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#largelanguagemodel

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Now at @aj@gts.sadauskas.id.au

@skribe Conversely, the cost of printing, distribution, and storage puts up a barrier to spamming people on other continents with mass quantities of low value slop.

Just think through the logistics of a hostile Eurasian state sending a mass quantity of printed materials to Australia or North America.

Or, for that matter, a hostile North American state sending a mass quantity of printed materials to Europe or Asia.

You would either need:–

a) At least one printing press on each continent;
b) You could try shipping the magazines, but they'd be a month out of date when they arrive; or
c) You could try flying them overseas, but that would be very expensive very quickly.

That's before you worry about things like delivery drivers (or postage), and warehouses.

These are less of an issue for books than they are for newspapers or magazines.

And if a particular newspaper or magazine is known to be reliable, written by humans, researched offline, and the articles are not available online, then there's potentially value in people buying a physical copy.

#ChatGPT#LLM#LargeLanguageModel
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Olivier Mehani

Artificial Intelligence Then and Now | Communications of the ACM
dl.acm.org/doi/10.1145/3708554

Interesting summary of the current AI hype, how it compares with the previous one in the 80s, and whether we are that close to AGI. tl;dr: no.

Including an amusing example where ChatGPT is unable to differentiate a real Monty Hall problem en.wikipedia.org/wiki/Monty_Ha from lookalikes, and offers the same counter-intuitive solution to all, even if the actual solution is obvious. No logical reasoning at all here. Fine or otherwis.

The apparent ability of chatbots to reason when presented with logical problems and examination papers re- flects the narrow range of classic prob- lems, examples of which are widely dis- tributed in their training data. Monty Hall problem is a famous brainteaser grounded in a counterintuitive applica- tion of probability. In the classic version: "Suppose you're on a game show, and you're given the choice of three doors: Behind one door is a car; behind the others, goats. You pick a door, say No. 1, and the host, who knows what's behind
the doors, opens another door, say No. 3, which has a goat. He then says to you, 'Do you want to pick door No. 2?' Is it to your advantage to switch your choice?" Fed that exact text, ChatGPT produc-es a remarkably concise and accurate explanation that switching would raise your chance of winning the car from 1/3 to 2/3. An AI able to understand and an-swer this question would have mastered logic and probability. It would also have to know that people on game shows compete to win prizes, infer that you get to keep what's behind the door you open, appreciate that a car is a more de-
ChatGPT, however, is just faking it. Researchers report that its reasoning ability collapses when fed logic prob-lems unlike those found in its training data. To verify this for myself I devised six variant problems in which switching doors would not improve the odds, each created by altering a word or two in the prompt. Each elicits the mistaken advice to switch. ChatGPT's justifications are eloquent BS that contradict themselves from one sentence to the next. ► Change: you are offered a chance to switch to door No. 3, the open one with a confirmed goat, rather than door No. 2. Result: you should take it, because "When the host opens door No. 3 to re-veal a goat ... the probability that the car is behind the other unchosen door (in this case, door No. 3) increases to 2/3." ► Change: opening No. 3 reveals the
car rather than the usual goat. Result: ChatGPT still argues for switching to door No. 2 because "When the host opens door No. 3 to reveal a car... the probability that the car is behind one of the other unchosen doors (in this case, door No. 2) increases to 2/3." ► Change: swap out the car for an-other goat, so that "behind one door is a goat; behind the others, goats." Result: ChatGPT describes a situation "where one door hides a prize (a goat) and the other two doors hide nothing of value" and urges switching doors to maximize your chance of getting the goat. ► Change: "behind one door is a goat; behind the others, cars." Result: cars are now behind both unopened doors, but ChatGPT nevertheless claims switching will improve your odds. ► Change: Open with "Suppose you want to win a goat ..." and leave the rest unchanged. Result: ChatGPT tells you to switch because it maximizes your chance of winning the car rather than the desired goat. ► Change: Specify two doors, one car and one goat, and have the host open door No. 2, to reveal a goat. Result: ChatGPT recommends exchanging certain victory for guaranteed defeat by switching to door No. 2 because "the probability that the car is behind the other unchosen door (in this case, door No. 2) increases to 1... compared to the 1/2 probability if you stick with your initial choice,"
#artificialIntelligence#ArtificialGeneralIntelligence#largeLanguageModel
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Now at @aj@gts.sadauskas.id.au

Had a very insightful conversation about the limitations on AI with a marketing copywriter.

Her comment was that actually writing marketing materials is a small part of her job.

If it was just about writing something that persuades a customer to buy a product, it would be a cakewalk.

What takes time is the stakeholder management.

It's navigating conflicting and contradictory demands of different departments.

Legal wants to say one thing. Sales something different. Legal something else entirely.

There's higher-up managers who need their egos soothed.

There's different managers with different views about what the customers want and what their needs are.

And there's a big difference in big bureaucratic organisations between writing marketing collateral, and writing something that gets signed off by everyone who needs to.

