In the last episode of 2019 I speak with Filip Piekniewski about some of the most worth noting findings in AI and machine learning in 2019. As a matter of fact, the entire field of AI has been inflated by hype and claims that are hard to believe. A lot of the promises made a few years ago have revealed quite hard to achieve, if not impossible. Let's stay grounded and realistic on the potential of this amazing field of research, not to bring disillusion in the near future.
Join us to our Discord channel to discuss your favorite episode and propose new ones.
I would like to thank all of you for supporting and inspiring us. I wish you a wonderful 2020!
Francesco and the team of Data Science at Home
Bridging the gap between data science and data engineering: metrics (Ep. 95)
A big welcome to Pryml: faster machine learning applications to production (Ep. 94)
The dark side of AI: bias in the machine (Ep. 92)
The dark side of AI: metadata and the death of privacy (Ep. 91)
The dark side of AI: recommend and manipulate (Ep. 90)
The dark side of AI: social media and the optimization of addiction (Ep. 89)
More powerful deep learning with transformers (Ep. 84) (Rebroadcast)
How to improve the stability of training a GAN (Ep. 88)
What if I train a neural network with random data? (with Stanisław Jastrzębski) (Ep. 87)
Deeplearning is easier when it is illustrated (with Jon Krohn) (Ep. 86)
[RB] How to generate very large images with GANs (Ep. 85)
More powerful deep learning with transformers (Ep. 84)
[RB] Replicating GPT-2, the most dangerous NLP model (with Aaron Gokaslan) (Ep. 83)
What is wrong with reinforcement learning? (Ep. 82)
Have you met Shannon? Conversation with Jimmy Soni and Rob Goodman about one of the greatest minds in history (Ep. 81)
Attacking machine learning for fun and profit (with the authors of SecML Ep. 80)
[RB] How to scale AI in your organisation (Ep. 79)
Replicating GPT-2, the most dangerous NLP model (with Aaron Gokaslan) (Ep. 78)
Training neural networks faster without GPU [RB] (Ep. 77)
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