Machine Learning has been used to increase profit for businesses and solve tough problems in research, but what if ML was being harnessed to help people? Well, there are few things that affect people’s lives more than work.
On this week’s episode, Fred Goff, CEO of Jobcase, talks about harnessing the power of ML to empower workers and build community, with some powerful economic and political repercussions.
Read on for the transcript!
Hey, it’s Monte. If you'd like to see my newest project, a real-time feature store, we're holding a hands-on workshop at TWiMLCon on January 28, where you can actually play with it. Learn more at mlminutes.com/events.
Hi, I’m Monte Zweben, CEO of Splice Machine. You’re listening to ML Minutes, where cutting-edge thought leaders discuss Machine Learning in one minute or less.
This episode, our guest is Fred Goff, founder and CEO of Jobcase, a social media platform dedicated to empowering workers. Fred is a leading voice on issues related to worker advocacy, and how AI will impact the future of work. Welcome, Fred.
Thanks, Monte. Great to be here.
Well, tell us about your journey. How did you get to where you are now?
So look, where I started out was I'm a first generation college grad that just had the good fortune of finding his way to Carnegie Mellon. I then ended up in New York, I had the good fortune of finding my way into Wall Street and managing about a billion dollars. I launched a machine learning based hedge fund, and eventually then ported the technology that was underneath that into the consumer internet space. But I think what's most relevant for the journey is a moment: In the consumer internet space, I found myself realizing that most people are talking data, and they mean people. And I've been in rooms where they talk about commodity labor, and I think that's people. That's my cousin, my dad, and people are not commodities. And I started thinking about how could we leverage AI and machine learning and big data for individual people? How do we be a complement that while Neil Bussery is finding actionable insight and big data, to help companies, we can find it to help individual people. And so that's how I find myself to founding Jobcase and running this company.
That's fantastic. There's been many uses of data, and machine learning to help companies, help enterprises, help governments. And I love the fact that you're focused on helping people.
Tell us more. What's the problem you're trying to solve with Jobcase?
So in 2013, Oxford came out with a study that McKinsey amplified that said 47% of jobs can be automated through computer models, computerization, that's pretty scary. That's scary to economic systems, that has fallout into political systems. When we got focused on how you manage this whole future of work. Increasingly, we realize that people don't really have the tools or a community of other people to help them go forward. But if we could have a platform for that, the same elements that might be incredibly scary, the elements that might take you as a cashier and displace your job, if you actually lean into that mode, it can be incredibly empowering, free online education to teach you skills that can come with a salary and benefits and etc. But how are you going to solve that? So we decided that if we could form and democratize data to help people help themselves navigate the future of work, this wouldn't be just important for them, but it actually has system wide economic impact.
Excellent. So you're helping people navigate work? Maybe you can give us a day in the life of a job case member?
Yes, well, I'll give you a day in the life today, as I'm sitting here talking to you still in the middle of the pandemic. We have an office manager, say, Teresa, who's lost her job recently. And she's she's over 40. And let me tell you, that is not old, but you face ageism. And so what can she do to move forward? She'll come to Jobcase and start talking about this in our community. Other people will help her navigate how to deal with ageism. In our community, other people will will bolster her emotionally in terms of you can do this, our team is going to look at her behavior on site is going to look at what kind of jobs she's clicking on are going to find the kind of job alerts to help with what she needs. Now in Teresa's case, it's not five year career navigation or education to get to a new career, if she needs a job now, and we will help her land that with our internal technology and all the employers that are active on our site, and she'll be encouraged to achieve it because of the community that supports her.
Terrific. So you're really helping people, especially in today's economic constraints and economic pressures, find positions when they may be left out of work. You're doing it through community and not just job listings. Maybe you can tell our listeners how community helps.
Sure Monte, I'd love to. A lot of your listeners are probably very familiar with LinkedIn, LinkedIn is great for knowledge workers to connect with their first and second degree connections. And they leverage those to figure out what to do next about opportunities that are open and how to pursue it. You know, for a lot of folks in the workplace, all those that we celebrate today as frontline workers, medical billing specialists, and hospitals, waiters, everybody bringing the packages to your door: they're not on LinkedIn. And as they look to figure out what to do next, it may not be their first and second degree connections that'll help them; it may be, but there's frequency of job changes, and keeping in touch with people is hard. Or it may be that you decide you want to be a nurse, and you don't have anybody you know, that can do that. Having a community that can help you navigate that and support you and say you can do it is incredibly, incredibly important. And so those are the aspects just at the beginning of how community and network helps. Jeff Wiener talks about the networking gap in this country holding people back and being part of the economic injustice. Fundamentally, we're fixing the networking gap problem.
That's fantastic. Using a community social media site, to connect workers with people that can help them sounds like a fantastic mission. Now, this is ML Minutes, so let's get to the machine learning. Where do you apply models in your community to help workers and help employers?
Monte, ML is at the core of everything we do. When when we had roots as a hedge fund we were offering and inventing algorithms, right now our philosophy is to be at the cutting-edge and be the best at the world at harnessing ML and AI for the benefit of our members. So we harness deep learning networks like TensorFlow to better predict lifetime value of a member, which helps informs our bids on how we advertise opportunities. We shamelessly steal NLP practices to embed in clever ways into into recommender systems. Regarding recommender systems, it can be off the shelf, I mean, leveraging Redshift for simple Jaccard distance calculations on our collaborative filtering that help us understand when someone says, I've been a cashier, and it's up to us to decide how do we what jobs are relevant for that. It's not just cashier, right, as you get to lower wage jobs, it's a very broad, diverse set of jobs that might be applicable. And leveraging ml to understand implicit and explicit signals to land relevance is critically important.
That's great. So you seem to have deployed many different technologies from data warehousing technologies like Redshift, all the way through to deep learning, like with TensorFlow, and maybe some of the new NLP approaches that are attention-based. This is a robust deployment of AI. Can you identify one specific challenge that you faced along the way of deploying ML?
We've both been around long enough that we have memory, where your gating factors are compute power, or your gating factor as storage, or maybe even the cleverness of the heuristics or algorithms themselves. Our problem the HR landscape is data, old, good old fashioned data. It's not stitched together, most people's work histories are in silos at individual companies. There's no common ontology to stitch together. And I'm not a believer in unstructured data having a lot of value, despite having seen business models for 20 or 30 years that it does. So if you think about the--it's not so much in the deployment of models, but in when we try to form how do we forecast the right career navigation? And as you try to find whether it's collaborative building, whether it's an expert system, whatever recommender system you have, we have difficulty in stitching together the data all the way down to the end result that we're really looking for: hires, retentions, growth. And so fundamentally, at the end of the day, it's not a technology problem, it's not a deployment or engineering problem. It's still data problem. And that's what we're focused on.