Re: [FFmpeg-devel] VDD conference invitation - Dublin 22-24 Sept 2023

[Michael Niedermayer <michael@niedermayer.cc>]

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On Sat, Sep 09, 2023 at 09:04:25AM +0200, Tomas Härdin wrote:
> fre 2023-09-08 klockan 15:09 +0200 skrev Michael Niedermayer:
[...]

> > AI based filters are neglegted at a time everything is shifting
> > to neural networks and AI. 
> 
> Good. AI is a meme. Polynomial regression is just as good.

Theres a lot i could reply but lets pick 2 choices
1. FFmpeg is about data compression so lets look at compression
ill make it easy, just a "toy" project from fabrice, beat this with
Polynomial regression
https://bellard.org/ts_server/ts_zip.html

2. lets just ask AI, and while we can argue about this, i REALLY like to see
your Polynomial regression producing anything that resembles english text
heres chat gpts reply, first attempt:

If someone were to say, "Good. AI is a meme. Polynomial regression is just as good," I would respond with the following points:

AI is not a meme: Artificial Intelligence (AI) is a field of computer science that focuses on creating systems and machines capable of performing tasks that typically require human intelligence, such as problem-solving, learning, and decision-making. AI has made significant advancements in various domains, including machine learning, natural language processing, computer vision, and robotics. It is a legitimate and impactful field of research and application.

Polynomial regression vs. AI: Polynomial regression is a specific machine learning technique used for modeling relationships between variables. While polynomial regression can be effective in certain situations, it has limitations compared to AI and other machine learning approaches. AI encompasses a wide range of techniques and algorithms, including deep learning, reinforcement learning, and neural networks, which can handle complex and high-dimensional data and perform tasks that go beyond what polynomial regression can achieve.

Applicability: The choice between AI and polynomial regression depends on the problem at hand. Polynomial regression is suitable for problems where a simple polynomial equation can accurately represent the data's underlying patterns. However, AI is more versatile and can tackle tasks like image recognition, language translation, autonomous driving, and more, where the relationships are highly non-linear and complex.

Evolution of AI: AI has evolved significantly over the years, leading to breakthroughs in various domains, including healthcare, finance, entertainment, and scientific research. It has the potential to bring about transformative changes in society, whereas polynomial regression is just one tool within the broader AI toolkit.

In summary, while polynomial regression can be a useful tool in certain contexts, it is not a replacement for AI. AI is a diverse and rapidly advancing field with a wide range of applications and techniques that go far beyond simple regression models. Both AI and polynomial regression have their places in data analysis, but they serve different purposes and should not be considered interchangeable.

[...]
-- 
Michael     GnuPG fingerprint: 9FF2128B147EF6730BADF133611EC787040B0FAB

If a bugfix only changes things apparently unrelated to the bug with no
further explanation, that is a good sign that the bugfix is wrong.

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