9 Millennial AI Leaders to Follow in 2019
Based in the Bay Area, these millennials are defining what it means to build in the era of AI. I have had the pleasure of meeting or working with each of these innovators, and as much as I want to selfishly keep them to myself, it’s time to share the wealth.
We know the Musks, the Ngs, the LeCuns, and the Deans — but what about the next generation of AI leaders?
The Bay Area is a hot spot for up-and-coming AI talent, and it’s not hard to see why. Between powerhouse universities like Stanford and Berkeley, accelerators like Y-Combinator and Lambda School, and companies creating new machine learning applications, you’ve probably passed an AI genius on the street and not even known it.
My name is Allie Miller, and in my role as Lead Product Manager at IBM Watson, I’ve had the honor of meeting, working, toasting, and even kitesurfing with some of the smartest folks in AI — comparing notes on everything from the best APIs for computer vision to government strategies on digitalization.
I wanted to introduce a few of the people who have particularly caught my eye. Follow them, connect with them, and say hi to them.
These are the new faces of artificial intelligence.
Andrew Zaldivar, Google AI
Where do you work, and what do you do?
My name is Andrew Zaldivar, and I am a Developer Advocate for Google AI in SF. Specifically, I work on a research-based team focused on developing and promoting socio-technical strategies that can advance positive outcomes from AI long-term. In my role, I act as a servant to the public’s interest in developing ethical AI systems.
What did you study, and how did you get into AI?
I completed my doctorate degree in cognitive neuroscience, but my studies were complemented with informatics, psychology and data science, which helped prepare me to examine the interplay of people and technology and what it means for our future.
What are you working on? Any recent projects you can share?
To help developers take on the challenge of building fairness into their machine learning models, I helped develop a short, self-study Fairness Module that is part of our Machine Learning Crash Course. I also recently co-authored a paper on Model Cards, a documentation framework that encourages transparency between developers, users, and stakeholders of machine learning models and systems.
Why does AI excite you?
It’s remarkable the see and experience the major advances and the resulting positive impact that AI is having in our society. Yet, I can sit down, think of a problem to work, come up with a novel solution using AI and contribute right back into the community. That’s because a lot of what’s out there is publicly available: the research papers, the datasets, the open source models. AI is really empowering on so many levels.
What’s most surprising about working in AI?
AI is not just an area of computer science; it’s reshaping the fabric of our practices, interactions and environments. Whether you are in the field of AI, software development, design, tech, policy, civil society, business, academia, politics, art, or otherwsie — everyone has a say in the ethical and socially responsible development of AI.
Where can people follow you?
While I do have a Twitter account, I am more active on LinkedIn. Feel free to connect with me as I will be announcing new projects in the coming months.
Alyssa Simpson Rochwerger, Figure Eight
Where do you work, and what do you do?
I am the VP of Product at Figure Eight in San Francisco.
What did you study, and how did you get into AI?
I studied American Studies and Photography at Trinity College (a liberal arts education). I got into AI when I transferred as a Product Manager into the Watson group at IBM. I didn’t know anything about AI at the time, but I was able to learn from some wonderful colleagues and researchers on the job. I immersed myself in reading as much as I could about AI — and specifically computer vision which was my focus. I did online learning courses such as Coursera, video tutorials, and spent lots of time out in the field learning from customers.
What are you working on? Any recent projects you can share?
Today, data scientists spend 80% of their time wrangling data in preparation for AI. At Figure Eight, we work on applying machine learning to our data annotation platform to make getting the data needed to train models a lot more time and cost efficient. One recent example is ML-assisted video object tracking — some of our customers have seen a 51x efficiency improvement.
Why does AI excite you?
AI (or more specifically machine learning) is an exciting technology because I believe it has the power to positively transform our world if harnessed, applied, regulated, and controlled by thoughtful people. As a product person, I love working on unsolved problems and creating products where there hasn’t been one before which delight users. AI is full of these problem spaces — it’s the Wild West right now.
What is the biggest misconception about working in AI?
The biggest misconception about working in AI is that it’s only a field open to nerdy data scientists with a background in computer science from a fancy school. The AI industry needs people from all walks of life, with all types of experiences to mature into a technology which is widely used and successfully harnessed. It’s also easy to learn! It’s not rocket science — just like learning a new skill or language it requires patience, practice and commitment — but it’s no different from learning to ride a bike!
Where can people follow you?
Miguel Gonzalez Herranz, FounderNest
Where do you work, and what do you do?
My name is Miguel Gonzalez, and I am the Co-founder and COO / CPO at FounderNest, where we have the mission to leverage artificial intelligence to connect investors and founders in an efficient, transparent and democratic way. I am based in Palo Alto, California, where I oversee our company’s product and operations.
