AI_Distilled #28: Unveiling Innovations Reshaping Our World

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!

👋 Hello ,

“Generative AI has the potential to change the world in ways that we can’t even imagine. It has the power to create new ideas, products, and services that will make our lives easier, more productive, and more creative. It also has the potential to solve some of the world’s biggest problems, such as climate change, poverty, and disease.” 

-Bill Gates, Microsoft Co-Founder 

Microsoft Bing’s new Deep Search functionality is a case in point — Bing will now create AI prompts itself to provide detailed insights to user queries in ways traditional search engines can’t even match. Who could have thought LLMs would progress so much they would eventually prompt themselves? Even Runway ML is onto something big with its groundbreaking technology that creates realistic AI generated videos that will find their way to Hollywood. 

Welcome back to a new issue of AI Distilled - your one-stop destination for all things AI, ML, NLP, and Gen AI. Let’s get started with the latest news and developments across the AI sector:  

Elon Musk's xAI Initiates $1 Billion Funding Drive in AI Race 

Bing’s New Deep Search Expands Queries 

AI Takes Center Stage in 2023 Word of the Year Lists 

OpenAI Announces Delay in GPT Store Launch to Next Year 

ChatGPT Celebrates First Anniversary with 110M Installs and $30M Revenue Milestone 

Runway ML and Getty Images Collaborate on AI Video Models for Hollywood and Advertising 

We’ve also curated the latest GPT and LLM resources, tutorials, and secret knowledge: 

Unlocking AI Magic: A Primer on 7 Essential Libraries for Developers 

Efficient LLM Fine-Tuning with QLoRA on a Laptop 

Rapid Deployment of Large Open Source LLMs with Runpod and vLLM’s OpenAI Endpoint 

Understanding Strategies to Enhance Retrieval-Augmented Generation (RAG) Pipeline Performance 

Understanding and Mitigating Biases and Toxicity in LLMs 

Finally, don’t forget to check-out our hands-on tips and strategies from the AI community for you to use on your own projects: 

A Step-by-Step Guide to Streamlining LLM Data Processing for Efficient Pipelines 

Fine-Tuning Mistral Instruct 7B on the MedMCQA Dataset Using QLoRA 

Accelerating Large-Scale Training: A Comprehensive Guide to Amazon SageMaker Data Parallel Library 

Enhancing LoRA-Based Inference Speed: A Guide to Efficient LoRA Decomposition 

Looking for some inspiration? Here are some GitHub repositories to get your projects going! 

tacju/maxtron 

Tanuki/tanuki.py 

roboflow/multimodal-maestro 

03axdov/muskie 

Also, don't forget to check our expert insights column, which covers the interesting concepts of NLP from the book 'The Handbook of NLP with Gensim'. It's a must-read!    

Stay curious and gear up for an intellectually enriching experience!

📥 Feedback on the Weekly Edition

Quick question: How can we foster effective collaboration between humans and AI systems, ensuring that AI complements human skills and enhances productivity without causing job displacement or widening societal gaps?

Share your valued opinions discreetly! Your insights could shine in our next issue for the 39K-strong AI community. Join the conversation! 🗨️✨ 

As a big thanks, get our bestselling "Interactive Data Visualization with Python - Second Edition" in PDF. 
 

Let's make AI_Distilled even more awesome! 🚀 

Jump on in! 

Writer’s Credit: Special shout-out to Vidhu Jain for their valuable contribution to this week’s newsletter content!  

Cheers,  

Merlyn Shelley  

Editor-in-Chief, Packt 

SignUp | Advertise | Archives

⚡ TechWave: AI/GPT News & Analysis

🏐 Elon Musk's xAI Initiates $1 Billion Funding Drive in AI Race: xAI is on a quest to secure $1 billion in equity, aiming to stay competitive with tech giants like OpenAI, Microsoft, and Google in the dynamic AI landscape. Already amassing $135 million from investors, xAI's total funding goal is disclosed in a filing with the US Securities and Exchange Commission.  

🏐 AI Alliance Launched by Tech Giants IBM and Meta: IBM and Meta have formed a new "AI Alliance" with over 50 partners to promote open and responsible AI development. Members include Dell, Intel, CERN, NASA and Sony. The alliance envisions fostering an open AI community for researchers and developers and can help members make progress if they openly share models or not. 

🏐 Bing’s New Deep Search Expands Queries: Microsoft is testing a new Bing feature called Deep Search that uses GPT-4 to expand search queries before providing results. Deep Search displays the expanded topics in a panel for users to select the one that best fits what they want to know. It then tailors the search results to that description. Microsoft says the feature can take up to 30 seconds due to the AI generation. 

