Tod Loofbourrow of ViralGains On Five Things You Need To Create A Highly Successful Career In The AI Industry
Approach problems with a collaborator’s heart. Individuals impact history. But teams move mountains. AI is moving at such a rapid pace that a collaborative approach to learning and spreading knowledge is essential to career success and staying on the cutting edge. All 8 of the authors of Google’s seminal 2017 paper on the Transformer architecture which launched the latest AI revolution have moved on to other organizations, where they are sharing their knowledge and launching a thousand new AI ships. Don’t be a loner — the best team wins.
Artificial Intelligence is now the leading edge of technology, driving unprecedented advancements across sectors. From healthcare to finance, education to environment, the AI industry is witnessing a skyrocketing demand for professionals. However, the path to creating a successful career in AI is multifaceted and constantly evolving. What does it take and what does one need in order to create a highly successful career in AI?
In this interview series, we are talking to successful AI professionals, AI founders, AI CEOs, educators in the field, AI researchers, HR managers in tech companies, and anyone who holds authority in the realm of Artificial Intelligence to inspire and guide those who are eager to embark on this exciting career path.
As part of this series, we had the pleasure of interviewing Tod Loofbourrow.
Tod Loofbourrow is the CEO and Chairman of ViralGains, a leading marketing technology for finding, understanding, nurturing and acquiring customers across all phases of the digital advertising journey. Tod is a serial entrepreneur, having served as President of iRobot (NASD:IRBT), where he helped grow market capitalization to $1B, Founder, Chairman and CEO of Authoria (now Peoplefluent), and CEO of artificial intelligence consulting firm Foundation Technologies, Inc. He is the author or editor of eight books — including a bestseller on computer science and robotics written at the age of 16 and an anthology book series on Artificial Intelligence and Machine Learning. Tod was educated at Harvard and Oxford Universities, and has lectured at Stanford, MIT Sloan School, Harvard Business School, Babson College, and others.
Thank you so much for joining us in this interview series! Before we dive in, our readers would like to learn a bit about your origin story. Can you share with us a bit about your childhood and how you grew up?
I grew up in a family of engineers — my father worked for Bell Labs, the research arm of AT&T, and my mother ran a local bookstore. My brothers went on to jobs with Microsoft, Apple and DreamWorks. I built robots as a kid and when I was 16, I published a book on robotics — it went on to sell 20,000 copies, and the robot became the basis for the iRobot Roomba — which has now sold more than 40 million units. Later, I served as president of iRobot.
Can you share with us the ‘backstory” of how you decided to pursue a career path in AI?
My initial fascination with AI was through robotics — I saw a display of American Presidents as animatronic robots at an exhibition in Montreal when I was six years old and was fascinated with the idea of using computers to create intelligence. Later I got fascinated by all aspects of artificial intelligence (AI) and devoured everything I could read, built robots, created expert systems, neural networks, and genetic algorithms and even started a company doing AI consulting around the world with my friend and colleague Erik Brynjolfsson.
Can you tell our readers about the most interesting projects you are working on now?
My company — ViralGains — and I are working to reinvent how digital advertising works using AI based on two fundamental principles — the right to individual privacy and a commitment to a genuine and authentic conversation between individuals and the companies who serve them. We combine authentic conversations with individuals plus the use of AI to try to understand people’s preferences, sentiments, perceptions and interests on a large scale and then we use that understanding to help advertisers find their natural customers, expand their customer base, launch new products and build loyalty among their customers. It is a fascinating problem because it is one of the largest data problems in the world and yet based on something as simple as an honest conversation.
None of us are able to achieve success without some help along the way. Is there a particular person who you are grateful for who helped get you to where you are? Can you share a story about that?
There are so many: From my father, I got a curiosity about how things work and a desire to make things better. From my mother, who read two books a day, I got an understanding of the world at large and the greater things that drive progress for humanity. From my documentary filmmaking professor in college Ross McElwee, I got an understanding of storytelling and the importance of building connections. From my college debate partner Tony Blinken — now our Secretary of State — I learned the importance of understanding both history and individuals for making progress in the world. And from my college roommates Erik Brynjolfsson, Jon Koomey, Ephraim Heller and Mark Morland, I learned to sharpen my ability to make an argument supported by fact and evidence, as we would all quickly pounce on each other for any flaws — no matter how tiny — in our logic. And I credit the Montessori education system which I experienced as a young child for giving me a sense of control over my own destiny.
As with any career path, the AI industry comes with its own set of challenges. Could you elaborate on some of the significant challenges you faced in your AI career and how you managed to overcome them?
