ai language models

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Timestamps:

(0:00) Sunny joins Jason to dive into this week's AI news and demos.

(1:39) AI, LLMs, and Groq infrastructure discussion.

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(11:00 ) Meta's AI landing page, new search engine, and investment strategies

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(23:19) Zuckerberg's approach towards Google's dominance and the impact of Meta's search engine

(30:19) HiddenLayer. HiddenLayer’s AI Detection & Response Solution secures your Generative AI & LLMs from malicious attack. Visit https://www.HiddenLayer.com/TWiST to learn more.

(32:11) Importance of a dedicated domain name and Google's commitment to Google Plus

(34:36) Meta's progress in AI and the strategy of open sourcing

(45:34) The industrial revolution in technology, AI development, and rights issues

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Great 2023 interviews: Steve Huffman, Brian Chesky, Aaron Levie, Sophia Amoruso, Reid Hoffman, Frank Slootman, Billy McFarland

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Ready for a little LLM secret? Big tech companies are lying to us. They tell us their models are connected to the internet in real time. Are they really? Not really. We're doing an LLM breakdown, as we compare the internet connectivity of popular large language models like ChatGPT, Gemini, and Copilot. 

Newsletter: Sign up for our free daily newsletter
More on this Episode: Episode Page
Join the discussion: Ask Jordan questions on LLMs

Related Episodes:
Ep 246: No that’s not how ChatGPT works. A guide on who to trust around LLMs
Ep 204: Google Gemini Advanced – 7 things you need to know

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:
02:00 LLMs we are testing
05:33 RAG importance in AI and internet connection.
06:50 Browsing with Bing queries words in URL.
13:04 Comparing AI accuracy
15:40 Rating and evaluating ChatGPT and Copilot performance.
19:34 Acknowledgement of Google's helpful search feature.
26:14 Uploading files to ChatGPT.
29:21 Unofficial language model results from Internet connection.
31:14 Large language models being integrated everywhere.

Topics Covered in This Episode:
1. Testing Large Language Models
2. Using Large Language Models in Business
3. In-Depth Comparison of Different Language Models
4. Implications for Future Use

Keywords:
ChatGPT, Google Gemini, Microsoft Copilot, large language models, internet information retrieval, web reading platforms, information interpretation, accuracy, business use, safeguards against inaccuracies, up-to-date information, real-time data, live testing, language model performance, Everyday AI, Jordan Wilson, market cap, largest US companies, podcast episodes description, language models comparison, browse with Bing feature, web reader GPT feature, outdated information, information veracity, data retrieval, PDF hidden information, artificial intelligence, integrated AI, internet connectivity, knowledge workers.

Get more out of ChatGPT by learning our PPP method in this live, interactive and free training! Sign up now: https://youreverydayai.com/ppp-registration/

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Today’s show:

Adept Co-Founder and CEO David Luan joins Jason to discuss his experience at OpenAI (2:45), demo Adept's AI-powered workflow builder (13:47), what facets of AI he is most excited about, what keeps him motivated (32:33), and more!

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Timestamps:

(0:00) Adept founder and CEO David Luan joins Jason.

(2:45) David's experience at OpenAI and the evolution of deep learning

(5:22) DALL-E origins and its "a-ha moment"

(9:24) The next great well of AI training data

(11:21) Brave - Try the Brave Search API at http://www.brave.com/jason

(12:35) Adept's Northstar since day one

(13:47) Demos: showcasing the power of Adept AI

(15:52) The next battlefield for AI

(18:34) A look into Adept AI Experiments – a sandbox for AI enthusiasts

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(21:51) How Adept aims to streamline job processes

(24:54) Strategies to outperform large competitors

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(32:33) What excites David about the future and motivates his daily efforts?

(36:29) The present and expected advancements in AI

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Great 2023 interviews: Steve Huffman, Brian Chesky, Aaron Levie, Sophia Amoruso, Reid Hoffman, Frank Slootman, Billy McFarland

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AI chatbots have gotten pretty good at generating text that looks like it was written by a real person. That’s because they’re trained on words and sentences that actual humans wrote, scraped from blogs and news websites. But research now shows when you feed that AI-generated text back into the models to train a new chatbot, after a while, it sort of stops making sense. It’s a phenomenon AI researchers are calling “model collapse.” Marketplace’s Lily Jamali spoke to Clive Thompson, author of “Coders” and contributing writer for the New York Times Magazine and Wired, about what could be a growing problem as more AI-generated stuff lands on the web.