I found Google AI remarkably well informed about SOTA, not just answers to hard facts like “What are the five nearest SOTA summits to Dent railway station?” but subjective things like the challenges and needs of SOTA activating, pro and cons of using straight keys versus paddles on summit, etc which makes me feel a snapshot of this reflector was added to its database.
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It isn’t a case of what you want AI to do. The geni is out of the box and for a lot of things will do away with lots of tasks
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…and genies are well known for having the power to grant wishes but also for being either tricksters or malicious, so they’ll twist what you say. In LLMs we have invented a genie which is powerful but also full of BS…
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It’s a tool. Very powerful if you handle it skillfully. Very poor if you don’t.
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I think there’s a ‘Catch 22’ here. To become skilful at handling publicly-accessible LLMs like Gemini, (unless you know an AI expert) you have to ask the AI itself how to avoid problems and it gives you different (sometimes contradictory) instructions about how to do that depending on how you ask the question or if you focus on one particular problem with AI but not another. It’s like building a house on shifting sands.
I’ve had to rework the ‘rules’ I give it several times since I created this topic, and even now [because my trust in its answers is low] I’m still not sure they solve (or minimize) the AI problems we’ve discussed.
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…and with the general LLMs the quality of the training seems to be deteriorating as the stuff they ingest for training contains more slop…
As LLMs proliferate, do they learn from earlier versions of competitors, or can they tell if their training sources are AI generated BS…?
Gemini says: AI models do not know facts the way humans do. They can distinguish between authoritative sources and general internet ‘slop’. The type of source is a factor in assessing reliability.
As everything is done based on calculating probabilities, ‘slop’ can sometimes slip through, especially for very niche topics and rapidly developing news. In that case, would it be called “slip slop”?
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They can now, but what will underpin the authoritative sources in a few years time? 
I find my dabbling with AI quite fun and useful - it can be much quicker than using application help menus (I can ask a question in plain language), and the answer is easily tested. I wouldn’t take the results on trust for anything important.
It will be interesting to see how it develops and settles into mainstream use - I’d guess its here to stay, for better or worse.
What is SOTA in hamrado and how it’s done?
In ham radio, SOTA stands for Summits on the Air, an award and operating program that encourages amateur radio operators to make contacts from mountain summits. It is organized by Summits on the Air.
How SOTA works
There are two main roles:
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Activator – hikes to a qualified summit and operates a radio station from there.
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Chaser – contacts activators from home, a mobile station, or another location.
Both activators and chasers earn points.
Activating a summit
To activate a summit successfully, an operator typically:
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Chooses a summit listed in the SOTA database.
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Hikes to the activation zone (usually near the summit, following SOTA rules).
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Sets up a portable station:
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Transceiver (HF, VHF, UHF, etc.)
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Antenna
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Battery power
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Calls CQ and makes contacts.
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Logs the contacts and uploads them to the SOTA database.
Most associations require at least four valid contacts to qualify an activation.
Typical equipment
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Common gear includes:
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Lightweight QRP transceivers
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Telescopic or wire antennas
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LiFePO₄ batteries
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Logging app or paper log
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Hiking and safety equipment
Popular operating modes
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Voice (SSB/FM)
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Morse code (CW)
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Digital modes such as FT8
Many SOTA operators prefer QRP (low-power) operation to keep equipment light.
Example
Suppose you hike to a qualifying summit in Slovenia:
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You set up a 10-watt HF radio and antenna.
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Call “CQ SOTA”.
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Make contacts with four or more stations.
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Upload your log.
You receive activation points based on the summit’s difficulty/height, while the stations that contacted you receive chaser points.
Why people enjoy SOTA
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Combines hiking and amateur radio
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Encourages portable operating skills
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Tests antenna and low-power techniques
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Provides awards and achievements
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Lets operators contact stations from unique locations
It’s often described as a mix of mountaineering, field operations, and amateur radio, with an emphasis on self-sufficient portable stations.
Well done AI 
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I have tried from time to time to use AI to put together an itinerary for activating a summit. It gets pretty close I think, as long as I pay attention to some of the details 
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73, Jared, N7MAW
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I should have given a more nuanced answer, e.g. “they can mostly distinguish …”
The problem is already here, e.g. when a very-prominent but non-expert, politically-appointed head of a reputable organisation expresses his opinions (e.g. on vaccine safety) which contradict established science. That situation presents the absolute highest degree of difficulty for an AI when ranking its source data.
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i know they arent programmed that way but from a high level view thats what they do. they have no intelligence they should not be called intelligent as they are not.
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But they don’t. If/then/else would give a highly deterministic output. AI is based on predicting the probability of the next word and then rolling the die to pick the word. So if you ask the same question to the same AI model with the same context (e.g. a fresh session) you will get different output.
It is an unfortunate name. It was coined by an American computer scientist John McCarthy in 1955 at Dartmouth College. It was controversial back then. If Herbert Simon and Allen Newell had had their way we would be debating “Complex Information Processing” instead of AI. Not as catchy though.
Dartmouth College were early pioneers in AI, time sharing computers and the BASIC programming language.
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I prefer “Simulated Intelligence” as thats really what it boils down to.
As for how they actually work, that really all depends on the type of model thats being used. Decisions Trees and and Linear regression models are in essence nested if-then-else that in simple terms solve simultaneous with many variables. Only the neural network based models generally use the probability activation functions.
A point to note with Decision trees and linear regression models is that it is entirely possible to manually calculate the answers whereas with the Neural Nets it is virtually impossible.
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Which of the LLMs don’t use neural networks?
While Decisions Trees and and Linear regression models may be used within an AI system, they don’t replace the neural networks.
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Not all AI is a LLM though. Whilst they are impresisve feats of engineering (especailly the Transformer models) compared to text parsers of old ( anyone for Collosal Cave Adventure?), the ones that most people use in normal life (such as Chat GPT and Copilot) are not specialised enough to be really useful except in a generic sense. This is why the OP suggested that the output from the LLMs need to be taken with a pinch of salt and verified. If you need to check whats been produced from these things, why not just cut out the middle man.
Like most things, its about horses for courses. A well built and trained decision tree can outperform a neural network and is a lot cheaper in terms of computational costs but it does all depend on the application its being used for and knowing the limitations of the models that are being used.
For example, I had to build an AI model (for an assignment) to predict indicators of cognitive decline (to indicate onset of Alzheimers) based on a mobile app game (Sea Hero Quest) that tested spatial navigation. With only the 3500 data points for Romania the only real possibility was a decision tree. The accuracy, even on such a small dataset, was around the 70% mark. No neural net would have come close to that and would have taken more computational time to produce a worse result.
Now that model would be useless for giving instructions on how to bake a cake, but it is a real world useful application (the models built by professionals with more of the data will be anyway) for a very specific topic.
If anyone is interested the theory behind it can be found in the paper at https://www.pnas.org/doi/full/10.1073/pnas.1901600116
but unfortunately the main pages and data from Alzheimers Research are not now easily accesible.
An excellent resource and playground for more AI stuff can be found at Kaggle (https://www.kaggle.com/) for those wanting to play at building their own models. The Titanic Survival Prediction is an interesting one.
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Absolutely, but this thread is about LLMs and so my comments were about that.
Neural networks need huge amounts of data to train.
That’s what engineering is about.