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Print to Pixel: Optimize your learning experience with Packt
Several research studies have proven that printed books enhance comprehension, with the tactile experience of flipping pages and annotating the margins adding depth to the learning experience. However, developers can't overlook the practical benefits of eBooks, such as quickly finding relevant information or carrying an entire library on a single device.
Acknowledging the unique benefits of both formats, Packt is offering a 40% discount on all print books, plus a free eBook version of each purchase, from February 26th to February 29th.
Here’s what’s included:
A Vast Library: Enjoy 40% off on over 5,000 titles spanning topics from Cybersecurity to Generative AI.
Complimentary eBook: Each print book purchase includes a free eBook.
AI Assistant: Top 500 books come with a personalized AI that can simply complex topics to your learning style, offering an interactive learning experience.
“No Al is perfect, especially at this emerging stage of the industry’s development, but we know the bar is high for us and we will keep at it for however long it takes.”
Pichai acknowledges problems with Gemini AI, stressing the importance of unbiased information for users, and outlining steps to address issues and improve products. A rapidly progressing industry, AI development is a tricky game to master, with numerous pitfalls along the way.
Greetings readers! Our mission is to help you stay on top of the ever-changing AI landscape so you can advance your skills. Let’s get started with the latest news and developments across the AI field:
In a world that's always changing, learning is key to success. At Packt, we've updated our learning platform to help you stay ahead in the fast-moving tech world. Our platform makes learning easier and more effective, helping you overcome challenges and achieve your goals.
Microsoft has partnered with Mistral AI to provide their new LLM Mistral Large on Azure cloud services. This state-of-the-art AI model offers advanced NLP capabilities. Several companies have praised Mistral Large's performance in increasing productivity and aiding innovation.
Google's CEO recently said some responses from their AI model Gemini were unacceptable and biased. The company has been working to address these issues and sees improvements but will review what happened. They plan to relaunch Gemini in the coming weeks after fixing it.
GitHub has launched Copilot Enterprise, an AI coding assistant that integrates throughout the software development process. It provides customized code suggestions based on an organization's codebase, answers questions about internal systems, and generates summaries of code changes. Early testing found massive productivity gains from such AI tools.
Researchers have developed new optimized language models for mobile devices with under a billion parameters. Called MobileLLM, the models achieve higher accuracy than previous smaller models through innovative architecture and weight-sharing techniques. MobileLLM shows significant gains on conversation tasks and competes with much larger models for common on-device uses.
Researchers at Microsoft have developed new techniques to improve visual language models using structured knowledge graphs. By incorporating relationship maps between image elements like objects and attributes, models can generate richer images from text descriptions. Hierarchical prompting and dual-path encoding methods were also introduced to help models better understand complex language.
🌟 Secret Knowledge: AI/LLM Resources
🌀Mastering the Art of Prompt Crafting: Got a new NLP project that needs prompting? This guide covers the basics of effective prompt engineering for AI models like ChatGPT. Learn how clarity, conciseness, and context can improve responses. Also explore techniques like zero-shot learning and dynamic few shots, plus how temperature, top-p, and other settings can refine your model's "personality". From system messages to tailoring examples, these tips will help you leverage your LLMs' full potential.
🌀Breaking Down How Large Language Models Learn: This article provides a helpful breakdown of how LLMs are trained through causal language modeling and calculates loss. It visually explains how models generate text sequences, are pre-trained to predict the next token, and how cross-entropy loss compares predictions to true labels to update weights. The process is demonstrated through code showing how loss is manually calculated for an LLM matching the framework's automatic calculation. This gives developers valuable insights into how state-of-the-art models learn.
🌀Using AI to Level Up Live Games: This article discusses how generative AI can enhance live service games. Techniques like adaptive gameplay, personalized ads, and faster asset creation are described. The authors provide a framework for developing games using tools like Unity, GKE, and Vertex AI. They demonstrate how ML models can dynamically generate images, code and dialogue to customize the player experience. Whether deploying models on GKE or Vertex, cloud-based AI brings the benefits of lower costs and easier maintenance than self-hosted options.
🌀 Monitoring Large Language Models on AWS: As AI language models grow more advanced, ensuring they behave properly becomes more important. This article discusses techniques for monitoring LLMs deployed on AWS. Key metrics covered include semantic similarity of responses, sentiment analysis, refusal rates, and more. The proposed architecture takes in model outputs, runs metrics modules, and reports results to CloudWatch for aggregation and alerts. With the right monitoring in place, you can help keep your conversational AI acting as intended.
🔛 Masterclass: AI/LLM Tutorials
🌀 Fine-Tuning Models for Speech Recognition Made Simple: This article discusses how to fine-tune LLMs for automatic speech recognition tasks using Amazon SageMaker. It explains language models and ASR as well as the basic steps for fine-tuning a pre-trained model which includes preparing data, choosing a model, training, evaluating, and deploying. SageMaker is highlighted as a powerful yet easy-to-use platform for this process due to its scalability, integration with AWS services, and pay-as-you-go pricing.
🌀 Make Conversation Come Alive - Deploying Your Own AI Chat Partner: Tired of boring chatbots? This guide shows you how to bring the amazing Qwen AI model to your own server so you can have engaging discussions on any topic. The steps cover setting up your environment, installing dependencies, initializing the tokenizer and model, and using history to keep conversations flowing naturally. Once complete, you'll have a powerful AI assistant right at your fingertips. Best of all, it's completely open source.
🌀 Combining Geospatial and Semantic Data to Build Powerful Search Tools: This guide shows developers how to create an interactive campground search map using vector databases, NLP models, and geospatial data. Technologies like Qdrant, Llama2, and Streamlit allow embedding text and locations to enable semantic queries. The page explains setting up Qdrant cloud, loading campground CSV data, and parsing text into nodes. Developers can then embed nodes with HuggingFace and query the vector store to retrieve similar results. By leveraging tools that understand both spatial and semantic context, you can build customized applications to help users explore outdoor destinations.
🌀 Leveraging Notion, Supabase, and AI for Knowledge Retrieval: This tutorial shows how you can build a knowledge base by extracting data from Notion databases and storing it in a vector format in Supabase. It then demonstrates retrieving relevant information from the knowledge base using an AI model from OpenAI. By combining these tools, developers can query custom datasets and generate responses based on retrieved documents. The process involves loading Notion documents, storing embeddings in Supabase, and setting up a retrieval pipeline. With some enhancements, this could be a powerful way to access organizational information.
🚀 HackHub: Trending AI Tools
🌀 lucky-lance/expert_sparsity: Implements efficient expert pruning and dynamic skipping techniques for mixture-of-experts large language models to improve their efficiency and speed while maintaining strong performance.
🌀 facebookresearch/pearl: This open-source library provides a modular reinforcement learning framework for building and training production-ready AI agents, empowering developers with state-of-the-art techniques.
🌀 zhen-tan-dmml/llm4annotation: Curates papers on using LLMs for data annotation, which developers could reference to apply these techniques or learn about the current state of the art.
🌀 google/gemma.cpp: Provides a lightweight C++ library for running Google's Gemma models that developers can easily integrate into their own projects for experimenting with and deploying LLMs.
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