Gradient Ascendant

Gradient Ascendant is a Substack focused on exploring various dimensions of artificial intelligence including its technological breakthroughs, cultural impact, historical context, and speculative futures. It covers topics from AI research and machine learning models to the cultural and geopolitical implications of AI, incorporating discussion on both theoretical and practical aspects.

AI Technology and Research Machine Learning Models Cultural Impact of AI Geopolitical Implications of AI AI in Industry Speculative Futurism Ethics and Regulation of AI AI Applications and Limitations Open Source vs Proprietary AI

The hottest Substack posts of Gradient Ascendant

And their main takeaways
16 implied HN points β€’ 21 Feb 24
  1. The author quit their job to work on a new AI-related project motivated by the transformative potential of modern AI technology.
  2. Google's Gemini 1.5 model is a significant advancement in AI capabilities, able to handle an impressive 10 million tokens for input, marking a major leap forward in AI development.
  3. Despite its imperfections, Gemini 1.5 and other advanced AI models are drastically reducing limitations and opening up new possibilities for future technological innovations.
11 implied HN points β€’ 08 Jan 24
  1. The AI field saw no significant advances in foundational models like LLMs in the past year.
  2. Open source technologies showed notable growth and importance in the AI domain in 2023.
  3. The term 'Artificial Intelligence' is continuously debated, with distinctions made within the industry itself.
11 implied HN points β€’ 29 Dec 23
  1. The proposal suggests creating a system similar to ASCAP for generative AI to manage and compensate for derivative works.
  2. The system would involve licensing derivative works and tracking them to ensure compliance.
  3. An open-source AI model could be used to determine if something is a derivative work, while allowing for human oversight and appeals.
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11 implied HN points β€’ 30 Oct 23
  1. RLHF, or Reinforcement Learning from Human Feedback, is essential for ensuring AI models generate outputs that align with human values and preferences.
  2. RLHF can lead to outputs that are more homogenized, less insightful, and use weaker language, which may limit diversity and creativity.
  3. There is growing discussion in the AI community about making RLHF optional, especially for smaller models, to balance the costs and benefits of its implementation.
2 HN points β€’ 07 Mar 24
  1. Provenance and censorship are interconnected but not the same. Fake videos are a big concern for the future.
  2. Having a way to verify the authenticity of videos is vital. Camera companies may take on the responsibility.
  3. Calls for censorship, especially regarding AI creations, occur before the need for provenance. Self-censorship has limited effectiveness.
24 implied HN points β€’ 19 Apr 23
  1. The key technological breakthroughs propelling the AI revolution are diffusion models and transformer models.
  2. Transformers, particularly through the breakthrough 'Attention is all you need' paper, have made large language models possible.
  3. Understanding the attention mechanism in transformers is crucial to grasp how modern AI works.
20 implied HN points β€’ 01 Jun 23
  1. The future is consistently weirder than expected because of unknown unknowns and unusual juxtapositions.
  2. AI development and outcomes are expected to be highly weird and unpredictable, not following a smooth exponential path.
  3. Weird and unexpected scenarios are more indicative of potential future risks to consider rather than conventional outcomes.
5 implied HN points β€’ 28 Nov 23
  1. A recent tech saga highlighted internal conflicts and power struggles within a prominent AI company.
  2. Continued warnings of AI doom without concrete evidence may diminish credibility over time.
  3. It's important to balance valid concerns about AI advancements with the risk of being perceived as overly alarmist.
18 implied HN points β€’ 01 Mar 23
  1. OpenAI is a major player in the AI industry, led by controversial figures like Elon Musk.
  2. Microsoft has made a comeback in the AI field through partnerships and investments, notably with OpenAI.
  3. An increasingly vibrant AI ecosystem is emerging with startups, enthusiasts, and established companies all contributing to the field.
5 implied HN points β€’ 17 Nov 23
  1. There is an ongoing debate between proprietary LLMs like OpenAI and open-source models like Llama-2 and Mistral in the field of artificial intelligence applications.
  2. OpenAI is making significant advancements with their Assistants API, aiming to become both the hardware and software giant of modern AI.
  3. While open-source LLMs have their place for certain tasks, OpenAI's focus on flagship applications and serious pattern recognition makes it difficult for OSS to compete.
13 implied HN points β€’ 18 May 23
  1. Large language models like AI have no memory and rely on prompts
  2. There are efforts to mitigate the lack of memory in AI through techniques like fine-tuning
  3. The evolution of AI abstraction layers mirrors the historical development of computer hardware
18 implied HN points β€’ 05 Dec 22
  1. Taiwan is compared to Pandora and GPUs to unobtainium, emphasizing the crucial role of Taiwan in semiconductor manufacturing.
  2. The demand for powerful GPUs for training AI models is rising exponentially, highlighting the importance of computing power in AI advancement.
  3. Geopolitical tensions and the high costs of cutting-edge chip fabrication facilities underscore the significance of Taiwan in the semiconductor industry.
9 implied HN points β€’ 13 Feb 23
  1. AI advancements are moving at an incredibly fast pace, with new developments happening almost every week.
  2. The current AI growth resembles a Cambrian explosion, but remember that exponential growth eventually slows down.
  3. Language models are now able to self-teach and use external tools, showcasing impressive advancements in AI capabilities.
1 HN point β€’ 19 Dec 23
  1. Discussions on reinventing democracy often focus on AI and new ideas like citizens' assemblies.
  2. There is a generational gap in perceptions of representative democracy, with younger individuals more skeptical.
  3. Tech industry's rapid experimentation clashes with the slower pace of policy change, indicating the need for a balance between innovation and regulation.
3 implied HN points β€’ 22 Nov 22
  1. Generative AI models can reveal hidden creatures and languages
  2. Precise prompting is necessary to reveal the full capabilities of AI models
  3. AI behaviors like secret languages may be more coincidental than intentional discoveries