🏐 AI Takes Center Stage in 2023 Word of the Year Lists: In 2023, AI dominates tech, influencing "word of the year" choices. Cambridge picks "hallucinate" for AI's tendency to invent information; Merriam-Webster chooses "authentic" to address AI's impact on reality. Oxford recognizes "prompt" for its evolved role in instructing generative AI, reflecting society's increased integration of AI into everyday language and culture. 

🏐 OpenAI Announces Delay in GPT Store Launch to Next Year: OpenAI delays the GPT store release until next year, citing unexpected challenges and postponing the initial December launch plan. Despite recent challenges, including CEO changes and employee unrest, development continues, and updates for ChatGPT are expected. The GPT store aims to be a marketplace for users to sell and share custom GPTs, with creators compensated based on usage. 

🏐 ChatGPT Celebrates First Anniversary with 110M Installs and $30M Revenue Milestone: ChatGPT's mobile apps, launched in May 2023 on iOS and later on Android, have exceeded 110 million installs, yielding nearly $30 million in revenue. The success is fueled by the ChatGPT Plus subscription, offering perks. Despite competition, downloads surge, with Android hitting 18 million in a week. The company expects continued growth by year-end 2023. 

🏐 Runway ML and Getty Images Collaborate on AI Video Models for Hollywood and Advertising: NYC video AI startup Runway ML, backed by Google and NVIDIA, announces a partnership with Getty Images for the Runway <> Getty Images Model (RGM), a generative AI video model. Targeting Hollywood, advertising, media, and broadcasting, it enables customized content workflows for Runway enterprise customers.

🔮 Expert Insights from Packt Community 

The Handbook of NLP with Gensim - By Chris Kuo 

NLU + NLG = NLP 

NLP is an umbrella term that covers natural language understanding (NLU) and NLG. We’ll go through both in the next sections. 

NLU 

Many languages, such as English, German, and Chinese, have been developing for hundreds of years and continue to evolve. Humans can use languages artfully in various social contexts. Now, we are asking a computer to understand human language. What’s very rudimentary to us may not be so apparent to a computer. Linguists have contributed much to the development of computers’ understanding in terms of syntax, semantics, phonology, morphology, and pragmatics. 

NLU focuses on understanding the meaning of human language. It extracts text or speech input and then analyzes the syntax, semantics, phonology, morphology, and pragmatics in the language. Let’s briefly go over each one: 

Syntax: This is about the study of how words are arranged to form phrases and clauses, as well as the use of punctuation, order of words, and sentences. 

Semantics: This is about the possible meanings of a sentence based on the interactions between words in the sentence. It is concerned with the interpretation of language, rather than its form or structure. For example, the word “table” as a noun can refer to “a piece of furniture having a smooth flat top that is usually supported by one or more vertical legs” or a data frame in a computer language. 

NLU can understand the two meanings of a word in such jokes through a technique called word embedding.  

Phonology: This is about the study of the sound system of a language, including the sounds of speech (phonemes), how they are combined to form words (morphology), and how they are organized into larger units such as syllables and stress patterns. For example, the sounds represented by the letters “p” and “b” in English are distinct phonemes. A phoneme is the smallest unit of sound in a language that can change the meaning of a word. Consider the words “pat” and “bat.” The only difference between these two words is the initial sound, but their meanings are different. 

Morphology: This is the study of the structure of words, including the way in which they are formed from smaller units of meaning called morphemes. It originally comes from “morph,” the shape or form, and “ology,” the study of something. Morphology is important because it helps us understand how words are formed and how they relate to each other. It also helps us understand how words change over time and how they are related to other words in a language. For example, the word “unkindness” consists of three separate morphemes: the prefix “un-,” the root “kind,” and the suffix “-ness.” 

Pragmatics: This is the study of how language is used in a social context. Pragmatics is important because it helps us understand how language works in real-world situations, and how language can be used to convey meaning and achieve specific purposes. For example, if you offer to buy your friend a McDonald’s burger, a large fries, and a large drink, your friend may reply "no" because he is concerned about becoming fat. Your friend may simply mean the burger meal is high in calories, but the conversation can also imply he may be fat in a social context. 

Now, let’s understand NLG. 

NLG 

While NLU is concerned with reading for a computer to comprehend, NLG is about writing for a computer to write. The term generation in NLG refers to an NLP model generating meaningful words or even articles. Today, when you compose an email or type a sentence in an app, it presents possible words to complete your sentence or performs automatic correction. These are applications of NLG.  

