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a lot of these AI companies products are really terrible. Has anyone ever tried the AI API models from clarifai ? Just so unaccurate. It seems like a scam. I've

a lot of these AI companies products are really terrible. Has anyone ever tried the AI API models from clarifai ? Just so unaccurate. It seems like a scam. I've also had a really bad experience with watson's speech to text apis.

IshKebab 7y ago
Yeah their speech recognition API was by far the worst of the ~5 I tested a few years ago. Like, almost as bad as Sphinx.
linuxftw 7y ago
I'm pretty sure it's all a big scam, at least the crap that comes out of IBM.
- tolstoy77 7y ago
  +1
pmart123 7y ago
Watson’s is awful. Google’s default models are by far the best followed by Microsoft at a distance. Then, many of them are so bad that it is laughable.
- gbersac 7y ago
  Do you have any insight about AWS IA ?
  - pmart123 7y ago
    Haven’t tried it actually yet.
  - pmart123 7y ago
    I tested most of the solutions a year or two ago before AWS rolled out Amazon Comprehend. I found that Google’s API combined with some code and open source packages ended up being additive for entity recognition, classification, and semantic text. Unsurprisingly, Google’s custom search API’s are also worlds better than the other places, as is it’s Maps API. I haven’t tried any of the image classification API’s, but I won’t be surprised if Google won here too.
tabtab 7y ago
An AI bubble poppage is around the corner. The actual revenue of AI companies does not justify their stock price.
acdc4life 7y ago
Deep learning and machine learning don’t work. Quantitative math will always prevail, as it always has. Unfortunately, mathematical research isn’t there yet. We don’t have models for vision, audition and linguistics. Neuroscience and psychology are in their infancy, a good analogy would compare these fields to where physics was pre-Newton, Galileo era of understanding. I suspect that in the decades to come, these fields will influence mathematics the same way physics influenced calculus. Physics historically had a huge influence on math, in the coming century it will be neuroscience and psychology, in linking brains to behavior, and the quantitative laws that allow brains to give rise to minds.
- intuitionist 7y ago
  > Deep learning and machine learning don’t work. Quantitative math will always prevail, as it always has. What do you think machine learning is, if not “quantitative math”? Deep learning is just linear algebra and calculus, and things like random forests are even simpler mathematically.
  - acdc4life 7y ago
    Machine learning is glorified curve fitting. DL isn't even mathematically sound, back propagation has no proof of convergence. Quantitative math is about extracting natural laws, and mapping them to mathematical structures. You could use DL to predict planetary motion, and get pretty good at it. But this isn't a quantitative understanding of the world. You didn't learn anything. Physics in contrast has the laws of motion and gravitation. You can directly model arbitrary planets. Moreover, you can model arbitrary rigid bodies, from cars to space shuttles. Your ML, DL random forrest etc. all use math, sure. But so did the Keplarian models of motion. You aren't qualitatively deducing math that governs the world, but forcing an arbitrarily chosen mathematical structure to your data.
    - intuitionist 7y ago
      If we’re throwing out anything that doesn’t have a proof of convergence as “not mathematically sound,” you can kiss fluid mechanics goodbye, as well as lots of other subfields of physics that rely on partial differential equations.
m_ke 7y ago
I was an early clarifai employee and although I can't speak for what's there now, up until 2-3 years ago they had one of the most accurate models available (with google being the only real competitor). The generally available models will almost always be suboptimal due to the difference in the data that they're trained on vs the data that clients use it on. That's why most of these AI companies end up doing a ton of consulting and build specialized models for larger customers.

IshKebab 7y ago

Yeah their speech recognition API was by far the worst of the ~5 I tested a few years ago. Like, almost as bad as Sphinx.

linuxftw 7y ago

I'm pretty sure it's all a big scam, at least the crap that comes out of IBM.

tolstoy77 7y ago
+1

pmart123 7y ago

Watson’s is awful. Google’s default models are by far the best followed by Microsoft at a distance. Then, many of them are so bad that it is laughable.

gbersac 7y ago
Do you have any insight about AWS IA ?
- pmart123 7y ago
  Haven’t tried it actually yet.
- pmart123 7y ago
  I tested most of the solutions a year or two ago before AWS rolled out Amazon Comprehend. I found that Google’s API combined with some code and open source packages ended up being additive for entity recognition, classification, and semantic text. Unsurprisingly, Google’s custom search API’s are also worlds better than the other places, as is it’s Maps API. I haven’t tried any of the image classification API’s, but I won’t be surprised if Google won here too.

tabtab 7y ago

An AI bubble poppage is around the corner. The actual revenue of AI companies does not justify their stock price.

acdc4life 7y ago

Deep learning and machine learning don’t work. Quantitative math will always prevail, as it always has. Unfortunately, mathematical research isn’t there yet. We don’t have models for vision, audition and linguistics. Neuroscience and psychology are in their infancy, a good analogy would compare these fields to where physics was pre-Newton, Galileo era of understanding. I suspect that in the decades to come, these fields will influence mathematics the same way physics influenced calculus. Physics historically had a huge influence on math, in the coming century it will be neuroscience and psychology, in linking brains to behavior, and the quantitative laws that allow brains to give rise to minds.

intuitionist 7y ago
> Deep learning and machine learning don’t work. Quantitative math will always prevail, as it always has. What do you think machine learning is, if not “quantitative math”? Deep learning is just linear algebra and calculus, and things like random forests are even simpler mathematically.
- acdc4life 7y ago
  Machine learning is glorified curve fitting. DL isn't even mathematically sound, back propagation has no proof of convergence. Quantitative math is about extracting natural laws, and mapping them to mathematical structures. You could use DL to predict planetary motion, and get pretty good at it. But this isn't a quantitative understanding of the world. You didn't learn anything. Physics in contrast has the laws of motion and gravitation. You can directly model arbitrary planets. Moreover, you can model arbitrary rigid bodies, from cars to space shuttles. Your ML, DL random forrest etc. all use math, sure. But so did the Keplarian models of motion. You aren't qualitatively deducing math that governs the world, but forcing an arbitrarily chosen mathematical structure to your data.
  - intuitionist 7y ago
    If we’re throwing out anything that doesn’t have a proof of convergence as “not mathematically sound,” you can kiss fluid mechanics goodbye, as well as lots of other subfields of physics that rely on partial differential equations.

m_ke 7y ago

I was an early clarifai employee and although I can't speak for what's there now, up until 2-3 years ago they had one of the most accurate models available (with google being the only real competitor). The generally available models will almost always be suboptimal due to the difference in the data that they're trained on vs the data that clients use it on. That's why most of these AI companies end up doing a ton of consulting and build specialized models for larger customers.