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Merged Artificial Intelligence

Latest Copilot win:

For reasons I needed to remember the name of that stupid Australian celebrity chef, so I asked Copilot "who was the Australian celebrity chef who was stupid?" And it came back with "It seems like you might be referring to Pete Evans, an Australian celebrity chef known for his controversial views and promotion of pseudoscience." which is absolutely correct.
 
novaphile said:
Quick question:

Is there any change that participants here are mixing up sentience (the ability to feel) with sapience (the ability to think)?

I'm a bit confused with some of the arguments and wonder if that is the reason why.
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I went to find a link to how I am using it, and found one here: https://www.sciencedirect.com/topics/neuroscience/sentience

Sentience refers to the capacity of an individual, including humans and animals, to experience feelings and have cognitive abilities, such as awareness and emotional reactions. It encompasses the ability to evaluate actions, remember consequences, assess risks and benefits, and have a degree of awareness. Recent advancements in neuroscience, such as EEG, PET-scanning, MEG, and fMRI, have provided evidence for the presence of cognitive and emotional abilities in sentient beings.
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I thought that's a good one that matches how I use it... And then got to the end and blew my entire supply of irony meters....
AI generated definition based on:
Encyclopedia of Animal Behavior (Second Edition), 2019
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Perhaps I am nothing more than a stochastic parrot, and have totally different internal behaviours than the rest of you, which is why I think LLMs are telling us more than lots of people like about human sentience...

P-zombies unite!
 
Darat said:
On the financial side I agree, I'd say many have gone beyond blurring to out and out lying! It's on the if you like more philosophical side I was referring to by the "blurring"
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This is always going to be a barrier, for me at least - the elephant in the room. Discussions about AI used to be purely hypothetical so it was never a problem until recently; but it supposedly exists now. Especially if we're talking specifically about LLMs and diffusion image generators - they are real things here and now, and in my opinion the fact that they are commercial software applications can't be cordoned off from any discussion. LLMs and diffusion image generators are products, they were developed by companies who have an expectation of profit, not only through direct sales to consumers but also by attracting investment in their company. Every single thing any of these companies publicly says about their product and its capabilities, including expected future ones, is marketing and needs to be viewed and treated as such. They have a very strong incentive to make people think (or allow them to think) that their product is something that it isn't; and because it's such a new field and class of product, there are few to no experts and regulations around to keep the claims and propaganda in check.
 
https://arxiv.org/abs/2409.04109

Recent advancements in large language models (LLMs) have sparked optimism about their potential to accelerate scientific discovery, with a growing number of works proposing research agents that autonomously generate and validate new ideas. Despite this, no evaluations have shown that LLM systems can take the very first step of producing novel, expert-level ideas, let alone perform the entire research process. We address this by establishing an experimental design that evaluates research idea generation while controlling for confounders and performs the first head-to-head comparison between expert NLP researchers and an LLM ideation agent. By recruiting over 100 NLP researchers to write novel ideas and blind reviews of both LLM and human ideas, we obtain the first statistically significant conclusion on current LLM capabilities for research ideation: we find LLM-generated ideas are judged as more novel (p < 0.05) than human expert ideas while being judged slightly weaker on feasibility. Studying our agent baselines closely, we identify open problems in building and evaluating research agents, including failures of LLM self-evaluation and their lack of diversity in generation. Finally, we acknowledge that human judgements of novelty can be difficult, even by experts, and propose an end-to-end study design which recruits researchers to execute these ideas into full projects, enabling us to study whether these novelty and feasibility judgements result in meaningful differences in research outcome.
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That's the sort of bread and butter research that I think should form part of regulations for AIs going forward.
 
AlphaProteo:
AlphaProteo can generate new protein binders for diverse target proteins, including VEGF-A, which is associated with cancer and complications from diabetes. This is the first time an AI tool has been able to design a successful protein binder for VEGF-A.

…

Trained on vast amounts of protein data from the Protein Data Bank (PDB) and more than 100 million predicted structures from AlphaFold, AlphaProteo has learned the myriad ways molecules bind to each other. Given the structure of a target molecule and a set of preferred binding locations on that molecule, AlphaProteo generates a candidate protein that binds to the target at those locations.

…

To test AlphaProteo, we designed binders for diverse target proteins, including two viral proteins involved in infection, BHRF1 and SARS-CoV-2 spike protein receptor-binding domain, SC2RBD, and five proteins involved in cancer, inflammation and autoimmune diseases, IL-7Rɑ, PD-L1, TrkA, IL-17A and VEGF-A.

