Life with AI

Hey guys, in this episode I talk about Gato, a new DeepMind algorithm that is capable of solving more than 600 completely different tasks. This algorithm is for sure a first step to achieve general artificial intelligence, because with only 1 algorithm with 1 set of parameters is able to solve tasks like play games like Atari, caption images, chat with humans (chatbot) e even control a robotic arm to move some blocks.

Paper: https://arxiv.org/pdf/2205.06175.pdf

DeepMind's blog post about the paper: https://www.deepmind.com/publications/a-generalist-agent

Episode about Reinforcement Learning: https://open.spotify.com/episode/2w088ECqtC0CVuAe9l25A2?si=886d21cbbc8f4757

Episode about Transformers: https://open.spotify.com/episode/5qdXu3Y2aq2z6EqMGoM5YW?si=6e8d1139c04c4338

Episode about Vision Transformers: https://open.spotify.com/episode/7xcdNNkvLKjq8QBozluATo?si=952a89557afe4be8

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Hey guys, in this episode I finish a series of 3 episodes about Transformers talking about BERT, the model revolutionized the NLP field. If you want to better understand the architecture of the model and mainly how it was trained, go listen to this episode cause it is really nice!

Artigo do BERT: https://arxiv.org/pdf/1810.04805.pdf

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

Hey guys, in this episode I talk about Vision Transformers, the application of Transformers for computer vision (images). In the episode I explain the Vision Transformers architecture and also the idea of applying the technique that was firstly proposed for NLP to images and also its challenges. The episode is really nice and you will understand why Transformers took so long to be applied to images and the future of the computer vision field.

Vision Transformer original paper (ViT): https://arxiv.org/pdf/2010.11929.pdf

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

Hey guys, in this episode I finally explain the Transformers network architecture! The paper Attention is all you need proposed the Transformer network and it was groundbreaking for firstly NLP field and now for all the Deep Learning fields. In the episode I explain the attention, the self-attention and the multi-head attention mechanisms for both Transformers encoder and Decoder, and also the positional encoding. Go listen to this episode because it's probably my best technical episode! 

Original paper: https://arxiv.org/pdf/1706.03762.pdf 

Self-attention GitHub code: https://github.com/filipelauar/projects/blob/main/self_attention.ipynb 

Youtube video explaining the architecture: https://www.youtube.com/watch?v=TQQlZhbC5ps 

Nice blog post with code 1: http://peterbloem.nl/blog/transformers 

Nice blog post with code 2: https://nlp.seas.harvard.edu/2018/04/03/attention.html 

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

Hey guys, in this episode I talk about the problem of multimodal learning, where we train a network using paired modalities of data, like image and text. 

With this learning strategy we achieve more robust embeddings and enable some new applications, like the Google Multisearch.

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

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

Hey guys, in this episode I receive as guest my professor at Sorbonne Laure Soulier. In the episode she talk a lot about Information Retrieval and NLP, which is the subject of her PhD and she current research. We discuss about conversational models (chatbots) and also explainability from Data to Text models. 

In a second part of the episode, I asked her a lot of questions about her PhD and some advises that could give for people that are in their PhDs and for the ones that are undecided if they want to do it or not (me). Laure is a very good teacher and we had a really great talk, go listen to it!

Laure's linkedin: https://www.linkedin.com/in/laure-soulier-18829948/

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

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

Hey guys, in this episode I talk about Data2Vec, a revolutionary algorithm that is able to learn from 3 different data modalities, audio, text and image and be better or comparable than previous state of the art methods. Also this algorithm uses a self-supervised approach, which means that it doesn’t use labels to the training. If you want to better understand it go listen to the episode! 

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

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

Paper: https://arxiv.org/pdf/2202.03555.pdf 

Github code and models: https://github.com/pytorch/fairseq/tree/main/examples/data2vec

Hello everyone, in this episode I talk about the future of AI. I talk about applications that will impact our lives in the future, like metaverse recommendation systems, autonomous vehicles, robots, medicine and much more! 

Metaverse episode: https://open.spotify.com/episode/54zU0UvAygPvmSmS8LHCmo?si=QuO39tOaSl-avJuifpf11w

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

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Hey guys, in this episode I have as guest Joe Scarffe, Chief Market Officer at Twine. In this episode we talk about bias an ethics in the datasets and also in the artificial intelligence algorithms. We discuss about some well known bias present in video and audio datasets like gender, age, skin color, accent and also some non trivial bias like the combination of the previous ones, the inference of the demographic attributes based on other attributes and even the existence of a qualitative bias that usually is not taken into account. 

We also discuss about the ethics of using AI algorithms, where I ask Joe some really trick questions about usage of the algorithms and much more! Go listen the episode because it's a really important subject and the discussion is really interesting.

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

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

Joe's and Twine's contact:

Twine AI Page: https://www.twine.net/ai

Twine Blog: https://www.twine.net/blog/category/ai/

Data Collector Page: https://www.twine.net/freelance/data-collector

Joe's Linkedin: https://www.linkedin.com/in/joe-s-4b0a18101/

Joe's email: [email protected]