What did you study, and how did you get into AI?
I graduated with a dual degree (B.Eng. & M.sC.) in electrical engineering and computer science from both the Technical University of Madrid in Spain and the grande école TELECOM ParisTech in France. I then received my MBA from the Wharton School and a Master of Arts in International Studies from the University of Pennsylvania.
Since I was a kid and saw my father tinkering with technology at home, I have been passionate about technology and how its application can improve the world in limitless ways. This passion evolved into a deep vision to create technologies that helped us, humans, utilize our intelligence in ways where this unique ability of ours can truly make a difference. AI has been the avenue I have taken to realize this vision.
What are you working on? Any recent projects you can share?
At FounderNest, we are building an AI Investor Analyst that facilitates the job of a venture capital investor in two fronts: sourcing and evaluating investment opportunities. To accomplish this, we have developed a technology that automates the discovery of companies and the normalization of company data obtained from hundreds of sources; we have built proprietary algorithms in charge of evaluating companies vis-à-vis investors’ theses and producing the analysis investors need to make decisions. We do all of this while maintaining the highest standards of data confidentiality and privacy for our customers.
Why does AI excite you?
It is always difficult to realize that you are in front of a turning point in history while living through it. That is how I feel about AI. AI represents a historical leap as it lets humans, for the first time in our existence, process unthinkable amounts of data, from universal sources, at an unimaginable speed. What truly excites me about AI, though, is how it will free us from repetitive and low-ROI tasks while allowing us to spend our time tackling the biggest challenges of our time.
What is the biggest misconception about working in AI?
That AI will replace humans. AI does allow us to automate increasingly sophisticated tasks and experiment in faster cycles thanks to its capacity to process vast amounts of data in short time frames. Nevertheless, it is hard for me to imagine an AI machine capable of replacing, and even less so, improving the unique ability of humans to spontaneously make decisions. Since we wake up in the morning until we go to bed in the evening, our lives are all about continuously making decisions, mostly out of habit, in so many different contexts and with so many varied inputs. This essence is, in my humble opinion, irreplaceable, but this is just an opinion and AI may just eventually prove me wrong.
Where can people follow you?
Here is my LinkedIn!
Emily Yeh, Bolt AI
Where do you work, and what do you do?
I am a Product Manager at Bolt, a fintech AI company building a fraud-free hyper-optimized checkout experience.
What did you study, and how did you get into AI?
My first real encounter with AI and machine learning was through online dating. Not dating online, but at a career fair. I passed by a booth for a major dating site and fell in love with the data and mission-driven approach. OkCupid was one of the first AI products in the country; they use machine learning to statistically predict compatibility to help bring potential lifelong partners together for a date. As a double major in computer science and psychology, I’d worked with the accuracy and knowledge of data and research and, separately, the ability of tech to deliver impact at scale. I wasn’t sure how they would effectively be brought together, but AI was the bridge for that gap. During my time working on the core engineering and AI team there, I built the foundation for compatibility predictions based on hobbies and saw how elegantly it was able to help people build real, personal connections, and I was inspired to continue working with AI.
What are you working on? Any recent projects you can share?
I’m working on helping make it easier to shop and sell online. This may seem simple, but building e-commerce sites is surprisingly difficult. Technical and design challenges make the experience increasingly frustrating for customers and very challenging for small businesses to compete with Goliaths like Amazon. Specifically, I’m leveraging AI to detect fraud and provide dynamic purchasing UX/UI (think the checkout experience) to help make it as seamless as possible for buyers and easy to set up for businesses. Imagine that you have nowhere to sleep because you bought a new bed, but it wasn’t shipped since your card was declined at the last second. Without high-accuracy models and data sources, this can easily happen due to how much easier it is for fraudsters to attempt credit card fraud online instead of at a store.
Why does AI excite you?
AI enables us to efficiently help businesses grow, detect signs of depression from social media posts or cancer from scans, and recognize people with incredible accuracy. When used in combination with other disciplines and technology such as e-commerce, healthcare, or natural language processing, AI is one of the most powerful new tools we have today to scale the impact of countless industries. As it becomes more widespread, we’re also making our technology more adaptable to individual situations and decreasing costs of bringing high-quality goods and services to the general public.
What’s the next big thing in AI?
Picture walking into a hospital for a check-up and being greeted instantly without needing to fill out a clipboard of forms; the office’s lobby computer vision software detected you and notified the staff of your arrival. Behind the doors, you see busy but happy nurses walking around, their schedules semi-automatically optimized each week when their head nurse approves the AI-assisted schedule recommendation. When you walk into a doctor’s office, he uses predictive models, 3D diagrams, and his expertise to review your health. At the end, he gently suggests more exercise and sends it to your Health app, which recommends a personalized workout plan and convenient time and place on your calendar.
