Builder.ai did not "fake AI with 700 engineers"

We can also use the more general word "lie". It's not a taboo; use it when appropriate!

Yes.

Yeah, I feel like if their goal was to make custom software cheap and easy, this was basically just dogfooding.

I would assume they were collecting data. If you're building a chatbot to talk to clients, you need all the transcripts from meetings, chat logs, project info, analytics, metrics, and so on. It's the only way to train your model properly.

Do they have any patents related to using chatbots for project management?

Most likely. But that's exactly what someone who hasn't experienced enough cycles in industry would come up with :).

Probably, but casting such a wide net so early has its risks - as is visible in hindsight. Build one, then use that to build the second, the first two to build the third, etc, etc.

Doing everything all at once is a recipe for disaster.

One good thing that came out of that disjointedness is temporal.io, which is an ultimate microservice gluecode tool

Yeah, nice work - I mentioned "and others" for that reason; dangs was just the top comment.

They don't know the difference between AI and ChatGPT

But it was the flow in the examples.

I just saw the click-bait garbage headline, and from that alone I knew it was a lie, so I never even clicked it to read any articles.

You claim you have a queue and it takes up to 24 hours for your job to run?

It was obviously not prompt and get response model like chatgpt.

No, it’s not sustainable at any scale.

No, the definitions are very much up for debate, but there is an actual process here. "Reasoning" in this case means having the model not just produce whatever output is requested directly, but also spend some time writing out its thoughts about how to produce that output. Early version of this were just prompt engineering where you ask the model to produce its "chain of thought" or "work step by step" on how to approach the problem. Later this was trained into the model directly with traces on this intermediate thinking, especially for multistep problems, without the need for explicit prompting. And then architecturally these models now have different ways to determine when to stop "reasoning" to skip to generating actual output.

I don't have a strict enough definition to debate if this reasoning is "real" - but from personal experience it certainly appears to be performing something that at least "looks" like inductive thought, and leads to better answers than prior model generations without reasoning/thinking enabled.

An LLM was given the algorithm to Towers of Hanoi and was unable to solve it. There is no "reasoning".

> They found that - indeed - reasoning improves performance.

You're oversimplifying the results a bit here. They show that reasoning decreases performance for simple problems, improves performance for more complex ones, and does nothing for very complex problems.

I think you need to re-read the paper.

The LLMs don't "reason" by any definition of the term. If they did, then the Tower of Hanoi and the river problem would have been trivial for them to handle at any level because ultimately the solutions are just highly recursive.

What the LLMs do is attempt to pattern match to existing solved problems in their training set and just copy those solutions. But this results in overthinking for very simple problems (because they're copying too much of the solutions from their training set), works well for the somewhat complex problems like a basic Tower of Hanoi, and not at all for the problems that would require actual reasoning because...they're just copying solutions.

The point of the paper is that what LLMs do is not reasoning, however much the AI industry may want to redefine the word to suit their commercial interests.

What you think is an absurd question may not be as absurd as it seems, given the trillions of tokens of data on the internet, including its darkest corners.

In my experience, its better to simply try using LLMs in areas where they don't have a lot of training data (e.g. reasoning about the behaviour of terraform plans). Its not a hard cutoff of being _only_ able to reason exactly about solved things, but its not too far off as a first approximation.

The researchers took exiting known problems and parameterised their difficulty [1]. While most of these are not by any means easy for humans, the interesting observation to me was that the failure_N was not proportional to the complexity of the problem, but more with how common solution "printouts" for that size of the problem can typically be encountered in the training data. For example, "towers of hanoi" which has printouts of solutions for a variety of sizes went to very large number of steps N, while the river crossing, which is almost entirely not present in the training data for N larger than 3, failed above pretty much that exact number.

[1]: https://machinelearning.apple.com/research/illusion-of-think...

"The apple study" is being overblown too, but here it is: https://machinelearning.apple.com/research/illusion-of-think...

The crux is that beyond a bit of complexity the whole house of cards comes tumbling down. This is trivially obvious to any user of LLMs who has trained themselves to use LLMs (or LRMs in this case) to get better results ... the usual "But you're prompting it wrong" answer to any LLM skepticism. Well, that's definitely true! But it's also true that these aren't magical intelligent subservient omniscient creatures, because that would imply that they would learn how to work with you. And before you say "moving goalpost" remember, this is essentially what the world thinks they are being sold.

It can be both breathless hysteria and an amazing piece of revolutionary and useful technology at the same time.

The training set argument is just a fundamental misunderstanding, yes, but you should think about the contrapositive - can an LLM do well on things that are _inside_ its training set? This paper does use examples that are present all over the internet including solutions. Things children can learn to do well. Figure 5 is a good figure to show the collapse in the face of complexity. We've all seen that when tearing through a codebase or trying to "remember" old information.

Well, if the thing is truly capable of reason, then we have an obligation to put the kibosh on the entire endeavor because we're using a potentially intelligent entity as slave labor. At best, we're re-inventing factory farming and at worst we're re-inventing chattel slavery. Neither of those situations is something I'm personally ok with allowing to continue

Skepticism cannot, by definition, be religious.

You should re-think the bias that led you to this belief.

I agree that all AI has been fake AI since the term was first coined.

Researchers in the field used to acknowledge that their computational models weren't anywhere close to AI. That all changed when greed became the driving motivation of tech.