LLM "Model Collapse"

☕️ Greetings AI enthusiasts! It's Tuesday, August 29th. Welcome back to our daily exploration of the AI landscape. Together, we'll navigate the latest updates, trends, and insights steering the AI and the business world. Exciting times ahead—let's dive in!

🗞️ Today’s Highlights:

• LATEST TRENDS — In Reversal Because of A.I., Office Jobs Are Now More at Risk

• INDUSTRY | FUNCTION — How Microsoft, Salesforce Apply Generative AI’s Power to Customer Experience (CX)

• RESOURCES — Thermodynamics and a New LLM Science | Dr Stephen Wolfram

• WORK — Navigating the Future with Generative AI: Treating AI as a Trusted Colleague

• AI TOOLS — Kickresume: Create a beautiful resume quickly with the help of artificial intelligence and our customizable templates. Impress your future employer with a perfect resume created in minutes.

• CHARTS — The Most Popular AI Tools By Country 

🤖 According to an article published in the University of Toronto Engineering News, leading academics in AI research warn that the increasing prevalence of content created by machine learning models may undermine the development of future chatbots. These AI systems could end up learning more from previous iterations of themselves than from authentic human-generated data. The scholars call it "model collapse," a scenario that threatens to degrade the chatbots' ability to make accurate predictions. To mitigate this, the researchers recommend proactive data curation and scrutinizing the information that AI systems learn from.

Read the paper here: The Curse of Recursion: Training on Generated Data Makes Models Forget

🗞️ LATEST TRENDS

1️⃣ Previously, automation primarily impacted less-educated workers in manufacturing. However, advancements in AI, particularly large language models are targeting jobs requiring cognitive skills and higher education, especially office roles. While these AI tools can assist in various tasks across multiple occupations, they currently supplement rather than replace human workers. However, some jobs, like mathematicians, face significant automation exposure. The technology can boost worker productivity, especially for junior employees, and might potentially reduce income inequality. Still, tasks requiring human touch, empathy, and nuanced understanding remain challenging for AI.

Reference: (In Reversal Because of A.I., Office Jobs Are Now More at Risk)

2️⃣ As industrial adoption of Natural Language Processing (NLP) skyrockets, organizations are boosting investments to refine its capabilities. The article delves into the nitty-gritty of NLP—a fusion of AI, linguistics, and machine learning that powers search engines and chatbots. Key focus areas include essential components like NLU and NLG, the mechanics of text data analysis, and practical tasks that NLP accomplishes, from tokenization to text classification.

Reference: (Natural Language Processing (NLP) Essentials: Text Data Analysis Made Easy)

3️⃣ Humans quickly adapt to technological advances. From industrial revolutions to AI, we've embraced change. The pace of tech adoption is fastening: smartphones reached 25% penetration in just five years. ChatGPT, an AI chatbot, amassed 1 million users in five days, setting records. By January 2023, it had 100 million active users. AI integration has shifted digital interaction; its rapid rise prompts the question: What's next in AI? As companies innovate with next-gen AI, three criteria ensure success: technological feasibility, economic viability, and societal values. Future AI tech includes multimodal systems, AI legal aid, humanoid robots, and research assistance.

Reference: (Rapid adoption of generative AI begs the question: What will be the next innovation in AI to capture our imagination)

4️⃣  d-Matrix Inc. has unveiled Jayhawk II, its second-generation generative AI compute platform. The new silicon features an improved digital in-memory-compute (DIMC) engine, boasting 40x better memory bandwidth than leading GPUs. This enables 10-20x more generative inferences per second for large language models, offering a substantially better total cost of ownership. The DIMC engine, implemented in a 6nm process, is designed for versatility, supporting various data types and enabling efficient caching for generative AI models.

Reference: (Chiplet-base generative AI platform raises LLM performance)

5️⃣ Generative AI is revolutionizing software development by offering capabilities that extend from requirement analysis to support and maintenance. It streamlines tasks like code generation, debugging, and even architectural design. However, challenges such as reliability must be addressed for sustainable integration. The technology promises a collaborative approach between AI and human expertise, reshaping how software engineering is approached.

