The text discusses various aspects of LLMs, including non-determinism, copyright issues, best practices for implementation, industry investments, and ethical concerns. It highlights the impact of lawsuits, economic implications, and the preference for AI-generated content. The information also touches on the challenges of using pirated datasets and the need for tools to detect hallucinated facts in LLM-generated text.
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Ten of my LinkedIn posts on LLMs
1. Non-determinism in LLMs
The best LLM use cases are where you use LLM as a tool rather than expose it directly. One way to avoid non-deterministic behavior and associated risk is to generate a set of templates using LLMs and use an ML model to choose which template to serve. This also allows human oversight of generated text.
2. Copyright issues with LLMs
The New York Times is suing OpenAI and Microsoft over their use of the Times’ articles, which could potentially impact LLM APIs and open source LLMs. The lawsuit also emphasizes the importance of unique and high-quality content and may affect SEO and customer acquisition costs.
3. Don’t use a LLM directly; Use a bot creation framework
Use a higher-level bot-creation framework such as Google Dialogflow or Amazon Lex to avoid struggles with directly implementing LLM APIs or custom GPTs. This can save you from encountering adversarial attacks and expensive lessons.
4. Gemini demonstrates Google’s confidence in their research team
Google’s Gemini model showcases their confidence in their research team and their strategic approach to AI development and investment in multiple models, hardware, and talent.
5. Who’s actually investing in Gen AI?
Investments in Gen AI can be inferred from H100 shipments, providing insight into which companies are leading in this field. Understanding chip speed improvements can help gauge a company’s AI workload investment.
6. People like AI-generated content, until you tell them it is AI generated
People generally prefer AI-generated content over human-generated content until they are aware of its origin. This raises questions about labeling and perception of AI-generated content.
7. LLMs are plateau-ing
LLMs are showing signs of plateauing, which suggests a potential need for innovation and evolution in this area.
8. Economics of Gen AI software
Gen AI software has unique characteristics that affect its computational cost and data moat, leading to a shift in traditional software economics.
9. Help! My book is part of the training dataset of LLMs
Several LLMs include a training corpus that may contain pirated copies of books, raising ethical and copyright concerns.
10. A way to detect Hallucinated Facts in LLM-generated text
An update from Bard addresses the issue of hallucinated facts in LLM-generated text, offering a solution to identify potentially factually incorrect areas in the generated text.
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