It's an exciting time in artificial intelligence.
New offerings are cropping up seemingly every day.
Chatbots are writing recipes, generative AI is revolutionizing art, and robotic comedians are cracking us up with their witty one-liners.
So the joke goes "Why did the AI start a band?" And the answer is "It wanted to be an algorithm and blues singer." Hilarious!
Oh, maybe that's just me.
But look, when it comes to generating business value with artificial intelligence, there are a number of prevailing myths.
So let's take a quick peek at five of them, courtesy of a study by the IBM Institute for Business Value and the M.I.T / IBM Watson AI lab.
And look, they don't know I'm doing this, so this is my interpretation of their report detailing what is holding some businesses back from fully embracing AI.
So let's get started with number one--myth number one.
And that is that shortcuts in AI really don't work.
Now, if we think about kind of the history of AI, for years,
artificial intelligence systems have been built by data scientists training various data sets with very specific and very specialized objectives.
But with the advent of powerful foundational models, that's all changed.
And that's really the key to this, is foundational models.
We are witnessing a new era here of AI generalists that can adapt to various tasks with minimal fine tuning.
We're talking about technologies like GPT-4 and Lambda.
And surprisingly, these foundational models can often meet or even exceed the performance of their narrowly-focused counterparts.
Now, for sure, that's not always the case.
Adapting pre-trained models sometimes results in too large a drop in performance on new data, but when developing any new AI application,
it would be remiss not to consider how existing foundational models perform before taking a more specialized route.
That's myth number one.
Now number two.
Let's put this to say if it isn't deep learning then it isn't really AI.
So look, search, retail, streaming, all sorts of B2C platforms.
They've long adopted deep learning for recommendations, forecasts and other data-driven services.
But deep learning is just one piece of the AI puzzle.
Organizations employ different machine learning techniques depending upon the business problem.
Now, while something like 20 to 30% of organizations are using deep learning today,
just as many are also using other machine learning techniques. Things, for example, like linear regression--that's a popular one.
Decision trees and also random forest.
So things that aren't actually deep learning but still machine learning.
In reality, deep learning is just one tool among many in an enterprise analytics toolbox.
Now for myth number three, I'm going to phrase this one as
"AI is the answer. What's the question?"
So this is really that idea that AI is the answer to everything.
Like not every business challenge or desired outcome is fit for AI, despite the hype that might make it appear so.
Sometimes simpler solutions like just rule-based systems or straightforward data analysis is actually going to be sufficient for what you need to do,
and it can deliver equally effective results.
AI isn't always the silver bullet it's made out to be.
So rather than forcing AI to fit every problem, let's ask ourselves if it's truly the best solution for the task at hand.
And remember that sometimes simplicity can outshine even the most advanced technology.
Right, onto myth number four.
Now, this myth says about the sweet spot of AI .
What is the sweet spot of AI?
Well, the myth says cost reduction.
I think that's a bit cynical, don't you?
Look, sure, AI can help reduce costs by automating labor-intensive tasks and optimizing workflows, but that's just scratching the surface.
AI can enable competitive differentiation that can improve process efficiency,
and it can foster personalized customer engagements--all things that go way beyond simply keeping down expense.
And look, AI doesn't come for free.
The increased compute necessary to support AI solutions can result in higher expenses in the data center.
If you're looking as a purely cost saving measure, you're really missing the point.
Which brings us to myth number five.
And this one really says that the AI benefits, they're basically limited, and they're limited to the problem they're trying to solve.
And that's a very narrow view to take, because contrary to this belief, AI's impact often reaches far beyond its initial target.
So deploying AI in one aspect of a company can bolster adaptability and resilience in others.
AI's transformative capability isn't restricted to a single department or to a single team.
Once deployed, it can reshape entire organizations, or indeed industries.
In a nutshell, these five myths highlight a common theme, and that is the need to approach AI with an open mind,
recognizing its multifaceted potential and the importance of considering all aspects of its implementation.
And by debunking these myths, we can unlock a world of possibilities. And who knows? In the near future,
maybe I'll not be the only person chuckling away to a AI generated robotic comedians.
Or, or... maybe I will.
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