Data Scientology

2021-12-18 04:15:01 D Do large language models understand us?

Blog post by Blaise Aguera y Arcas.

Summary

Large language models (LLMs) represent a major advance in artificial intelligence (AI), and in particular toward the goal of human-like artificial general intelligence (AGI). It’s sometimes claimed, though, that machine learning is “just statistics”, hence that progress in AI is illusory with regard to this grander ambition. Here I take the contrary view that LLMs have a great deal to teach us about the nature of language, understanding, intelligence, sociality, and personhood. Specifically: statistics do amount to understanding, in any falsifiable sense. Furthermore, much of what we consider intelligence is inherently dialogic, hence social; it requires a theory of mind. Since the interior state of another being can only be understood through interaction, no objective answer is possible to the question of when an “it” becomes a “who” — but for many people, neural nets running on computers are likely to cross this threshold in the very near future.

https://medium.com/@blaisea/do-large-language-models-understand-us-6f881d6d8e75

/r/MachineLearning
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2021-12-18 03:14:47 The "Fa" to "La" ratio in the Christmas carol Deck The Halls. [OC]

/r/dataisbeautiful
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2021-12-18 02:14:53 Open / Comment

2021-12-18 02:14:37 You'd think red would mean bad and green would be good right?

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@datascientology Open / Comment

2021-12-18 01:14:45 Animation: If every State in India lived as densely as Delhi – they can fit within the blue regions [OC]

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2021-12-18 00:14:33 Submissions are open for Volume VI of The Atlas of Design
https://atlasofdesign.org/submit/

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2021-12-17 23:14:46 D Internship after ML phd?

Hello everyone,

I recently submitted my phd thesis focused on optimization and RL at a university in Europe. Since my advisor was against internships and my funding didn't allow for one, I graduated without any internship experience and it is difficult to land a full time job. I applied for many full time roles but I got rejections almost all the time.

In my case, does it make sense to apply for internships at big companies? I see that FAANG companies are hiring a lot of interns nowadays. Do you have any suggestions?

Thanks a lot for your help!

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2021-12-17 21:14:41 Simulation of Euler's number [OC]

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2021-12-17 20:14:42 D Why ViT does not beat CNN in the field of deep generative model?

Recently ViT beats the CNN in many field proving that ViT is superior backbone network than CNN. There are some papers who tried to use ViT as a discriminator architecture in GAN but their results simply do not look so good. Why ViT does not beat CNN in the field of deep generative model?

/r/MachineLearning
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2021-12-17 19:14:56 E What theory do I actually need in industry?

Currently I'm at a crossroads in terms of my career. I'm in my last year of undergrad, and at this time next year I'll either be enrolled in a graduate stats program or working some data analyst job. Even if I don't get into any grad schools, I'm still planning to self-study a lot of statistics to give me deeper understanding. The problem is that the few things I know are universally useful across all of statistics (undergrad-level real analysis, linear algebra, probability theory, and linear models) are things I've already studied. Beyond that, I'm having a lot of trouble deciding what courses/topics would be most useful to me. Stochastic processes, Bayesian statistics, causal inference,... they all seem extremely important regardless of where I end up applying my statistics knowledge in industry. I'm very aware that there's always more to learn, but I'm trying to construct some vague hierarchy of topics so I know what to focus on. The only things I know for sure are

1. I'm much more interested in the inference/model selection/causality side of statistics than the ML/AI side.
2. I can always understand mathematical results/theorems in terms of their assumptions and what the theorems say, but completely understanding the theory is sometimes beyond me. To clarify what I mean by this, I'm confident that I can completely understand any undergrad-level math topic given enough time, but I'm not sure about anything higher than that (graduate-level measure theory, manifolds, algebraic topology, etc.).

I realize this is a very nebulous question, but I was wondering if anyone here could point me in the right direction in terms of the most important things to study and/or the things I might find interesting. Right now the main topics I'm considering learning are stochastic processes, Bayesian statistics, multivariate analysis, causality, and model selection.

/r/statistics
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