By Rishon Blumberg, 10x Management Co-Founder
The business world is changing fast and finding a talented AI engineer can bring your company significant competitive advantages. While entrepreneurs have relied on their instincts and intuition to dictate the direction of their businesses for a long time, AI engineers are helping businesses verify or discredit some of their long-held beliefs.
An AI engineer has the ability to come into a company and transform the way we do business. And business leaders are using data to make decisions like never before. Executives can still rely on intuition, but AI is here to help us verify or discredit our beliefs.
As a tech entrepreneur myself working with some of the best AI engineers in the world, I’ve witnessed the transformative power an AI engineer can have on a business. I had the privilege of interviewing an AI engineer and prodigy that started university at age 12(!), Zack Dvey-Aharon, on how companies will begin to use AI in the new data-driven era for business.
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Rishon (in bold): Thanks for taking the time to speak with me Zack. What is your favorite use of AI that you have worked on personally?
Zack: As an AI engineer, I’ve helped healthcare companies analyze data to understand when their cure works best. I’ve helped cybersecurity companies identify abnormal network behavior for security purposes, helped energy companies better understand ocean drilling potential, commercial companies optimize their pricings and offerings, the list goes on… If I picked a favorite, I might get some angry letters in the mail from those I left out! All my clients are special to me and I truly enjoy working on every project I undertake.
Quite a diplomatic answer! What are some ways that you think AI will be monetized in the future?
I’ll use a simple example that demonstrates how AI can improve most existing services and products, and not necessarily create new ones. An AI engineer might develop a refrigerator that can manage the content inside the fridge and adjust the temperature to ideally match your groceries. The company that employs that AI engineer will monetize by simply selling more units than the competition. That’s just one example. Basically, the companies that really take advantage of the intelligence of AI will be able to monetize simply by being better than the competition.
Let’s compare it to baseball and the famous example of Moneyball and the Oakland Athletics for a second. In 2002, Oakland started using deep statistics to analyze and find undervalued players in the major and minor leagues before any other team. While most teams had scouts that would rely on instincts to evaluate a player, Oakland used objective statistics and algorithms to evaluate players. This allowed Oakland – with a payroll of $44 million – to compete with teams like the New York Yankees – with a payroll of $125 million. Data lets us evaluate the exact impact that a player has on the field. What percentage of the time does a player hit a curveball traveling 82mph into the infield vs. the outfield vs. over the fence? Just like baseball was transformed by statistics, the broader business world is being transformed by AI too. Any method (like Moneyball) that gives you a competitive advantage will monetize itself.
As a Yankees fan, I appreciate the baseball analogy. How is AI different than other technologies in the past?
Through data analysis, AI engineers can allow companies to work much more efficiently, adjust to changes, cancel unnecessary business processes and replace expensive alternatives, including human jobs.
AI is completely data-driven, so algorithms will help us understand where we can improve our processes as opposed to using intuition (as I just mentioned) or people analyzing data. This has never been the case before.
Data is a true goldmine and the sky’s the limit with how it can be used. By employing a single AI engineer or multiple AI engineers, companies have endless opportunities to better understand their business processes, improve them, optimize them and reveal new insights that can dramatically change the bottom line.
In lay terms, what are the differences between Data Science, AI, and Machine Learning?
Data science is the most general term for data analysis. Data can be analyzed manually without any algorithms or learning mechanisms, which means in certain circumstances, it’s not AI at all.
Artificial Intelligence (AI) covers all computerized/algorithmic ways to learn data and react better to it.
Machine Learning (ML) is a sub-domain of AI. Machine learning features self-learning mechanisms that become smarter as they have more data.
So the difference between Machine Learning and AI is that AI can include hard-coded formulas that do not learn from the data, whereas Machine Learning engineers will always build self-learning mechanisms.
What company do you think will dominate the AI landscape in the future? For instance, 68% of internet searches in the U.S. are done on Google. Will there be a Google of AI?
It’s tough to say that one company will monopolize the industry. My prediction is that several years from now, AI, and more specifically Machine Learning, will be naturally integrated everywhere and by everyone. Just like Google and its search engine are everywhere, AI and machine learning will be everywhere. An AI engineer will be a very lucrative position to have at any company.
What are the biggest challenges for companies looking to embrace AI?
The clear number one challenge is to find a strong enough AI engineer to help a company or join a company. If we compare AI to playing chess, there are close to a billion chess players in the world, but only a thousand grandmasters. Although many people present themselves as expert engineers, there are perhaps a few dozen AI engineers or teams out there with a truly strong, diversified project experience in machine learning. Building a great AI solution is difficult at the moment because the talent is so rare.
What are the biggest misconceptions regarding AI?
In movies, we often see machines that are ‘smart’ like human beings that can adapt their language and behavior to unpredictable situations. It’s been a fantasy for humans for a long time, especially since it was realistically posed as a challenge by Alan Turing in the 1950s. The truth is that technology like that is still out of our reach, so I’d say that’s the biggest misconception. AI engineers are working hard to get us there, but we’re not that close.
What’s your favorite use of AI technology being applied today?
As an AI engineer, it’s tough to choose a favorite. I find the revolution itself amazing. Insurance companies better understand their clients, media companies better evaluate their artists, airline companies better optimize their seat ticket prices, the list goes on.
What’s the one example of an application of AI that feels inevitable to you, yet today no one you know is really working on it?
I think AI that takes text written about a person, and by that person, from many different sources and gathers a smart, integrated analysis and report would be useful for personal clients, companies and intelligence agencies. Imagine trying to find out information about a potential client, and having to go from point A to point B and all sorts of places to find relevant information. AI could make that process so much easier by aggregating useful data and giving you ONE useful report as opposed to hundreds of sources with bits of useful information.
What advice would you give a company trying to source AI talent?
It’s important to do research on AI engineers that have been contracted by competitors or other companies in the field. My firm has delivered more than 40 AI projects to clients, and in each area, my past AI engineer experience with similar problems turned out to be a crucial factor.
Companies that source AI engineers and development talent must understand two key parameters:
- How strong and experienced is the engineer?
- How easily can their work be integrated with the company, its IT team and the general “data DNA” of the firm?
In today’s economy, even inexperienced data scientists and AI engineers have become very expensive, so building a team seems less realistic for most companies.
Did you really start university at age 12?
I sure did. As I child, I always looked for new challenges and new ways to learn. I convinced my parents to let me try a university class, and when I was able to keep up with the class, I enrolled in more. I was able to finish my university degree before high school graduation.
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