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Transcript:

Henrik de Gyor: This is Tagging.tech. I’m Henrik de Gyor. Today I’m speaking with Jason Chicola.

Jason, how are you?

Jason Chicola: Doing great, Henrik. Thanks a lot for taking the time.

Henrik de Gyor: Jason, who are you and what do you do?

Jason Chicola: I’m the founder of Rev.com. Rev is building the world’s largest platform for work from home jobs. Our mission is to create millions of work from home jobs. Today, we have people working on five types of work. Jobs they could do in their pajamas. And the main ones are audio transcription and closed captioning. Several of my co-founders and I were early employees at Upwork which is the largest marketplace for work at home jobs. Rev takes a different approach than Upwork. With Rev, we guarantee quality which means that the task of managing a remote freelancer, hiring the right one is something that our platform excels at. And so what that means is our customers have a very easy to consume service. You can think of it… you can think of us as Uber for work at home jobs. So if you wanted to come to us to get say for example this call transcribed as you know as a customer all you have to do is upload an audio file to a website and then couple hours later, you’ll get back the transcript. Now behind the scenes, there is an army of freelancers that are doing that work and we have built our technology to make their lives easier and make them productive. If I zoom out from all of this, I look at the world and see a lot of people who are sitting in cubicles that probably shouldn’t have to, while he was in traffic and it shouldn’t have to and I look at what are all the kinds of jobs you will do today at a computer. How many of those jobs need to be done in cube farm. How many of them could be done from home? We think many of them can be done from home. And our mission is to give more people the opportunity and the freedom to work from home which allows them not only to choose the location but also gives people more control over their lives because they can decide whether they want to be the morning versus early afternoon. It means you’re not tied to a single boss or employer. It means that you can work on one skill on Monday and a different skill on go on Tuesday and go surfing or hiking on Wednesday, if you feel it. So that’s how we think about our business is really centered on giving people this freedom that comes when they can be their boss and work from home. And as a segue to some of your next questions that you and I discussed the past, as we got deeper and deeper into creating jobs for transcriptionists, we have invested in technology to make their jobs easier, to make them more productive. And that has led us to develop some competency and familiarity with what you’re calling here AI transcription which means using a computer to transcribe audio so that what I call a relatively new area for us, an important area, especially in light of people being familiar with Amazon Alexa and Apple’s Siri. So that’s a new small business, but the core is giving people work they can do with a computer. Most of that work today listening and typing.

Henrik de Gyor: Jason, what are the biggest challenges and successes you’ve seen with AI Transcription?

Jason Chicola: It’s really early to judge that. I can give you a specific example in a moment. But it’s a little bit like asking someone today what are the biggest challenges and successes of self-driving cars. The answer is I think business cases that they have been small but possible successes in the future could be massive. I really believe that we’re truly… you’re not even in the first inning. Maybe we’re the warm-up for the first inning of this game and I think is going to be a pretty exciting decade ahead of us as computers have gotten better, as more audio is captured in digital formats and companies like Rev are innovating in a bunch of areas. Our success today in this area has been… we had success, but it’s been at the fringes of our business so I’ll give you a specific example: when the Rev transcriptionist type out an audio file like somebody might hear about this phone call, some customers request time stamps and the humans part of their job is to go into note for example at the end of every minute, this event occurred in three minutes, this event occurred four minutes or so forth. That was an additional task they performed manually while they did their job. We automated that using what you could call AI transcription. So now not only time stamps are inserted automatically but every single word is marked by the AI as when it was sent. So literally for every single word, we know this word occurred at 4:38 and get that word occurred at 5:02 . So that’s something that we’ve done that automated something previously done manually and it actually made it a much better experience for the customer because the timestamps are more accurate. That something we already have today. The challenge… the challenge list is longer. The biggest challenge to be aware of when it comes to automated transcription is that it’s garbage in, garbage out. Other people say you can’t make chicken salad out of chicken [****] that if you go to Starbucks and you sit outside by a noisy street and you record an interview with someone who you’re talking to for a book and you submit it to some automated engine you’re not going to get back anything that is very good. And that’s I mean it’s obvious why that is, but the quality of speech recognition depends I would say on three or four key factors other than the quality of the [speech recognition] engine itself. One is background noise. The less the better. Another is accents. The less the better. Another is how clearly the person is speaking. Are they annunciating? Are they slurring their words together? Are they speaking really quickly? Those tend to be the major factors. There is probably another one related to background noise which comes down to the quality of your microphone. How far you are from the microphone. You are a podcaster, so you probably know far more about how record clear audio than most people do. Most people throw an iPhone onto a table next to somebody else’s eating a bag of Doritos. [laugh] So you have great audio of someone eating a bag of Doritos which causes problems downstream and some of those people because they don’t think about it will say “Hey, I really annoyed. You didn’t get this word right.” And that’s because somebody was eating a bag of Doritos during the time that word was said. So part of our job… as we try to get better at helping people transcribing quickly and cheaply part of our job is to help customers understand that you need to record good audio if you want to get to get a good outcome.

Henrik de Gyor: Jason, of January 2018, how much of the transcription work is completed by people versus machines?

Jason Chicola: Are you talking to the work that Rev does?

Henrik de Gyor: Sure.

Jason Chicola: Depends on how you slice it, but I’ll say 99% percent people, 1% machine.

Henrik de Gyor: Fair.

Jason Chicola: We actually have…I’ll be a little more clear on that, we recently released a new service called Temi. Temi.com. That is an automated transcription service where people are not doing the work. Machines are and then are core service rev.com is done basically entirely by people. We believe that that’s required to deliver the right level of accuracy. This is I don’t answer your next questions but we clearly see these two blending and merging a little more over time, but today if you want to get good accuracy you need people to do it. If I give you kind of the external contacts in an earnings call used to be transcribed for Wall Street analysts and machine does it and they make a mistake on, you know, a key number or you know, the CFO said that something happened or something didn’t happen, that’s a big problem. Or if a movie is captioned for HBO. Game of Thrones is captioned by HBO. Those captions need to be right. So any use case where people want a transcript that is accurate today, they need to have people in the loop.</p