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Agreed. The billions of dollars thrown at anything claiming “AI” is quite disappointing. It’s a small branch of statistics the same way that statistics is a s

by jfghi 4y ago

Agreed. The billions of dollars thrown at anything claiming “AI” is quite disappointing. It’s a small branch of statistics the same way that statistics is a small branch of math. At this point, even a rolling average is labeled as AI.

blitzar 4y ago
The billions of dollars thrown at anything claiming “AI” but would be lukcy to be high school level statistics under the hood is disappointing There fixed it for you ... However, that is the cycle of society. 1990's all you had to do wat put '.com' in your company name and you were a paper millionaire overnight. 2000's "Social, 2010's mobile first. Metaverse, AI and beyond.
wiz21c 4y ago
Out of curiosity, what are some other, powerful, statistical branches besides those used in "data sciences" ? (honest question, I just completed a MSc in data sciences but I must admit I don't know much besides that)
- jfghi 4y ago
  I’d recommend generalized linear models which are a subset of linear mixed models. These are the driving force for financial services. The model building process for these translates well to other types of models. Decision trees and random forests show up too. I think data science is great in that it exposes one to programming and technology moreso than a focus in math or stats, I was mostly complaining about the marketing I’ve seen surrounding AI. I recall listening to a few mba consultants claiming that they could improve a business process significantly by using ML even though there was sparse inaccurate data available and they had no modeling background. Overall I think AI models are interesting and often useful (although not sure what fits under the umbrella), but am tired of the hype, strong opinions, and amount of claimed expertise.
bee_rider 4y ago
> It’s a small branch of statistics the same way that statistics is a small branch of math. Is this sarcastic or something? Math understanding is a fractal, the closer you look at a branch the bigger it gets. Statistics in particular seems to have countless real world applications.
- jfghi 4y ago
  Not sarcastic, looking at statistical modeling there is a vast library of modeling techniques and research of which “AI” is a small component. In my experience, non-AI models tend to outperform the AI ones in many applications. This is different than how AI is marketed.
deltasevennine 4y ago
Statistics is the basis of science. Probability is one of the axioms of reality. Probability just works and is assumed to work and nobody knows why. It is fundamentally the reason why Entropy always goes up. It is much more then a small branch of math given it's foundational relation to the real world. Intuitively AI is much more just a branch of math. We are getting closer and closer to understand theoretically how our own brains work. The philosophical connections are profound and you are using language categories to limit your thinking.
zmgsabst 4y ago
Deep learning is statistics weighted graphs, but graphs are a way to encode all of mathematics. Which is why things like manifolds and topology are useful for analyzing our models, because all of the ways to encode mathematics are equivalent and you can transport ideas between them.
Banana699 4y ago
This is as ridiculous as claiming that a neural network is going to make Skynet. First, statistics is not a small branch of math, proability theory is (not a small branch by the way, not by a long shot, just a branch, one of the fundamental and very large ones). Statistics is the art and science of dealing with data, it uses probability theory like physics uses math, but is far more than probability theory just like physics is far more than math. Second, Neural nets and machine learning in general is an extremly powerful way of solving problems of certain structure and form, and it solves problems not encountered in statistics. Claiming it's a small branch of statistics is like claiming that the entirety of computer science is a small branch of logic.
- jfghi 4y ago
  What are the top 3 things accomplished and implemented by the “extremely powerful” neural networks and machine learning? How significant are these top 3 things? Have you ever built a production model for a large company or have a tier 1 phd? If not, what are your qualifications for labeling a claim ridiculous.
  - Banana699 4y ago
    >What are the top 3 things accomplished and implemented by the “extremely powerful” neural networks and machine learning? Off the top of my head and in no particular order : 1- Playing human games at superhuman performance from raw pixels 2- Recognizing objects and people in images at human or better performance 3- Recognizing human voice in real world noisy environments and parsing it into text at human or better performance >How significant are these top 3 things? They generate billions of dollars of profit and beat human knowledge and expertise that took thousands\millions of years to craft and\or evolve with nothing but a GPU and a lot of electricity. >Have you ever built a production model for a large company or have a tier 1 phd? No. >what are your qualifications for labeling a claim ridiculous. I have a brain that can spot ridiculous hyperbolic claims, and experience-backed knowledge with neural networks and machine learning that can explain with examples why they are ridiculous and hyperbolic. If you do have the qualifications you request, start with using them to support your claims. Here's a small challenge that should pose no difficulty to you. Show me, with derivations and citations, how an RNN is "just" an obscure statistical model. Which statistician first published or applied it ? Is it taught in a statistics course you know of ? Which ones ? Are research statisticians publishing on it right now or ever ? In what sense is it part of the science of statistics like, say, the normal distribution is ?
elcomet 4y ago
I don't understand this criticism. What matter are the results, not the technology used. If your company is capable of solving a new problem that is worth billion of dollars, then does it matter that it uses basic statistics ? Of course if they overpromise, then reality will catch them and the company will fail.. but this has nothing to do with statistics.

