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100%. What this all boils down to in 2018 is data. Data acquisition, data cleansing, data filing, etc. But what else is there, now? You're going to create a

100%. What this all boils down to in 2018 is data. Data acquisition, data cleansing, data filing, etc. But what else is there, now? You're going to create a new, novel ANN network? Cool, can't wait to peruse your paper. Maybe someday it's considered groundbreaking. Right now it's not solving immediate problems. Immediate problems were solved with 1970s ideas + 2018's infrastructure.

itronitron 9y ago
Yes, and there are some exceptional papers from the 1960's as well.
leblancfg 9y ago
A word against hasty generalisation - just because you don't interact with it on a daily basis doesn't mean it isn't being worked on elsewhere. There is an increasing number of ML-first companies out there. They are solving problems like object detection, portfolio strategy optimization, medical diagnosis, etc. Things that are 100% not possible to do with just SQL and Bash, and much less expensive than to have humans run those tasks. In those fields, these companies will outperform their competition on average. I think it's smart for investors to look for in knowledge and talent in the companies they look into. If there's AI/ML talent in the team, their value for acquisition (acqui-hire) is higher. If you want to solve problems for your existing company, you certainly don't need AI/ML. You can tackle many, many low-hanging fruits with just SQL and Bash. If you want to build the kind of company that will be around in 30 years, you need to have an ML-first mindset, and you probably need to start now.
- nkozyra 9y ago
  My point is less that everything can be done with SQL, but that most of what people are doing is based on things possible 10, 20, 40 years ago. This leaves ml engineers dealing more with software modeling than ml modeling.
- Mahn 9y ago
  Maybe we could all just agree that AI and ML have their use cases, but they are not necesarily a good fit to any and all computing problems indiscriminately?
- zitterbewegung 9y ago
  I think the 10% is exActly what these AI first companies should be targeting .

itronitron 9y ago

Yes, and there are some exceptional papers from the 1960's as well.

leblancfg 9y ago

A word against hasty generalisation - just because you don't interact with it on a daily basis doesn't mean it isn't being worked on elsewhere. There is an increasing number of ML-first companies out there. They are solving problems like object detection, portfolio strategy optimization, medical diagnosis, etc. Things that are 100% not possible to do with just SQL and Bash, and much less expensive than to have humans run those tasks. In those fields, these companies will outperform their competition on average. I think it's smart for investors to look for in knowledge and talent in the companies they look into. If there's AI/ML talent in the team, their value for acquisition (acqui-hire) is higher. If you want to solve problems for your existing company, you certainly don't need AI/ML. You can tackle many, many low-hanging fruits with just SQL and Bash. If you want to build the kind of company that will be around in 30 years, you need to have an ML-first mindset, and you probably need to start now.

nkozyra 9y ago
My point is less that everything can be done with SQL, but that most of what people are doing is based on things possible 10, 20, 40 years ago. This leaves ml engineers dealing more with software modeling than ml modeling.
Mahn 9y ago
Maybe we could all just agree that AI and ML have their use cases, but they are not necesarily a good fit to any and all computing problems indiscriminately?
zitterbewegung 9y ago
I think the 10% is exActly what these AI first companies should be targeting .