In this thought-provoking episode, we sit down with the renowned AI expert, Filip Piekniewski, Phd, who fearlessly challenges the prevailing narratives surrounding artificial general intelligence (AGI) and the singularity. With a no-nonsense approach and a deep understanding of the field, Filip dismantles the hype and exposes some of the misconceptions about AI, LLMs and AGI.
Join us as we delve into the real-world implications of AI, separating fact from fiction, and gaining a firm grasp on the tangible possibilities of AI advancement.
If you're seeking a refreshingly pragmatic perspective on the future of AI, this episode is an absolute must-listen.
Filip Piekniewski is a distinguished computer vision researcher and engineer, specializing in visual object tracking and perception. He approaches machine learning with a pragmatic mindset, recognizing its current limitations. Filip earned his Ph.D. from Warsaw University, where he explored neuroscience and later joined Brain Corporation in San Diego. His extensive study of neuroscience inspired him to develop innovative, bio-inspired machine learning architectures. Filip's unique blend of scientific curiosity and software engineering expertise allows him to quickly prototype and implement new ideas. He is known for his realistic perspective on AI, debunking AGI hype and focusing on tangible advancements.
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References
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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