It’s 2023 and we’re living in a brave new world. And while it feels a lot like science fiction, this one isn’t devised by Aldous Huxley and it’s very real.
Nowadays you can’t throw a (virtual) rock without hitting some form of artificial intelligence (AI). Whether it’s autonomous vehicles, customer service chatbots, or virtual assistants like Alexa, everything is in some way affected or augmented by generative computer programs or deep learning algorithms.
Seeing that it’s taking over our lives, let’s dive into what that really looks like. What is AI? How did it get here? And what are the impacts we can expect—and are some of them not scary?
What’s the deal with artificial intelligence (AI)?
Humans don't like to think in statistics. Personally, stats was my worst subject in college. Statistics don't jive with how I think, and more than likely, how most people think.
The human brain is more visual than it is numerical, and we experience the world through dozens of imperceptible cognitive biases any given second, meaning the data points we generate as individuals cannot be relied upon.
The magic of AI comes from the fact that computers don’t have any of this baggage of the human mind. They don't care about how things look; they just see algorithms. They simply observe all the data points, aggregate them, and spit out things like trend lines and inefficiencies.
A (fast and loose) history
We have to take a couple of steps back to understand why we’re seeing this explosion of AI now. It’s rooted in the fact that the age of data is more prevalent than ever—and that we've been approaching it for quite some time.
Something that may surprise you is that AI development goes all the way back to the eighties and nineties. The power of real-time statistical analysis was truly in the limelight when it became critically important for financial instruments and institutions to maximize their output.
Suddenly, humans could spot trends and inefficiencies that normally were missed. It was problem-solving through automation.
Fast-forward to the early 2000s. The concept of data science was popularized in mainstream culture by Michael Lewis' bestselling book Moneyball: The Art of Winning an Unfair Game (later made into an Academy Award-nominated film), about a statistical approach to assessing major league baseball players.
Today, we have had breakthroughs in advanced computing that not only supports the aggregation of all these data points but is actually able to sort them through ever-growing data sets, all the while continually training data to learn more. Computing power has become greater and greater in order to drive these AI algorithms—and then, of course, the fuel to the fire: the internet, with its infinite sources of data from every human interaction, wittingly or unwittingly.
Not every data is perfect. Not every data point is clean. But the idea is we've given ourselves the field on which to sow these seeds of the power of AI and machine learning (ML).
These phrases are thrown around a ton, which can be tricky when you’re someone who’s just catching up to this trend. Essentially the trajectory of AI comes from a few ingredients: decades of strengthening the power of statistical analysis, modern computers that can manage massive data sets, and now, with the internet, an endless set of data streaming in feeding AI systems.
The shrinking chasm between AI and human intelligence
Some folks who, like me, grew up with the beginnings of AI may remember messaging with SmarterChild, a very rudimentary AI chatbot. Those who remember using SmarterChild might agree that the experience was a good encapsulation of the early perception of AI—as in something that was trying to be human but was very obviously not. This is called the “uncanny valley” effect, the valley referring to the gap between what is human and what is artificial.
By stark contrast, what we are seeing today are products like ChatGPT, created by OpenAI. ChatGPT runs where SmarterChild could only crawl (if that).
In point of fact, the natural language processing (NLP) of ChatGPT is so advanced, it passes the Turing test. The test is named for its creator Alan Turing, who theorized that a computer was capable of “thinking” if it “acts, reacts, and interacts like a sentient being.” The test itself is simple: if an impartial, unknowing person cannot determine if they’re communicating with a computer program or another human being, then that program passes the Turing test.
ChatGPT and other AI tools like it are crossing the “uncanny valley” by effectively mimicking the way the human brain functions, only with algorithms instead of neural networks. They are generating human speech and communication indistinguishable from that of a human mind. And yes, this is real life, not the Twilight Zone.
The unsung benefits of AI
1. A tidal wave of new coders.
This is where I think it gets really interesting. When we consider the evolution of communication over the past few decades, the largest growing form is arguably code.
Code is language with formalized logic, rules, and syntax. Now we have AI tools that are able to adequately mimic coding languages to the point of writing very high-quality code and pseudo-code. Access to these tools opens the door for a much larger chunk of the population to design digital applications and products themselves.
