#6 – Laura Montoya, Founder of Accel.AI

Laura Montoya, Founder of Accel.AI (Artificial Intelligence)

Laura Montoya, Founder of Accel.AI (Artificial Intelligence)Laura Montoya founded Accel.AI to offer artificial intelligence & deep learning workshops in Oakland, CA.

Selected Topics: Artificial intelligence, joining a tech community, cultural identity, and persistence & perseverance.

About Laura: Laura is the Founder & CEO of Accel.AI, the first accelerated program that trains artificial intelligence engineers. Laura graduated from high school at 16 and worked full time while in college. She is the first in her family to earn a Bachelor’s degree and completed a self-crafted major in Biology, Physical Science, Human Development & Diversity at Eastern Michigan University. Following her software engineering education at DevBootcamp, she worked at Intuit, became a Certified Scrum Master and stepped up as Chapter Director for Women Who Code. She also organizes meetings for the Deep Learning Enthusiasts Meetup group and TechLore, a book club exploring social issues through the lens of technology. Connect with her on LinkedIn or email her at info@accel.ai


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Mindset: The New Psychology of Success by Carol Dweck

Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy by Cathy O’Neil


Note: The text below reflects constructive editing of the published audio for clarity and flow. Time stamps indicate a change of topic.

Erin Allard: In today’s episode we catch up with Laura Montoya, founder of Accel.AI, an artificial intelligence education program based in Oakland. Don’t miss Laura’s episode if you’re interested in artificial intelligence, finding your way into a tech career, funding the learning you need to do to get yourself there, and why it’s important to be persistent and dedicated. And if you’re a person who has struggled with the challenges of cultural identity and assimilation, you may find that Laura’s story resonates.

Welcome to another episode of the WITtalks Podcast. I’m Erin, your Producer and Co-Host. Our beloved co-host Tammy is not with us today. She is sick, but I am here today with Laura Montoya who is the founder of Accel.AI, and I will let her tell you all about what that means in the course of the podcast. Say Hi Laura!

Laura Montoya: Hi everyone.

Erin: Just to set the scene a little bit, we are in Laura’s co-working space in downtown Oakland, it’s raining outside and there are other folks working in the office, so we’re getting a true startup environment here.

Laura: Yes.

Erin: Laura and I were just talking before we started recording about how we are both coding boot camp graduates. It’s different being a coding boot camp grad as opposed to someone who came out of a traditional CS program, so I feel like I already have this connection with you.

Laura: Yes, I agree.

Erin: So to get us going here, maybe you could tell us a bit about your life thus far, whatever is relevant for our conversation today and whatever will help us have a better understanding of the Laura who exists now.

Laura: Yeah, so I want to go a little further back. I think that in order to understand part of the work and why I’m doing it the way I’m doing it now—and my mission—if you understand a little bit of the way I was raised and, you know, those circumstances.

Erin: Please, yeah.

Laura: So, I did come from kind of an unstable household when I was growing up. I was raised by a single mother. My father passed away when was about two years old. I also have a sister. So my mom had two kids and I know it was hard for her as we were growing up. She was a very strong and empowered woman. She was in the financial services industry, had always wanted to run her own business as well, but because of circumstances that was very challenging for her to do.

I always looked up to her drive and her ambition, and she has been one of my biggest role models because of that. But again, unfortunately, things weren’t always the way we’d hoped they would be. We tended to move a lot, oftentimes annually. Things were unstable and I felt that was kind of hard growing up.

Erin: And in the course of moving a lot did you change schools a lot as well?

Laura: Yes, that’s correct, we moved at least twenty times before I moved out on my own. I feel like a lot of kids have that experience where they have a best friend growing up, over a certain number of years, and I don’t think I really had that. I was moving constantly. I’m not quite sure how that affected my interactions with people today, but I’m sure it has had some impact as well.

Erin: Yeah, of course. So Laura, with all this moving around, were you primarily moving around within one city, within one state or were you much more mobile than that?

Laura: Yes. I was actually born in California, in Santa Monica. I remember I was very young and my dad got sick, so we moved across the country to Miami, Florida. My mom chose to do that because her parents were there at the time so they could help take care of him. Things didn’t go as planned and he ended up passing away. My grandparents passed away shortly after that.

Erin: Oh my gosh.

Laura: So we ended up moving a lot within that area, within Miami, oftentimes it was rougher neighborhoods so we lived in Hialeah, we lived in Opa-locka, we lived in Carol City. I don’t know if you’re familiar with any of those.

Erin: A little bit familiar with Miami but not the others.

Laura: So those neighborhoods were what a lot of people would consider the rougher or “ghetto” environments. I lived in a lot of urban sprawl. I lived with a lot of immigrants and I grew up mostly around Cubans and Haitians and that’s who’s there in Miami, especially in those areas. That brought in a lot of different aspects of my personality and helped develop who I am today. I also struggled a lot with those parts of my identity as well. But maybe we can go into that more later.

Erin: And I know that Tammy also moved around a lot as a kid, and something she has said to me in the past is that she went to three different schools for first grade. Over the course of her childhood and moving around so much, she had to find some centering component to her life because she felt like the only constant thing she had in her life was herself. So I wonder if you also had to come up with some sort of centering element as a kid to keep your sanity intact.

Laura: Yeah, I did. I would say I was probably a little less social when I was a child and I found a lot of ways to creatively entertain myself. My mom used to say I had a really great imagination because I could spend hours on my own entertaining myself without any need for—

Erin: Imaginations are great!

Laura: Yeah, yeah. I especially was very interested in animals when I was young and for a little while I wanted to be a veterinarian. Being in Miami was great because you could run around outside.

Erin: OH MY GOSH, all those bugs and reptiles!

Laura: Oh yes, actually I was kind of obsessed with reptiles when I was a kid. I would run around and catch lizards and play with them and keep them. I would actually get them to clasp on to my ear with their mouths and have them dangle like earrings.

Erin: Oh my God!

Laura: That was my favorite pastime…

Erin: That is crazy!

Laura: Yeah.

Erin: Please tell us there are pictures of this!

Laura: I really wish I had some pictures. I think that would be amazing. Unfortunately, I don’t. I fell in love with reptiles for a long time, reptiles and amphibians. That led to what I wanted to study when I got older. I think that connection with animals and empathy with animals is a different way of connecting. I think it just didn’t matter so much if I connected to other people because I was moving around so much.

