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- What is RAG?
☕️ Greetings, and welcome to my daily dive into the Generative AI landscape.
My goal is to streamline this newsletter size, making it a concise, under-five-minute read containing 1500 words or less. Today’s newsletter is 1667 words long which I think is still too long. For those interested in deeper dives, I’ll provide references for extended reading. Most of this content springs from my ongoing research and development projects at INVENEW.
In today’s issue:
Generative AI is poised for significant advancements in 2024
Discover AI Tools at AI Tools Masters
Can Oracle Cash-In On The AI Cloud 'Gold Rush'
AI Phone Revolutionizes Communication with Generative AI-Powered Efficiency and Intelligence
Can AI Predict Death
The Do’s & Don’ts of Using Generative AI Tools Ethically in Academia
And more
🔔 Please forward this newsletter to your friends and team members and invite them to join. This will help me grow my reach. Thanks, Qamar.
♨️ WHAT IS RAG?
Retrieval-Augmented Generation (RAG) is a novel approach in artificial intelligence that combines the power of language models with external knowledge retrieval to enhance the generation of text. This method utilizes a pre-trained language model for generating responses and supplements it with relevant information extracted from a large corpus of documents. RAG significantly improves the quality, relevance, and factual accuracy of generated content, particularly in complex domains where specific knowledge is essential.
Key Points:
RAG integrates language models with external knowledge sources to produce more informed and accurate text.
It employs a pre-trained language model for initial response generation, then refines these responses with information retrieved from extensive document databases.
This approach enhances the quality and factual accuracy of AI-generated content, especially in areas requiring specialized knowledge.
📈 TRENDS
Generative AI is poised for significant advancements in 2024, expanding beyond text and image applications to audio and video, with a focus on improved accuracy and specific use cases. This evolution will see widespread adoption in various sectors, including business and consumer technology. Key developments will involve major tech companies and semiconductor giants, with a special emphasis on AI-equipped devices and addressing the challenges of AI model creation.
Key Points:
Expansion Beyond Text and Images: Generative AI applications will extend to audio and video, moving past the current focus on text and images.
Tailored AI Models: Companies will release AI models for specialized purposes, enhancing sectors like weather forecasting, security, and more.
Accuracy Enhancement: Efforts to reduce AI-generated errors and "hallucinations" will be a priority, aiming for more reliable outputs.
Widespread Usage: Generative AI will become more prevalent in everyday use, impacting various professional and personal domains.
Business and Consumer Impact: The technology will have a notable presence in both business and consumer sectors, with potential new products and services.
Major Tech Companies' Involvement: Tech giants like Nvidia, AMD, Intel, and Qualcomm are expected to play significant roles, particularly in the semiconductor space.
AI-Equipped Devices: The introduction of PCs and other devices capable of performing AI tasks locally, reducing cloud dependency.
Challenges for Startups: Smaller companies may face difficulties competing with larger corporations in AI model development.
Focus on Model Building: The complexity and cost of AI model creation will be a central challenge, likely dominated by major tech firms.
Innovative Technologies: New approaches like "retrieval augmented generation" will emerge to enhance generative AI capabilities.
🗞️ IN THE NEWS
AI Phone Revolutionizes Communication with Generative AI-Powered Efficiency and Intelligence: AI Phone, launched in San Jose, revolutionizes phone communication with AI. It offers live transcription, real-time translation in over 15 languages, and AI-generated call summaries, enhancing user experience. Designed for diverse users, including professionals and language learners, the app boosts productivity, breaks language barriers, and improves accessibility, marking a significant technological advancement in telecommunication. Download it for free on the Apple App Store. Read more at accesswire.com.
The 3 Most Important AI Innovations of 2023: 2023 marked a pivotal year in AI understanding and application, seeing chatbots go viral and governments recognizing AI risks. Key advancements included multimodality in AI, like OpenAI's GPT-4, Google DeepMind's Gemini, and the concept of 'Constitutional AI' for value alignment. Additionally, the rise of text-to-video technology signaled a transformative shift in creative processes. Read more at Time.com.
Can Oracle Cash-In On The AI Cloud 'Gold Rush': Oracle's artificial intelligence initiatives are driving demand for its cloud products, but challenges in scaling infrastructure are impeding stock growth. Despite Oracle's accelerated data center expansion and GPU investments for AI, slower revenue growth in its cloud business and market competition have impacted investor confidence. Oracle, known for its database software, faces stiff competition from leading cloud providers like Amazon, Microsoft, and Google. The company's recent performance suggests potential yet raises concerns about its capacity to dominate the AI-driven cloud market. Read more at investors.com.
