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Perhaps this is a hot take, but when I see ML being used (for the most part), it's because people do not fundamentally understand OR know how to solve the probl

Perhaps this is a hot take, but when I see ML being used (for the most part), it's because people do not fundamentally understand OR know how to solve the problem in question. And instead of understanding what's going on, and solving the problem efficiently, instead it's "throw GB's or TB's of data at an algo that we tweak and hope for the best". Sure, it gets results for massive processing times and data. And sure it's "fuzzy" and can fail on really stupid stuff and provide bad answers confidently. But it'll get the next VC funding line, won't it?

axg11 4y ago
Let's look at a few of the most impressive applications of machine learning: - Classification in computer vision - Protein folding (AlphaFold) - Image generation (Dall-E 2) - Answering general language queries (GPT-3) It's unclear how any of these applications could have been tackled _without_ machine learning. We had no solid grasp on these problems before ML, with the exception of protein folding. I think you are being cynical. Of course not every ML project will result in a home run success. Should that make us sceptical of the entire field?
- Jensson 4y ago
  But most people using neural nets in the industry aren't working on those things. They just slap it on any data inference task, even though in most cases traditional statistical/data science models works much better.
  - r-zip 4y ago
    In my experience, people use appropriate tools for the job. Most data scientists won't reach for a neural network if the problem can be solved by other, simpler means.
    - Jensson 4y ago
      Which is why I said "using neural nets" instead of "data scientist".
PartiallyTyped 4y ago
How would you solve problems like Q/A? How would you solve problems in RL scenarios? How would you solve image recognition problems? In the end, ML is not that different to what we are doing with all models, ie use a set of data to create a crude model of the process that we are trying to learn/figure out.

axg11 4y ago

Let's look at a few of the most impressive applications of machine learning: - Classification in computer vision - Protein folding (AlphaFold) - Image generation (Dall-E 2) - Answering general language queries (GPT-3) It's unclear how any of these applications could have been tackled _without_ machine learning. We had no solid grasp on these problems before ML, with the exception of protein folding. I think you are being cynical. Of course not every ML project will result in a home run success. Should that make us sceptical of the entire field?

Jensson 4y ago
But most people using neural nets in the industry aren't working on those things. They just slap it on any data inference task, even though in most cases traditional statistical/data science models works much better.
- r-zip 4y ago
  In my experience, people use appropriate tools for the job. Most data scientists won't reach for a neural network if the problem can be solved by other, simpler means.
  - Jensson 4y ago
    Which is why I said "using neural nets" instead of "data scientist".

PartiallyTyped 4y ago

How would you solve problems like Q/A? How would you solve problems in RL scenarios? How would you solve image recognition problems? In the end, ML is not that different to what we are doing with all models, ie use a set of data to create a crude model of the process that we are trying to learn/figure out.