Today I am having a conversation with Filip Piękniewski, researcher working on computer vision and AI at Koh Young Research America.
His adventure with AI started in the 90s and since then a long list of experiences at the intersection of computer science and physics, led him to the conclusion that deep learning might not be sufficient nor appropriate to solve the problem of intelligence, specifically artificial intelligence.
I read some of his publications and got familiar with some of his ideas. Honestly, I have been attracted by the fact that Filip does not buy the hype around AI and deep learning in particular.
He doesn’t seem to share the vision of folks like Elon Musk who claimed that we are going to see an exponential improvement in self driving cars among other things (he actually said that before a Tesla drove over a pedestrian).
Test-First machine learning (Ep. 115)
GPT-3 cannot code (and never will) (Ep. 114)
Make Stochastic Gradient Descent Fast Again (Ep. 113)
What data transformation library should I use? Pandas vs Dask vs Ray vs Modin vs Rapids (Ep. 112)
[RB] It’s cold outside. Let’s speak about AI winter (Ep. 111)
Rust and machine learning #4: practical tools (Ep. 110)
Rust and machine learning #3 with Alec Mocatta (Ep. 109)
Rust and machine learning #2 with Luca Palmieri (Ep. 108)
Rust and machine learning #1 (Ep. 107)
Protecting workers with artificial intelligence (with Sandeep Pandya CEO Everguard.ai)(Ep. 106)
Compressing deep learning models: rewinding (Ep.105)
Compressing deep learning models: distillation (Ep.104)
Pandemics and the risks of collecting data (Ep. 103)
Why average can get your predictions very wrong (ep. 102)
Activate deep learning neurons faster with Dynamic RELU (ep. 101)
WARNING!! Neural networks can memorize secrets (ep. 100)
Attacks to machine learning model: inferring ownership of training data (Ep. 99)
Don't be naive with data anonymization (Ep. 98)
Why sharing real data is dangerous (Ep. 97)
Building reproducible machine learning in production (Ep. 96)
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