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

Anthropological Intelligence

5 implied HN points • 30 Dec 24
🕹 Technology Startups AI Innovation User Experience Market fit
  1. Many tech startups are not really pushing new technology; they're mostly testing if people will use what already exists in new ways. Uber and AirBnB combine known tech in ways that challenge social norms.
  2. AI startups are even more focused on understanding user relationships with technology. It's still unclear how people want to use AI, making early experiments tricky.
  3. The success of AI startups might depend not just on the technology but also on user appeal. AI that feels more charming or relatable might win out over others, even if the tech is similar.

Inconsistency as a service

13 implied HN points • 10 Dec 24
🕹 Technology Artificial Intelligence Software Development Engineering Quality Assurance Data processing
  1. Testing is really important for both hardware and software, especially when things can fail sometimes. In making chips, a lot of resources go into making sure they work properly.
  2. With AI like LLMs, you have to keep checking their outputs because they can be unpredictable. It's smart to set up a test system to know if what you're getting makes sense.
  3. We're still figuring out the best ways to test AI technology. Just like with traditional software, it will take time to develop good practices for making sure LLMs work well and reliably.

Agentic

16 implied HN points • 03 Dec 24
🕹 Technology AI Innovation Science Energy Economics
  1. Many people feel like life is getting worse even though, in many ways, it is improving globally. We're healthier and living longer, but people feel they have less control over their lives.
  2. There are two main ways to create wealth: by making something new (the 'forge') or by taking from existing resources (the 'siphon'). The siphon can lead to corruption and inequality, while the forge creates opportunities for everyone.
  3. Modern AI has the potential to help people gain more control and agency over their lives, but it can also take it away if it is used in ways that benefit only a few. It's important for designers to focus on increasing people's agency.

Intertextuality

15 implied HN points • 25 Nov 24
🎭️ Culture Literature Art Technology Copyright Society
  1. The legal issues around AI and reading published work are complex. While people can read anything published, there's ongoing debate about whether AI should be allowed to learn from those works.
  2. Many artists feel that AI trained on their work could be considered stealing, but it hasn't been legally restricted before. Trying to change the rules now might not be fair or practical.
  3. A new way to share revenue from AI outputs with creators might be good, but it would need new laws to make it happen. Limiting access to information in new ways could harm society as a whole.

Another Brick In The Wall

16 implied HN points • 19 Nov 24
🕹 Technology AI Engineering Policy Research Startups
  1. AI models are hitting a point where progress is slowing down. This means that just getting more data or tweaking algorithms might not lead to big breakthroughs anymore.
  2. Even if AI isn't changing dramatically right now, it's still a useful tool for many people. Startups in this space might find it easier to succeed without the threat of a huge game-changing model wiping them out.
  3. With the slowdown in AI development, concerns about AI risks might lessen. Policymakers will have to address how people continue using current chatbots, even with their flaws.
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Leviathan wakes

16 implied HN points • 21 Feb 24
🕹 Technology AI Forecasting Generative AI Models Artificial Intelligence
  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.

AI Grab Bag 2024

11 implied HN points • 08 Jan 24
🕹 Technology AI Open Source Artificial Intelligence
  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.

An Immodest Proposal to OpenAI and the NYT

11 implied HN points • 29 Dec 23
🕹 Technology AI Copyright AI Ethics
  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.

Attention is all you need to understand

24 implied HN points • 19 Apr 23
🕹 Technology AI Neural Networks Training
  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.

It was the best of RLHF, it was the worst of RLHF

11 implied HN points • 30 Oct 23
🕹 Technology Artificial Intelligence Machine Learning Human feedback Information processing Data Training
  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.

Who's Who in A.I.

18 implied HN points • 01 Mar 23
🕹 Technology AI Tech Giants Startups Academia Biotech China
  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.

LLMemento

13 implied HN points • 18 May 23
🕹 Technology AI Models Prompt engineering Fine-tuning
  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

Taiwan is Pandora, GPUs are unobtainium

18 implied HN points • 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.

On Crying Wolf

5 implied HN points • 28 Nov 23
🕹 Technology AI Tech industry Artificial Intelligence Innovation Ethics
  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.

OSS vs OpenAI

5 implied HN points • 17 Nov 23
🕹 Technology AI Software Hardware Cloud Computing API
  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.

Occasional Exponential AI Grab Bag

9 implied HN points • 13 Feb 23
🕹 Technology AI Machine Learning Generative models Language Models Artificial Intelligence
  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.

Provenance and censorship

2 HN points • 07 Mar 24
🕹 Technology AI Censorship Algorithm Social Impact
  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.

RecreAIting DemocrAIcy

1 HN point • 19 Dec 23
🕹 Technology AI Policy Regulation Tech industry Social media
  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.

Hidden Monsters, Secret Languages

3 implied HN points • 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