Artificial intelligence (AI) is a fascinating technology that has been under development for decades. The premise of AI is that computers can be built to function like the human brain, endowed with the ability to think and reason. In the 50’s and 60’s scientists were convinced they could build such a machine, but later realized the computing power necessary to accomplish such a task did not exist.
From an everyday use standpoint, AI has slowly integrated its way into our lives via the electronic devices we use everyday. Siri, Alexa, Google Home, X.AI, and Astro are just a few of the many “artificial-intelligence-like” platforms available to consumers. One could argue that since none of these platforms can “think”, then technically they do not meet the definition of artificial intelligence. In a University of Washington research paper on the History of Artificial Intelligence, the authors write:
“No one can refute a computer’s ability to process logic. But to many it is unknown if a machine can think. The precise definition of think is important because there has been some strong opposition as to whether or not this notion is even possible. The argument is that since computers would always be applying rote fact lookup they could never ‘understand’ a subject. The main advances over the past sixty years have been advances in search algorithms, machine learning algorithms, and integrating statistical analysis into understanding the world at large. However most of the breakthroughs in AI aren’t noticeable to most people. Rather than talking machines used to pilot space ships to Jupiter, AI is used in more subtle ways such as examining purchase histories and influence marketing decisions.” 
A couple of weeks ago I had the opportunity to interview Shelby Vanhooser, former Artificial Intelligence Researcher at the University of Oklahoma and current Forward Deployment Engineer at Palantir in New York City, to get his perspective on AI and how he sees it playing a role in the world now and in the future. See the summarized interview Q&A below:
Q:How do you see A.I. affecting the world in the next five years?
A:I think the broader perspective is that to think about AI you really need to think about three different categories altogether: AI, Machine Learning (ML), and Data Science. Artificial intelligence implies logic and reason, and quite frankly, we’re just not there yet. We’re just now getting to a point where AI is accessible and usable at a commercial level in a few industries, but applying it universally to everything is going to take a lot of time and money. One of the biggest problems with AI that many people don’t think about is how hard it is for engineers to “define a problem” and then set up the inputs correctly to solve that problem. Using AI to do this is difficult because the inputs are so complex and can change in real time. For example, many startup companies that claim to have AI enabled platforms, really don’t. What they’ll do instead is hire a bunch of people to manually respond to user inputs until they’ve gathered enough data to give the computer a chance to answer at some point in the future (hopefully correctly). In essence, these companies will just fake it until it works. This is why data science (or data gathering) is critically important to development of AI and machine learning, because without data these platforms are limited in their effectiveness.
Q: What are your thoughts on machine learning?
A: Machine learning is interesting. Basically, machine learning is when a machine (i.e. computer) can take millions of bits of data and quickly determine an optimal way to assemble that data to achieve a desired result. Over time as that machine is given more data, say for a specific task like reading medical x-rays, the machine can more accurately and precisely determine a near error proof outcome.
Q: With that being said, are there any particular industries that are in jeopardy?
A: The way I see it, any job or task that requires any sort of pattern recognition or repetition is in trouble. Secretaries, cashiers, financial services jobs, and even medical professionals, are at risk. Medicine in particular is going to change dramatically, especially around diagnostics. Radiologists in particular will likely transition from actually giving a patient a diagnosis, to simply collecting the data and waiting for a machine to provide the outcome. Think about it; a machine never gets tired, is never emotional, and has the ability to reference millions of x-rays to provide an accurate diagnosis (and likely a treatment plan) in seconds. My parents are radiologists so it pains me to think like this. However, they’re brilliant people and they’re well aware of how technology is making its way into every part of our lives.
Q: What advice would you give to help people understand and embrace the role that AI and ML will have in our lives in the future?
A: Overall, I think this technology needs to be embraced because when it’s used correctly, it can make society better. Despite the concerns about privacy (which are legitimate), we can use AI/ML to optimize our energy usage, lower our food costs, and make medicine more accessible and more accurate. I would also encourage parents to tell their children to avoid going into jobs that likely won’t exist in the future. We need to create better educational programs to get kids excited about jobs in technology because that’s where the future is going.
Q: Any other thoughts or recommendations on AI and ML?
A: If people are really interesting in learning more about AI, I recommend they read a book written by Russel & Norvig about artificial intelligence. It is a technically a textbook, but is great for anyone who really wants to dive in and learn it. Also, I want people to understand that AI is not going to take over the world. That is simply SO hard to do because the complexities that still exist around the technology are difficult to figure out. However, I would encourage everyone to at learn about how AI works at a high level.
In summary, I’m excited about what the future holds, especially around AI and Machine Learning. Admittedly, the science behind how it all works is a lot to process, but from a conceptual standpoint it makes total sense. I hope you take time to learn a little bit AI and ML. It’s a fascinating topic. Cheers – KM