Scale (YC S16) Raises $100M from Accel and Founders Fund at $1B Valuation • Hacker News Board

Re: Scale (YC S16) Raises $100M from Accel and Founders Fund at $1B Valuation

#41

Post by codesushi42 » Tue, Aug 06, 2019, 1:09 AM UTC

ChuckMcM wrote: post #4

I really dislike this sort of journalism. Theranos was founded by a 19 year old too. That one didn't work out so well. Was it because the founder was so young? The board so oblivious? (a bit of both if you read the book) What does it really matter how "old" the founder is, does the business have a workable business plan? Can it be profitable? Do people pay enough money for its goods and services to return a net incom…

Welcome to the bubble.

Don't fret, it is not likely to last much longer.

Re: Scale (YC S16) Raises $100M from Accel and Founders Fund at $1B Valuation

#42

Post by troquerre » Tue, Aug 06, 2019, 1:10 AM UTC

Congrats Alex! It's been amazing watching your journey from the first pivot to Scale to now.

Re: Scale (YC S16) Raises $100M from Accel and Founders Fund at $1B Valuation

#43

Post by fnbr » Tue, Aug 06, 2019, 1:15 AM UTC

khannate wrote: post #35

I'm a little confused about what the business model here is. It sounds like they are selling labeled data to companies, and doing this by "label[ing] most of the objects automatically" and then having humans review these labels. So does this mean they are using some unsupervised method to label data, and then selling that to people who want to train supervised models? Why aren't they instead just beating out the peop…

Presumably they have a supervised model that they've trained on all their labeled data so far (possibly pooled across clients). They'd use this to estimate labels for their data, and then have humans correct it. They're basically doing the standard supervised data training loop.

If I had to guess, the long term plan probably is to move up the stack and sell the models to their clients.

Re: Scale (YC S16) Raises $100M from Accel and Founders Fund at $1B Valuation

#44

Post by samclearman » Tue, Aug 06, 2019, 1:32 AM UTC

sindergirl wrote: post #32

In essence, the company pays third worlders a pittance to transfer humanity's skills to the machine. The skill transfer is limited to what can be done with a mouse and screen, but since that's where most human ability is currently manifested, it's hardly a limitation. What happens to the serfs once the transfer is complete? Do they realize they are exchanging temporary wages for eternal futility? I like how the inves…

I work at Scale - I've met a bunch of people that work on our platform, and seeing the impact that it's had on their lives is actually a huge source of inspiration to me. There's a writeup highlighting some of their stories at https://scale.com/blog/positive-externalities - based on my personal experience, I can say that it's not bullshit.

Re: Scale (YC S16) Raises $100M from Accel and Founders Fund at $1B Valuation

#45

Post by scottrogers86 » Tue, Aug 06, 2019, 1:42 AM UTC

whats made you more successful than other players in this space?

Re: Scale (YC S16) Raises $100M from Accel and Founders Fund at $1B Valuation

#46

Post by sindergirl » Tue, Aug 06, 2019, 1:44 AM UTC

Reminds of Sleep Delaer: https://en.wikipedia.org/wiki/Sleep_Dealer

Re: Scale (YC S16) Raises $100M from Accel and Founders Fund at $1B Valuation

#47

Post by streetcat1 » Tue, Aug 06, 2019, 2:10 AM UTC

So what is your competitive advantage? I.e. what cannot be replicated?

From a technical perspective, can someone just post labelling task to mechanical Turk? what is the difference here?

Re: Scale (YC S16) Raises $100M from Accel and Founders Fund at $1B Valuation

#48

Post by notrlyai » Tue, Aug 06, 2019, 2:12 AM UTC

Big deal. Exploiting slave labor is hardly a new idea.

Re: Scale (YC S16) Raises $100M from Accel and Founders Fund at $1B Valuation

#49

Post by plinkplonk » Tue, Aug 06, 2019, 2:18 AM UTC

kareemsabri wrote: post #25

Bit of an AI novice here, I did Norvig's course a few years ago and never worked in the field, but how can a machine take a "first pass" at labelling without being trained? What information is it using to apply labels to the first set of data? How does this approach differ from a conventional classifier? Would the initial guesses essentially be random?

The business model is, initially, selling human labeling services to owners of data (like Uber or Google), using third world cheap labor to keep costs low. This is very much like a call center service.

Once a sufficiently large corpus of human labeled data is available (across clients and datasets probably), that labeled data is used to train a 'first pass' labeling system.

It then becomes a virtuous cycle. Now the labeling is done in two phases. The first pass system makes its best guess, which is then reviewed by the existing human work force. Over time the first pass labeler gets better and better, till only very tricky/borderline cases need human intervention.

The end game is anyone's guess. Pretty clever biz model hack.

Re: Scale (YC S16) Raises $100M from Accel and Founders Fund at $1B Valuation

#50

Post by streetcat1 » Tue, Aug 06, 2019, 2:21 AM UTC

kareemsabri wrote: post #25

Bit of an AI novice here, I did Norvig's course a few years ago and never worked in the field, but how can a machine take a "first pass" at labelling without being trained? What information is it using to apply labels to the first set of data? How does this approach differ from a conventional classifier? Would the initial guesses essentially be random?

So they have a set of pretrained model that they are running as first pass and either verify the result with humans, or filter out the low confidence results and give them to humans.

Note that this creates a positive feedback loop. I.e. as they get more results they can improve the initial stage.