by jfghi 4y ago

blitzar 4y ago

The billions of dollars thrown at anything claiming “AI” but would be lukcy to be high school level statistics under the hood is disappointing There fixed it for you ... However, that is the cycle of society. 1990's all you had to do wat put '.com' in your company name and you were a paper millionaire overnight. 2000's "Social, 2010's mobile first. Metaverse, AI and beyond.

wiz21c 4y ago

Out of curiosity, what are some other, powerful, statistical branches besides those used in "data sciences" ? (honest question, I just completed a MSc in data sciences but I must admit I don't know much besides that)

jfghi 4y ago
I’d recommend generalized linear models which are a subset of linear mixed models. These are the driving force for financial services. The model building process for these translates well to other types of models. Decision trees and random forests show up too. I think data science is great in that it exposes one to programming and technology moreso than a focus in math or stats, I was mostly complaining about the marketing I’ve seen surrounding AI. I recall listening to a few mba consultants claiming that they could improve a business process significantly by using ML even though there was sparse inaccurate data available and they had no modeling background. Overall I think AI models are interesting and often useful (although not sure what fits under the umbrella), but am tired of the hype, strong opinions, and amount of claimed expertise.

bee_rider 4y ago

> It’s a small branch of statistics the same way that statistics is a small branch of math. Is this sarcastic or something? Math understanding is a fractal, the closer you look at a branch the bigger it gets. Statistics in particular seems to have countless real world applications.

jfghi 4y ago
Not sarcastic, looking at statistical modeling there is a vast library of modeling techniques and research of which “AI” is a small component. In my experience, non-AI models tend to outperform the AI ones in many applications. This is different than how AI is marketed.

deltasevennine 4y ago

Statistics is the basis of science. Probability is one of the axioms of reality. Probability just works and is assumed to work and nobody knows why. It is fundamentally the reason why Entropy always goes up. It is much more then a small branch of math given it's foundational relation to the real world. Intuitively AI is much more just a branch of math. We are getting closer and closer to understand theoretically how our own brains work. The philosophical connections are profound and you are using language categories to limit your thinking.

zmgsabst 4y ago

Deep learning is statistics weighted graphs, but graphs are a way to encode all of mathematics. Which is why things like manifolds and topology are useful for analyzing our models, because all of the ways to encode mathematics are equivalent and you can transport ideas between them.

Banana699 4y ago

This is as ridiculous as claiming that a neural network is going to make Skynet. First, statistics is not a small branch of math, proability theory is (not a small branch by the way, not by a long shot, just a branch, one of the fundamental and very large ones). Statistics is the art and science of dealing with data, it uses probability theory like physics uses math, but is far more than probability theory just like physics is far more than math. Second, Neural nets and machine learning in general is an extremly powerful way of solving problems of certain structure and form, and it solves problems not encountered in statistics. Claiming it's a small branch of statistics is like claiming that the entirety of computer science is a small branch of logic.

jfghi 4y ago
What are the top 3 things accomplished and implemented by the “extremely powerful” neural networks and machine learning? How significant are these top 3 things? Have you ever built a production model for a large company or have a tier 1 phd? If not, what are your qualifications for labeling a claim ridiculous.
- Banana699 4y ago
  >What are the top 3 things accomplished and implemented by the “extremely powerful” neural networks and machine learning? Off the top of my head and in no particular order : 1- Playing human games at superhuman performance from raw pixels 2- Recognizing objects and people in images at human or better performance 3- Recognizing human voice in real world noisy environments and parsing it into text at human or better performance >How significant are these top 3 things? They generate billions of dollars of profit and beat human knowledge and expertise that took thousands\millions of years to craft and\or evolve with nothing but a GPU and a lot of electricity. >Have you ever built a production model for a large company or have a tier 1 phd? No. >what are your qualifications for labeling a claim ridiculous. I have a brain that can spot ridiculous hyperbolic claims, and experience-backed knowledge with neural networks and machine learning that can explain with examples why they are ridiculous and hyperbolic. If you do have the qualifications you request, start with using them to support your claims. Here's a small challenge that should pose no difficulty to you. Show me, with derivations and citations, how an RNN is "just" an obscure statistical model. Which statistician first published or applied it ? Is it taught in a statistics course you know of ? Which ones ? Are research statisticians publishing on it right now or ever ? In what sense is it part of the science of statistics like, say, the normal distribution is ?

elcomet 4y ago

I don't understand this criticism. What matter are the results, not the technology used. If your company is capable of solving a new problem that is worth billion of dollars, then does it matter that it uses basic statistics ? Of course if they overpromise, then reality will catch them and the company will fail.. but this has nothing to do with statistics.