Enter Big Data and mix in a fair amount of commercial pressure, i.e. “Are you getting the most out of your data?” And you have a $100 billion industry that is exploding with promises of what could be.
“Don’t you want to do more with your data? In the modern age, it’s cheap to obtain, cheaper to store, and we can analyze it and develop all sorts of profitable initiatives with it.”
What do you get when you tell million-dollar corporations that there’s more money to be made here and that the cost associated with unlocking that potential is low?
You get an almost overnight $100 billion industry.
Which again, is not to understate the value of Big Data. Given its proper application, Big Data can be all of those things. It’s just not all of those things to all users. And at worst, it’s a false promise based on commercialized pressure to be a part of the next big thing.
Is AI Our Last Hope?
Hardly. One of the advantages AI and ML have over the Big Data conundrum is there is a lot more opportunity to identify solutions that can be realistically implemented. Those solutions can have real, noticeable impact.
That said, the notion of all of these concepts; AI, ML, Big Data, etc as offering limitless value or being the saviors of society, humanity, or simply just business is overblown. We would argue that positioning these technologies in this way is a fundamentally flawed position.
Rather than wondering if AI will be the savior, we see it a bit differently. It’s more of a rush to provide an answer to Big Data’s failure to live up to its promises as the last big savior.
Consider The Gold Rush
This irrational hope that there’s limitless possibilities and capabilities for AI and ML are flawed. We would compare it to the mentality of a Gold Rush. That is, thousands of people flocking to the promise of something amazing. What’s more, thousands of people who had no interest in prospecting feeling pressure to get in on the action for fear of missing out.
We know what happened there. The vast majority didn’t strike it rich. The majority of people who did strike it rich, did not do so by finding gold. Rather, they sold tools, lodging, supplies, and other ancillary accessories to those looking to strike it rich.
It would be lazy to make a reference to fool’s gold at this point. However, it should be stressed that AI, ML, and Big Data are not inherently fool’s gold. Nor is actual gold.
However, the false promises and hyper-commercial pressure to make these systems into something they’re not can sometimes make it feel that way.
There is immense social and commercial pressure to chase the shiny object. Whether that’s gold or Big Data, or even AI. However, we should have an ethical standard for how quickly we react to such impulses.
As new technologies and processes come into use, there will be continued pressure to adopt them. From a pragmatic standpoint, we should resist the urge to adopt for the sake of adoption. Rather, we should focus on the practical applications of these tools and how they can best serve both our personal and business needs.
What Does That Mean For AI?
Big Data, while useful, truly failed to deliver on their lofty promises. Never-ending storage, widespread practical application, immeasurable profit-generating analysis, the list goes on.
To avoid falling into the same pitfall, it should be noted that both AI and ML have already achieved some of these goals in various applications. And they will continue to innovate and explore.
AI and ML will likely bring about a massive disruption to one, or more industries. However, knowing precisely where, when, or how that will take shape is difficult to predict.
For now, those of us in this space should take up an ethical mantra not to over-hype, oversell, and under-deliver. There are enough amazing possibilities with these technologies that we don’t have to promise the world.
Is AI our last hope? Probably not. It’s just the latest one. But in order to have true, ground-breaking, and even disruptive force; it doesn’t have to be the end all, be all of technological innovation.
It just has to work. And more importantly, work for what you need it to do.