With developers from different backgrounds, industries, and interests, we’ll see an increase in solutions like these for problems that couldn’t be solved before. We currently have access to an incredible amount of information, and AI will help us digest and take advantage of it. We’ll be able to leverage data to help democratize processes like getting clothes delivered from a high-quality personal stylist, easily shopping at new stores, finding love, and even hitting those exercise goals.
Where can people follow you?
Find me on LinkedIn.
Jason Benn, Sourceress
Where do you work, and what do you do?
My name is Jason Benn, and I work at Sourceress, where I’m the lead ML Engineer.
What did you study, and how did you get into AI?
I actually studied economics in college (UVA), and learned programming and AI afterwards. I was in one of Dev Bootcamp’s first cohorts back in early 2013, learned computer science by taking all of the courses at the Bradfield School of Computer Science through 2015, then started learning about ML and AI in mid-2016. I figured it would be helpful for my job to know NLP, so I just started reading about it and organizing book clubs. About a year later, I was having coffee with an AI researcher and he told me that you don’t need a PhD to be a Machine Learning Engineer (MLE) or an AI researcher…! So I set my sights on that, quit my job to study, and a few months later I got a job as an MLE at Sourceress.
What are you working on? Any recent projects you can share?
Well, there’s always more to learn! I track my progress at a high level here. The paper club I organize has just recently started streaming our group programming efforts and posting the videos to YouTube, too.
Why does AI excite you?
For the right business, it can have a huge impact. My project at Sourceress last quarter was to “improve the company’s profitability by X%”, and we were able to do it. If your goal as an engineer is to make yourself indispensable, then it’s hard to better than that.
But more broadly, I just believe in the transformative potential of the technology. Relatively simple architectures are performing mind-bogglingly well on tasks that we used to think would require far more advanced AIs (question answering and automatic captioning both blow my mind). The field is still very young, it’s going to grow vastly larger, and it only takes a year or two of studying to go from “competent programmer” to “I can understand and use the state of the art in this field”.
What’s your favorite way to stay up-to-date on AI?
My favorite news sources are arxiv-sanity, lobste.rs/t/ai, Hacker News, and Google Scholar alerts on my favorite papers. For reading papers and remembering interesting things about them, I’m a huge fan of MarginNote and Anki on my iPad.
Where can people follow you?
Follow me on Twitter!
Charu Sharma, NextPlay.ai
Where do you work, and what do you do?
My name is Charu Sharma, and I’m the CEO of Nextplay.ai in San Francisco.
What did you study, and how did you get into AI?
Studied Physics and Economics, went to programming bootcamps. I didn’t need a degree in AI myself; I recruited engineers and advisors who did.
What are you working on? Any recent projects you can share?
Our AI-driven mobile app at scale perceives employees’ goals and blindspots, matches them to mentors and experts within their organization, gives them conversation prompts for effective conversations, and give employees real-time analytics for self-awareness, performance improvement, and career advancement.
Why does AI excite you?
This is a very exciting time as AI allows me to augment our intelligence about mentorship in the workplace to level the playing field and help massive workforces at scale.
What advice would you give to someone trying to get into AI?
Like any technology, AI is an enabler. What’s exciting to me is how AI is being applied to more and more industries whether it be agriculture or healthcare. My advice to entrepreneurs would be to focus on the problem you want to solve as opposed to focusing on being an AI company. To engineers, my advice would be to get coffee with people in the various AI roles (researcher, data scientists, ML engineers, etc.) and understand the path you want to follow. AI is becoming a massive field, and each stream requires a different set training and skills. So do informational interviews and go find mentors!
Where can people follow you?
Matt Beale, Intel Corporation
Where do you work, and what do you do?
My name is Matt Beale, and I’m a Business Development Manager in Intel Corporation’s AI Products Group, where I lead our efforts in the public sector.
What did you study, and how did you get into AI?
I worked as an analyst for the US Government and got an up-close view of the challenges and opportunities that the government faces with the vast amounts of data it collects. In business school at Berkeley’s Haas School of Business, I got my first close look at artificial intelligence when I organized a workshop on AI for other Haas students with Matthew Stepka, formerly VP of Special Projects at Google, and Ori Brafman, a Berkeley lecturer and author. When I saw an opportunity at Intel to help apply this type of technology to public sector and nonprofit problems, I jumped at the chance.
What are you working on? Any recent projects you can share?
