What if the biggest challenge in AI isn’t datasets or algorithms, but the lack of talent?

In a world where demand for AI solutions is skyrocketing, businesses (and now governments) are facing a critical shortage of skilled machine learning engineers. Yes, the biggest challenge in AI and machine learning today isn't data sets or training data, its a human resources problem.

With only 300,000 skilled engineers globally - and only around 600 truly brilliant engineers - demand far outstrips supply.

How can businesses meet the soaring demand for AI-powered solutions? With salaries for the best developers as much as €2 million per year, how can the average company keep up?

Neo is the worlds first autonomous machine learning engineer. It's designed to bridge this gap by automating tedious processes and enabling engineers to focus on the more important, creative tasks downstream.

You'll learn how Neo’s multi-agent system outperforms traditional workflows, why Kaggle grandmasters are the gold standard for AI expertise, and what this means for the future of work.

The Vij brothers also share insights from their journeys, including the transformation of GPUs for AI and lessons from working at CERN.

And because this is Thinking on Paper, you'll also get the ethical implications of hyper-intelligent systems, how automation might redefine creativity and human ingenuity and what it all means for business, culture and your day-to-day existence.

Please enjoy the show.

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Timestamps

(00:00) Why Are There So Few Machine Learning Engineers?(01:54) Meet Gaurav Vij and Saurabh Vij(02:57) Lessons Learned from Working at CERN(04:45) How to Explain The Importance Of A.I. to Your Parents(07:24) The World’s First Autonomous Machine Learning Engineer: What AI Problem Does NEO Solve?(08:17) AI Competitions and Kaggle Grandmasters(11:06) How Many A.I./ML Engineers Do We Need?(17:30) Fixing The A.I. Hallucination Problem(18:09) Hot Buttons: 5 AI Questions In 30 Seconds(18:46) Hollywood: Doomed by A.I, or Reborn?(20:26) AI News: Nvidia Digits Explained(21:51) Moore's Law And Could AI Models Be Motivated by Rewards?(25:42) AI And Quantum Computing(29:45) The Thinking on Paper Carry-Over Question(30:16) After Hours: Backstage Extra

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Check out NEO: https://heyneo.so/Learn more about the show: www.thinkingonpaper.xyzFollow Thinking On Paper On Instagram: https://www.instagram.com/thinkingonpaperpodcast/

Stoke The Fire #024 – Brian Tsao (Chef)