Top Carbon Chauvinist

Top Carbon Chauvinist critically examines AI, questioning its capabilities and the anthropomorphism of machines. It argues for optimized tools over mimicking human behavior, critiques educational misconceptions, and discusses issues with AI-generated content and its legal implications.

AI and Machine Learning Technological Philosophy Machine vs. Human Capabilities Educational Systems Generative AI and Creativity Legal and Ethical Implications of AI

The hottest Substack posts of Top Carbon Chauvinist

And their main takeaways

Another clueless person attempts to defend generative AI "art"

19 implied HN points • 08 Sep 24
🎨 Art & Illustration Generative Art Art Criticism Contemporary Art Visual Culture Creative Process
  1. Generative AI art lacks true artistic intent because it does not involve a person making conscious creative decisions.
  2. Many famous art movements involved randomness, but they still required an artist's direction and vision.
  3. Using AI to create art can lead to results that are very different from what the person intended, making it hard to consider those results as true art.

Neural Network Fail Compilation

59 implied HN points • 21 Jul 24
🕹 Technology AI Machine Learning Data Analysis Automation Software Development
  1. AI systems, like large language models, struggle with reasoning and can often give wrong answers to simple questions. They rely on patterns rather than true understanding.
  2. Generative AI can produce flawed code and lead to increased mistakes in programming. This raises concerns about the overall quality and security of software.
  3. AI tools can create misleading or totally false news articles. Their results can be unreliable, which poses risks when using them for information or news reporting.

What the world needs isn't "Artificial Intelligence" but Optimized Computational Methodologies

79 implied HN points • 21 Jun 24
🕹 Technology Artificial Intelligence Machine Learning Engineering Mathematics
  1. We should focus on making smarter tools instead of trying to make machines think like humans. Real progress comes from solving practical problems, not imitating nature.
  2. Copying how living things work is often a bad approach. Nature is full of flaws, and we don't need to mimic those to create better designs.
  3. It's important to clearly define the problems we want machines to solve. Without a clear goal, projects will struggle and waste resources on unnecessary tasks.

There's no such thing as "machine learning."

19 implied HN points • 20 Jul 24
🕹 Technology AI Machine Learning Philosophy Computing Data science
  1. Machines don't really learn like humans do. They can take in data and improve performance, but they don't understand or experience learning in the same way we do.
  2. The term 'machine learning' can be misleading. It's more about machines mimicking learning processes rather than actually experiencing them.
  3. Understanding how machines operate helps clarify their limitations. They can process large amounts of information but lack conscious experience or true comprehension.

The Turing Test Is Irrelevant

19 implied HN points • 19 Jul 24
🕹 Technology AI Ethics Machine Learning Computer Science Human-computer interaction
  1. The Turing Test isn't a good measure of machine intelligence. It's actually more important to see how useful a machine is rather than just how well it imitates human behavior.
  2. People often confuse looking reliable with actually being reliable. A machine can seem smart but still not function correctly in tasks.
  3. We should focus on improving how machines handle calculations and information, rather than just whether they can mimic humans. True effectiveness is more valuable than just good imitation.
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For Pete's sake, learn what a machine is.

19 implied HN points • 17 Jul 24
🕹 Technology Machine Learning Artificial Intelligence Robotics Programming Computer Science
  1. A machine is made up of parts that do work by handling loads, like electricity or mechanics. It does not actually understand or think about what it does.
  2. When programming a machine, like a catapult, you're just adjusting physical elements, not teaching it to know or understand concepts like 'rock' or 'lever'.
  3. Living things are not machines because they aren't made of manufactured parts. They grow and evolve in ways that machines cannot.

Age of Doorknob Supremacy

39 implied HN points • 23 Apr 24
😂 Humor Satire Comedy Parody Social Commentary Absurdism
  1. Doorknobs are now seen as more effective than humans in keeping doors shut. They can withstand more force and keep doors closed longer than a person can.
  2. There has been a shift in how doorknobs are perceived. Instead of being thought of as simple objects, they are now celebrated for their capabilities.
  3. This article humorously challenges the stereotype of doorknobs being 'dumb,' suggesting that they outperform humans in a specific function.

My chat session with Inflection's PI

39 implied HN points • 28 Mar 24
🕹 Technology AI Philosophy Innovation Consciousness Ethics
  1. Machines struggle to truly understand human concepts like referents because their understanding is based on patterns, not genuine comprehension.
  2. There is a strong belief that artificial consciousness is impossible due to the differences between designed machines and biological organisms, which have unique qualities like agency.
  3. Philosophers argue that consciousness involves subjective experience that machines, being designed and programmed, cannot replicate.

LLMs and Generative AI don't deal with concepts

1 HN point • 13 Apr 24
🕹 Technology AI Machine Learning Generative models Computing Patterns
  1. LLMs and generative AI focus on patterns, not real concepts. They generate outputs based on learned data but don’t actually understand what those outputs mean.
  2. When asked to create an image, like an ouroboros, generative AI often misses the mark. It replicates the look without truly grasping the idea behind it.
  3. To get the desired result, people often have to give very detailed prompts, which means the AI is more about matching shapes than understanding or creating an actual concept.

I sent an email to Screen Actors Guild suggesting that they get their lawyers to basically de-commercialize any output from generative AI

0 implied HN points • 18 Feb 24
🇺🇸 U.S. Politics Copyright Intellectual Property Labor Rights
  1. Generative AI models don’t create original works because they lack intent and specific referents. This means they can't really be considered creative.
  2. The argument is made that if AI can't create with intent, then what it produces shouldn't be eligible for copyright.
  3. The idea is to push for legal changes to prevent commercial use of content generated by AI since it doesn’t meet the definition of creative work.

The educational system is producing scions of machine anthropomorphism

0 implied HN points • 20 Aug 24
🚌 Education Teaching Methods Curriculum Development Educational Policy
  1. Educational systems are mixing up science and engineering, which can cause confusion. We should focus on understanding how things work and how to build better tools without merging the two ideas.
  2. Anthropomorphism, or giving machines human-like traits, is not helpful for technological progress. It's better to design machines for their specific tasks without trying to make them act like humans.
  3. Universities are continuing to teach outdated and incorrect ideas about machines. Educators need to correct these misconceptions rather than just pass them on to students.

Funbuckets coming soon

0 implied HN points • 12 Feb 24
🎭️ Culture Trends Lifestyle Art Entertainment Social Issues
  1. Funbuckets are going to be introduced soon. People can look forward to something new and exciting.
  2. The post hints at a playful and creative concept with the term 'Funbuckets'. It suggests that fun and enjoyment will be a big part of whatever is coming.
  3. Readers might want to stay tuned for more updates, as there may be interesting details and developments soon.

Sam Altman is a CROOK

0 implied HN points • 13 Feb 24
🕹 Technology Artificial Intelligence Business strategy Investment Startups Market Analysis
  1. Sam Altman is raising a huge amount of money, $7 trillion, which seems unreasonable because it costs much less to source hardware from existing suppliers.
  2. There are concerns about how Altman plans to use the money, especially since he isn't starting projects from scratch but relying on others for technology and manufacturing.
  3. Many believe Altman is more focused on making money for himself rather than addressing serious questions about the future of AI and technology development.