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YeGoblynQueenne

24,929 karma · joined September 26, 2015

This a common question on this board:

What is reasoning?

In computer science and AI when we say "reasoning" we mean that we have a theory and we can derive the consequences of the theory by application of some inference procedure.

A theory is a set of facts and rules about some environment of interest: the real world, mathematics, language, etc. Facts are things we know (or assume) to be true: they can be direct observations, or implied, guesses. Rules are conditionally true and so most easily understood as implications: if we know some facts are true we can conclude that some other facts must also be true. An inference procedure is some system of rules, separate from the theory, that tells us how we can combine the rules and facts of the theory to squeeze out new facts, or new rules.

There are three types of reasoning, what we may call modes of inference: deduction, induction and abduction. Informally, deduction means that we start with a set of rules and derive new unobserved facts, implied by the rules; induction means that we start with a set of rules and some observations and derive new rules that imply the observations; and abduction means that we start with some rules and some observations and derive new unobserved facts that imply the observations.

It's easier to understand all this with examples.

One example of deductive reasoning is planning, or automated planning and scheduling, a field of classical AI research. Planning is the "model-based approach to autonomous behaviour", according to the textbook on planning by Geffner and Bonnet. An autonomous agent starts with a "model" that describes the environment in which the agent is to operate as a set of entities with discrete states, and a set of actions that the agent can take to change those states. The agent is given a goal, an instance of its model, and it must find a sequence of actions, that we call a "plan", to take the entities in the model from their current state to the state in the goal. This is usually achieved by casting the planning problem as pathfinding over a graph with a search algorithm like A*. Here, the agent's model is a theory, the search algorithm is the inference procedure, and the plan is a consequence of the theory. Deductive reasoning can be sound, as long as the facts and rules in the theory are correct: from correct premises we can deduce correct conclusions. We know of sound deductive inference rules, e.g. A*, and Resolution, used in automated theorem proving and SAT-Solving, are sound.

The classic example of inductive reasoning is inferring the colour of swans. Most swans are white (apparently) so if we have only seen white swans we have no reason to believe there are any other colours: we are forced to infer that all swans are white. We may only be disabused of our fallacy if we happen to observe a swan that is not white, e.g. a black swan. But who is to say when such a magnificent creature will grace us with its presence, outside of Tchaikovsky's ballets? Induction is thus revealed to be unsound: even given true premises we can still arrive at the wrong conclusions. Another example is the scientific method: imagine an idealised scientist, perfectly spherical, in a frictionless vacuum. She starts with a scientific theory, then goes out into the world and makes new observations about a phenomenon not described by her theory. She constructs a hypothesis to extend her theory so as to explain the new observations. The hypothesis is a set of rules, where the premises are the consequences of the rules in her initial theory. Then, being an idealised scientist, she goes looking for new observations to refute her hypothesis. Science only gives us the tools to know when we're wrong.

Abductive reasoning is the mode of inference exemplified by Sherlock Holmes. We can imagine Sherlock and Watson standing outside a tavern in London, watching as a gentleman of interest steps out of the tavern with egg on his lapel. "Ah, my dear Watson, what can we conclude from this observation?". "Why my dear Holmes, we can conclude that the man had eggs for breakfast". Holmes and Watson can arrive at this conclusion, about a fact that they have not directly observed, because they have a theory with a rule that says "if one eats eggs, one may get some on one's lapels". Working backwards from this rule, and their observation of egg on the man's lapels, they can guess that he had eggs even if they didn't directly observe him doing so. Abduction is also unsound: the man may have swapped coats with an accomplice, who was the one who had eggs for breakfast instead.

And now you know what "reasoning" means. So the next time someone asks: "what is reasoning?", you can let them know and turn the discussion to more interesting, more productive directions.

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YeGoblynQueenne··on The end of Microsoft Research's AI Frontiers group
>> We definitely haven't hit the ceiling and everyone's noticing "Hey this Opus 5.5 with the increased parameters seems much better than the last one".

Opus is better (not necessarily "very much") than Sonnet in coding. That's because of a gigantic engineering effort to make it better at coding. That is a regression: we've gone from "sparks of AGI" and systems that were said to be "genearl-purpose" to systems that do one thing well and everything else... well, there's no real measure of how better Opus is from Sonnet in replying to your emails so it's hard to say.