There are two things that make AI difficult:
One is that the rate of change is very high, so it’s important to stay up to date with the latest innovations. There was a massive wave of AI when I first came out of college leading to thousands upon thousands of innovative applications and there’s another massive wave going on now with large language models and generative AI, sparked by a 2017 paper written by Google researchers. The rate of change is mind-boggling but also exciting if you approach it with a sense of curiosity rather than fear.
The second thing that makes AI difficult is either skepticism or fearmongering from people about the power of these tools and technologies. Throughout my career in artificial intelligence, I’ve had to overcome skepticism from those who don’t think that computers can do things, and the reality is if you build it, you prove them wrong. Witness the impact of ChatGPT, where computers could suddenly do things that no one thought possible, and people only believed it when they sat down and played with it. And few people thought a robot could navigate and clean a house until I built the first one which proved you could do so, and 40 million Roombas later, they believe.
On the fearmongering side, worries that our latest generation of tools will wake up one day and become the Terminator have been with us since the first telephone networks were built in the late 1870s. Folks need to approach each generation of technology with a healthy curiosity, an open mind, and a careful approach to safety, but not an irrational fear. We are in the hype and fear stage now but moving quickly past it to the era of useful applications.
Ok, let’s now move to the main part of our interview about AI. What are the 3 things that most excite you about the AI industry now? Why?
Bill Gates summarized this latest revolution in artificial intelligence best when he said the fundamental breakthrough is this — “Computers can now read, and computers can now write.” This revolution builds on 40 years of work in neural networks and artificial intelligence to create truly novel ways of interacting between people and computers, allowing computers to understand what we ask them in plain natural language and produce text, images, video and other outputs that are quite clever. So, what three things are most exciting?
- With a simple ability to write what you want and have your computer understand you, you can get detailed explanations of things, detailed analyses, and detailed breakdowns of complex subjects within seconds.
- Much of the work we do now is tedious work — researching things, finding answers, assembling the things we find into sentences and paragraphs — a lot of this work can be automated and these latest breakthroughs in artificial intelligence allow for that routine work of research and assembly to be automated by machines.
- Just as Google made simple webpage searches available to billions of people around the world, so too will these new artificial intelligence tools make information, knowledge, and analytics available to billions of people around the world — this is tremendously exciting in its impact both on productivity and on economic inequality.
What are the 3 things that concern you about the AI industry? Why? What should be done to address and alleviate those concerns?
These new breakthroughs are not without their limitations — many observers and researchers think we’re on an exponential curve of development in the capabilities of these generative AI systems and large language models. I do not. I think we’re on an S-curve, meaning that we’re in the exponential phase of rapid change but that the limitations of these systems — such as their tendency to make things up and make substantial errors — will prove to be much more significant limitations than most mainstream AI researchers think. That’s the top of the “S” in an S-curve, where the exponential rate of improvement flattens out and the challenges grow. Time will tell. But this technology is both revolutionary and overhyped now. These are my largest concerns:
- Bias and errors: These AI systems are trained on existing human knowledge — to the degree that their training set incorporates systematic bias or errors, so too will their outputs incorporate systematic bias and errors. In fact, these systems can magnify errors through a process called confabulation, which in plain English means combining things in such a way that they no longer contain accurate information.
- Fear: Decisions driven by fear rather than knowledge. While this revolution is tremendously exciting (perhaps the biggest breakthrough in the history of computing), it’s also substantially overhyped, and I fear a rush to regulate based on misinformation and fear could do more harm than good.
- Bad actors: Whether we are talking about state-sponsored purveyors of disinformation, or people foolish enough to hook up AI systems to control things they shouldn’t be controlling, bad actors can cause a lot of damage by misusing AI systems.
For a young person who would like to eventually make a career in AI, which skills and subjects do they need to learn?
This is an excellent question, and I would strongly urge young people to get involved in artificial intelligence, as this is a fundamental technology breakthrough that will have a transformative impact on global society.
The first place I would urge someone to start is to simply play with these technologies — go to OpenAI and play with ChatGPT, figure out how to use Discord and start playing with Midjourney, and build extraordinary images based on words you type into the prompt, subscribe to some newsletters about AI and if a tool strikes you as interesting, go play with it. The great news about this industry and this technology is that it’s not gatekept by universities or closed systems or large organizations preventing access — it’s wide open and anyone can go play. Play, play, play. There are tons of resources that you can avail yourself of on YouTube, on TikTok, and on the open web. There are great newsletters on Medium and newsletters like Ben’s Bites that you can subscribe to in your email.
There are hundreds of conferences springing up — many of them free, and many of them offering remote access. Times like this are few and far between in the technology industry where technology breaks through so quickly and where access to it is so broadly open — take advantage of this time, because these times are rare and incredibly empowering if you just simply take the plunge. And of course, there are solid programs at the universities too, from undergraduate courses in data science and machine learning, all the way up to PhDs to break new ground in an increasingly fertile area.
