Life with AI Filipe Lauar

Hey guys, in this episode I talk about 3 very important models that use contrastive learning, CLIP, SigLIP and JinaCLIP. They are image-text embedding models that allow us to for instance do image-text retrieval.



CLIP paper: https://arxiv.org/pdf/2103.00020

SigLIP paper: https://arxiv.org/pdf/2303.15343

JinaCLIP paper: https://arxiv.org/pdf/2405.20204

Github of similarities and contrastive loss: https://github.com/filipelauar/projects/blob/main/similarities_and_contrastive_loss.ipynb

Instagram of the podcast: https://www.instagram.com/podcast.lifewithai

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Hey guys, in this episode I talk about two very important technical concepts in Deep Learning, constrastive learning and cosine similarity. They are very useful when training embedding models or doing RAG.



Very good blog post about contrastive losses: https://lilianweng.github.io/posts/2021-05-31-contrastive/

SimCLR paper: https://arxiv.org/abs/2002.05709

Instagram of the podcast: https://www.instagram.com/podcast.lifewithai

Linkedin of the podcast: https://www.linkedin.com/company/life-with-ai

Hey guys, in this episode I talk about the FineWeb dataset, the best pre-training open source dataset to date. In the episode I explain how they created the dataset and I also share some results.



Link to the huggingface blog: https://huggingface.co/spaces/HuggingFaceFW/blogpost-fineweb-v1

Instagram of the podcast: https://www.instagram.com/podcast.lifewithai

Linkedin of the podcast: https://www.linkedin.com/company/life-with-ai

Fala galera, nesse episódio eu falo com o João Batista, Technical Product Manager da Stackspot AI. No episódio a gente falou bastante sobre o uso de LLMs como um copilot usando os próprios documentos da empresa para dar as respostas usando RAG.



Hey guys, in the brazilian version of the podcast I discussed with Joao from Stackspot AI. In the episode I talk about how they are developing enterprise copilot assistants using RAG.



In the episode we talk both about technical and product aspects, like similarity metrics, how many documents to use, how to show the answer to the user, how to metrify the quality of the answers...



Linkedin do Joao: https://www.linkedin.com/in/joaobatista-cordeironeto/

Linkedin da Stackspot AI: https://www.linkedin.com/company/stackspot/

Instagram do podcast: https://www.instagram.com/podcast.lifewithai

Linkedin do podcast: https://www.linkedin.com/company/life-with-ai

Hey guys, in this episode I talk about two papers, BitNet and 1.58 bit Transformer. These two papers from microsoft tell a new receipe to train 1 bit transformers, improve hugely the memory and energy consumption along with lower inference times.



BitNet paper: https://arxiv.org/pdf/2310.11453

1.58 bit paper: https://arxiv.org/pdf/2402.17764

Instagram of the podcast: https://www.instagram.com/podcast.lifewithai

Linkedin of the podcast: https://www.linkedin.com/company/life-with-ai

Extra episode about Llama 3.