Same goes for OpenAI in maths. Industry wide, there is a regression from general-purpose to special-purpose systems, like in Good, Old-Fashioned AI. LeCun is absolutely right to complain that this is not the path to general intelligence. I don't think his preferred approach, JEPA, is going anywhere either, but at least he's trying. Other luminaries of the field have expressed similar doubts, e.g. Richard Sutton's tweet was in my news feed yesterday saying that brute force is ngmi and algorithmic advances are needed [1]. Schmidhuber I think has said multiple times something along the lines of "there is no intelligence without physical intelligence".

We are very far from that. The recent trend for Vision-Language-Action Transformers (VLA-Transformers) in robotics has conclusively demonstrated that you can't solve the real world by overfitting to a set of discrete tasks, and there is no source of data that we can harness to repeat the feat of LLMs. Algorithmic advances will be necessary, but as the article suggests, these have now be put on hold.

There is a dumbing down, slowing down of research as all resources are spent to try and scale what we have now. What we have now is not enough for intelligence, but that doesn't matter. The industry's goal is not intelligence, it's making more money than anyone has ever made before; in fact more money than all humans put together have ever made before. That's the current race: who will win the money game. Obviously that's not likely to do anything for intelligence.

____________

[1] Brute force computation is not enough for intelligence. Clever new methods are needed. The science of AI is far from finished. IMHO

https://x.com/RichardSSutton/status/2119142310894334262?a=ry...

YeGoblynQueenne··on I struggle to believe that AI R&D is ingenious
>> Its fairly plausible that the original ideas could be sensory gradient-triggered impulses in simpler creatures' proto-minds that (physical and cultural) evolution has gradually built up through more complex neural machinery supporting more complex representational language.

I'm sorry, I don't understand what this means and so I don't understand why it is plausible. What are "sensory gradient-triggered impulses in simpler creatures' proto-minds"? I understand the words on their own but put together they say nothing I can make sense of.

>> Similarly, its not a stretch to see art as built from earlier attempts at representation in physical space - starting with mundane attempts at information exchange by colony insects or pack creatures.

How does that work? We're not descended from insects.

Moreover, I understand all of the above to be speculation, not something there's any kind of concrete evidence for. If you say there are no novel ideas, only combinations, we can all agree what you mean because we know what it means to take the combinations of the elements of a set. You should make your argument at least as concrete and straight-forward if you want it to move beyond the realm of imagination. Fantasizing is great, but it doesn't alone help us understand what is really going on in the real world. You also need to base your fantasies on concrete reality.

YeGoblynQueenne··on I struggle to believe that AI R&D is ingenious
>> Oh no, I don't think you understand how big the problem space actually is.

Again: what's the number of combinations of elements of ∅?

(∅ is the empty set, a.k.a. {})

YeGoblynQueenne··on I struggle to believe that AI R&D is ingenious
>> If they were really stupid, a Prolog program, a genetic algorithm or a SAT solver would be enough to reach the goal. Clearly it was not the case.

"Clearly" as in "somebody tried that and failed" or "clearly" as in nobody ever thought to try it and now nobody will because there's no point?

YeGoblynQueenne··on I struggle to believe that AI R&D is ingenious
If you want to argue, as I think you do, that there are no new ideas other than new combinations of old ideas, then you have to explain where the old ideas came from. It can't just be combinations all the way down. At some point, some initial set of ideas must have been created where none existed before otherwise we would have nothing to combine. After all the combinatoric closue of ∅ is ∅.

Ultimately any claim that there exist only combinations of old ideas must tackle this chicken-and-egg problem convincingly. For example- explain art. There was no art until it appeared fully formed and out of nowhere in early anatomically modern humans' dwellings (Neanderthals also had art but it was simpler and it is subject to the same chicken-and-eggness). So where did art come from? What pre-existing ideas were combined to bring it into existence from non-existence?

Btw, since I've had this conversation before- if the answer is that we looked around us in the world and saw things; yes, but we didn't see art. Where did art come from? Where did the idea come from of abstracting shapes and colours so that they resemble the shapes and colours of existing things, without actually being those things, or being composed of pieces of those things? How about numbers? How about writing? How about all our technology and science? Where did those things come from? What did we combine to create them?

Obviously I think that the argument that there are only new combinations is wrong: we are a creative species. We can create new things. Somehow. It would be very interesting to understand how. We have not yet created machines that can match us in that and we can't understand the "how" by studying the machines we have now. By denying the mystery of this our ability, by denying that it is a mystery, by dismissing it and accepting any facile explanation that presents itself in our current era -it's god, it's nature, we're like LLMs- we are making ourselves a great epistemic disservice, denying ourselves the chance to find out what it all means, to understand what kind of creature we truly are.

