Replication Index

> I don't think we are near general AI

Nor do I. But I don't agree with your definition of general AI at all.

I think by your definition we are well on the way towards it. I'd note that you didn't address the idea that compression of input concepts into a lower dimensional space is exactly the "reliably turning symbols into meaning" idea you suggest.

The fact the latent representations of concepts can be manipulated in ways that make logical sense is a good indication that the symbols have meaning. The classic Word2Vec experiments showing how the relationships Paris->France ~= London->England and King - Man + Woman = Queen show this well. Modern large language models are much more complicated of course, but the principles remain.

> I subscribe to the view we think of the human mind as a pattern matching computer simply because this is the current major tech, much like people in the past thought of "humors" or "steam machines". I think some of the analogies are useful, to a point, but I don't think there's hard evidence the mind is like a neural net (irony notwithstanding) or a pattern marching GPT-like algorithm.

I don't think the computational approach is an interesting question. No one thinks brains operate like software neural networks (not sure what "pattern marching GPT-like algorithm" means - it is just a neural network). That doesn't matter because all computational methods are ultimately equivalent.

I think outcomes on metrics like benchmarks is important, and…

A tiny bit of searching:

https://replicationindex.com/2018/01/05/bem-retraction/ and a lovely quote:

“I’m all for rigor, but I prefer other people do it. I see its importance—it’s fun for some people—but I don’t have the patience for it. If you looked at all my past experiments, they were always rhetorical devices. I gathered data to show how my point would be made. I used data as a point of persuasion, and I never really worried about, ‘Will this replicate or will this not?” (Daryl J. Bem, in Engber, 2017)

also http://osc.centerforopenscience.org/2014/06/25/a-skeptics-review/ but I don't like this: "If ever there was a paper that showed the futility of meta-analysis, this one is it. Here we have one of the most ridiculous claims that intelligent people have ever dared to make (yes; the hypothesis that aliens built the pyramids is more plausible than people being able to look into the future) – and a meta-analysis supports this claim. The unavoidable conclusion is not that psi exists; rather, it is that meta-analysis is a tool that is fraught with danger". The other points are perhaps valid.

You talk about science but don't have the basic critical thinking to look for opposing viewpoints. You should be seriously embarrassed.

You mean the same Kahneman who got:

- A Nobel Prize in Economics.
- Whose book has almost 20,000 reviews with an
average of almost 5 stars.
- Whose book was winner of the National Academy of Sciences Best Book Award in 2012

and then ...

got most of the findings undermined by this blog analysis:

"Reconstruction of a Train Wreck: How Priming Research Went off the Rails"
https://replicationindex.com/2017/02/02/reconstruction-of-a-train-wreck-how-priming-research-went-of-the-rails/

and then...

commented as himself in the same blog
https://replicationindex.com/2017/02/02/reconstruction-of-a-train-wreck-how-priming-research-went-of-the-rails/comment-page-1/#comments

stating and I quote :
"I accept the basic conclusions of this blog..."

You mean that one ?

In case you are thinking anybody could create an account under his name ...I provide you with an additional reference:
https://retractionwatch.com/2017/02/20/placed-much-faith-underpowered-studies-nobel-prize-winner-admits-mistakes/

Lets just accept most of Academy nowadays is not far
from a total joke.

I read the paper, and agree with your impression of its quality. With training, it is possible to navigate a shaky field like social psychology, so I'll provide my estimates here.

The paper was written in 2013, so it's not pre-registered.

The sample size of 39 is suspicious given its date. People became upset about junk papers two years before, so the authors should have known better. However, I would hesitate to draw conclusions about the integrity of the paper's authors, because each participant was paid $60, which makes expanding the sample size somewhat expensive.

The authors declared no conflicting interests, but I looked up the first author and he has major conflicts of interest.

The last author, DeSteno, is ranked very poorly at https://replicationindex.com/2021/01/19/personalized-p-values/.

Combining these factors, I'd adjust the p-value from .02 to .45, if it were a pre-registered paper which you have pre-committed to read. This paper is worthless.

A good time to remind everyone that for almost 2 decades now it's been determined that the majority of published scientific findings are wrong. [0][1] Including possibly even the very determination that the majority of published scientific findings are wrong.[2] ([3])

So, if that's the case, when a new result comes out, the appropriate reaction is to assume there's a better than average likelihood it will be refuted. And honestly that's what makes science great. Unlike with some other fields of human endeavor, it is possible to firmly refute bad science. And often learn something new in the process.

[0] https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.0020124
[1] https://www.annualreviews.org/doi/10.1146/annurev-statistics-060116-054104
[2] https://replicationindex.com/2019/01/15/ioannidis-2005-was-wrong-most-published-research-findings-are-not-false/
[3] https://en.wikipedia.org/wiki/Epimenides_paradox

> The results were staggering; the more prevalent the idea, in any academic field, the fewer women and people of color participating in that field. This outcome held across all thirty subjects in the study.

This is not evidence that natural talent is not needed, it is at best some evidence that minorities and women maybe don't think they have the requisite natural talent.

> Negative messages, either explicit (“I think you’d be happier if you didn’t take that hard mathematics class”) or implicit (“I’m just not a math person”), both imply that only some people can succeed.

To say the evidence for stereotype threat is "weak" is charitable at best [1], and multiple replication attempts have failed entirely.

Overall, the evidence that was cited is simply not sufficient to justify the principles you list.

[1] https://replicationindex.com/2017/04/07/hidden-figures-replication-failures-in-the-stereotype-threat-literature/