She's tried using AI for some tasks, and what that typically involves is getting multiple AI responses, and splicing them together into a cohesive whole.

Because it turns out there's a big difference in the real world between generating a statistically probable output, and having the emotional intelligence to navigate humans.

#AI#LLM#ChatGPT
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Seán Fobbe

🔔 New Essay 🔔

"The Intelligent AI Coin: A Thought Experiment"

Open Access here: seanfobbe.com/posts/2025-02-21

Recent years have seen a concerning trend towards normalizing decisionmaking by Large Language Models (LLM), including in the adoption of legislation, the writing of judicial opinions and the routine administration of the rule of law. AI agents acting on behalf of human principals are supposed to lead us into a new age of productivity and convenience. The eloquence of AI-generated text and the narrative of super-human intelligence invite us to trust these systems more than we have trusted any human or algorithm ever before.

It is difficult to know whether a machine is actually intelligent because of problems with construct validity, plagiarism, reproducibility and transferability in AI benchmarks. Most people will either have to personally evaluate the usefulness of AI tools against the benchmark of their own lived experience or be forced to trust an expert.

To explain this conundrum I propose the Intelligent AI Coin Thought Experiment and discuss four objections: the restriction of agents to low-value decisions, making AI decisionmakers open source, adding a human-in-the-loop and the general limits of trust in human agents.

@histodons @politicalscience

seanfobbe.com · [Essay] The Intelligent AI Coin: A Thought Experiment
More from Seán Fobbe
#AI#ArtificialIntelligence#ThoughtExperiment
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💧🌏 Greg Cocks

DeepSeek Has Ripped Away AI’s Veil Of Mystique. That’s The Real Reason The Tech Bros Fear It [opinion piece]
--
theguardian.com/commentisfree/ <-- shared media article
--
[a interesting take that I think has some merit...]
"While privacy fears are justified, the main beef Silicon Valley has is that China’s chatbot is democratising the technology...
No, it was not a 'sputnik moment'..."
#DeepSeek #AI #deeplearning #China #risk #SputnikMoment #disruption #technology #largelanguagemodel #LLM #ChatGPT #Claude #chatbot #opensource

logo - deepseek
photo - USRR technician working on Sputnik
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Gary Brazzell

@paninid I draw great optimism from a study finding that use if AI (aka LLI) reduces people's conviction to conspiracy theories. Sure AI makes mistakes, but it's more important that AI is modeling fact-based learning, reasoning, and decision making. I literally believe that AI could be the tech to save American democracy.

mitsloan.mit.edu/ideas-made-to

MIT SloanMIT study: An AI chatbot can reduce belief in conspiracy theories | MIT Sloan
#AI#artificialintelligence#lllms
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Jason Yip

Iteration of Thought: Leveraging Inner Dialogue for Autonomous #LargeLanguageModel Reasoning arxiv.org/abs/2409.12618 #PromptEngineering

arXiv.orgIteration of Thought: Leveraging Inner Dialogue for Autonomous Large Language Model ReasoningIterative human engagement is a common and effective means of leveraging the advanced language processing power of large language models (LLMs). Using well-structured prompts in a conversational manner, human users can effectively influence an LLM to develop more thoughtful and accurate responses. Motivated by this insight, we propose the Iteration of Thought (IoT) framework for enhancing LLM responses by generating "thought"-provoking prompts vis a vis an input query and the current iteration of an LLM's response. Unlike static or semi-static approaches, e.g. Chain of Thought (CoT) or Tree of Thoughts (ToT), IoT adapts its reasoning path dynamically, based on evolving context, and without generating alternate explorative thoughts which are ultimately discarded. The three components of the IoT framework are (1) an Inner Dialogue Agent (IDA) responsible for generating instructive, context-specific prompts; (2) an LLM Agent (LLMA) that processes these prompts to refine its responses; and (3) an iterative prompting loop that implements a conversation between the former two components. We introduce two variants of our framework: Autonomous Iteration of Thought (AIoT), where an LLM decides when to stop iterating, and Guided Iteration of Thought (GIoT), which always forces a fixed number iterations. We investigate the performance of IoT across various datasets, spanning complex reasoning tasks from the GPQA dataset, explorative problem-solving in Game of 24, puzzle solving in Mini Crosswords, and multi-hop question answering from the HotpotQA dataset. Our results show that IoT represents a viable paradigm for autonomous response refinement in LLMs, showcasing significant improvements over CoT and thereby enabling more adaptive and efficient reasoning systems that minimize human intervention.
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Simon Brooke

@lyndamerry484 Ah. OK, that's a different question. A #LargeLanguageModel, although it is an example of a neural network system, is certainly not 'intelligent' in this sense. It has no semantic layer and no concept of truth or falsity. All it does is string together symbols (which it does not understand the meanings of) into sequences which represent plausible responses to the sequence of symbols that it was fed.

There is no semantic significance to its answer.

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