**Edit (5pm PT):** Thanks so much all for really great questions - I'm going to pause now but will take a look over next 24 hours and try to...
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The original was posted on /r/machinelearning by /u/WillSen on 2024-10-30 19:31:11+00:00.

Edit (5pm PT): Thanks so much all for really great questions - I’m going to pause now but will take a look over next 24 hours and try to answer any more questions. V grateful for chance to do this and to others who helped answer some of the Qs too from their perspective (shoutout u/Rebeleleven)

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I recently had the opportunity to attend the Berlin Global Dialogue, which has been likened to Davos but with a stronger focus on technology and AI . The lineup was impressive: Hermann Hauser, the founder of ARM, executives from OpenAI and ASML, and a mix of founders from emerging startups tackling everything from quantum ML to supply chain optimization. Even leaders like President Macron and the German Vice Chancellor were there, engaging with critical tech issues that impact us all.

As the CEO of Codesmith – a small, independent tech school with a data science and machine learning research group (last year we contributed to TensorFlow) – I was invited to announce our latest endeavor: Codesmith’s AI & ML Technical Leadership Program.

I shared this experience in an AMA on r/technology and had a great conversation—but the depth of questions around ML/AI didn’t quite match what I’d hoped to explore. I spoke to the mods here and am grateful for them supporting this AMA. 

Proof:

My real passion, inherited from my parents who were both educators, is teaching and making ML more accessible to a broader audience. I’m currently developing an AI/ML workshop for Frontend Masters, and I want to hear from those navigating the ML field. What’s the biggest challenge you’re facing in this space?

A few of my takeaways from the event:

  • Chip manufacturers are shifting to new architectures rather than further miniaturization due to physical limits. High-bandwidth memory (HBM) is a central focus for future roadmaps.
  • Europe is fixated on finding a ‘tech champion,’ but there’s a distinct emphasis on core industries rather than consumer internet—think ASML and ARM.
  • Quantum ML is gaining momentum and receiving government support, particularly for applications like climate forecasting (e.g., Germany’s Klim-QML initiative). While promising, these efforts are still in the prototype phase.
  • There was also, candidly, a lot of talk without much substance. Even OpenAI execs demonstrated a need for more leaders with deep technical insights.

Looking forward to diving deeper into these issues and the broader challenges in ML/AI in an AMA!