And then also I think moving around affected my ability to reinvent myself. Every time you move you are with a new group of people, and if there’s something from your past that you don’t necessarily like or want to hold onto anymore, you could say to yourself, “I don’t have to be that person.” I enjoyed that part. I was able to say to myself, “This time I’m going to try on something new and be this new person.” I found that a little bit empowering.

Erin: Absolutely.

Laura: Yeah.

Erin: And so you grow up in Miami, you move around a lot, you really love animals, what happens after that? You graduate high school, I presume, and you go off to college.

Laura: Yeah, so the story’s not quite that smooth. [Laughter]

Erin: They never are, I made a presumption. [Laughter]

Laura: So, we ended up moving to Michigan when I was fourteen or fifteen years old. My mom was a stockbroker at the time and she was trying to make it on her own as an independent. And that’s about the time that 9/11 hit and the stock market crashed. She was really struggling and she couldn’t do it. And fortunately the company that she was contracted with, they had a position available in their headquarters in Michigan. It was for compliance regulations and they said to her, “Hey, come move up here. You can work in our headquarters and it will be much more stable for you.”

And she agreed, so that was our first really big move—other than when we were young because otherwise it was all within Florida. We moved to Michigan and I really wanted that transition. I looked forward to it a lot. This comes back to the identity piece: When I was really young I kind of rejected my Hispanic ethnicity. You know, society imbues you so much with this idea about the white purity of everything, so I refused to speak Spanish when I was a child. I didn’t want an accent. I thought that the only way I would be accepted or successful in life was if I was white.

Erin: Those are pretty intense feelings for a child to have.

Laura: Yeah.

Erin: For anyone to have, really, but especially a kid.

Laura: Yeah, and so when I thought of this idea to move to Michigan, I was like, “Oh great! I can go be with all the white people!” [Laughter] And that sounds horrible, but—

Erin: If Tammy were here right now, she’d probably chime in with something along the lines of being a biracial child growing up in a military family, moving around a lot.

Laura: Yeah, I can totally relate to that. I felt that whiteness was valued a lot more [than other ethnicities]. My hair is naturally a little bit wavy and can be very frizzy, especially in the Miami humidity. There were several people when I was young who asked, “Why don’t you blow dry and straighten your hair? It would be so much prettier.” They would constantly relate beauty to this very white or Caucasian affectation and that always stuck with me. In the media it’s very much portrayed that white is what is attractive. Even in the way you talk, people don’t take you seriously if you don’t speak very fluently [in English].

There was something else that really stuck with me in regards to speaking a certain way. [When I transferred to one] of the schools I had transferred to, I had a little bit of an accent from being around so many Spanish-speaking people all the time. Even though I could speak and read well, they decided to enroll me specifically in ESL courses. Even though I was born in the U.S., I could speak English, I could read English, yet they decided to enroll me ESL classes and hold me back a year.

Erin: Oh my gosh.

Laura: Yeah, and I think also because I was more internal. I was a shy child because of all the moving. They didn’t think that I could speak English well, and so I think that really impacted me negatively. At the time I wanted to fight against it so I decided, “I’m going to be a white person then. I’m going to make sure I only speak English and I’m going to project myself that way.”

In a lot of ways I did assimilate. I assimilated to the majority, to the dominant culture, because that’s what was stated for me. I think projecting myself in this way has made me more successful, but is that right? I don’t think so. I think that’s wrong. As an adult I’ve had to go back and do a lot of internal seeking—getting back in touch my roots, getting back in touch with my people—to find my identity. It’s been a whole journey.

So, my mom got this position and I started going to high school there. I thought it was great. I am lighter skinned and in a lot of ways I could pass for white, especially once I wasn’t in the sun all the time. I lost my tan, started dying my hair, then I started speaking very properly. I think I passed.

After a time I realized my mom still wasn’t doing very well in Michigan, even being at the company that she was with. At a certain point she had decided she wanted to move back to Miami but I did not want to do that. I felt like I was where I was supposed to be. I was finally, you know, “being a white person”.

Erin: And perhaps you also felt like you finally had a bit of stability.
Laura: Right, in a way, yes. And at this point I was between fifteen and sixteen years old. I realized that if I wanted to live my life the way I thought I should live my life, then I would have to move out on my own. So, I started working really hard. I took online courses, I took extra courses in regular school, I enrolled in summer school, I dual-enrolled in college and I graduated high school at sixteen. I took a full-time job and I told my mom, “You can move to Miami and I’m going to stay here. And I’m going to take care of myself.”

Erin: I’m sure that went over well.

Laura: I was always a very independent child and really rebellious for most of my life. Obviously, because I rebelled against my own culture. So I think in a way she understood—

Erin: And so you figured out how to graduate early, you got yourself a job, you worked full time, was it in your mind that you wanted to go to college or did you just want to work?

Laura: I had dually enrolled in college already in order to graduate high school early. And that school at the time was called Washtenaw Community College. It was just there in my backyard and fortunately it was actually also really good, one of the best community colleges in the U.S. which is great.

Erin: Wow.

Laura: That’s the other thing I really valued about moving to Michigan, their education system is way, way better than anything in Miami. I felt like within the time that I was [in Michigan] I was able to learn a lot more and fill in a lot of gaps. From there I was on that track where I thought I wanted to be a veterinarian, so I ended up getting into Michigan State University. I don’t know if you guys are familiar with it but a lot of people joke that it’s a “farm college” because they’re very well known for their veterinary program, specifically with agriculture.

I started going there for a little while and I took an internship at a veterinary hospital, and I realized it was not actually what I wanted to do with the rest of my life. I found that I had too much empathy for animals, to the point that seeing them put under and cut open and everything—it was too much for me.

Erin: And that’s why you take an internship first!

Laura: Yes! Yes. Also at the time I had started dating my partner and he was going to the University of Michigan. It was quite a distance for us to be apart and he was really in support of me when I moved out on my own. It was really hard to be away from him, because in a lot of ways he became part of my stability. He became my rock and my family once my actual biological family wasn’t there anymore.

So I decided to leave Michigan State and then enrolled in Eastern Michigan University, which was just around the corner from University of Michigan [where my partner was]. By then I had taken a lot of biology courses, a lot of chemistry courses, a lot of physical science courses, and I was also trying to explore [different things]. I didn’t know what I was going to do with my life because veterinary school wasn’t going to work out. I knew I didn’t want to be a doctor because I didn’t like people that much, but I did really enjoy animals still.

I considered research so I did some exploration there. I also considered psychology. I took a lot of psychology courses, a lot of social science and political science courses, so that’s how I ended up with the degree that I have, which is an interdisciplinary degree in biology, physical science, human development and diversity.

