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Pivot to AI: Hallucinations worsen as the money runs out

๐ŸŒˆ Abstract

The article discusses the current state of the venture capital-funded AI and machine learning industry, highlighting the issues of hallucinations and the impending collapse of the AI hype bubble.

๐Ÿ™‹ Q&A

[01] The Current State of AI

1. What are the key issues with large language models (LLMs) discussed in the article?

  • LLMs are prone to "hallucinations" - generating convincing-sounding but factually incorrect output.
  • The hallucination problem is not decreasing, but rather getting worse.
  • LLMs work by generating output based on statistical patterns in the training data, rather than true understanding.
  • The venture capital-funded AI industry is running on the promise of replacing humans with LLMs, even in areas where details matter.

2. How are AI companies trying to address the hallucination issue?

  • The current workaround is to hire fresh master's graduates or PhDs to try to fix the hallucinations.
  • AI companies try to underpay these new hires on the promise of future wealth or high-status positions.
  • There are also attempts to train AIs on the output of other AIs, which is known to make the models collapse into gibberish.

[02] The AI Hype Bubble

1. What is the current state of the AI venture capital bubble?

  • There is enough money floating around in tech VC to fuel the current AI hype for another couple of years.
  • However, the money and patience are running out, as the AI systems don't have a clear path to profitable functionality.
  • The article cites the example of Stability AI, which ran out of money for their AWS cloud computing bill.
  • The article suggests the AI VC bubble will likely last another 3 quarters (9 months) before collapsing.

2. How will the AI bubble's collapse impact the broader tech industry?

  • AI stocks are currently holding up the S&P 500, so when the AI VC bubble pops, the tech sector as a whole will crash.
  • The article also suggests that the collapse of the AI bubble will lead to a downturn in the cryptocurrency market, as "whenever the NASDAQ catches a cold, bitcoin catches COVID."

[03] Emerging AI Capabilities

1. What are the claims around "emergent capabilities" in AI?

  • AI companies are starting to talk up "emergent capabilities" again, where an AI suddenly becomes useful for things it wasn't developed for.
  • The article states that every claim of "emergent capabilities" has turned out to be either an irreproducible coincidence or data the model was already trained on.
  • The article dismisses these claims as "magic" that does not actually happen.

2. How does the article characterize the recent claims of AI models gaining "reasoning" capabilities?

  • The article analyzes a Financial Times article that reported on OpenAI and Meta's claims of developing AI models capable of "reasoning".
  • The article shows how the FT article's claims slowly decay from the initial splashy headline to the admission that the companies haven't actually figured out how to achieve true reasoning in their models.
  • The article portrays these claims as overhyped and not reflective of the actual capabilities of current AI systems.
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