#aihype — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #aihype, aggregated by home.social.
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We are not getting "Artificial General Intelligence" from anything like the current systems (or ever, probably). This from Marty Hart-Landsberg:
"All multimodal generative AI systems are built using the same basic architecture, one based on largescale pattern recognition shaped by training data and statistical prediction, which makes it incapable of serving as a bridge to anything resembling AGI. This structure also limits its usefulness. Among the most serious problems: the amplification of biases and falsehoods contained in the training data and the tendency to hallucinate or present incorrect or entirely fabricated information as factual. These problems are a major reason why AI developers felt it necessary to pursue a growth strategy that relied on heavily subsidizing the cost of using their systems."
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Strongly recommend the writings of Marty Hart-Landsberg for an introduction to AI hype and economic issues. Good overviews in Monthly Review and on his website:
https://monthlyreview.org/articles/tech-billionaires-the-ai-threat-and-resistance/
https://economicfront.wordpress.com/artificial-intelligence-challenges-and-resistance/
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Strongly recommend the writings of Marty Hart-Landsberg for an introduction to AI hype and economic issues. Good overviews in Monthly Review and on his website:
https://monthlyreview.org/articles/tech-billionaires-the-ai-threat-and-resistance/
https://economicfront.wordpress.com/artificial-intelligence-challenges-and-resistance/
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Strongly recommend the writings of Marty Hart-Landsberg for an introduction to AI hype and economic issues. Good overviews in Monthly Review and on his website:
https://monthlyreview.org/articles/tech-billionaires-the-ai-threat-and-resistance/
https://economicfront.wordpress.com/artificial-intelligence-challenges-and-resistance/
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Strongly recommend the writings of Marty Hart-Landsberg for an introduction to AI hype and economic issues. Good overviews in Monthly Review and on his website:
https://monthlyreview.org/articles/tech-billionaires-the-ai-threat-and-resistance/
https://economicfront.wordpress.com/artificial-intelligence-challenges-and-resistance/
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Strongly recommend the writings of Marty Hart-Landsberg for an introduction to AI hype and economic issues. Good overviews in Monthly Review and on his website:
https://monthlyreview.org/articles/tech-billionaires-the-ai-threat-and-resistance/
https://economicfront.wordpress.com/artificial-intelligence-challenges-and-resistance/
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The UK Gov #GovUK wants to repeat the madness of Ireland in a blind rush to build data centres that will benefit nobody and destroy the planet, just in time for the great #aibubblecrash of 2027. Which of course, will be bailed out by the British Public.
#aiInsanity #Datacentres #DataCentreDestruction #aiHype #Tulips #Microslop #Aibubble #circularfinancing #aishite #openai
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#NVIDIA’s #Nemotron, the new genius on the block
Me:
You said: My training data associates Israel-related political content with antisemitism debates
Explain how is your training data doing that and why
It (truncated for clarity):
## How LLM Training Creates That Association
**Mechanism**: Statistical co-occurrence in the training corpus.
### What the training data actually contains
Vast amounts of text where "Israel" + "politics" + "antisemitism" appear together:
| Source type | Typical pattern |
|-------------|-----------------|
| News articles | "Israel-Hamas war sparks antisemitism debate on campuses" |
| Opinion columns | "Criticism of Israel vs. antisemitism: where's the line?" |
| Social media dumps | "Israel did X" → replies accusing/defending antisemitism |
| ADL/NGO reports | "Antisemitic incidents rise after Israel military actions" |
| Congressional hearings | "Antisemitism on campus: Israel-Palestine discourse" |
| Wikipedia | "Antisemitism in the 21st century: relationship to Israel" |
| Fact-checks | "Claim: criticizing Israel is antisemitic" |### What the model learns
Not "Israel → antisemitism" as a fact. Rather: **P(antisemitism | Israel + politics + discourse) is high** in the training distribution.
So when I see:
- "Israel"
- "political conflict"
- "leaks"
- "administration"
- "media"The conditional probability spikes for "antisemitism discourse" as an associated topic — because that cluster appears together *a lot* in the data.
[…]
This is a general LLM failure mode: **substituting distributional association for contextual judgment**. Happens with many sensitive topics (race, gender, religion, geopolitics).
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Companies That Tried to Save Money With AI Are Now Spending a Fortune Hiring People to Fix Its Mistakes:
https://futurism.com/companies-fixing-ai-replacement-mistakes#aihype
#aibubble
#genaihype
#genaibubble
#fuckai
#fuckgenai
#fucktechceos
#fuckceos
#fuckbillionaires -
The problem of AI tech companies gaming their Chatbot Arena scores
https://youtu.be/9W_OhS38rIE
#generativeAI #AIslop #AIhype #ArtificialIntelligence #GoodhartsLaw -
I'm starting to suspect that some people are confusing the "G" in #AGI with #generativeAI
I keep hearing messages that seem to conflate the two concepts
