Ask HN: Why do so many startups claim machine learning is their long game? • Hacker News Board

Re: Ask HN: Why do so many startups claim machine learning is their long game?

#21

Post by solidasparagus » Wed, Nov 13, 2019, 9:42 PM UTC

It is one of the two obvious economies of scale for pure software companies (along with network effects). I can't think of a company that should not have ML in their long-term plan.

Some people probably talk about it without truly understanding it (it can take years before you have enough data to build good ML models, particularly if there are seasonal trends), but I certainly wouldn't judge a company poorly for seeing ML as an important long-term moat. I would judge a young company that sees it as a short-term moat - if you can acquire valuable data quickly, so can your competitors and it's not really an economy of scale that gives you defensibility.

Re: Ask HN: Why do so many startups claim machine learning is their long game?

#22

Post by buboard » Wed, Nov 13, 2019, 9:42 PM UTC

stereolambda wrote: post #18

buboard wrote: post #5

It's like the gold rush except the gold (data) is easy to make, and very very cheap. hmm better sell shovels.

Yeah, people say that, but it's hard to see what selling shovels would amount to. I mean, hardware/cloud is commoditized, ML software is free. Maybe Uber for annotators /s (read, Mechanical Turk). (Of course, there is the ever more popular route of making services for enterprises wanting to outsource basic stuff.)

i think most ml jobs are for finding/cleaning data anyway . I suppose an "ebay of data" might be successful

Re: Ask HN: Why do so many startups claim machine learning is their long game?

#23

Post by [deleted] » Wed, Nov 13, 2019, 9:42 PM UTC

axegon_ wrote: post #15

peterwoerner wrote: post #12

Because there is a real moat with data ownership and pipelines. If you want to do any analysis you quickly find that learning to properly use scikit-learn and tensorflow (or your machine learning algorithm of your choice) is atleast an order of magnitude lower of work than getting the data. For instance, I wanted to build a machine learning algorithm which took simple data from the SEC filed 10-Q and 10-K, which are…

So much this!!! In all fairness, it doesn't matter what you pick up, you'll spend north of 80% of your time preparing and pre-processing data, which is almost always catastrophically tedious and boring. Annoyingly that also applies to publicly available datasets - pre-processing is still most of the work. For instance, the tensorflow team invested a lot of time and effort into tf.data for that reason but imo it doesn…

[deleted]

Re: Ask HN: Why do so many startups claim machine learning is their long game?

#24

Post by tetha » Wed, Nov 13, 2019, 9:44 PM UTC

Ugh. Maybe I'm in a different world by now, but I dislike such statements on multiple levels.

> This typically comes with assertions such as "data is the new oil" and "once we have our dataset and models the Big Tech shops will have no choice but to acquire us".

Maybe it's me, but I dislike the attitude to work to be acquired. Interestingly, this is a rift I see quite a bit if I interview more development oriented guys, or more infrastructure oriented guys. Tell me I'm wrong, but development oriented guys tend to be more fast paced and care less about long-term impacts. Infra inclined guys tend to be slower paced, but longer-term oriented. Build something to last and generate value for a long time.

> 2. Is it reasonable to be highly skeptical of startups that make these claims, seeing it as a sign they have no real vision for their product?

From my B2B experience over the last few years, and working towards a stable business relationship with large European enterprises, yes. My current workplace is moving into the position of becoming a cutting edge provider in our place of the world. This is a point where machine learning and AI becomes interesting.

However, we didn't get here by fancy models and AI. We got here by providing good customer support, rock-solid SaaS operation, delivering the right features, strong professional services, and none of those features were AI. It's been good, reliable grunt work.

Different forms of AI are currently becoming relevant to our customers, because we have customers that handle 5k tickets per day with our systems and they have 3-4 people just classifying tickets non-stop. We have customers with 30k - 40k articles in their knowledge base, partially redundant, partially conflicting.

This is why we entered a relationship with a university researching natural language processing among other - and they will provide us with a big selling point in the future. And they are profiting from this relationship as well, because they are getting large, real world data sets they couldn't access otherwise. Even with a good amount of pre-processing by the different product teams.

But as I maintain, nothing of that form has brought us where we are.

Re: Ask HN: Why do so many startups claim machine learning is their long game?

#25

Post by solidasparagus » Wed, Nov 13, 2019, 9:44 PM UTC

justapassenger wrote: post #13

tlb wrote: post #4

There are surely some startups for which this is bullshit. But the good version of it is: - take some valuable task that's never been successfully automated before - do it manually (and expensively) for a while to acquire data - build an automated system with some combination of regular software and ML models trained on the data - now you can do a valuable task for free - scale up and profit The risk is that it's har…

> The risk is that it's hard to guess how much data you'll need to train an accurate, automated model. Maybe it's very large, and you can't keep doing it manually long enough to get there. Maybe it's very small and lots of companies will automate the same task and you won't have any advantage. I think you forgot most important option. It may not be a data problem. You may have all the data in the world and still not…

I can't think of a company that wouldn't have valuable data. A core use of ML is reducing costs by making better spending decisions/reducing waste and that is relevant to almost every company I think.

