knowledge cutoff

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In today's episode, we're diving into the 7 most common mistakes people make while using large language models like ChatGPT. 

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Related Episodes:
Ep 260: A new SORA competitor, NVIDIA’s $700M acquisition – AI News That Matters
Ep 181: New York Times vs. OpenAI – The huge AI implications no one is talking about
Ep 258: Will AI Take Our Jobs? Our answer might surprise you.

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Topics Covered in This Episode:
1. Understanding the Evolution of Large Language Models

2. Connectivity: A Major Player in Model Accuracy

3. The Generative Nature of Large Language Models

4. Perfecting the Art of Prompt Engineering

5. The Seven Roadblocks in the Effective Use of Large Language Models

6. Authenticity Assurance in Large Language Model Usage

7. The Future of Large Language Models

Timestamps:
00:00 ChatGPT.com now the focal point for OpenAI.

04:58 Microsoft developing large in-house AI model.

09:07 Models trained with fresh, quality data crucial.

10:30 Daily use of large language models poses risks.

14:59 Free chat GPT has outdated knowledge cutoff.

18:20 Microsoft is the largest by market cap.

21:52 Ensure thorough investigation; models have context limitations.

26:01 Spread, repeat, and earn with simple actions.

29:21 Tokenization, models use context, generative large language models.

33:07 More input means better output, mathematically proven.

36:13 Large language models are essential for business survival.

38:53 Future work: leverage language models, prompt constantly.

40:47 Please rate, share, check out youreverydayai.com.

Keywords:
Large language models, training data, outdated information, knowledge cutoffs, OpenAI's GPT 4, Anthropics Claude Opus, Google's Gemini, free version of Chat GPT, Internet connectivity, generative AI, varying responses, Jordan Wilson, prompt engineering, copy and paste prompts, zero shot prompting, few shot prompting, Microsoft Copilot, Apple's AI chips, OpenAI's search engine, GPT-2 chatbot model, Microsoft's MAI 1, common mistakes with large language models, offline vs online GPT, Google Gemini's outdated information, memory management, context window, unreliable screenshots, public URL verificat

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Why do AI chats lie? It probably starts with understanding the model's knowledge cutoff. Why does an AI's knowledge have an expiration date, and how does this impact our interaction with technology?  We're cutting through the tech jargon to give you a clear view of how AI thinks and learns.

Newsletter: Sign up for our free daily newsletter
More on this Episode: Episode Page
Join the discussion: Ask Jordan questions about AI and LLMs
Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup
Website: YourEverydayAI.com
Email The Show: info@youreverydayai.com
Connect with Jordan on LinkedIn

Timestamps:
[00:01:50] Daily AI news
[00:05:50] Importance of knowledge cutoff in LLMs
[00:07:55] How LLMs are trained
[00:10:00] Knowledge cutoff is like a text book
[00:14:30] ChatGPT modes and knowledge cutoff dates
[00:21:50] Anthropic Claude knowledge cutoff date
[00:27:35] Microsoft Bing Chat modes and knowledge cutoff dates
[00:31:30] Google Bard knowledge cutoff date
[00:33:40] Recap of LLM knowledge cutoff dates
[00:35:30] Final thoughts

Topics Covered in This Episode:
1. Understanding the Knowledge Cutoff in Large Language Models
2. Understanding Learning Models and Knowledge Cutoffs
3. Knowledge Cutoff Dates in Different Generative AI Models

Keywords:
AI, generative AI, Sports Illustrated, investigation, fake author names, AI-generated profile images, Symphony, Google, voice analytics, financial firms, natural language processing, Amazon, reInvent conference, Bedrock service, knowledge cutoff, large language models, web scraping, training, transparency, Anthropic Claude, Microsoft Bing Chat, human confirmation, GPT 4, Bing Chat modes, Google Bard, Palm 2, learning models, textbook, GPT 3.5, prompting, ChatGPT.