It seems you can’t have a conversation in business these days without some mention of AI. And as CEO of Gradient Ascent, where AI is the meat and bones of our company, I get faced with questions on AI both on and off the job every single day.
AI is not a new term. We’ve been using it and exploring its potential through science and art through the past half-century and beyond. But now AI is far more than just an idea -- it is an insanely powerful beast of technology that will define our future in more ways than we can imagine. Already it has the potential to help us find cures, solve (and prevent) crimes, and determine what we want to watch, eat, or buy.
We’re going to dive into all this at the Gradient Ascent Insights blog, but for now let’s look at three basic AI questions I encounter all the time.
At its most basic, AI can be described as intelligence displayed by machines as opposed to the natural intelligence displayed by humans and other living creatures. I tend to view it as a little more complicated than that. I also believe “AI” as a term is marketing in many ways.
When you break it down, the term itself doesn’t mean much. No disrespect to marketers and salespeople, but over the last decade or two it has been used as an umbrella concept and tied to cautionary tales of misuse, such as this story of Amazon’s AI-assisted hiring approach, and the grand promise and opportunities it presents.
So, though it has been awash in a lot of marketing speak, the basic premise of AI involves getting computers to make more complex, more subtle, more hard-to-explain decisions. In other words, it enables us to do things that are otherwise hard to describe. As machine learning computer scientist Yann LeCun stated, “Our intelligence is what makes us human, and AI is an extension of that quality.”
“Our intelligence is what makes us human, and AI is an extension of that quality.”
Machine learning, often used interchangeably with AI, describes how a machine learns from data and develops predictions based on pattern recognition. Fundamentally, machine learning is what allows machines (computers) to behave as if they are learning and displaying intelligence.
A lot of our own intelligence comes from learning, recognizing, and making decisions based on patterns we have seen in the past. Machine Learning allows computers to detect and learn from patterns too. The power of pattern recognition allows machines to mimic some of our seemingly complex behaviours.
Since AI and machine learning are frequently used interchangeably, always ask what the speaker means when they use one of these terms. Ultimately, both use pattern recognition to simulate intelligence,
If you care about job security, the job security of your children, or the competitiveness of your company, you should probably care about AI. But it goes beyond that.
AI is insanely empowering, and we are just starting to see what it is capable of for almost any and every business. We’ve long said that AI will be central to almost all businesses by 2030 and that any organization that hasn’t already either implemented a strategy or is in the process of doing so will fall behind, and we stand by this claim.
Fundamentally, AI lets us do things we couldn’t do before, and it enables us to do it faster, better, more efficiently, and without being encumbered by manual human activity. Some examples of AI being applied across many different businesses include:
Also, AI relies on data, data, data. As Founder and CEO of Sinovation Ventures Kai-Fu Lee put it: “Data is the new oil.” Data is the cornerstone of all AI and the fundamental version of truth that it acts upon.
“Data is the new oil.”
AI is also equipped with algorithms that help it understand how to apply this data, whether that means finding the best ticket prices, recognizing your voice on a smart home device, or helping a bank determine whether or not your credit application can be approved.
By collecting as much data as possible and analyzing it, AI can unlock the potential -- and therefore truth -- of the data and empower us to make better decisions, or equip our machines to make better, more informed, and more accurate decisions.
So, that’s all?
We haven’t even scratched the surface, but we figured we’d get the big questions out of the way. We’ll be following this post up with more questions in the coming weeks. In the meantime, send us any key questions you’d like answered and we’ll do our best to tackle them. We love educating people on the potential of AI, so write us.