As you know, there are not that many women in the AI industry. Can you advise what is needed to engage more women in the AI industry?
I know many women in the industry, so I’m not sure what data you are using in the premise of the question, but it’s always important to get more women and minorities involved in the factors and forces that drive progress in society, and this is one of the biggest. For those who have something to teach, I would urge them to seek out students in non-traditional places; for those who are curious to learn, resources abound.
Ethical AI development is a pressing concern in the industry. How do you approach the ethical implications of AI, and what steps do you believe individuals and organizations should take to ensure responsible and fair AI practices?
Number one — make sure that your training set does not include implicit or explicit bias, or these things will be replicated by your AI systems. As we say in computing: Garbage in, garbage out.
Number two — test, test, test. Generative AI systems are black box systems meaning that you can’t look inside and see exactly what they’ve learned, so the only way to root out bias or incorrect information is to test, test, test, test. Many of the large generative AI companies are now starting to crowdsource this testing which is an interesting and novel approach.
Number three — don’t connect your AI to control systems if you don’t understand exactly what it’s doing, and similarly don’t publish work written by AI. Have an expert look at whatever it produces and use it as a draft and not as a final output except for very simple examples. That said, I know both writers and programmers who have improved their productivity between 50 and 80% by using AI to produce the first drafts of their work. But there is a big difference between using it to produce 50% to 80% of your work and using it to produce the final result.
Number four — if you’re a student, disclose your use of AI in any work that you do. There’s nothing wrong with using AI to organize your research and generate ideas and analysis, but there’s something very wrong with presenting its output as your own work.
Now, just for fun, I asked ChatGPT the same question — here is part of its answer (not bad at all):
Approach to Ethical Implications of AI:
Transparency and Comprehension: Understand and be able to articulate how the AI models work. This is especially important for models making decisions that impact human lives, such as in medical diagnosis or criminal sentencing.
Bias and Fairness: Actively seek to recognize and correct biases in AI data and algorithms. This includes understanding the sources of bias, both overt and subtle, and applying techniques to mitigate them.
Accountability: Determine who is responsible when AI models make incorrect or unethical decisions. This could be the developers, the users, or a combination of both.
Privacy and Data Rights: Ensure that AI respects user privacy, which includes not only protecting user data but also giving users control over their own data.
Safety and Security: Ensure that AI models are robust and reliable and that they are resistant to adversarial attacks.
Collaboration: Engage a diverse set of voices and stakeholders in the AI development process. This helps to ensure that multiple perspectives are considered.
Addressing the ethical implications of AI is not a one-time task but a continuous process of reflection, adaptation, and improvement. Organizations and individuals need to be proactive in their approach and open to feedback to ensure that AI serves as a force for good in society.
Not too bad, ChatGPT, not too bad.
Ok, here is the main question of our interview. Can you please share the “Five Things You Need To Create A Highly Successful Career In The AI Industry”? If you can, please share a story or an example for each.
1 . A career in AI starts with curiosity and a willingness to play. AI tools are broadly accessible, easy to use, and extremely powerful. My company ViralGains is an AI-based marketing software company, and we felt it was very important that every one of our employees — not just the technical ones — was well-versed in, and comfortable with, the latest AI tools. So, we created an event called AI Play Day and gave everyone a couple of weeks to learn the latest AI tools and present something fun that they had done with them. One person had illustrated a children’s book she wrote about Fiona the turkey using MidJourney. Another employee had automated 80% of his programming work using AI tools. Still, another had used ChatGPT to automate most of his email writing. The first characteristic you need to succeed in AI is a willingness to learn and play.
2 . Next, you need to cultivate an experimental mindset. Success with AI involves a lot of iterations and a lot of trial and error. Be data driven. And go where the data takes you. Some years ago, at the beginning of my career, I was tasked with developing an AI-based fraud detection system for a credit card company. The company had many experts in fraud detection, so we codified their rules of thumb — or heuristics — in an expert system. But we also built a neural network-based system for finding and recognizing patterns that the experts had never seen before and discovering novel types of credit card fraud. It turned out that the best solution to the problem was a combination of these two types of systems, bringing the strengths of each to a truly hybrid system that best solved the problem. But we got there only through experimentation and a ton of trial and error.
