In bemoaning how things are getting worse everyday, we often tend to forget that the state of the world is becoming monotonically more observable. 1/
It may not be so much that there is monotonically increasing suffering in this world, but that it is monotonically more observable--we can be aware of it, if we choose to. 2/
Wars become forever stalemates because both parties have much better observability into the state of the adversary. As my son says, Normandy-like surprise attacks are much harder in this era of satellite/cell signal observability (Ukraine being a textbook case in point..) 3/
On the whole, this observability is a force for good, IMHO--in as much as it actually gives us a choice of using our knowledge to improve the state of affairs: realize the impossibility of surprise wars or make it harder to look away from the suffering in the far corners. 4/
So on this #Festivus day, let's celebrate the human ingenuity that has been the main driver of the increased observability of our world!
May all our POMDPs become MDPs! 5/
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One paper, lead by @kayastechly (w/ @mattdmarq), evaluated the claims over a suite of graph coloring problems. The setup allows for GPT4 guessing a valid coloring in stand alone and self-critiquing modes. There is an external sound verifier outside the self-critiquing loop. 2/
GPT4 has <20% accuracy in guessing colorings, as should perhaps be expected given its reasoning (in)capabilities. What is perhaps more surprising is that the accuracy *FALLS* in the self-critiquing mode (2nd bar👇)--running counter to all the claims of self-improvement! 3/
It is hilarious that LLMs are making traditional symbolic #AI relevant, and in the process merrily exposing the ignorance of the post-Alexnet yung'uns who skipped their Intro #AI's to do MORE LAYERS, only to find themselves busy with ersatz natural science with LLMs. 🧵 1/
Without background in combinatorial search and logical inference, you are susceptible to conflating brute force search (or forest of jumbled thoughts prompting) as something to be proud of instead of seeing them for their "Rube Goldberg" silliness.. 2/
[Paradoxes of Approximate Omniscience:] 🧵We all know, by now, that our intuitions *suck* at high dimensions.
We haven't yet come to grips with the fact that our intuitions about *approximate omniscience* suck too!
(And this explains some of our puzzlement at LLMs)
1/
Re: high-D, we all have been surprised by the core-less apples. George Dantzig famously explained the surprising efficiency of (worst case exponential) Simplex algorithm with a pithy "One's intuition in higher dimensional space is not worth a damn!" 2/
LLMs have laid bare our lack of intuitions about approximate omniscience as acquired by LLMs trained on web-scale text. (Something certainly demonstrated by some of the responses to my recent threat 😋) 3/
Making plans in the world involves (1) discovering actions (and their precondition/effect causal dependencies), and (2) sequencing an appropriate subset of available/discovered actions to achieve the agent's goals. 1/
The former requires *broad knowledge* about actions available in the world and their individual effects, while the latter requires deep drilling-down over a given set of actions to ensure that all goals are supported (causal chaining) without any undesirable interactions. 2/
LLMs have an edge on the former--they do indeed have web-scale broad knowledge! They are however *lousy* at the second--they can't do any real combinatoric search if their life depends on it .. 👇3/
🧵Been reading several recent arXiv entries claiming planning capabilities of #LLM's. This area is so full of anthropomorphisms--"Chain of Thought Prompting", "Inner Monolog"--that it cries out for a cleansing read of Drew's #AI meets Natural Stupidity 1/
One popular line claims that while LLM's may give wrong plans, they can improve with right prompting (which, in one case, is claimed to even induce "inner monolog" all West World host-like).
The prompts in the appendices however seem to suggest a Clever Hans effect in action 2/
Clever Hans, btw, is the ChatHorseGPT of its time--that showed very un-horselike arithmetic prowess by stomping its foot the right number of times, as long as the questioner was in front and knew the answers. Crucially, no fraudulent intent was needed. 3/ en.wikipedia.org/wiki/Clever_Ha…
So @TheEconomist tells me now that #LLMs can do planning and reasoning after all. Obviously our own dismal experience of their planning performance (c.f. the 🧵 at
) must be a clear outlier.. 🙄 Thank goodness I pay big bucks for my subscription.. 1/
Interestingly, I was just telling someone today how several of the papers on "LLMs for Task Planning by Prompting" are rife with the Clever Hans effect (c.f. en.wikipedia.org/wiki/Clever_Ha… ). I guess I will have to do a thread.. 2/
(While we should all be used to #LLM hype-expertise in the press by now, this particular case was prickly as it is my cocky son who airily pointed this article out to me at dinner with barely concealed delight.. 😡 ) 3/