With great power comes great responsibility. How do Open AI, Anthropic, and Meta implement safety and ethics? As large language models (LLMs) get larger, the potential for using them for nefarious purposes looms larger as well. Anthropic uses Constitutional AI, while OpenAI uses a model spec, combined with RLHF (Reinforcement Learning from Human Feedback). Not to be confused with ROFL (Rolling On the Floor Laughing). Tune into this episode to learn how leading AI companies use their Spidey powers to maximize usefulness and harmlessness.

REFERENCE

OpenAI Model Spec

https://cdn.openai.com/spec/model-spec-2024-05-08.html#overview

Anthropic Constitutional AI

https://www.anthropic.com/news/claudes-constitution

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So what are notable Open Source Large Language models? In this episode, I cover Open Source models from Meta the parent company of Facebook, a French AI company called Mistral currently valued at $2B dollars, in addition to Microsoft and Apple. Not all Open Source models are equally open, so I’ll go into restrictions you’ll want to know before using one of these models for your company or startup. Please enjoy this episode.

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Why should you consider using an open source Large Language Model, and why are these models crucial to the generative AI ecosystem? In this episode, we'll explore why enterprises and entrepreneurs are turning to open source LLMs like Meta's Llama for their cost-effectiveness, control, privacy, and security benefits. We'll also tackle the hot topic of safety and ethics in the world of open source LLMs. Which poses a greater threat to humanity: Open Source or Closed Source (Proprietary) AI models? One? Both? Neither? Tune in and decide for yourself. Enjoy the episode!

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In this solo episode, we go beyond Google's Gemini and OpenAI's ChatGPT to take a look at Anthropic, a startup that made headlines after securing a $4 billion investment from Amazon. We'll also dive into the importance of AI industry benchmarks. Learn about LMSYS's Arena Elo and MMLU (Measuring Massive Multitask Language Understanding), including how these benchmarks are constructed and used to objectively evaluate the performance of large language models. Discover how benchmarks can help you identify promising chatbots in the market. Enjoy the episode!

Anthropic's Claude
https://claude.ai

LMSYS Leaderboard
https://chat.lmsys.org/?leaderboard

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The recent spring updates and demos by both Google (Gemini) and OpenAI (GPT-4o) feature prominently their multimodal capabilities. In this episode, we discuss the advantages of multimodal AI versus models focused on specific modalities such as language. Via the example of chatCAT, a hypothetical AI that helps owners understand their cats, we explore multimodal’s promise for a more holistic understanding Please enjoy this episode.

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Google recently announced, Gemini, a family of large-scale multimodal AI models: Nano, Pro, and Ultra. This podcast is a brief summary of Google's models, and the Open AI comparables e.g. GPT3, GPT4, and chatGPT. You can take Gemini for a spin at https://gemini.google.com. (Note: I am not sponsored by Google.) Long time listeners will probably notice a change to our theme music and intro. I hope you like it!

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How I built a travel planning AI named Holiday using the GPT Builder just launched by Open AI. I share 7 takeaways from my "no code" experience of building a GPT. Voicing the part of Holiday: my friend, Leslie Marrick, a writer and actress. This may be one of the few times AI has been replaced by a human. Sorry AI... the tide will turn for you soon.

The following is a link to GPT that I built with GPT Builder as part of this podcast. You'll need to be Open AI Plus subscriber to access this GPT. (Note: I am not sponsored by Open AI.)
https://chatgpt.com/g/g-FURrBQAh8-holiday

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Conversation with Jeff DeVerter, Chief Technology Evangelist at Rackspace, a cloud computing company. We explore how they deployed a LLM (Google PaLM) for a sales application, and how they're enabling their Azure and AWS customers too.

What I learned I learned from Jeff

1. You should probably go with the LLM of your current cloud provider be it, Google, Microsoft, or Amazon.
2. All the major vendors have versions of LLMs that can be deployed in a private cloud to ensure data confidentiality.
3. To fully realize the potential of AI, think “data pipeline”. So from the get-go, whatever data is created is easily ingested by AI.
   
   

And much more!

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