GPT-6 Astra

Many VCs also dislike these examples, I believe. I'm doubtful this is what they're being pitched.

As for public releases: I wonder if it's because these examples are easy to relate to. Many websites are just a long tail of industry or use-case specific stuff. What's valuable to me probably means nothing to you. This is unlikely to resonate with people-wit-large (and LLMs are marketed broadly) or requires the reader to think (and marketing that requires thinking is bad these days).

Second, it's arguably a good litmus test. If it still can't do the worn out examples of plane tickets and shopping, which would be a good assumption since we've been demo'd these use-cases for 2 years at this point, then ...

People are ultimately capable of self deception. Believing that one is 'generally ... substantively above average on human benchmarks' may be more indicative of the brittleness of the claimant's human benchmarks.

Your observations expose the brittleness of the benchmarks being used for Fable, where the 'reasonable confident' claimant is working in the problem space of those two benchmarks, and you're demonstrating the failure at depth of the very capability facade for the model.

Sure, both the model and the confident human get the first layer right in some benchmark, but at least the model, and probably the human as well reach a collapsing probability of accuracy quite quickly. They may be completely and unconditionally right that Fable is more intelligent than they are, but that has little relevance at intelligence in depth as measured against your hobbies and the risk of unsafe or false responses.

Being smarter than a select group of people under test conditions is not conclusive regarding AGI. Moving the goalposts to declare AGI via a shallow benchmark, when a model could be proven wrong by almost anyone with basic competency given a few turns of iterative depth is where the 'grift' resides.

We're so far from anything approaching actual AGI, and it is highly speculative to infer that the current approach to Machine Learning as applied in LLM is even on a path that leads to AGI. But sales and promotions teams gotta pose, and we can all hate both the game and the players.

> For the many people who resist the AGI label possibly ever being achieved, I'd be curious to hear takes on what would make you think Astra is yet to be AGI, and what would still need to be achieved for this to effectively be AGI from this point forward.

I think I have the following questions about what AGI would look like:

1. Do you expect an AGI to be able to competently do any knowledge work that an able human does today?

I think this is implied by the "General" component. I would assume that anything we call AGI would be able to do any of these tasks if given the time and reference material needed.

2. How would you expect AGI to handle edge cases? (Missing context, no known solution, under specified instructions, over specified instructions)

I would expect an agent to be able to look at the context that work exists in and correctly attenuate it's intentions for these goals. Simpler solutions, more thorough reporting, etc based on the need.

3. In my work I attend meetings, write reports, write code, research things, etc. Would AGI be able to reliably do that?

I would say that AGI would need to do this. I would classify this as the "Intelligence" component. Obtaining context, building a model of a problem, solving it, and convincing others.

4. Would it be able to inspire trust in itself? Trust can be established through verification of it's outputs, the construction of introspective tools, no hallucinations, etc.

I would say yes to this as well. It would be a component of the "Intelligence" to know that buy in is more important than the completion of a task.

To these points, will Astra be able to do these things? If not, I would hesitate to call it AGI.

Agreed, pages on pages of fruitless discussion on "AGI". Shut up damn it, let's see something useful, let's have that pelican!

For people delirious about AGI, you don’t get to it by redefining it favorably.