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What would be the criteria to say ML is becoming another commoditized skillset sort of like how cloud computing or mobile app development eventually became? I f

What would be the criteria to say ML is becoming another commoditized skillset sort of like how cloud computing or mobile app development eventually became? I feel like there is a big increase in these types of resources that make it possible for anyone (with necessary background) to learn.

panpanna 6y ago
We are already past that. Remember when everyone needed a little computer knowledge, maybe even some minimal programming skills, even if you were a mathematician or a social scientist? Well, now we have a similar situation with data sciences.
tikhonj 6y ago
There are resources out there to learn any tech skill with the necessary background—and resources to pick up the necessary background if you don't have it. At the same time, tech as a whole is not (fully) commoditized, at least in the sense that there is a wide range to the market and a lot of differentiation towards to top end of the skill distribution. My impression is that ML is getting to the point where you can do useful ML for some businesses without needing particularly deep expertise or creativity so I could see a commoditized lower end to the market developing (just like you can get a generic mobile app from a random consultancy), but there will still be a lot of important business problems that are too hard to tackle that way, leaving a lot of room for experts in the market as well.
jariel 6y ago
1) It's considerably harder and 'up the intellectual stack' - which will be a limiting factor. 2) It requires data, and a lot of it, and the cleaning and maintenance of it. Very few people have access to the necessary data, and companies are protective of it. 3) AI has very few obvious applications. There are a lot of 'apps' because they do something useful for us. But how many 'AIs' have you downloaded or used, i.e. things that are mostly AI? AI is an 'approach' not a 'solution'. So because of those things, we're not seeing a 'sea of developers' like wit iOS, rather, AI is caught up in the 'big companies'. Because 1) AI researchers are still expensive and 3) they are generally not profitable. Because of 2) there are few places regular people can practice AI. So you end up with massive companies that can support major R&D operations (Google, Amazon), and a ton of secondary companies with tons of data, but can barely manage their own IT, let alone, do 'fancy AI'. Think airlines, most banks, insurance companies. So the first real applications of AI come out of Google. We will start to see some financial products etc.. We'll some nice experimental apps, but generally not a lot of 'small companies winning hard on an AI play'. That said - you may see startups who are 90% regular company, but who have some AI tweaks that make them differentiated. FYI - a lot of AI is still mundane: Spotify playlists, Shopify support - we would not miss it if the AI were removed, it's mostly superficial gains. AI is a weird subject because 'it will change the world' but mostly by providing incremental jumps in some areas, that allow bigger jumps in others, like 'self driving cars' - the improved image recognition means cars cross a treshhold whereby they can start to understand enough around them so as to actually, possibly drive on their own.

panpanna 6y ago

We are already past that. Remember when everyone needed a little computer knowledge, maybe even some minimal programming skills, even if you were a mathematician or a social scientist? Well, now we have a similar situation with data sciences.

tikhonj 6y ago

There are resources out there to learn any tech skill with the necessary background—and resources to pick up the necessary background if you don't have it. At the same time, tech as a whole is not (fully) commoditized, at least in the sense that there is a wide range to the market and a lot of differentiation towards to top end of the skill distribution. My impression is that ML is getting to the point where you can do useful ML for some businesses without needing particularly deep expertise or creativity so I could see a commoditized lower end to the market developing (just like you can get a generic mobile app from a random consultancy), but there will still be a lot of important business problems that are too hard to tackle that way, leaving a lot of room for experts in the market as well.

jariel 6y ago

1) It's considerably harder and 'up the intellectual stack' - which will be a limiting factor. 2) It requires data, and a lot of it, and the cleaning and maintenance of it. Very few people have access to the necessary data, and companies are protective of it. 3) AI has very few obvious applications. There are a lot of 'apps' because they do something useful for us. But how many 'AIs' have you downloaded or used, i.e. things that are mostly AI? AI is an 'approach' not a 'solution'. So because of those things, we're not seeing a 'sea of developers' like wit iOS, rather, AI is caught up in the 'big companies'. Because 1) AI researchers are still expensive and 3) they are generally not profitable. Because of 2) there are few places regular people can practice AI. So you end up with massive companies that can support major R&D operations (Google, Amazon), and a ton of secondary companies with tons of data, but can barely manage their own IT, let alone, do 'fancy AI'. Think airlines, most banks, insurance companies. So the first real applications of AI come out of Google. We will start to see some financial products etc.. We'll some nice experimental apps, but generally not a lot of 'small companies winning hard on an AI play'. That said - you may see startups who are 90% regular company, but who have some AI tweaks that make them differentiated. FYI - a lot of AI is still mundane: Spotify playlists, Shopify support - we would not miss it if the AI were removed, it's mostly superficial gains. AI is a weird subject because 'it will change the world' but mostly by providing incremental jumps in some areas, that allow bigger jumps in others, like 'self driving cars' - the improved image recognition means cars cross a treshhold whereby they can start to understand enough around them so as to actually, possibly drive on their own.