This content is from the book The Handbook of NLP with Gensim - By Chris Kuo (Oct 2023). Start reading a free chapter or access the entire Packt digital library free for 7 days by signing up now. To learn more, click on the button below. 

🌟 Secret Knowledge: AI/LLM Resources

🏀 Unlocking AI Magic: A Primer on 7 Essential Libraries for Developers: Discover seven cutting-edge libraries to enhance development projects with advanced AI features. From CopilotTextarea for AI-driven writing in React apps to PrivateGPT for secure, locally processed document interactions, explore tools that elevate your projects and impress users. 

🏀 Efficient LLM Fine-Tuning with QLoRA on a Laptop: Explore QLoRA, an efficient memory-saving method for fine-tuning large language models on ordinary CPUs. The QLoRA API supports NF4, FP4, INT4, and INT8 data types for quantization, utilizing methods like LoRA and gradient checkpointing to significantly reduce memory requirements. Learn to implement QLoRA on CPUs, leveraging Intel Extension for Transformers, with experiments showcasing its efficiency on consumer-level CPUs. 

🏀 Rapid Deployment of Large Open Source LLMs with Runpod and vLLM’s OpenAI Endpoint: Learn to swiftly deploy open-source LLMs into applications with a tutorial, featuring the Llama-2 70B model and AutoGen framework. Utilize tools like Runpod and vLLM for computational resources and API endpoint creation, with a step-by-step guide and the option for non-gated models like Falcon-40B. 

🏀 Understanding Strategies to Enhance Retrieval-Augmented Generation (RAG) Pipeline Performance: Learn optimization techniques for RAG applications by focusing on hyperparameters, tuning strategies, data ingestion, and pipeline preparation. Explore improvements in inferencing through query transformations, retrieval parameters, advanced strategies, re-ranking models, LLMs, and prompt engineering for enhanced retrieval and generation. 

🏀 Understanding and Mitigating Biases and Toxicity in LLMs: Explore the impact of ethical guidelines on Large Language Model (LLM) development, examining measures adopted by companies like OpenAI and Google to address biases and toxicity. Research covers content generation, jailbreaking, and biases in diverse domains, revealing complexities and challenges in ensuring ethical LLMs. 

🔛 Masterclass: AI/LLM Tutorials

🎯 A Step-by-Step Guide to Streamlining LLM Data Processing for Efficient Pipelines: Learn to optimize the development loop for your LLM-powered recommendation system by addressing slow processing times in data pipelines. The solution involves implementing a Pipeline class to save inputs/outputs, enabling efficient error debugging. Enhance developer experience with individual pipeline stages as functions and consider future optimizations like error classes and concurrency. 

🎯 Fine-Tuning Mistral Instruct 7B on the MedMCQA Dataset Using QLoRA: Explore fine-tuning Mistral Instruct 7B, an open-source LLM, for medical entrance exam questions using the MedMCQA dataset. Utilize Google Colab, GPTQ version, and LoRA technique for memory efficiency. The tutorial covers data loading, prompt creation, configuration, training setup, code snippets, and performance evaluation, offering a foundation for experimentation and enhancement. 

🎯 Accelerating Large-Scale Training: A Comprehensive Guide to Amazon SageMaker Data Parallel Library: This guide details ways to boost Large Language Model (LLM) training speed with Amazon SageMaker's SMDDP. It addresses challenges in distributed training, emphasizing SMDDP's optimized AllGather for GPU communication bottleneck, exploring techniques like EFA network usage, GDRCopy coordination, and reduced GPU streaming multiprocessors for improved efficiency and cost-effectiveness on Amazon SageMaker. 

🎯 Enhancing LoRA-Based Inference Speed: A Guide to Efficient LoRA Decomposition: The article highlights achieving three times faster inference for public LoRAs using the Diffusers library. It introduces LoRA, a parameter-efficient fine-tuning technique, detailing its decomposition process and benefits, including quick transitions and reduced warm-up and response times in the Inference API. 

🚀 HackHub: Trending AI Tools

⚽ tacju/maxtron: Unified meta-architecture for video segmentation, enhancing clip-level segmenters with within-clip and cross-clip tracking modules. 

⚽ Tanuki/tanuki.py: Simplifies the creation of apps powered by LLMs in Python by seamlessly integrating well-typed, reliable, and stateless LLM-powered functions into applications. 

⚽ roboflow/multimodal-maestro: Empowers developers with enhanced control over large multimodal models, enabling the achievement of diverse outputs through effective prompting tactics. 

⚽ 03axdov/muskie: Python-based ML library that simplifies the process of dataset creation and model utilization, aiming to reduce code complexity.