Erin: Wow, that sounds pretty cool.

Laura: Yeah, it is cool I think. From there I was looking into research. I then decided to take a position doing some research at Eastern and I worked in their spider lab.

Erin: Really? You were working with spiders.

Laura: Yeah, working with Spiders.

Erin: Wow.

Laura: I started helping with a study there in their spider lab, which is cool. They have a professor who’s really well known for it, actually.

Erin: I had no idea that was a thing.

Laura: But I guess it goes back to my comfortability with reptiles and amphibians and then all the other creepy crawly creatures.

Erin: And actually… it is occurring to me now, not only did you have a deep empathy for animals, you particularly had empathy for animals that other people were terrified of.

Laura: Right, yes.

Erin: The reptiles and amphibians and spiders.

Laura: Yes, I’m not quite sure how that happened but I enjoyed it. So I did that [research] and the more I looked into doing research, the more I realized I didn’t like the way research is structured in academia: The further you go—get your master’s and PhD—a lot of it is very hierarchical, a lot of it is very political, most of the time you’re relying on grants and can’t necessarily even study the things that you want to study or are interested in. At that time I realized well maybe research isn’t the path that I would go into. I decided that it was probably time to just get more real world industry experience.

I was also still working full time to support myself while I was going to college. So, I started out working for an insurance agency, first being a receptionist and filing paperwork and stuff. I was so young at the time. Then I moved beyond that and I started working with clients and doing some sales. I actually became really professional and knowledgeable about insurance.

Erin: Awesome.

Laura: Yeah, and I considered doing that for a second and then realized no one really likes insurance and I didn’t really like insurance. It was one of those necessary things that you have to have, so I said, “Okay, this isn’t going to work for me either.”

Erin: No one dreams as a child of being an insurance agent but for some people it works out that way.

Laura: Right, exactly. But I started to do some consulting. I did consulting, research and project management. I worked for awhile with the University of Michigan Hospital for an orthopedic oncologist and that was great. I started helping her host workshops for her residency program. My partner and I had gotten married and we decided to move out here to the Bay Area. He had never lived anywhere outside of Michigan and I felt like things were getting kind of stale, especially being used to moving so much.

Erin: How did you alight upon the Bay Area? I mean, it’s an awesome place but it has its downsides, so how did you guys decide to go for it?

Laura: We’re attracted to things that are very unique or different. It’s kind of a rebellion inside of ourselves. Because we had been together from such a young age—we started dating when we were sixteen—I think we settled down into a very comfortable “older” relationship than most people would. I think we both had some craving for adventure and wanted to do some exploration. When the thought came up of where we could move… it just seemed like one of those places. It’s very well known for having a very unique culture.

Erin: Yeah, definitely.

Laura: We decided to move here and the first job that I got here was working at the Mathematical Sciences Research Institute in Berkeley and I was helping to do project management there. I was also organizing their workshops. But once you’re in the Bay Area, it’s kind of hard to escape tech and entrepreneurship and start ups. So I was doing that job for a little while and I decided that the work that I was doing there wasn’t really what I would consider career worthy. It was great work and I enjoyed it but I felt like I had kind of hit a plateau and I wanted to go further.

I wanted to see what else I could accomplish so that’s when I thought maybe I could learn to be a software engineer and do some coding. I felt like I had enough technical background to pick it up and enough background in mathematics so that it would be fine.

Erin: Wow, okay, so you just shared with us this really broad range of career… I won’t call them experiments… but something sort of like that, where you fairly quickly were able to identify things that didn’t feel like a good fit for you. It seems like you were pretty open to leaving those and going on to find something else that you thought might be interesting or something else you wanted to try.

Laura: Yes.

Erin: Do you think that ability to let go of something stems from the fact that you had to move around a lot as a kid? Do you think that made you more resilient in a way?

Laura: I definitely think that that was part of it, yes. Again, that idea of being able to reinvent myself. Every time that I moved I really enjoyed that aspect, being around a new group of people and trying new things and trying on a new personality. I think that that was helpful in this area.

I think also having a very stable partner gave me more confidence in the fact that I could make a career shift. In general, people need stability in some aspect of their life. Oftentimes they’ll find that in their career, but that can sometimes be debilitating because they end up feeling stuck. Then, they’re not able to make that leap forward if they don’t have other very secure parts of their life. But because I had such a secure partnership and we had moved around so much when I was a kid, it helped me to be able to explore careers.

Erin: Once, a good family friend of ours explained to us how he felt when he got married. He said he found it to be very freeing because suddenly, there was this enormous life purpose that he didn’t have to try to find anymore — he had found it. And having found it, he felt this sense of freedom and safety. He described it as, “It’s not complacency but I have this foundation now. And from here, I can only build up.” Obviously, that really resonated with me because I still remember it and I feel the same as you do about the importance of having that.

Laura: Yes, definitely. From there, I realized that if I was going to become a software engineer, the best way to find out if that was right for me as early as possible would be to surround myself with other software engineers. That’s when I started going to meet-up groups and that’s when I found Women Who Code (WWCode). I started attending meet-ups and study sessions and I found this space really welcoming and really who were just starting out and also women who were professionals in the industry that could help and guide the other ones who were just getting started. I thought, “This is great. Maybe this is right for me. I feel comfortable here.” So I started doing tutorials online and going through tutorials on Codecademy and all that kind of thing.

But then I felt that the process wasn’t moving fast enough, maybe because I was used to change and I wanted the change to happen quicker. I started hearing about these boot camp programs (which have obviously taken off recently) and I was like, “Okay, maybe that’s the way to do it.” I still was pretty adamantly set that I didn’t want to get my master’s or my PhD, so this seemed like a good alternative education model that I could look into.

At the time, I didn’t feel financially secure enough to pay $13,000 to go through the program, but I discovered in one of my social media feeds that there were scholarships available. So I applied for a scholarship and fortunately I got it. That was super exciting. I felt secure enough both with my partner and with having the scholarship that I felt going into the program would enable me to make the career switch. I’m really grateful for having that opportunity. The scholarship that I got was through YesWeCode, which is a wonderful non-profit organization based here in Oakland as well.

Erin: I haven’t heard about them. Would you mind spending a minute describing what they do?

Laura: They specifically focus on underrepresented groups in tech, but more specifically on black and Latino individuals and people that come from nontraditional backgrounds. Their goal is to partner with coding schools and boot camps to get underrepresented people into those programs so that they can be successful.

