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
True Machine Intelligence just like the human brain (Ep. 155)
Delivering unstoppable data with Streamr (Ep. 154)
MLOps: the good, the bad and the ugly (Ep. 153)
MLOps: what is and why it is important Part 2 (Ep. 152)
MLOps: what is and why it is important (Ep. 151)
Can I get paid for my data? With Mike Andi from Mytiki (Ep. 150)
Building high-growth data businesses with Lillian Pierson (Ep. 149)
Learning and training in AI times (Ep. 148)
You are the product [RB] (Ep. 147)
Polars: the fastest dataframe crate in Rust - with Ritchie Vink (Ep. 146)
Apache Arrow, Ballista and Big Data in Rust with Andy Grove (Ep. 145)
Pandas vs Rust (Ep. 144)
Concurrent is not parallel - Part 2 (Ep. 143)
Concurrent is not parallel - Part 1 (Ep. 142)
Backend technologies for machine learning in production (Ep. 141)
You are the product (Ep. 140)
How to reinvent banking and finance with data and technology (Ep. 139)
What's up with WhatsApp? (Ep. 138)
Is Rust flexible enough for a flexible data model? (Ep. 137)
Is Apple M1 good for machine learning? (Ep.136)
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