Our system has highly-competitive binding success rates and best-in-class binding strengths. For seven targets, AlphaProteo generated candidate proteins in-silico that bound strongly to their intended proteins when tested experimentally.
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"in-silico" - how has that handy phrase passed me by for decades?

(ETA: I'm going to notice it everywhere now aren't I? :) )
 
OpenAI’s latest beta is apparently using “reasoning “

https://www.theregister.com/2024/09/13/openai_rolls_out_reasoning_o1/

…snip…

Alon Yamin, co-founder and CEO of AI-based text analytics biz Copyleaks, told The Register that o1 represents an approximation of how our brains process complex problems.

"Using these terms is fair to a point, as long as we don't forget that these are analogies and not literal descriptions of what the LLMs are doing," he stressed.

"While it may not fully replicate human reasoning in its entirety, chain of thought enables these models to tackle more complex problems in a way that 'starts' to resemble how we process complex information or challenges as humans.

…snip….
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https://www.zdnet.com/article/how-c...f-code-in-seconds-and-saved-me-hours-of-work/

Interesting article about using an a LLM in a real-life scenario. I like how they describe some aspects of using ChatGPT
...snip...

As is often the case with ChatGPT, the conversation was a lot like talking to a brilliant grad student who is somewhat stubborn and uncooperative

...snip...

When trying to wheedle an answer from a chatbot, I usually think of it as a talented student or employee. Sometimes, I'll even use "please" and "thank you" to keep the conversational tone going. But as anyone you talk with might get distracted or stubbornly refuse to see your point, keep trying, change up your questions, ask questions in different ways, and clarify, even when you think what you're clarifying should be obvious.

...snip...
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Darat said:
OpenAI’s latest beta is apparently using “reasoning “

https://www.theregister.com/2024/09/13/openai_rolls_out_reasoning_o1/
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More here: https://openai.com/index/learning-to-reason-with-llms/

We are introducing OpenAI o1, a new large language model trained with reinforcement learning to perform complex reasoning. o1 thinks before it answers—it can produce a long internal chain of thought before responding to the user.

OpenAI o1 ranks in the 89th percentile on competitive programming questions (Codeforces), places among the top 500 students in the US in a qualifier for the USA Math Olympiad (AIME), and exceeds human PhD-level accuracy on a benchmark of physics, biology, and chemistry problems (GPQA). While the work needed to make this new model as easy to use as current models is still ongoing, we are releasing an early version of this model, OpenAI o1-preview, for immediate use in ChatGPT and to trusted API users(opens in a new window).

Our large-scale reinforcement learning algorithm teaches the model how to think productively using its chain of thought in a highly data-efficient training process. We have found that the performance of o1 consistently improves with more reinforcement learning (train-time compute) and with more time spent thinking (test-time compute). The constraints on scaling this approach differ substantially from those of LLM pretraining, and we are continuing to investigate them.
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How an AI ‘debunkbot’ can change a conspiracy theorist’s mind

In 2024, online conspiracy theories can feel almost impossible to avoid. Podcasters, prominent public figures, and leading political figures have breathed oxygen into once fringe ideas of collusion and deception. People are listening. Nationwide, nearly half of adults surveyed by the polling firm YouGov said they believe there is a secret group of people that control world events. Nearly a third (29%) believe voting machines were manipulated to alter votes in the 2020 presidential election. A surprising amount of Americans think the Earth is flat. Anyone who’s spent time trying to refute those claims to a true believer knows how challenging of a task that can be. But what if a ChatGPT-like large language model could do some of that headache-inducing heavy lifting?
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Thoughts?
 
It's a good idea, because it takes the ego out of the equation.
Also, LLMs are way more polite and tolerant to kooky theories than I would be.
 
arthwollipot said:
How an AI ‘debunkbot’ can change a conspiracy theorist’s mind

Thoughts?
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Someone brought this up a little while back but can't find it at the moment. My comment then was how long would it stick - they say they checked at 2 months, but as we know there is the phenomenon of "fringe reset". How many times have we apparently argued someone out of some craziness yet a couple of months down the line their reset button is pressed and hey presto back to the beginning?

Here is where it was posted: https://www.internationalskeptics.com/forums/showthread.php?t=372262&highlight=fringe
 
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