#aihype — Public Fediverse posts

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."

#AiAiAi #AI #AIHype

https://economicfront.wordpress.com/2026/08/01/artificial-intelligence-the-hype-the-dangers-and-the-resistance-part-i/

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."

#AiAiAi #AI #AIHype

https://economicfront.wordpress.com/2026/08/01/artificial-intelligence-the-hype-the-dangers-and-the-resistance-part-i/

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."

#AiAiAi #AI #AIHype

https://economicfront.wordpress.com/2026/08/01/artificial-intelligence-the-hype-the-dangers-and-the-resistance-part-i/

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."

#AiAiAi #AI #AIHype

https://economicfront.wordpress.com/2026/08/01/artificial-intelligence-the-hype-the-dangers-and-the-resistance-part-i/

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."

#AiAiAi #AI #AIHype

https://economicfront.wordpress.com/2026/08/01/artificial-intelligence-the-hype-the-dangers-and-the-resistance-part-i/

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:

#AI #AIHype #AiAiAi

https://monthlyreview.org/articles/tech-billionaires-the-ai-threat-and-resistance/

https://economicfront.wordpress.com/artificial-intelligence-challenges-and-resistance/

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:

#AI #AIHype #AiAiAi

https://monthlyreview.org/articles/tech-billionaires-the-ai-threat-and-resistance/

https://economicfront.wordpress.com/artificial-intelligence-challenges-and-resistance/

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:

#AI #AIHype #AiAiAi

https://monthlyreview.org/articles/tech-billionaires-the-ai-threat-and-resistance/

https://economicfront.wordpress.com/artificial-intelligence-challenges-and-resistance/

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:

#AI #AIHype #AiAiAi

https://monthlyreview.org/articles/tech-billionaires-the-ai-threat-and-resistance/

https://economicfront.wordpress.com/artificial-intelligence-challenges-and-resistance/

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:

#AI #AIHype #AiAiAi

https://monthlyreview.org/articles/tech-billionaires-the-ai-threat-and-resistance/

https://economicfront.wordpress.com/artificial-intelligence-challenges-and-resistance/

@zopyx

Not entirely clear on what exactly I'm disqualified for, but to make you happy / happier...

It's a lot more typing, but would "uninformed despecialized prompt-writing to cause an LLM to generate source code that might in some way look like it's an attempt at solving the described problem if you squint really hard and eat a few THC gummies" be a more acceptable description?

#hype #AIhype #apologia #SlopCoding

I think this is clearly a bit bullshit from Anthropic's side but anyway...:

"The US government, citing national security authorities, has issued an export control directive to suspend all access to Fable 5 and Mythos 5 by any foreign national, whether inside or outside the United States, including foreign national Anthropic employees. The net effect of this order is that we must abruptly disable Fable 5 and Mythos 5 for all our customers to ensure compliance. Access to all other Anthropic models will not be affected.

We received the directive from the government today at 5:21pm (ET). The letter did not provide specific details of its national security concern. Our understanding is that the government believes it has become aware of a method of bypassing, or “jailbreaking” Fable 5. We reviewed a demonstration of this specific technique being used to identify a small number of previously known, minor vulnerabilities. These vulnerabilities all appear relatively simple, and we have found that other publicly-available models are able to discover them as well without requiring a bypass."

https://www.anthropic.com/news/fable-mythos-access

#AI #GenerativeAI #Anthropic #USA #Claude #ClaudeFable #ClaudeMythos #AIHype #CyberSecurity

#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).

#aicon #aihype #aibias #antisemitism

File under: AI Resistance

I'm reading – and enjoying immensely – @emilymbender and @alex's book #TheAICon.

I've been learning a lot of things and something that really stood out recently is the need to be careful about the language we use to describe these systems. Bender and Hanna helpfully explain:

"It matters what words we use when we talk about these technologies. For instance, in our writing, we don’t use the term “hallucination” to discuss the errors of LLMs, for two reasons. First, if it’s used tongue-in-cheek, it is making light of what can be symptoms of serious mental illness. Second,
“hallucination” refers to the experience of perceiving things that aren’t there. But LLMs actually don’t have perceptions, and suggesting that they do is yet more unhelpful anthropomorphization. That means we also avoid assigning thought processes to these systems, or saying that they can
“think”. Metaphors have power, they structure the frames of discourse, and they can subtly and insidiously encourage certain ways of understanding technology and the social systems it is embedded in."

