Life with AI

In this episode I explain the solution I had to win a kaggle competition in my university (Telecom Paris). I explain the different solutions I had and what I did to improve the results. In the end I also talk about a possible solution that could win against my own using ensembles.

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

GitHub with the solution: https://github.com/filipelauar/Kaggle/blob/main/cancer_detection_challange_notebook.ipynb

In this episode I give various examples about racist algorithms in different domains like face recognition, healthcare insurance, crime probability, hate speech and much more! I talk not only about racial problems, but also about gender and religion problems caused by artificial intelligence algorithms. 

In this episode I explain how the machine learning algorithms based on trees and forests work. In the episode I explain in a simple way the idea behind the decision trees and also the powerful algorithms RandomForest and XGBoost and its respectives methods, bagging and boosting.

In this episode I talk about the next generation of instagram filters, that we will be able to edit easily specific attributes of our face or even mix two people. I also explain the artificial intelligence networks behind this next generation of filters.

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

Code: https://github.com/filipelauar/projects/tree/main/styleGAN_interfaceGAN_IDInvert_faceAttributeFAN

In this episode I talk about how artificial intelligence is generating people that doesn't exist. I explain how the GANs work, the Generative Adversarial Networks that are able to generate perfectly people from noise.

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

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

This person is real: https://thispersondoesnotexist.com/

Which face is real: https://www.whichfaceisreal.com/

In this episode I talk about how Facebook algorithms work. I explain the main goal of the platform and also how do they use our data to make money. I also give some examples of how powerful data is.  

In this episode we explain how computers understand texts. I talk about a representation based on the dictionary and a more efficient one based on the attributes/characteristics of the word so that the computer can understand them better.