YeGoblynQueenne··on Verification Is Not Knowledge: Science needs more than answers
The author is wading into an age-old debate, between instrumentalism and (scientific) realism in science. Briefly, instrumentalists are happy to have accurate models, realists also want to have explanatory theories.

I am far from well-informed on the intricacies of this debate myself, however I class myself as a realist. Not only do I have to know how and why things work, I also observe that an explanatory theory is at least as useful as an accurate model (of the same phenomenon). Assuming equal accuracy, the explanatory theory is just as good at predicting future observations as the model, and on top of that it is capable of explaining past observations. More, we seem to always be able to improve our predictive accuracy, compared to non-explanatory models, once we have an explanatory theory and can understand why models work and why they fail. Understanding ultimately improves prediction but prediction alone does not necessarily improve understanding.

I think this is the crux of the debate with AI mathematics: AI proofs are nice to have but if we don't understand them, what are we going to do with them to further advance our knowledge?

I see this also in the way neural nets work in general. We can train very powerful neural nets, but can we reuse one trained neural net to train another? Not unless we use the first net to label some data. I mean to say, we can't just use a trained neural net as a feature of another neural net. Which is why most of the time we have to learn everything from scratch, end-to-end, and consequently why neural nets need so much damn training data.

Finally, say that AI takes over mathematics research. What happens a few years down the line when all we have is yes/no answers? How are we going to make new mathematics from that? How are we going to keep training AI mathematicians? Let alone human ones?

YeGoblynQueenne··on Astra can read and write weights to improve Qwen
Who is "we" and how are these "we" going to train foundation-scale LLMs? With what money? What compute?
YeGoblynQueenne··on Google will turn off search for schools that reject AI
>> When I subscribed, these features were provided by traditional ML, perhaps some BERT-like encoder architecture at most, however now I cannot keep that previous functionality if I don't wish to grant LLMs access to my data.

Sorry for the sidetrack, but why is it so common to consider BERT something else than an LLM? It is one of the earliest LLMs so it's nowhere near the size of more recent ones, but even wikipedia classifies it as an LLM:

  Bidirectional encoder representations from transformers (BERT)
  Original author Google AI
  Release October 31, 2018[1]
  Type 

    Large language model
    Transformer
    Foundation model

  License Apache 2.0
  Website arxiv.org/abs/1810.04805 
  Repository github.com/google-research/bert
https://en.wikipedia.org/wiki/BERT_(language_model)

And in any case it's an autoregressively-trained Transformer, albeit encoder-only. If it's not an LLM, then what is it?

YeGoblynQueenne··on US boy names that became girl names and vice versa
Interesting, I guess my two favourite SF/F writers are Pratchett and Iain M. Banks (who also wrote basically space opera) (my favourite kind of opera).

I'll have to check out the Vorkosigan books. I used to read a lot more and older Sci-Fi but I started finding it repetitive and samey.

YeGoblynQueenne··on Let Me Ch...
As I was reading it occurred to me, too, that I'm reading something written by an LLM. I was not sure and I'm still not sure, and nothing says that your "tells" are exclusive to LLMs.

The point though is that regardless of whether this was written by an LLM or not, the reason that I thought it might be, is that I found the writing really poor. Not to say that all poor writing is LLMs but I guess that doesn't rally matter. Bad writing is bad writing.

YeGoblynQueenne··on Astra can read and write weights to improve Qwen
I wonder. I mean I agree that it's cool af but if an LLM could do it that must mean someone else has been able to do it before (in whole or in parts) and yet you didn't hear about that, the lone hacker doing something cool af in their spare time, you only hear about the billion-dollar product of the stratospherically hyped startup.

That is not cool at all.

YeGoblynQueenne··on Astra can read and write weights to improve Qwen
I don't think we're "democratizing" anything if it's just two companies who can make it and it costs billions or trillions even. That's like the polar opposite of "democratizing".

(Two companies can make it work best, tbc).

YeGoblynQueenne··on Astra can read and write weights to improve Qwen
This year ICLR had more papers than every previous year combined. How is a new PhD* to stand out in such an over-crowded field, not least one saturated with extravagant claims and dominated by industry orgs with unlimited budgets?

The only chance to stand out these days is to go viral.