Antropomorphizing AI contributes to AI hype. Thanks Emily and Alex for helping me see things this way!

#AIcritique #AIhype #NoAI #AIresistance #AI #books

«KI macht kaputt — Ein Dutzend Beispiele dafür, wie der KI-Hype alles erstickt. Beflügelt von KI?
Wir erleben gerade die größte technologische Disruption aller Zeiten – und wenn nicht die größte, dann sicherlich die schnellste. Enorme Produktivitätssteigerungen, wissenschaftliche Durchbrüche. Sie kennen das Narrativ.»

Gibt es über die KI auch positive Artikel die fachlich und keine Werbungen sind?

🤔 https://www.computerwoche.de/article/4137800/ki-macht-kaputt.html

#kikritik #kihype #ki #kritik #hype #aihype #aicriticism #aislop #tech

#LeShow w/ #HarryShearer

#AI expert #GaryMarcus on the AI illusion and why it isn't intelligence

https://harryshearer.com/le-shows/december-21-2025/#
#artificialIntelligence #tech #BigTech #AIhype

"My gut instinct is that this is an industry-wide problem. Perplexity spent 164% of its revenue in 2024 between AWS, Anthropic and OpenAI. And one abstraction higher (as I'll get into), OpenAI spent 50% of its revenue on inference compute costs alone, and 75% of its revenue on training compute too (and ended up spending $9 billion to lose $5 billion). Yes, those numbers add up to more than 100%, that's my god damn point.

Large Language Models are too expensive, to the point that anybody funding an "AI startup" is effectively sending that money to Anthropic or OpenAI, who then immediately send that money to Amazon, Google or Microsoft, who are yet to show that they make any profit on selling it.

Please don't waste your breath saying "costs will come down." They haven't been, and they're not going to.

Despite categorically wrong boosters claiming otherwise, the cost of inference — everything that happens from when you put a prompt in to generate an output from a model — is increasing, in part thanks to the token-heavy generations necessary for "reasoning" models to generate their outputs, and with reasoning being the only way to get "better" outputs, they're here to stay (and continue burning shit tons of tokens).

This has a very, very real consequence."

https://www.wheresyoured.at/why-everybody-is-losing-money-on-ai/

#AI #GenerativeAI #BusinessModels #LLMs #Chatbots #AIHype #AIBubble

new #Krazam feat. #AlexisGay

„AI Boyfriend”
https://youtu.be/KiPQdVC5RHU

» Grandpa!...«

#aiHype #robotHype #AILover
#robotLove #aiHumor #TechHumor #robotHumor

ps: see also previous infamous #krazamtv on #microservices

new #Krazam feat. #AlexisGay

„AI Boyfriend”
https://youtu.be/KiPQdVC5RHU

» Grandpa!...«

#aiHype #robotHype #AILover
#robotLove #aiHumor #TechHumor #robotHumor

ps: see also previous infamous #krazamtv on #microservices

new #Krazam feat. #AlexisGay

„AI Boyfriend”
https://youtu.be/KiPQdVC5RHU

» Grandpa!...«

#aiHype #robotHype #AILover
#robotLove #aiHumor #TechHumor #robotHumor

ps: see also previous infamous #krazamtv on #microservices

new #Krazam feat. #AlexisGay

„AI Boyfriend”
https://youtu.be/KiPQdVC5RHU

» Grandpa!...«

#aiHype #robotHype #AILover
#robotLove #aiHumor #TechHumor #robotHumor

ps: see also previous infamous #krazamtv on #microservices

I guess this pretty sums it up:
" The answers generated on the basis of this data do not include
references to understand where certain information was taken from, and may be
biased. Additionally, #ChatGPT excels at providing answers that sound very plausible,
but that are often inaccurate or wrong".

Would you trust a person whose answers are #biased, #inaccurate or outright #wrong? I wouldn't.
#Europol #Report #LLM #AIHype #TechWatchFlash
Link to report
https://www.europol.europa.eu/publications-events/publications/chatgpt-impact-of-large-language-models-law-enforcement