They realize that oftentimes people from underrepresented groups and nontraditional backgrounds don’t have the financial resources to be able to pay the tuition and probably already have student loan debt —like I do, because I had to work through school and pay my own way. The thought of having to take out more loans to go through this kind of program is daunting, and as a student you don’t want to have to do that, so YesWeCode helps with that.

Since then, other programs and scholarships have been have been created. I think Facebook has now partnered with DevBootcamp, and Adobe has created scholarships and partnership programs with Dev Bootcamp specifically to help with the diversity initiative.

Erin: That’s awesome. I’m so glad you were able to find YesWeCode as a resource.

Laura: Yes, there are many different resources currently available and a lot of organizations that are trying to list all of these resources in one place. Women Who Code, for example, tries to send out scholarships in their monthly newsletter. If you subscribe for their monthly newsletter, you will get a list of scholarships available for these types of programs and also for some traditional computer science programs if you want to go back to school for a master’s or PhD.

Also, there’s another Podcast that started recently called Breaking into Startups. They focus on underrepresented groups in tech, as well as people of color. They also focus on people who have gone through the military and people from prison populations who have also broken into tech and become entrepreneurs. They list a lot of different scholarships available.

There is another really great resource called Course Report. They list not only scholarships and grants that are available for coding bootcamps but they also give you reports of every boot camp that’s currently available. It includes what their stats are for graduation rates, people getting careers after they go through the program, what they ended up IPO-ing for.

Obviously, computer science is such a large field. When you’re first starting out, who really knows if you should learn JavaScript, Python, Ruby, or if you should go into data science? You don’t really know because you don’t really understand what it all means, so they also guide you through that process and give you some background on what all of these languages are, how they apply to the industry, and what positions you could potentially get if you choose a certain one. So yes, I think Course Report would also be a really great resource.

Erin: At this point, would you say that we have a critical mass in terms of providing resources to people from underrepresented groups to help them get into technology, or do you think we still have some work to do in that regard?

Laura: I think there is still a lot of work to do, an insane amount actually. I think that there’s no one easy answer to solve this problem. The diversity problem in tech, in general, is a really big problem. A lot of tech companies—even these alternative education programs—are currently missing the mark.

Sustaining underrepresented people in tech careers is another major problem that’s happening right now and it’s a problem that’s been happening for a long time. This goes way back to when women actually used to be more prominent in computer science and then got pushed out. I’ve actually talked with a lot more women lately who were in these careers 10 years ago that felt unwelcomed, but now they’re starting to see that there are these resources available and these communities that are popping up that are helping them think, “Maybe I could come back, maybe I should re-up these skills, maybe there is a place for me in this world.”

Erin: That’s really good to hear.

Laura: Yeah, definitely. I think part of the problem is that in a lot of ways, we’re all very unique — we all have our own experiences and our own life stories. Finding your place in the world is hard, but finding your place in tech and finding a community that you feel like you belong to is even harder. Even if there are tons of Meetup groups or organizations that are doing this, no single one of those group or organizations is going to be able to help everyone who falls under the umbrella of underrepresented groups in tech.

There are communities and organizations that specifically help women of color, there are groups that specifically help LGBTQ individuals, there are some groups that specifically help trans individuals, like Trans*H4CK. There are also groups that specifically help people with disabilities. So if anything, there just needs to be more support and more communities to help people. No single one of these groups is going to appeal to the entire umbrella of underrepresented groups in tech, in my opinion.

Erin: I want to make sure we save enough time to talk about what you’re doing right now, which is Accel.AI. Tammy, in particular, really wanted to talk to you about this. You went from being a boot camp graduate to founding your own thing entity to teach artificial intelligence. Just from looking at your LinkedIn profile, you don’t have any experience in AI, so it seems like a really brave thing to do.

Tammy and I have also talked about how what you’re doing reminds us of Dorothy Vaughan in the movie Hidden Figures, where she senses that these new IBM machines are going to be a thing, so she trains her group of workers on how to use them so that the workers themselves don’t become obsolete. We see a similar thing happening, where you’re looking towards the future and trying to think about how to prepare people for the skills that we’re going to need in a few years.

Laura: Thank you. Honestly, when I first heard that comparison, my thought was, “I don’t feel worthy enough.” [Laughter] Dorothy Vaughn is an amazing person. Her work is so inspiring and touching, so I really appreciate that very much.

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Laura: I guess how I came to artificial intelligence is… Once I became a software engineer, I realized that computer science is such a huge field and a huge industry, and there are so many different ways you can specialize. I was always saying, “I like that. I like engineering. What else can I do with this? What else can I do with that?” When I was at Dev Bootcamp they would bring in speakers from different companies, and I realized the one who inspired me the most was the speaker who was from IBM who talked about Watson. They brought in this little teddy bear, hooked up this voice box and had us interact with it, and I thought at the time that it was the coolest thing ever and I was like, “Maybe that’s the thing [I should do]. Maybe it’s artificial intelligence.”

I started looking into it more and my creativity took off. I thought that maybe I could figure it out and start doing it. Dev Bootcamp teaches Ruby on Rails, JavaScript, SQL, and all of those standards of web development. I knew I would need a very different set of skills to do AI. A lot of the traditional programming languages used for artificial intelligence, data science and machine learning are Python or used frequently in scientific work, so I started teaching myself Python. I’m now self-taught in Python and in Flask for web development. Then, I started learning machine learning and deep learning. Deep learning is the newest craze and what a lot of people think of as artificial intelligence.

What I’ve come to discover is that “artificial intelligence” is really just an umbrella term that covers all aspects of deep learning, machine learning, parts of robotics, data analysis, data science, and data engineering. So even within artificial intelligence, there are so many other little ways you can specialize, which is fascinating to me.

When I started doing self-study in AI, I took a very similar approach to how I did when I first decided to go into engineering: I surrounded myself with people who are doing this and who are learning this because I think that’s the best way. I think immersing yourself in a culture and in a language is the best way to learn. So I found some Meetup groups that were learning the latest research and started attending them, and I just quickly fell in love with AI.

So that’s what spawned Accel.AI originally. When I started attending this meet-up group we were focusing on deep learning, going through Stanford’s CS231n course on computer vision. It’s a really great lecture series and their videos and course notes are all available online. There are a lot of other self-study programs as well. Udacity now has machine learning nano-degrees and AI nano-degrees. Those are all online. They’re MOOCs, Massively Online Open Courses.

I also found that if I was going to make the career switch I wanted to have a quicker solution, just like what I wanted for the transition to software engineering. I asked myself, “Why isn’t there an accelerated program for artificial intelligence? Why does that not exist?” I started doing research and the closest things I could find are programs like Metis and Galvanize.

