101–110 of 178 posts
Slightly over a year back, we were contacted by IBM to try out Watson under some program for startups. Starting out, they asked us to give them a try. We gave them a NLP related task and they were confident they would be able to do a good job of it. After multiple meetings over the next 2 months, they barely produced any useful output. In the meantime, an intern with us got a pretty decent solution using just Python.…
Python is included in the most recent versions of Watson Studio ML. Were Jupyter notebooks included when you originally tried the tasks? I’m curious why the included python didn’t work and the standalone did.
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I suspect what happened was that Watson started out as a single product or focused suite of products. I'm just guessing, but then as the marketing induced hype started and growth/results were not on target , they realized there was a lot of interest in Watson so IBM pivoted and started rolling everything under the Watson brand - products, consulting, cloud stuff, etc. Basically that way they could say Watson was a "s…
I worked there (Watson Health) a few years back. All that happened is IBM. That's right. They bought us (a small start-up) and this is what they did in a year to us: 1. replaced managers with their own who didn't get anything, kind of old date executives taken away from mainframe 2. banned remote work (this costed them a few brilliant engineers) 3. opened one huge open space full of noise and chatter, I mean sales te…
I think big companies trying to do things like "Capture a market segment that will be a trillion (I made this number up) dollars in 2025" suffer terrible analysis paralysis. The 5 year plan has an extreme revenue ramp up and insane targets. If you combine that with politics, there is a lot of business and financial justification that has to go into every decision, and many decisions will be safe ones that look innovative (we're going to build on Insert Latest Cloud and use AI!) but have no real value to many customers.
The end result is crazy hiring (and firing a few years later) and groups with opposite experiences. Some groups have no work to do and other groups are working 80 hour weeks trying to make it seem like the marketing and growth curves are all true.
It's a comedy (if you are able to stay out of the mess and politics) or a tragedy if you have a manager who feels they want to be the shining star that supports this mad rush.
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This reminds me of a bit in the first The Expanse book I read recently. The onboard medical computer had to be overridden because it calculated coldly that the correct course of treatment was palliative care.
Given the context of that scene, it was more an indication of how much characters A and B had been affected by the situation.
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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 fie…
>Deep learning and machine learning don’t work. Quantitative math will always prevail, I have a neural net onboard my phone which automatically detects songs offline and tells me what they are. Is that semantically 'quantitative math' and not machine learning?
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I suspect what happened was that Watson started out as a single product or focused suite of products. I'm just guessing, but then as the marketing induced hype started and growth/results were not on target , they realized there was a lot of interest in Watson so IBM pivoted and started rolling everything under the Watson brand - products, consulting, cloud stuff, etc. Basically that way they could say Watson was a "s…
I worked there (Watson Health) a few years back. All that happened is IBM. That's right. They bought us (a small start-up) and this is what they did in a year to us: 1. replaced managers with their own who didn't get anything, kind of old date executives taken away from mainframe 2. banned remote work (this costed them a few brilliant engineers) 3. opened one huge open space full of noise and chatter, I mean sales te…
This all rolls down a few levels of mgmt to the first line guys, who are told your in a growth area, and we expect to need to hire X people over the next 24 months, who will be tasked with XYZ (frequently fancy words which when analyzed boils down to support the product we are going to sell).
This goes on for a couple years until the projected vs real revenue divergence is so large that even an CEO can't ignore the lack of growth. At which point the plug gets pulled and the next adventure starts somewhere else.
Sadly though, IMHO none of that is a problem, the real problem is that the CEO's can't actually tell or make strategic decisions about why these projects are failing to have exponential growth. (see intel & mobile chips/wireless connectivity, those are so strategically necessary for their growth that they need to keep trying until they die). So, they ax them, sometimes just as they are getting a solid product portfolio together. But they don't know that because they have been fed the same line for the past 24-36 months.
Watson is more a marketing term than a technical connection between products. I invite you to test "Watson Tone Analyzer" https://tone-analyzer-demo.ng.bluemix.net/ : - "I like this product." => "this is an analytical opinion with neutral emotion." - "I like it" => "Tentative, 50% happy answer." - "It's not a bad product." => "analytical". - "It's not a bad tool" => "joyful answer".
As far as I can tell there's absolutely no connection between the products being labeled as 'Watson' and often times very little machine learning taking place either.
I've interviewed several candidates from IBM over the last ~3 years and almost all of them worked on something with 'Watson' in the name. When we whiteboarded out the architecture of what they worked on it was mostly automation with _maybe_ a touch of machine learning thrown in by a module written by someone else.
Very few of them were able to pass the technical interview.
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Quantitative math, or applied math isn't based on fitting data to an arbitrary mathematical structure. It's looking at real life, and deriving the mathematical laws that govern what you see. You could have a neural net predict planetary motion. However, it doesn't know jack shit about physics. >I have a neural net onboard my phone which automatically detects songs offline and tells me what they are. MP3 uses somethin…
" If actual mathematicians worked on this problem, I guarantee you they'd do a better job" No, this is wrong. Some of the most brilliant people in the world have been working on image recognition, voice recognition etc. and AI is crushing all of their work. "Your neural network doesn't tell you what features make songs distinct, it's not a quantitative model at all" - it doesn't matter at all if our objective is dete…
This is very true. I take my stronger statements back, MAINSTREAM mathematicians attempting this problem are all wrong, and have been wrong for 50 years. But you do need the right theory, and the right math that realizes this theory.
"AI" is superficially beating the work in computer vision. Computer vision is complete bogus. The gabor filters, fourier transfroms etc. are all wrong conceptually. The known methods do abysmally on basic tasks like object recognition, texture segmentation etc. But they keep trying it.
I would take this one step further: computer vision, audio and NLP researchers have been stuck in a rut for the past 50 years. DL is beating THEIR math, but this is because of data and computation speed, not because of any insights. But DL is also wrong, and giving you an illusion of progress. Both of these things are doomed to go the way of GOFAI.
I can go into great detail and carefully explain why MAINSTREAM contemporary ideas in math for vision, audition and language are completely wrong, and have been wrong for 50 years. What is the right model? Like I mentioned before, the right ideas are emerging, neural networks will dominate, just not DL.