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I think "deep learning startup" may be more exciting than company using "decades old technology" : - Applied deep learning is still not easy. Not many people h

by kozikow 10y ago

I think "deep learning startup" may be more exciting than company using "decades old technology" : - Applied deep learning is still not easy. Not many people have know-how about training models. There are no easy answers in hardware space. - Translate academia success to businesses. After DL achieved remarkable things in Academia, people expect that same success will be translated to business. Business success mostly only came from Google.

karambahh 10y ago
Why would that be? I built a company on translating academia research to business so I can totally relate to that feeling. I consider that a company exists to provide value to its consumers. If a simple linear regression provides value, why go the DL route? I am absolutely not saying that DL has no use-case, we use it internally and on customers projects, I am just arguing that if decades old stuff does the job, don't bother with DL, in particular because applied DL is indeed very hard. To me, it is like an extension of the "if it aint broke, don't fix it" philosophy: "if it can be fixed with a simple solution, dont over-engineer". I often hear young software engineers laugh about banks still using COBOL for their core business. COBOL is lame, it's monolothic, etc. It may be all fashioned (and the little COBOL I've done I've hated it :-) ) but it goddamn works, why touch it and risk breaking stuff? If a new technology efficiently solves a use-case then fine, go for it, but I hate the "hype" phenomenon. For instance, about 5 years, the hype was RoR, 2 years ago Scala, now it's DL and Pony, etc... I do some pretty nice DL, it's useful. I really like the concepts of Pony. But I'm not going to promote a deep learning framework in Pony to replace SWIFT money transfer, even though I'd be ticking many boxes of hype with that...
- kozikow 10y ago
  Previous approaches to AI couldn't beat game of go. There is not as much qualitive difference between pre-decesors of RoR, Pony and Scala. This makes me think that DL hype may be warranted. Anyway, I am working on deep learning and it enable us to solve problems of our users better than previous computer vision approaches.

by kozikow 10y ago

karambahh 10y ago

Why would that be? I built a company on translating academia research to business so I can totally relate to that feeling. I consider that a company exists to provide value to its consumers. If a simple linear regression provides value, why go the DL route? I am absolutely not saying that DL has no use-case, we use it internally and on customers projects, I am just arguing that if decades old stuff does the job, don't bother with DL, in particular because applied DL is indeed very hard. To me, it is like an extension of the "if it aint broke, don't fix it" philosophy: "if it can be fixed with a simple solution, dont over-engineer". I often hear young software engineers laugh about banks still using COBOL for their core business. COBOL is lame, it's monolothic, etc. It may be all fashioned (and the little COBOL I've done I've hated it :-) ) but it goddamn works, why touch it and risk breaking stuff? If a new technology efficiently solves a use-case then fine, go for it, but I hate the "hype" phenomenon. For instance, about 5 years, the hype was RoR, 2 years ago Scala, now it's DL and Pony, etc... I do some pretty nice DL, it's useful. I really like the concepts of Pony. But I'm not going to promote a deep learning framework in Pony to replace SWIFT money transfer, even though I'd be ticking many boxes of hype with that...

kozikow 10y ago
Previous approaches to AI couldn't beat game of go. There is not as much qualitive difference between pre-decesors of RoR, Pony and Scala. This makes me think that DL hype may be warranted. Anyway, I am working on deep learning and it enable us to solve problems of our users better than previous computer vision approaches.