Erin: Has Galvanize expanded into AI or are they still only doing data science? I knew that they have a data science program but I didn’t know that they were doing an AI.

Laura: They are specifically data science and so is Metis. Since deep learning has become the new biggest thing that everyone wants to figure out and do, they have added supplements onto their course material for deep learning as well, but they do focus specifically on data science. Also the thing that I found with a lot of these programs—because they are more mathematically intensive—is they tend to have a higher population of men that fit within that “standard groups in tech” demographic… you know, white males who are very competitive.

Erin: Why do you think that there are more men?

Laura: I think that a lot of times computer science is not marketed to women very well. A lot of women don’t realize that engineering is incredibly creative and collaborative. You can work with other people, plan out these beautiful models and make amazing visualizations with data. If you get a chance, go to a website called Information is Beautiful and look at the gorgeous graphics they’ve created with data.

So yes, I think from a young age women are told, “Science is not for you. Math is not for you. Why don’t you go do art? Why don’t you play with these pretty pink things?” From a young age, we are really encouraged not to be interested in that. It’s funny thinking back now on my own education, I did take one programming course when I was going to community college. I think I was 16 or 17 when I took an Intro to C++ course. Probably because I was a teenager and working full time and everything, I had so many other responsibilities and stressors in my life that I didn’t really find the course inspiring at the time.

We’re trained from a young age to think that it’s not for us, it’s not fun, and it’s not creative. We have to change the language that we use around computer science, especially with young girls and even with women. We have to learn how to empower and inspire them to make those switches in their careers in order to go into these positions, because in a lot of ways, these are the careers of the future.

The more research and reading I did about AI, the more I saw how everything is becoming automated—not just blue-collar work, not just factories, not just agriculture, but even white-collar work. Administrative assistants are going to be, in a lot of ways, replaced with things like Amy.ai or Clara.ai to accept meetings, do scheduling and stuff for people. If programs are going to start taking people’s places, what are professionals in every industry going to do once all of these tasks become automated? They’ll have to switch to doing knowledge work and they’ll have to learn how to learn again.

So that’s when I decided that if there wasn’t a program out there that was right for me, I needed to create it for myself. And I knew there wouldn’t be a program out there for people like me. I knew that because I’ve heard from other women, especially women of color, who have tried to get into other accelerated programs—just to be in web development, even—who still felt rejected.

They had gone to classes at General Assembly or Telegraph Academy and whether by their peers or by their teachers, they sort of felt pushed out. They thought that they couldn’t keep up in some way or that they weren’t good enough. They had major impostor syndrome. Even if they got through the program and got into their first job, they felt pushed out at work. They felt that they couldn’t keep up [or were somehow “less than”] Master’s and PhD graduates.

Something is still not right and someone has to solve this problem. Why is this still happening to women? That’s why I decided that if this program didn’t already exist I was going to create it myself.

Erin: So there was a particular product that you envisioned being out there in the market: A really safe, nurturing, and supportive learning space, especially for underrepresented people in tech. You didn’t see it in the world already so you wanted to create it. How does Accel.AI solve this market problem and what do you teach?

Laura: One of the main functions—and part of our mission, really—is to bring in the community aspect. A lot of that has to do with physically being in the community created by people of underrepresented groups. We want to help foster and grow these existing communities within our technological landscape, specifically within the area of artificial intelligence. I’ve spent a lot of time working within these groups and building partnerships with these groups. Being here in Oakland, fortunately, allows me to be more connected with these communities and also gives me the insight that tech is definitely moving into Oakland.

Uber is building a huge headquarters here. Other companies are already here, like ask.com and Pandora. The startup ecosystem here is huge! The Oakland tech scene is also being built upon by places like Devlabs (a seed-stage venture capital firm) and The Kapor Center for Social Impact. Part of The Kapor Center’s mission is to improve not only inclusion and diversity in tech, but specifically here in Oakland to help black and Latino people thrive in technology. And then of course the WomenWhoCode East Bay chapter we have here is incredibly diverse and we try to support—

Erin: Of which you are a director, right?

Laura: Yes, of which I’m a director. We try to support the Oakland ecosystem now as well. The thing is though, as technology’s been moving into Oakland—and as people from San Francisco have been getting priced out of San Francisco and now are moving into Oakland because rent is cheaper here—the people and culture of Oakland that’s been here for years has been getting pushed out. As people with more money move to Oakland, rents here start rising. That gentrification process is happening and it’s really unfortunate.

I think if technology companies are going to be here in Oakland, those jobs have to be representative of Oakland’s culture and communities. What we have to do is train those who are likely to be displaced to compete for these jobs, so that they can be contributing members of this new ecosystem and not be forced out. Whether they’re trained as engineers or as entrepreneurs, they should remain a part of this ecosystem. Their voices must be heard and they should be able to contribute to the system that’s being created here.

That’s part of what we’re trying to do with Accel.AI—to help foster and grow the larger community within artificial intelligence. That’s also a mission of the meet-up groups that we host, which I am constantly trying to make more diverse and inclusive. Even so, part of what we’re doing in our weekend workshops is building traction, developing people’s interest, helping them see that this really is relevant.

Our workshops include people with potentially no prior experience. We give them a really intense introduction to a the very broad range of fields that comprise artificial intelligence. We take our participants through what machine learning is, what Python and libraries like NumPy and Scikit-learn are, what TensorFlow is. We give them an introduction to all of that and to what industries and jobs these skills could be applied.

Erin: Can I go? It sounds really interesting!

Laura: Yeah! We actually just had one this past weekend. I’m really excited about how it went. It was super successful. I even partnered with the Kapor Center for Social Impact on this one, which is one of our sponsors. It’s just great.

Erin: If listeners are interested in either attending one of these events or supporting what you’re doing in some other way, how can they get plugged in and participate?

Laura: The main resource will be of course our website, www.accel.AI. If you want to join our meet-up group, the meet-up group is Deep Learning Enthusiasts. We also have a Facebook group, which is AI and Deep Learning Enthusiasts in the Bay Area. We have a Facebook page, a Twitter page and all that stuff.

Erin: Are the events that you host only in Oakland or all over the Bay Area?

Laura: They’re in Oakland and San Francisco primarily. We’re trying to host more and more in Oakland because, again, part of our mission is to help foster and grow underrepresented groups in tech. The diverse people we’re trying to reach are in Oakland, so we want to be where they are. The people who really need this training—the people who are getting pushed out and getting displaced—are people who have a hard time even making it over the bridge to San Francisco. Just the idea of going into San Francisco is a challenge, you know? The idea of going to one of these other bootcamp programs is a challenge for them.