AI solutions are bringing about an era in which companies aren’t required to limit their talent search to folks with an exhaustive developer background and a four-year computer science degree anymore. They may not even require short-term coding boot camps or year-long intensive programs.
With the advent of AI technologies such as ChatGPT, I’m predicting an oversaturation of basic coding capabilities which may be cause for concern.
And, in fact, there are many “white collar” vocations that may feel a similar impact, but with coding and web development engineers, I’d forecast a shift perhaps toward outsized supply for the demand.
2. When algorithms rule, humans teach.
Indulge me for a moment and let’s once again look back to the nineties when the internet wave was just beginning to surge. Household names like Google were first establishing themselves and introducing the possibility of AI use cases for the layperson, no longer limited to major institutions.
Suddenly, anyone like you or me could interact with this magical technology that could deliver the result of an arduous chore like going to a library or calling someone—handing you the information you wanted in a matter of seconds and with an easy-to-use interface.
At that same time, companies realized that with the shifting emphasis on Google Search, their rankings drastically changed their prospects and engagements with potential customers. So the salient point here is that even though there was this new AI tool that in a sense replaced the job of a librarian or a 411 operator, innovations like the birth of search engine optimization (SEO) also opened up new jobs.
For those who may be worried about all that AI is capable of, stories like these are important to remember. With AI and machine learning models such as ChatGPT and LensAI, we are bound to see marked inefficiencies around how best to leverage those tools.
These may be expert systems, but not all users are experts. Even when algorithms rule, we still need to know how best to comply with them.
Thus, I predict a new area of AI specialists will be born to help everyday users maximize the benefits of those tools. After all, SEO is going on 30 years and we still have specialists in that field.
3. Resources that don’t require revenue.
From the standpoint of the entrepreneur, newer companies such as startups that are strapped for resources are suddenly able to reach for AI tools to help them build their business. They could leverage AI for digital graphics, logos, or even an API integration for a prospect.
AI tools are allowing more and more people to achieve more with fewer resources and less funding. They could be the difference between a startup getting funding and a founder becoming unemployed.
4. Open source education.
If you want to educate yourself in a new skill, a new technology, or even just something you're curious about, chatbot tutors are an accessible way to gain that knowledge without expensive courses or even a new degree. These educational AI applications can help you start a career or make a move into a different area that you're more interested in.
Oftentimes the hardest part of learning something new is starting, and if we can lower that bar to make it easier for people to be curious and explore, there may be a benefit not only to the individual but to humanity as a whole. The more we learn and the fewer concepts that are foreign to us, the more we can share with others, and maybe feel a bit more connected to one another.
5. Prototyping for your next product launch.
A colleague of mine, Daniel Bacon, recently wrote about the potential to fuel product development and engineering creativity using visual AI tools. Companies partnering with AI platforms are bound to see a benefit from the stream of continuous information from consumers—primarily how consumers might envision the next iteration of their product. With just a few keywords and specifications, engineers and CAD designers can create a mockup of their next product, and get consumer feedback before they even need to factor in supply chain needs.
Tools for tomorrow’s product company
While we have to acknowledge these AI products, tools, and advancements will have a profound impact on our job market ecosystem, they will impact product companies as well. How?
Coupled with AI architectures, machine learning is becoming more and more intrinsic to the decision-making and production process behind supply chains.
But AI tools didn’t come from nothing; it isn’t as though we snapped our fingers and they were born. These tools are trained on data sets. These data sets have to be constantly updated. They have to be constantly curated. And after all that, the results need to actually comply with what humans expect.
Today’s companies, of course, recognize the importance of data—be it product data, consumer data, or process data. All this is to say that these recent advancements in ML and AI solutions are going to allow companies to outpace their competitors and their market by identifying patterns and trends that give them an edge.
This can be accomplished in myriad ways, and with unique approaches tailored to a specific company’s needs. But success is dependent on producing the right data and selecting the right tools to aggregate and automate that data for their particular processes.
For those who may be daunted by introducing AI into their product lifecycle and business workflows, there are use cases for AI solutions for essentially every scenario that would not replace your job—but, in effect, act in partnership with you. A tool you can partner with, to reduce your headaches, and maximize your successes.
What other innovations can we expect this year? Read our predictions for 2023.