Why AI Researchers Are Suddenly Obsessed With Whirlpools (Ep. 297) [RB]

Why AI Researchers Are Suddenly Obsessed With...

Up next

Your Favorite AI Startup is Probably Bullshit (Ep. 298) [RB]

The brutal truth about why Silicon Valley is blowing billions on glorified autocomplete while pretending it's the next iPhone. We're diving deep into the AI investment circus where VCs who can't code are funding companies that barely understand their own technology. From blockcha ...  Show more

AGI: The Dream We Should Never Reach (Ep. 296)

Also on YouTube Two AI experts who actually love the technology explain why chasing AGI might be the worst thing for AI's future—and why the current hype cycle could kill the field we're trying to save. Want to dive deeper? Head to datascienceathome.com for detailed show notes, c ...  Show more

Recommended Episodes

464: A.I. vs Machine Learning vs Deep Learning
Super Data Science: ML & AI Podcast with Jon Krohn

In this episode, I tackle three often conflated terms - AI, machine learning, and deep learning - to shine some light on what exactly they are. Additional materials: www.superdatascience.com/464 

MLG 001 Introduction
Machine Learning Guide

Show notes: ocdevel.com/mlg/1. MLG teaches the fundamentals of machine learning and artificial intelligence. It covers intuition, models, math, languages ...

  Show more

Feature Processing for Text Analytics
Linear Digressions

It seems like every day there's more and more machine learning problems that involve learning on text data, but text itself makes for fairly lousy inputs to machine learning algorithms.  That's why there are text vectorization algorithms, which re-format text data so it's ready f ...  Show more

MLG 004 Algorithms - Intuition
Machine Learning Guide

<div>

Machine learning consists of three steps: prediction, error evaluation, and learning, implemented by training algorithms on large datasets to build models that can make decisions or classifications. The primary categories of machine learning algorithms are supervised, un ...

  Show more