I met a gentleman at the workshop this past weekend who said that has a daughter and he works full time already. He told me, “I’ve heard about this stuff but I didn’t know the tech was in Oakland. I didn’t know I could learn this stuff in Oakland. That’s so amazing! There’s no way I can go into San Francisco, so thank you so much for bringing this here.”

Erin: That’s really lovely to hear.

Laura: Yeah, and that’s inspiring to me to know that this is good work and it’s helping people.

Erin: I wanted to add something earlier when you were talking about gentrification and this high price bubble that just keeps rippling out. My background is in real estate, so for me, this is doubly interesting not only from the tech aspect but also the real estate aspect. I couldn’t agree with you more in terms of why it is important to bring tech to Oakland and to train Oakland folks on how to be involved.

The thought I was having a few minutes ago was that it’s almost like you have to battle fire with fire. Think of the rising rent issue as one kind of fire, and think of those people being affected by this fire as the people being pushed out, who can’t afford to stay in Oakland. If you equip them with the skills that the industry is demanding—when that industry is the thing causing the problem—then you are essentially giving them the skills they need to make enough money to continue living here. Because then, they’re making the same amount—in theory—as the software engineers who are actually coming in and displacing them. So if we’re able to train them and get their salaries up so that they can afford to stay in their homes… I feel myself getting so passionate about that!

Laura: That’s right. Me too. Definitely. That’s part of why I want to [grow Accel.AI] and why I’m working hard to do it. There’s a lot of concern about ethics, especially in areas like artificial intelligence and data science. People say, “Terminators are going to take over!” or they reference Robocop. These are fictional, far-fetched ideas that come to people’s minds. But the methods that are being built into machine learning and data science right now have been causing problems.

For example, Microsoft put out a Twitter bot named Tay, and after being trained by hateful trolls on Twitter, it just started spouting racial slurs and a lot of hate. There was a machine learning program that was used to judge a beauty contest and it ended up being racially biased. It judged only white people as attractive, and that’s not okay. We must have not only human oversight, but diverse human oversight, as we develop these algorithms.

These are things that we need to take into account before—or even while—creating these models and creating these systems that are going to affect everyone in the world. And they really are affecting everyone in the world. These systems are built into technology. We’re using these systems every second of the day. We have our mobile phones with us all the time, and machine learning and AI are in these pieces of hardware that we carry around with us all the time. It’s going to affect you and if you don’t have those considerations from the start then that’s a problem.

Erin: Yeah, there’s an imperative to make sure that cognitive diversity is embedded within technology that touches everyone, so that everyone who’s a recipient of that technology is then represented in and by that technology.

We were talking with another guest over the weekend about the company that she’s working at. A lot of the engineers are men but it turns out that a lot of the consumption of their product is made by women. Men are creating a product for women and might not even understand fully what it is that the female consumer wants, but they’re just projecting their own unconscious biases about how the product should be built.

Laura: Yeah, and that can cause a lot of problems. I read a marketing book once that talked about when seat belts were first being developed for cars. Mostly, the engineers who were designing and testing the seat belt products were men. They created test dummies for the seat belts that were based on the average male size.

But since women tend to be shorter and smaller than the average man—and even though these cars passed the safety tests with the dummies—manufacturers were having a lot of problems with accidents because women were getting hurt and even killed by the seat belts. There was a major recall in order to restructure the design.

If women were in the room at the time when these systems were being considered and developed they could have said, “Sure that works for a man—but we’re not all men.” Stories like this impact the models and tools we’re talking about with AI. So that’s part of the problem.

Again, I believe if we’re going to have people making artificial intelligence that’s going to affect us for the rest of our lives, then it has to be a diverse group of people creating it. So really the mission of Accel.AI is to lower barriers to entry, to help people understand how to engineer artificial intelligence, and also promote community, diversity and inclusion in tech.

That doesn’t mean, of course, that we wouldn’t take a white man as one of our students. But our goal is to maintain at least 70% diversity within both our workshops and the full program that we’re developing I’m glad to say that we reached that goal this past weekend, which is awesome.

Erin: Congratulations!

Laura: Thank you. I’m going to publish the demographics and the logistics that came out of the workshop this weekend, which I’m really happy about. I think we had more than 70% of attendees who identify people of color and over 50% women in attendance. I even got reports from some of the attendees who told me that this was the most rigorous event that they’ve ever been to in the tech industry, especially for subjects so specialized as artificial intelligence. They were all very impressed and I’m excited about that.

Erin: I’m going to ask a devil’s advocate question in the spirit of Tammy, who often does this. For WITtalks, we have gotten questions like, “Why are you targeting this only for women? Why aren’t you targeting your podcast towards men when men clearly make up most of the population of tech workers? We think you’re cutting out huge swaths of your potential audience.”

Tammy and I have our own reasons for not explicitly targeting men, which we can talk about at a later time. My question for you though would be the same, in the sense of really appealing to the demographic of people of color—what about the “non people of color”? And this has been coming up recently where if you focus on one group, you’re by default pushing away another. What would your response be to that devil’s advocate question?

Laura: I have a few frames of thought when it comes to this topic. One of them is this: Saying that being more focused on the needs of an underrepresented group is equivalent to being biased towards the majority is blasphemous and unfair. That majority is already favored by society, by tech companies, by universities where they are students or faculty. So saying that every other minority group would also have to include the majority is just unfair to begin with. Is that somehow counter racist or counter biased?

From a psychological standpoint, everyone has implicit bias towards the group that they feel most aligned with. That’s going to be the same for white men towards other white men. Women towards other women. Black people towards other black people. It’s where you feel most comfortable in a lot of ways, it’s where you feel most at home, it’s where you can be yourself.

But being aware of that bias is important. You need that awareness in order to fight against the bias. You need to have empathy for the other side. Building community is the key in this way, especially when thinking of white men in spaces of diversity who should also have a voice in the diversity conversation.

I believe white men need to join the table as well. If the only people who are empowered to talk about diversity are people of color, women, and other underrepresented groups, you are actually leaving out a portion of the population. The question then becomes: How can you create an environment or culture where these men feel empowered to empathize and connect with those underrepresented groups? If they don’t also have a seat at the table, this is very difficult to achieve.