Anthropic Builds Methods for Reducing Bias in Generative AI – But Doesn’t Recommend AI for High-Stakes Decisions: Anthropic's research on reducing bias in AI models, specifically their LLM Claude 2, involves prompt engineering techniques. The study demonstrates how changing prompts can mitigate both positive and negative discrimination in AI responses. It highlights the importance of ethical AI practices in high-stakes applications, though Anthropic advises against using generative AI for critical decisions. Read more at www.techrepublic.com
Using sequences of life events to predict human lives: The 2023 study by Sune Lehmann, life2vec, introduces an AI algorithm that predicts life expectancy with 78% accuracy, using data like profession, health, and residence. It analyzed 6 million Danes from 2008-2020, identifying key longevity factors without informing individuals of their personal predictions. The study emphasizes ethical use and future applications for understanding broader life-span determinants. Read more at Nature.com.
🗣️ CULTURE
Barna and Gloo's study reveals growing curiosity and skepticism about AI among U.S. adults. While 31% use AI regularly, many hesitate to integrate it into their lives. Millennials lead in adoption, but AI is less trusted for personal advice or faith matters. Usage varies across generations and professions, with younger groups and working Christians more inclined to use AI. However, there's caution in applying AI to nuanced issues, especially in faith, indicating a preference for human interaction in complex matters. Read more at Barna.com.
🏢 INFRASTRUCTURE
The surge in AI model parameters, particularly beyond hundreds of billions, has dramatically improved AI capabilities in language understanding and logical reasoning. This growth necessitates advanced network infrastructure for efficient AI training, focusing on low latency and high throughput. Key challenges include managing large-scale networks, optimizing GPU communication, and ensuring network reliability and efficiency. FS's product portfolio addresses these needs, offering high-quality connectivity solutions for data center networks supporting large-scale AI models.
Reference: fs.com (2023). Deep Dive into Network Requirements for Large AI Models
🔬 QUESTIONS
OpenAI experienced turmoil with CEO Sam Altman's temporary ousting and return, influenced by Microsoft, its major investor. This triggered a UK Competition and Markets Authority probe into their relationship, reflecting increased vigilance over tech giants' dominance and its effects on competition and consumer choice, signaling a pivotal moment in regulating the evolving AI and tech landscape.
Reference: Read more at TheConversation.com.
💡 IDEAS
Generative AI is revolutionizing content creation and analysis in academia, necessitating ethical use and awareness of its limitations. Paperpal outlines best practices for academicians to responsibly use AI as a supportive tool rather than a substitute for their expertise, emphasizing adherence to policies, careful review of AI outputs, and mindful integration into academic work.
Key Points:
Utilize AI as an assistant, not a replacement for expert judgment.
Follow institutional guidelines on AI use, ensuring proper citation.
Be vigilant against biases and inaccuracies in AI outputs.
Use plagiarism tools for integrity, not for masking originality.
Employ AI for efficient literature discovery and summarization.
Regularly update knowledge on AI advancements and policies.
👩💻👨💻 SKILLS & CAREERS
AI is revolutionizing learning, work, and connectivity, reshaping global dynamics. Microsoft prioritizes equipping individuals worldwide to navigate and excel amidst evolving skill demands and economic shifts. The emphasis is on staying agile and informed in a swiftly transforming landscape. Visit AI SKills at Microsoft Learning.
📚LEARNING
AI Builder in Microsoft's Power Platform enables businesses to optimize processes using AI models. It offers both ready-made and customizable models, easily integrated with Power Apps and Power Automate. This tool automates processes and provides insights from data, requiring no coding skills. It involves selecting AI model types, connecting data, tailoring, training, and utilizing insights for business solutions. Although some features are in preview, AI Builder is a powerful, accessible tool for enhancing business intelligence.
Reference: Overview of AI Builder
🔬 RESEARCH
SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models
Transfer learning with pre-trained transformer models has been successful in various NLP tasks, and Federated Learning (FL) further extends this by using data from distributed, private edge clients.
Efficient fine-tuning is essential due to the limited resources of edge devices and the large size of transformer models, making federated training feasible.
The study investigates the application of parameter-efficient fine-tuning (PEFT) in FL settings for language tasks, noting challenges with diverse user data.
A novel method, SLoRA, is introduced, enhancing LoRA for high data heterogeneity with a data-driven initialization approach, achieving near-full fine-tuning performance.
SLoRA delivers comparable results with sparse updates (about 1% density) and reduces training time by up to 90%, addressing the limitations in high data variability contexts.
Reference: arXiv:2308.06522 (2023). SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models
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