week! Meme shared by ghost_in_the_machine TAI Curated section Article of the week Solving...