Re: Ask HN: Why do so many startups claim machine learning is their long game?

#26

Post by LoSboccacc » Wed, Nov 13, 2019, 9:48 PM UTC

because VC money is hunting for ai startups, so everyone does what they did when it was data analysis, predictive intelligence, augmented reality and whatnot, they attach the buzzwords to the pitch in hope to get a foot in

Re: Ask HN: Why do so many startups claim machine learning is their long game?

#27

Post by papln » Wed, Nov 13, 2019, 9:51 PM UTC

peterwoerner wrote: post #12

Because there is a real moat with data ownership and pipelines. If you want to do any analysis you quickly find that learning to properly use scikit-learn and tensorflow (or your machine learning algorithm of your choice) is atleast an order of magnitude lower of work than getting the data. For instance, I wanted to build a machine learning algorithm which took simple data from the SEC filed 10-Q and 10-K, which are…

> data ownership and pipelines

not ML.

By your argument, the money is in data hoarding and brokering, and renting that data (with DRM) to ML outfits, not doing ML.

Anyway, cleaning dirty data isn't execeptionally hard, it's just boring work; getting the raw data is the hard part.

Re: Ask HN: Why do so many startups claim machine learning is their long game?

#28

Post by mchen076 » Wed, Nov 13, 2019, 9:54 PM UTC

stereolambda wrote: post #18

buboard wrote: post #5

It's like the gold rush except the gold (data) is easy to make, and very very cheap. hmm better sell shovels.

Yeah, people say that, but it's hard to see what selling shovels would amount to. I mean, hardware/cloud is commoditized, ML software is free. Maybe Uber for annotators /s (read, Mechanical Turk). (Of course, there is the ever more popular route of making services for enterprises wanting to outsource basic stuff.)

Realistically the "shovels" are the compute services. And the shovel makers are really doing well.

What you don't really see is the ML startups making billions yet. What you do however see is companies like Microsoft with their Azure product and Amazon with AWS Compute making bank off the tech startups doing ML. I really think the people who are cashing in the most with the ML craze are the cloud/compute providers.

I would be curious to see just how much VC money is being passed indirectly to MS Azure through ML startups.

Re: Ask HN: Why do so many startups claim machine learning is their long game?

#29

Post by variaga » Wed, Nov 13, 2019, 10:02 PM UTC

solidasparagus wrote: post #25

justapassenger wrote: post #13

Earlier quoted context omitted.

> The risk is that it's hard to guess how much data you'll need to train an accurate, automated model. Maybe it's very large, and you can't keep doing it manually long enough to get there. Maybe it's very small and lots of companies will automate the same task and you won't have any advantage. I think you forgot most important option. It may not be a data problem. You may have all the data in the world and still not…

I can't think of a company that wouldn't have valuable data. A core use of ML is reducing costs by making better spending decisions/reducing waste and that is relevant to almost every company I think.

Recurring expenses (i.e. the kind that will generate enough data to adequately train a ML system) are already handled pretty well by most businesses through traditional (non-ML) methods. Operations Research has been a thing for 60+ years. Businesses that don't handle their recurring expenses well now are likely to have organizational issues (e.g. senior management that ignores the advice of their reports on how to do things) that will prevent them from doing so even if ML is added to the mix.

The place where businesses get in real trouble (and hence would see significant value from better decision making) is when doing things that they have _not_ done 10K+ times before. Things like "Should we expand into $NEW_MARKET?". ML isn't going to help them with that, because there will not be any useful historical data to train them with.

Re: Ask HN: Why do so many startups claim machine learning is their long game?

#30

Post by variaga » Wed, Nov 13, 2019, 10:10 PM UTC

peterwoerner wrote: post #12

Because there is a real moat with data ownership and pipelines. If you want to do any analysis you quickly find that learning to properly use scikit-learn and tensorflow (or your machine learning algorithm of your choice) is atleast an order of magnitude lower of work than getting the data. For instance, I wanted to build a machine learning algorithm which took simple data from the SEC filed 10-Q and 10-K, which are…

Agree 100% - the single largest problem with applying ML to anything is getting (1) enough (2) accurate (3) representative (4) correctly labeled data to train and validate the models.

Everything else is noise. For any proposed ML project, the first question you should ask is "Where are you going to get the training data?"

If someone tells you "Oh, getting data will be the easy part", back away very slowly :)