_________

* The first author listed is a first-year Phd according to their X profile: https://x.com/rikiyatakehi/status/2117313742891327994.

YeGoblynQueenne··on Astra can read and write weights to improve Qwen
Yep. This is weight-space learning with LLMs. The authors even got Schmidhuber'd about it on X:

https://x.com/SchmidhuberAI/status/2117446822855888956

And btw it's not RSI:

a) There is no evidence that a model can weight-train a larger, more powerful model.

b) There is direct evidenct that a model cannot weight-train new capabilities to another model (see section "Additional experiment" also cited by OP, where they fail to improve Gemma 3 270M which doesn't loop).

No RSI without those things.

YeGoblynQueenne··on US boy names that became girl names and vice versa
Once more I find myself wondering whether English really needs different vowels, if they can all be substituted for each other.
YeGoblynQueenne··on US boy names that became girl names and vice versa
Wow. Kind of blast from the past. I haven't read that btw, how good is it?
YeGoblynQueenne··on US boy names that became girl names and vice versa
In English, maybe. Several other languages have gendered nouns, so names are not interchangeable. For example "Ιωάννης" in Greek is the male form of "John" given exclusively to male children, whereas "Ιωάννα" is the female form given exclusively to female children. No exceptions. AFAIU if a person wished to change their name from "Ιωάννης" to "Ιωάννα" they would have to go to court and change their birth gender also.
YeGoblynQueenne··on US boy names that became girl names and vice versa
As a non-native English speaker I've learned never to gender English speakers from their names. I mean in emails and so on, when I haven't had a chance to meet them. No "Ask Ashley if she would like to join us...". "Would Ashley like to join us?" is the safe option.
YeGoblynQueenne··on AI isn't replacing SREs, it's deskilling them
Who said anything about anyone being stupid?

And yes, I'm more capable than Claude. Every human with normal mental development is. It's common these days to underestimate human intelligence, but being able to churn out lots of AWS CLI text is an achievement measured against itself. One-year old humans learn to speak without having ingested an entire civilization's textual output: adjust your metrics accordingly.

YeGoblynQueenne··on Florilegium: Procedural Flowers in LaTeX
Why should anyone care, full stop.
YeGoblynQueenne··on AI isn't replacing SREs, it's deskilling them
>> I will never be able to use AWS CLI as well as claude does.

Are you sure that it's superhuman; and not super -you- man?

YeGoblynQueenne··on Early progress on accelerating drug development with Claude
Why? Two birds, one stone: reduce care costs, feed poor people.
YeGoblynQueenne··on New Records Obtained by EFF Expose More Problems with Medicare's AI Experiment
>> EFF has long argued that AI has no place in consequential government decisions about peoples’ rights or access to benefits, such as immigration enforcement and policing decisions. That’s because using automated decision-making in these contexts risks decisions that violate people’s rights or deprive them of benefits they’re entitled to based on biased or inappropriate considerations and without proper due process or accountability if there is an error.

But that's the whole point.

YeGoblynQueenne··on The recently proposed o(n log n) integer multiplication algorithm [pdf]
>> What do you see as the problem with it (let alone "crisis")?

The fact that he didn't read the paper and he's talking about it as if he did.

YeGoblynQueenne··on Dessa: Conversation Model trained only on speech
This is why we have a flag.
YeGoblynQueenne··on The recently proposed o(n log n) integer multiplication algorithm [pdf]
Oh, I read further, for example where he says he didn't read the paper. I don't understand how someone of the obvious mathematical ability of David Harvey would write something like that. I just hope he has someone to speak with because he sounds like he's going through some kind of crisis maybe.
YeGoblynQueenne··on Marshall Islands Stick Chart
Thanks but I was looking for a specific brand which was from Iran.
YeGoblynQueenne··on The recently proposed o(n log n) integer multiplication algorithm [pdf]
>> I have not checked most of the details, but I think that the claimed o(n log n) bound is plausible.

Oh? Well I have not checked most of the details either, but I think that the claimed o(n log n) bound is not plausible at all.

Can someone please check most of the details of the paper and resolve our disagreement with Mr Harvey?

YeGoblynQueenne··on WorldTUI: I trained a 360M model to pretend to be a terminal
Great way to get people to type in their passwords.
YeGoblynQueenne··on How the Elizabeth Holmes documentary “You Can See Everything” is deceiving
Which one? Whichever it is, it would hardly be sponsored by the meat industry innit.
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