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- AI Brew: Your Weekly Dose of AI Caffeine! ☕🚀
AI Brew: Your Weekly Dose of AI Caffeine! ☕🚀
Your weekly AI Caffeine shot, tailored to fuel captivating AI discussions with your clients, boss, colleagues, friends, and family (and discreetly show off your expertise) ☕️🧠
📌 This week's AI Brew summary:
AI Spotlight on 🚀: ChatGPT-4 Plugins
Real-Life AI Stories 🤖
AI Jargon Buster 🤷♂️
Cool Corner 😎: Coca-Cola
Poll of the Week 🗳️: Are you using ChatGPT at work?
AI Spotlight 🚀: ChatGPT-4 Plugins
ChatGPT-4 is an incredible language model, but it does have its limits. It can only use the information it learned during training, which means it can miss out on real-time, personalized, and specific data. That's where ChatGPT-4 Plugins come in!
These plugins will expand ChatGPT-4's abilities to access fresh, up-to-date information beyond its training data. Don't believe us? Check out this tweet from Sam Altman! 😲
we are starting our rollout of ChatGPT plugins.
you can install plugins to help with a wide variety of tasks. we are excited to see what developers create!
openai.com/blog/chatgpt-p…
— Sam Altman (@sama)
5:12 PM • Mar 23, 2023
Real-Life AI Stories 🤖
👩⚕️ AI Detects Breast Cancer 4 Years in Advance - Revolutionary Advancement in Early Diagnosis!
👖 Levi's and Lalaland.ai Team Up to Boost Diversity and Sustainability Using AI Models 🌱
AI Jargon Buster - Large Language Models
LLMs, or Large Language Models, are advanced AI systems that can understand and generate human-like language by breaking down language into smaller units and analyzing their relationships. Chat GPT is an example of an LLM that can generate text in response to a given prompt.
When you ask Chat GPT , "What's the weather like today?," this is how it generates a human like response:
Tokenization: it takes your question, "What's the weather like today?" and breaks it down into smaller units of meaning called tokens, like "weather," "today," and "like."
Encoding: It uses word embeddings to convert each token into a numerical representation that can be processed by the model.
Processing: It, then, uses its deep learning architecture to process the encoded tokens and generate a response. It analyzes the relationships between the tokens and predicts the most likely next word or sequence of words based on its training data.
Decoding: It converts the numerical representation of the generated response back into human-readable text.
Response Generation: Finally, it puts together the tokens in a way that sounds natural and coherent, and produces a response that sounds like something a person might say. In the case of the weather question, it might say "It looks like it's going to be sunny and warm today, with a high of 80 degrees."
I found this video really helpful to understand how ChatGPT works:
Cool Corner 😎
This week’s award goes to —» Coca-Cola's new AI platform, "Create Real Magic." The platform is only available until March 31, so don't miss your chance to create something truly magical.
Poll of the Week 🗳️
📧 Got a question or an AI topic you'd like us to cover? Email me at [email protected]
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