I think that the key here is helping men realize that having a seat at the table sometimes means just listening and it doesn’t always mean speaking. Just being in the room and being in support of others without saying, “This is what I think. This is my projection of how I assume this person is feeling.” A lot of people call this “mansplaining”, which is another part of the problem.

In order for men in tech to fully empathize with the experience of women or people of color, men have to listen to them. And these men also have to feel that those people are listening to them as well ,and can empathize with their experiences and with their challenges.

We all are privileged in some circumstances and we’re all not privileged in others. Just because a white man had an easier upbringing and is able to achieve success more quickly in society—because of the way the system is structured and the way it’s designed—that doesn’t mean he has never felt hardship, or doesn’t have fear or anger or strife in his life. Being able to distill our commonalities to those baser instincts is one way the two groups can work together to mitigate some of these implicit biases we all have.

Erin: It seems like you’re really making your own way in tech, forging your own path, and creating things that you think should exist in the world. What would you recommend to other people who might really want to do that, but who either don’t know where to start or who might be feeling impostor syndrome?

Laura: First is forging your own path and finding that empowerment within yourself. It’s something that I definitely struggled with when I moved here, when I went through a phase of self discovery. I think a lot of people do this, especially when you’re going through your 20s, which is one of those life shifting times.

Erin: Yes, I’m pretty eager for that to be over.

Laura: [Laughter] Yeah, me too. Psychology is one of my other major interests and I took a lot of time to do a lot of self education in it, specifically in self-empowerment. I read a lot of self-help books, especially when I was dealing with my very unstable childhood and also figuring out my identity and how I want to identify with my gender, with my race, with everything. All those big life questions that you have to answer at some point.

One of the books that really stuck with me when going through that process was a book by Carol Dweck called Mindset. It talks about the fixed mindset versus the growth mindset, which is something I think a lot of people struggle with. An example of the fixed mindset would be how some people who haven’t been in the tech industry yet tend to think of the idea of engineering as being complicated. For example, they may say things like, “I’m not smart enough for that. I’m not mathematically inclined enough.”

Another fixed-mindset example in the book is about children. Say they do really well on a test and they take it home and show it to their parent. Their parent says, “You’re so smart!” The child might start to believe they’re gifted, saying, “I did well on this test because I’m really smart.” But then they come across a challenge or a test that they don’t do really well on, and they’ve already made this association in their mind of, “I’m really smart and that’s why I did well.” Now, they’re not doing well and the next logical step is, “Well, then I must be dumb.”

Researchers found this a lot in children who were labeled as “gifted” when they were young. Instead of being encouraged to work hard, their success is based on being smarter. The book tries to show you how to retrain your mindset such that if you come across something that you think is challenging, it’s not that you’re not smart enough to figure it out or to achieve it. It’s that you need to take on a different mindset: That in order to do this new thing or figure out this new problem, you have a lot more hard work to do. This is the growth mindset. The book tries to show you that discipline, change in behavior, empowerment can help you keep going and do the work.

That’s something that really stuck with me. I had my own doubts when thinking about a career switch, especially having been out of college for a while. I did linear algebra when I was first starting out in community college. I was 16 or 17 when I took calculus, which was years ago.

When I thought of engineering—especially about algorithms because that’s a huge interview topic—I had my own expectations about how I would perform in an algorithms interview. I had to fight the mindset that if I’m not getting it right away, it’s not that I can’t ever get it. It’s just that I have to work harder to get it, and that hard work is going to be more rewarding in the long term.

It’s like when you’re first starting to learn to ride a bike. It’s super challenging. And then you get better at riding the bike, and after a while riding a bike gets kind of boring.

It’s the same thing with engineering. You find people who have been engineers a really long time who can almost do it in their sleep, and that’s because they’ve been doing it so long. It’s not that it’s easy for them because they’re any smarter than anyone else, it’s just that they’ve become accustomed to it.

Just because it seems hard now doesn’t mean it’s going to be hard forever. You just have to get used to it. You have to fight through that uncomfortable feeling. You have to find room to grow in yourself, to push and to make it through so you can say, “Okay, I got this.”

So that’s what I would say: Find that self empowerment, find the shift in mindset, and like I said earlier, find your people. Surround yourself with the people who are doing the thing you want to do. Always strive to be the “dumbest person in the room”.

Erin: Along with finding the thoughts and the empowerment within yourself, has there been any impactful piece of advice or mentorship that you’ve carried with you as you go off and create things you wish to see?

Laura: Yeah. I wouldn’t say there was one word of advice, but I think meeting other people I find inspiring has been helpful. Part of what attracts me so much to the WomenWhoCode organization is that they have working tech professionals leading workshops. They’re there purely to help others. These WWC leaders might be end up being role models and examples for those who are just starting out. They’re not just women who earned their Bachelor’s, Master’s or PhD in computer science, but also women who went through boot camp programs or are self-taught.

Finding those sources of inspiration has stuck with me more than anything, and has also inspired me to want to be that inspiration for others. I think that’s why I don’t mind figuring out my own way in AI because right now there really aren’t very many women who are well known for artificial intelligence. There’s maybe a handful who I can name off the top of my head and that’s it. It’s funny, I actually had a conversation today with a guy I was meeting with, and he told me that he didn’t really get the whole diversity thing.

Erin: Oh dear…

Laura: He was like, “Why is it such a big deal?”

Erin: Oh god…

Laura: Yeah [laughter]… We got into a long conversation and at the end of it, I asked him, “How many women can you think of who are in artificial intelligence? Can you name off the top of your head five women who are well known?” He struggled very hard to find two. That’s a problem to me, that’s a serious problem. How can you expect to foster, empower, and create an environment for people if they can’t see themselves reflected in the space? That comes back to not just diversity for women but diversity for people of color, LGBTQ people, and people with disabilities. There has to be a correction to this problem.

Erin: So Laura, wow, we have spent a long time talking about really big heavy topics. What would the Laura, who is sitting in front of me today, tell 10-year old Laura with lizards hanging off her ears to keep her going? I ask this question as a way of asking: What do you tell people who are interested in getting into tech now? How do you encourage them?

Laura: The biggest things are to 1) Find your people and 2) If you’re uncomfortable or you feel rejected, don’t give up. Even in my short time in the tech industry, there have definitely been spaces that I’ve gone into where I felt completely unwelcome and where I felt people didn’t take me seriously. And the thought did cross my mind of, “Maybe tech isn’t for me. Maybe AI isn’t for me. Maybe engineering isn’t for me.” Looking back now, I know that if I would have given up after any of those points, I would be completely unhappy with myself and I wouldn’t be accomplishing the things that I am right now.

