Posted in TWIL

TWIL: April 16, 2023
Am I the only one who thinks everything is moving really fast? I've been on vacations for a week and so much has happened I haven't had a chance to catch up yet. I still got to listen to two
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TWIL: April 8, 2023
AI research continues to make headlines with new papers coming out every day. I'm highlighting the paper on HuggingGPT with a video explaining how GPT-4 can improve itself. Also, repos for JARVIS
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TWIL: April 2, 2023
This week I want to recommend you Lex Fridman's incredible podcast and, in particular, the conversation with Sam Altman which is really interesting. But also, a ChatGPT episode on AI Portugal's
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TWIL: March 26, 2023
The world of AI and machine learning is constantly evolving, and the latest developments are pushing the boundaries of what is possible. From ChatGPT to GPT-4, Azure ML to MONAI, the possibilities
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TWIL: March 19, 2023
What a week for AI! Open AI released GPT-4, Microsoft announced Microsoft 365 Copilot, Anthropic launched Claude, Google offers access to PaLM and Baidu unveils Ernie. Also, two awesome podcast
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TWIL: March 12, 2023
Generative AI continues to dominate headlines as companies start leveraging it for a set of diverse use cases. My focus this week was still on Artificial Intelligence topics, like deep learning and
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TWIL: March 6, 2023
Artificial Intelligence hype is at an all-time high, with a lot of people writing about it (even if they don't always understand it) and great repositories of samples and accelerators to get you
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TWIL: February 26, 2023
This week I dabbled a bit into Large Language Models (LLM) and Vector Similarity Search (VSS), important concepts when building real world use cases for GPT-3. I'm also highlighting two GitHub repos
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TWIL: February 19, 2023
This was a very intense week with a lot of work, but I still managed to learn new stuff. My focus was still on Azure OpenAI Service but I got to read a little more about Transformers, Self-Attention
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TWIL: February 12, 2023
This week most of my learning was focused on Azure Open AI Service, and how to use GPT-3 models for specific use cases, either by fine tuning them or by leveraging embeddings. I'm also highlighting
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