We work closely with public sector and nonprofit customers and partners to help develop and scale AI solutions. Intel has been a key supporter of NASA’s Frontier Development Lab, an AI R&D accelerator that focuses on problems useful to the space program, like how to automate the mapping of the Moon’s poles to enable lunar exploration. Closer to home, remote sensing and AI also offer huge potential benefits for disaster response, and Intel has several research projects to adapt existing models to this domain and to explore novel architectures. I am also really proud of Intel efforts to use AI for good, like using our technology to help protect endangered wildlife from poachers by triggering a real-time alert system when an Intel-powered camera’s AI algorithms detect humans within captured photos.
Why does AI excite you?
I think AI is an exciting space because of the potential for impact just with what we have already created. Even if we stopped making breakthroughs right now and simply explored additional applications of models that have already been developed, there is a tremendous opportunity. And of course, there is a huge amount of ongoing innovation.
What’s the next big thing in AI?
It is hard to pick just one next big thing, but certainly a very exciting area is the intersection of security and deep learning. Intel believes that increased automation should not translate to less privacy protection. Growing concerns about privacy make research areas like homomorphic encryption (HE) an attractive solution to resolve the seemingly conflicting demands that machine learning requires data, while privacy requirements tend to preclude its use. Intel has released open-source tools to enable deep learning computation on encrypted workloads.
Where can people follow you?
You can reach me via LinkedIn.
Anamita Guha, IBM Watson
Where do you work, and what do you do?
My name is Anamita Guha, and I am a Lead Product Manager at IBM Watson in San Francisco, CA.
What did you study, and how did you get into AI?
I studied Cognitive Science at UC Berkeley where I unpacked my desire to understand what motivates humans to do what they do. My coursework introduced me to AI by helping me understand human behavior through computational models of the mind (i.e. what is the algorithm for love?) and exposing me to the history of AI (SmarterChild, anyone?). I first applied my AI knowledge as a Product Manager for an ad-tech startup where I was optimizing ad experiences, and now I apply it every day at IBM!
What are you working on? Any recent projects you can share?
At IBM Watson, I am a Product Manager building tools for developers, with a focus on all things AI and AR/VR. I work with a team of Product Managers and engineers that are strategically creating the next generation of IBM products, platforms, and experiences that developers love. In my role, I recently helped launch “Chatbots for Good: Introduction to Empathetic Chatbots”. It’s a free cloud-based learning experience where anyone — even those with no prior bot development experience — can use Watson Assistant and Tone Analyzer services to design, test, and build a chatbot. My goal is to expose the course to as many individuals as possible, so that they develop a solid foundation to start building chatbots with Watson to help solve problems of the world.
Why does AI excite you?
I like to think of AI to be augmented intelligence, as it exists to make our world more efficient and easier. As we continue to collect more data on ourselves, and frankly human behavior, I believe that AI will not only enable us to live our best personalized lives, but also to solve loftier problems about the human experience that we are only beginning to tackle like solving for poverty or cancer.
What is the biggest misconception about working in AI?
AI is only as good as the data fueling it and the ML algorithms powering it. Most misconceptions about AI come from people thinking it is an out of the box solution that is omniscient — it’s not. You have to train your AI, you have to maintain your AI, and at times, you might have to completely redo your AI.
Where can people follow you?
Matt Bleifer, Twitter Cortex
Where do you work, and what do you do?
My name is Matt Bleifer, and I am a Product Manager for Twitter’s Cortex team, which develops machine learning technologies that help power the Twitter product.
What did you study, and how did you get into AI?
I studied Computer Science at Cal Poly, San Luis Obispo before joining Workday as an Associate Product Manager. While at Workday, I became increasingly excited about the potential business applications of machine learning and began teaching myself through online classes. Eventually I joined Workday’s ML team and worked on delivering machine learning applications within Workday’s extensive suite of enterprise products.
What are you working on? Any recent projects you can share?
These days I am focused on further developing the Cortex ML Platform with technologies that help spark exponential growth in ML at Twitter. Problem spaces I focus on include automation, experimentation, and data.
Why does AI excite you?
AI has already changed that nature of software and product development and is only just getting started. From a technology perspective, I think we will see massive decreases in the cost and complexity of effectively leveraging AI / ML in applications which will dramatically increase product innovation. This is similar to what we’ve seen in web development or cloud technologies. As useful abstractions and managed solutions become accessible, companies are able to achieve much more.
What is the biggest misconception about working in AI?
I think the biggest misconception is that it requires a PhD to be an effective contributor in the field of AI / ML. Firstly, the amount of quality online resources for learning AI / ML has grown steadily in recent years. In my experience, when you look beyond the math of ML, you’ll find that the higher level concepts can be remarkably intuitive. Secondly, as with the development of the software industry, AI / ML will continue to require contributors of all backgrounds including design, business, product, legal, human resources, and more. If you’re interested, jump in and help!
Where can people follow you?
People can (obviously) follow me on Twitter or find me on LinkedIn.
