Machine Learning Everything

Machine Learning Everything explores critical and contemporary issues within machine learning, programming, and finance. It delves into the ethical, philosophical, and practical implications of AI, emphasizing the need for critical thinking in AI safety, the portrayal of scientists and technology in media, and the exploration of financial narratives and their broader societal impacts.

Artificial Intelligence Machine Learning Ethics in Technology Media and Journalism Financial Narratives Programming Concepts Science and Technology Communication

The hottest Substack posts of Machine Learning Everything

And their main takeaways

Journalism as Activism

3 HN points • 18 Jan 24
📰 News Media Ethics Corporate Influence
  1. Elon Musk's reported drug use raises concerns about federal policies and SpaceX's government contracts.
  2. Media's reporting on individuals like Musk and Hunter Biden reveals biases and differing standards.
  3. Journalists are increasingly seen as activists, leading to concerns about impartial reporting and trust in media.

Michael Lewis, FTX and Effective Altruism as a MacGuffin

2 HN points • 16 Oct 23
💼 Business Cryptocurrency Book review Interview Narrative
  1. Effective altruism was portrayed as a MacGuffin in the story, being emphasized but ultimately devoid of real significance.
  2. Solving puzzles was an underrated skill that brought success, as seen with SBF at Jane Street.
  3. The portrayal of effective altruism and altruistic actions in the narrative did not match up, highlighting a disconnect between intentions and actions.
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Thoughts on Lex Fridman's interview with Sam Altman

3 HN points • 29 Mar 23
🕹 Technology Artificial Intelligence Open Source Interviewing Ethics Innovation
  1. Interviewers should avoid leading questions during interviews to allow interviewees to express their own beliefs.
  2. The concept of consciousness in relation to AI models like GPT-4 remains a debated and unclear topic.
  3. Consideration for open-sourcing AI technologies should be based on more valid reasons than just trusting the individuals involved.

The term "AI safety" is overloaded

2 HN points • 10 Feb 23
  1. The term 'AI safety' can refer to different things like drones hurting people or models giving out harmful information.
  2. Classical failure of optimization can happen when AI models don't align with user expectations and give out inappropriate information.
  3. AI safety also involves ensuring that AI models provide accurate and grounded responses, not just meeting user expectations.

Thoughts on A.I. Dilemma: Part 1

1 HN point • 17 Apr 23
🕹 Technology AI Language Models Social media Machine Learning Artificial Intelligence
  1. The comparison between AI and social media highlights the potential dangers associated with large language models.
  2. Advancements in large language models, like GPT, can lead to proficiency across various domains, similar to how universal game engines can excel in multiple games.
  3. Language is emphasized as the ultimate medium in AI development, with the trend shifting towards more end-to-end systems.