3 . Approach AI with a beginner’s mind. The concept comes from Buddhism but is fundamental to learning anything radically new. Don’t be afraid to be a novice, and come to the task with no assumptions, but just a willingness to learn, experiment, and play. You will make many wrong turns, hit many barriers, and scale many mountaintops that turn out to only be foothills, but the open beginner’s mind will allow you to quickly switch gears to a new approach, a new technology, or a new way of looking at the problem. When I first built Mike the robot at the age of 15, I wanted to give him the ability to navigate a room. I experimented with impact sensors, proximity sensors, infrared lasers, and ultrasonics before I hit the approach that worked best, a combination of impact sensors and ultrasonics. A similar approach of impact sensors and later cameras powered the iRobot Roomba to success. Later when I was President of iRobot, we experimented with game cameras from a Wii console, LIDAR, soft robotics and micro cameras to achieve navigation and manipulation, and extended that beginner mind to telepresence robots and bomb-sniffing robots, even working at one point with a sensor designed to mimic a dog’s nose. Not all of it worked, but the beginner’s mind was the key to both learning and innovation.
4 . Live in dogged pursuit of the impossible. And from that will emerge the possible. When the pioneers started the AI industry, they considered games like chess to be good places for AI to experiment, but the idea that AI would one day beat a human was a distant impossible dream. Until IBM did it with Deep Blue. When the organizers of the DARPA Grand Challenge created robot vehicle races across the desert, they dreamed that one day cars would be able to navigate themselves. Now that elusive impossible dream comes closer by the day. When my friend and colleague Erik Brynjolfsson wrote The Second Machine Age, heralding the second industrial revolution emerging from robotics and AI, he considered blue-collar jobs like truck driving to be those most suitable for automation through AI, and the white-collar jobs to be the most secure. Now with generative AI, it is the white-collar jobs like writing and analysis that will be most affected by automation, delivering another blow to the impossible. Erik talks today about inverting the pyramid he pioneered in the Second Machine Age, as AI’s new capabilities affect knowledge workers more profoundly than blue-collar workers.
5 . Approach problems with a collaborator’s heart. Individuals impact history. But teams move mountains. AI is moving at such a rapid pace that a collaborative approach to learning and spreading knowledge is essential to career success and staying on the cutting edge. All 8 of the authors of Google’s seminal 2017 paper on the Transformer architecture which launched the latest AI revolution have moved on to other organizations, where they are sharing their knowledge and launching a thousand new AI ships. Don’t be a loner — the best team wins.
Continuous learning and upskilling are vital in a dynamic field like AI. How do you approach ongoing education and stay up-to-date with the latest advancements in the AI industry? What advice do you have for those looking to grow their careers in AI?
Study the basics. Get an education — traditional or non-traditional. There are lots of great online courses. And lots of great university ones. But don’t get hung up on the credential — it’s about what you know. There are lots of great newsletters and resources on YouTube, TikTok, Medium and other platforms. Dive in. The water’s warm.
What is your favorite “Life Lesson Quote”? Can you share a story of how that had relevance to your own life?
“It’s all made up.” As a child, I thought the world worked a certain way because that’s just the way things had to be. But that’s not true, and history shows that radical change comes to all orthodoxies and all systems. Everything we see and experience, all the customs and institutions and scientific orthodoxies, business practices, and ways of doing things we see were just made up by some other people. It’s all made up. So don’t be afraid to make it better. To break barriers. To question everything. Invent. Improve. Innovate. And leave the world a better place.
Everything I do in my life is governed by this simple maxim. I invented the first microcomputer-controlled self-navigating robot, and now 40 million Roombas clean people’s homes. I proposed a new way to fund electronic medical record adoption to the White House in 2009, it got passed into law, and now 96% of hospital-based physicians (and 78% of office-based physicians) use electronic medical records, up from 9% in 2008. My cofounder and I at ViralGains found the privacy practices of the digital advertising industry to be unacceptably invasive, and so we reinvented it, and now billions of advertisements later, advertisers have found a new and better way to build authentic relationships with prospective customers.
J. Robert Oppenheimer said it best when he advised:
“See things not as they are, but as they might be.”
You are a person of great influence. If you could start a movement that would bring the most amount of good to the most amount of people, what would that be? You never know what your idea can trigger. 🙂
Lead with curiosity and the rest will follow. Not fear. Not old assumptions. Not biases. Not ossified orthodoxies. Curiosity and an open mind are the keys to making your own particular dent in the universe. You know less than you think. And so does everybody else. Be curious. Be fearless. And just get started.
How can our readers further follow your work online?
For my commercial work transforming advertising with AI, check out the ViralGains website www.viralgains.com.
To follow my nonprofit work on creating jobs for those facing barriers to employment, including the Center for Artificial Intelligence and the Future of Work, check out www.jff.org/work/jff-labs/jfflabs-incubation/jfflabs-artificial-intelligence or more broadly www.jff.org.
For my very occasional thoughts on other things, check out my Twitter/X at @todatmit.
This was very inspiring. Thank you so much for joining us!
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