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How Clara Labs (YC S14) Is Using Humans to Build AI

blog.claralabs.com

11–20 of 34 posts

Re: How Clara Labs (YC S14) Is Using Humans to Build AI

#11

Hi! I'm the author of the post and happy to answer any specific questions here.

How much human tagging time does it take to process 1000 meeting requests or more general how does the annotation side work?

Do you have people overlap items since there can be a fatigue issue as you mentioned in the post?

Re: How Clara Labs (YC S14) Is Using Humans to Build AI

#12
post #11

Hi! I'm the author of the post and happy to answer any specific questions here.

How much human tagging time does it take to process 1000 meeting requests or more general how does the annotation side work? Do you have people overlap items since there can be a fatigue issue as you mentioned in the post?

Clara has a 1-hr SLA for the processing of an incoming message. While I cannot give numbers on the speed of annotators (or volume), I can say that our platform is designed to enable quick and accurate work via incentive mechanisms. We avoid fatigue in part by making it easy for CRAs to navigate and work with data. Wrt to overlapping annotator schemes, these are known to be effective. We'll be writing more about how our human backend works in future posts.

Re: How Clara Labs (YC S14) Is Using Humans to Build AI

#13
post #2

This is SO interesting! I am surprised by how companies are trying to solve AI problems... Machines normally take you 80-90% there, and using humans for that 10-20% left is an amazing idea. It's also a great monetization model for many companies.!!! Also, Clara is awesome :)

Not only that, but there are a lot of domains where you don't need to be terribly accurate in the first place, because existing methods are so terrible. A great example I encountered recently is identifying people who may be interested in switching jobs soon. How much do you think recruiters and companies would pay to increase their pool of interested candidates 5-10%?

Re: How Clara Labs (YC S14) Is Using Humans to Build AI

#16
post #11

Earlier quoted context omitted.

How much human tagging time does it take to process 1000 meeting requests or more general how does the annotation side work? Do you have people overlap items since there can be a fatigue issue as you mentioned in the post?

Clara has a 1-hr SLA for the processing of an incoming message. While I cannot give numbers on the speed of annotators (or volume), I can say that our platform is designed to enable quick and accurate work via incentive mechanisms. We avoid fatigue in part by making it easy for CRAs to navigate and work with data. Wrt to overlapping annotator schemes, these are known to be effective. We'll be writing more about how o…

Thanks, in the future post would also be interested in the trade offs between building internal system vs. using a 3rd party (maybe you do?) like mechanical turk and the incentive structure.

Re: How Clara Labs (YC S14) Is Using Humans to Build AI

#17
post #16

Earlier quoted context omitted.

Clara has a 1-hr SLA for the processing of an incoming message. While I cannot give numbers on the speed of annotators (or volume), I can say that our platform is designed to enable quick and accurate work via incentive mechanisms. We avoid fatigue in part by making it easy for CRAs to navigate and work with data. Wrt to overlapping annotator schemes, these are known to be effective. We'll be writing more about how o…

Thanks, in the future post would also be interested in the trade offs between building internal system vs. using a 3rd party (maybe you do?) like mechanical turk and the incentive structure.

This is noted, thanks!
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