Erin: From my perspective, there are a whole lot of people who you wouldn’t have been able to reach through your work. So in a sense, you almost have to be a little bit selfless about your feelings. I felt this way when teaching at Girls Who Code as well. Yes, sometimes at two o’clock in the afternoon I was exhausted from standing up and lecturing about CSS all day. But there’s 20 people sitting in front of me who really want to know what I’m going to teach them, and that’s a good feeling.

Laura: Yes, exactly. That is incredibly rewarding, like having that guy at my workshop saying, “Thank you for bringing this to Oakland because I didn’t know this was possible here.” You’ll encounter and touch so many people’s lives just by being the example that you want to see in the world. I think that that should be what’s most empowering to you and what should keep you driven and going in this industry.

Erin: Alright. Do you have any big dreams for yourself in the next few years, professionally or otherwise?

Laura: Other than getting my own company off the ground and long-term success goals…

Erin: It’s not necessarily success goals. These can be dreams like, “I want to go to Berlin for the summer and eat ice cream every day.” That kind of stuff.

Laura: One of the things I haven’t had as much time to spend on just for fun and for exploration is robotics. My partner got me this robotics car kit this past holiday. It’s really cool and you literally build it from the ground up, from scratch. You use an Arduino motherboard and you add on all the sensors, the wheels and everything.

Erin: Do you know the name of it?

Laura: It’s called an Elegoo.

Erin: I’m thinking of a different one but I have seen something similar, where it comes with the micro computer, wheels, shell and all that.

Laura: Yeah, you just build the whole thing. You can also download all the software and program it yourself. From there, it’s completely hackable if you want to add onto it and stuff.

Erin: We might have to get together and noodle on that because I got a Raspberry Pi kit for the holidays, and I’ve figured out how to turn the LED bulb on and off with Python, and I’m pretty stoked to keep going!

Laura: Yes, definitely. I would really like to get into robotics more. Just separately from my company and the other areas of AI that I’m learning about. Just purely for curiosity and wonder and exploration, I think robotics would be really fun.

Erin: Awesome. So you mentioned the book Mindset by Carol Dweck. Are there any other books, blogs, or magazines that you’ve read recently that you want to share?

Laura: Yeah. So I decided to start a book club—

Erin: Yay!

Laura: [Laughter] I used to run one when I was really into psychology as a form of self empowerment. You have to find a community and others who are doing the thing you want to be doing. It’s essential to getting on and continuing on a path. I decided to start a book club specifically for books about tech. We also read about the topic of success, like entrepreneurship and social justice, because those are topics that are very near and dear to my heart right now. The book club that I’m starting is called TechLore, and the book we’re covering this month is called Weapons of Math Destruction.

Erin: Of math destruction?

Laura: That’s right, Weapons of Math Destruction. Cathy O’Neil is the author. This one fits our theme really well because she talks about how bias is being built into machines, and how that affects society. One of the extremely relevant topics she brings up in the book is how the rate of recidivism is judged by machine learning algorithms. The algorithms themselves are based on data derived from biased social systems that already falsely target black and Hispanic individuals as more likely to go back to prison or to commit crimes.

Erin: It sounds like the machine learning is extrapolating on data that’s faulty in the first place, and then coming up with even faultier data for the output.

Laura: That’s right, yes. There’s no human oversight saying, “Wait a second, are there other factors to be considered here?” Even though data scientists and computer scientists are not specifically identifying race as an input factor of the algorithm, the algorithm looks at where people live, their socioeconomic status and all these other things [which are often side effects of society’s racial bias]. When you think of the people that live in these poor areas, automatically you think of black and Hispanic populations. And if you’re going to train an algorithm to predict that people from these areas are more likely to commit crimes, you’re also targeting the minorities who live in these areas and that’s not fair.

Erin: Is it an invite-only book club or can anyone local attend?

Laura: Anyone local can attend. Even if you haven’t read the book, I encourage people to attend who want to just be part of the conversation or hear what it’s about. We’re meeting at the Kapor Center for Social Impact, which is a really great organization. I’ve been trying to foster community growth with them.

Erin: Where can listeners find out when your book club meets?

Laura: On Facebook, we have a group right now that’s called TechLore. We also have a website at techlore.org.

Erin: Awesome! So we’re down to the last three questions, which are always my favorite questions to ask. The first one is… cross-country trip by train or by car?

Laura: Because I’ve done cross-country trips by car many times in my life and I’ve never done one by train, I would say by train. I’ve also heard that it’s really nice and leisurely, that usually you can just see a lot of scenery and relax. Cross-country road trips by car is much more cramped.

Erin: Yes, and there’s work involved.

Laura: And there is work involved, yes.

Erin: I’m actually right there with you. One of the things on my bucket list is to take a trans-Canada train ride during the fall when I can see all the trees changing color. Alright, next question—fireplace or campfire?

Laura: Oh man, this is a really tough one for me actually. From the time I spent in Michigan, I grew really fond of campfires because it was a community gathering space. Even in the cold you could put up a campfire, go outside, look at the stars… There’s something about the smell of smoke and roasting marshmallows. So that’s a great favorite. But there’s also nothing like being in a house or a cabin that’s being kept nice and warm by a good fireplace. But I probably have to go to the community aspect and go with the outdoor campfire.

Erin: You’re really a person who values community—that comes through loud and clear.

Laura: Yeah, maybe it’s because I grew up without having that identity of a community. Now that I’ve discovered a lot about myself and have been able to put down roots here, I’m really seeking community in every aspect.

Erin: I think it’s great. I think that’s a really beautiful evolution as well.

Laura: Thank you.

Erin: My last question, which we’ve asked all of our guests so far: Would you rather have extra money or extra time?

Laura: Mmmm… that’s a tough one. I would have to say time. I feel like money can buy you a lot of things but it can’t buy you time, and in that way, time is so much more valuable. In having that extra time, you can always make more money.

Erin: Yeah, totally.

Laura: So yeah, I think time is way more valuable.

Erin: Laura, thank you so much for hanging out with me for the last couple hours.

Laura: Thank you so much, Erin.

Erin: It’s just been really, really fun. One of the reasons why I love doing this is because we probably never would have met in real life and I feel like we just have so much in common and you’re totally a person that I would go talk about books with.

Laura: Yes! I’d love to hang out at some point.

Erin: Well, this has been another episode of the WITtalks podcast. I’m Erin—signing out!

You can find Laura on LinkedIn and you can also reach her by e-mailing info@Accel.AI

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