Reference: (Generative AI in Software Engineering)

🗞️ INDUSTRY | FUNCTION

Generative AI is revolutionizing customer experience and worker productivity by serving as a "copilot" for both teams and customers. While Microsoft's 365 Copilot integrates with its existing suite to automate professional tasks, Salesforce's Marketing and Commerce GPT uses AI to offer personalized experiences. These advancements promise cost savings and heightened customer loyalty, but they require thoughtful integration and a learning curve for both staff and customers.

Reference: (How Microsoft, Salesforce Apply Generative AI’s Power to Customer Experience (CX))

🗞️ RESOURCES

Prompt engineering serves as a specialized toolkit for optimizing language models' performance across diverse tasks and research objectives. Going beyond mere prompt creation, it encompasses a multifaceted skill set to interact effectively with large language models. This practice not only enhances their utility in tasks like question-answering but also improves safety measures and augments them with domain-specific insights. It's an indispensable skill for those keen on leveraging the full potential of large language models in both research and development.

Reference: (Prompt Engineering Guide)

LLMs and Laws of Thermodynamics

In his discussion, Dr. Stephen Wolfram explores the applicability of thermodynamic laws, specifically the second law, to large language models (LLMs) like GPT. He likens LLMs' behaviors to gas molecules in a room, suggesting that similar statistical mechanics could explain emergent behaviors in LLMs. Wolfram also delves into the concept of "temperature" in LLMs, observing how it influences word selection and eventually impacts the coherence of the generated text. He highlights the nascent field of LLM science, emphasizing its potential significance in understanding scaling laws and practical applications of these models.

Source/Credit: Tech Stories 101 - Thermodynamics and a New LLM Science | Dr Stephen Wolfram

🗞️ WORK

With growing excitement surrounding generative AI tools like ChatGPT and Bard, it's crucial to treat these AI systems as you would a new employee. While they offer promising productivity gains, their integration into organizations necessitates clear policies, audit trails, and risk-based approaches. Tech luminaries caution against uncontrolled AI adoption, emphasizing the need for digital trust frameworks and transparent processes to validate AI outcomes. Overall, AI isn't just a tool but a member of your team that should abide by organizational standards and ethics.

Reference: (Navigating the Future with Generative AI: Treating AI as a Trusted Colleague)

🗞️ AI TOOLS

Broadcast: Stop wasting time manually writing weekly updates. Quickly draft updates in Broadcast with AI and share on Slack and Email. Broadcast makes sending weekly updates fast, and simple.

Zeda.io: AI-powered product discovery for customer-focused teams. Discover problems to solve for customers, decide what to build next based on actionable product intelligence, and create product strategies to drive business outcomes.

Memorable: High-accuracy AI to improve branding and the performance of every ad

Wope: New Era of Rank Tracking. Find out what's working and what's not to get more search traffic. Like an SEO consultant who can analyze millions of data.

Visual QR: Generate beautiful QR codes using AI.

Kickresume: Create a beautiful resume quickly with the help of artificial intelligence and our customizable templates. Impress your future employer with a perfect resume created in minutes.

Disclaimer: 1) The tool descriptions may include messaging from each tool site. 2) Please thoroughly read the site details before using and/or acquiring any of the tools listed above.

🗞️ CHARTS

 👀 The Most Popular AI Tools By Country

Source: ElectronicsHub

👀 The Most Popular AI Tools

Source: ElectronicsHub

Join my community by subscribing to my newsletter below:

🔴 Please reply to the confirmation email sent to you, after submitting your email address to start receiving the newsletter.

My Community

Join my professional communities on LinkedIn

_{I'm not a newsletter expert so you might find my approach a little different. In my daily dives into the world of AI, I handpick the latest gems, initially to support the AI projects that I’m working on. Realizing that these snippets might resonate with others, I thought, "Why not share this with my community and fellow AI enthusiasts?" I truly want this newsletter to be valuable to you so if there's anything on your mind—praises, critiques, or just a hello—please drop me a note. You can hit reply or shoot me a message directly at my email address: [email protected].}