Why we need more non-tech backgrounds working to shape artificial intelligence.
‘AI-Driven’, it’s a phrase we’ve added to more and more of our lives throughout this past decade. Not just when discussing phones, personal computers and cars, — but thought, theory and conjecture as well.
Regardless of where it has found a home though, the simple fact of the matter is that AI has assimilated with enough systems now to truly be seen as a force for change.
And this populational uptake is why the time has come to open up more of AI development to people from non-tech backgrounds.
The narrative surrounding artificial intelligence is one that reflects much of what we commonly associate with the peak of scientific and technological advance. That is to say that it’s the realm of digital geniuses, so far along the cutting edge that the pursuits of so-called tech gods are now near indecipherable to the common man.
This is not only disingenuous to many people who work with AI-driven systems outside of the technology sphere but sets a dangerous narrative for the future growth of the artificial intelligence sector.
Stagnation is equivalent to death in the tech landscape, this is a truth seen in the demise of everything from outdated apps, to digital music purchases lapsing in the face of streaming.
What could possibly have saved these bygone relics was innovation from parties outside the tech sphere.
This isn’t to say that people from other professional backgrounds should replace the coders, devs and tech experts who actively develop these AI-driven systems. Instead, the answer lies in opening the form to input from other skillsets and finding new ways for them to contribute.
The merit to this idea is clear, as in past AI-driven systems have struggled in their ability to rationalise themselves to their audience.
To many, the concept of AI is little more than robots doing the thinking for us. This is a narrow scope of view that could affect both the uptake and viability of future AI systems.
By allowing more backgrounds into the internal fold of AI, the disconnect between the system and the outside user will begin to shrink naturally.
And as more people gain an understanding of the inner workings of artificial intelligence.
The flow-on effect of adopting such a goal becomes clear.
AI will begin to do something it has only struggled to achieve in past, explain itself. This, in turn, will begin to move the needle on how, and most importantly, where people will see artificial intelligence as being able to assist them in achieving their own goals.
To ignore this potential for growth is a pathway that leads only to a greater disconnect and the stagnation mentioned earlier in this article.
But to address the devil’s advocate, there will be teething problems.
As with all proposed advances, attention soon turns to how such immense change can be achieved. After all, AI systems are incredibly complex and simply finding ways for those outside of specialist fields to contribute will be difficult.
While this is a valid concern, perhaps it is one born of a past definition of AI that has persisted even as it proliferated across industries and sectors throughout the world.
‘Artificial intelligence is the peak of scientific and technological advance’, as has already been mentioned. But a simple glance at where AI now sits in the everyday lives of its users tells that this doesn’t at all mean it’s out of reach.
Take our work at Mindhive as an example.
We’re bringing AI-driven collective intelligence to the fingertips of professionals worldwide. But without their input and expertise from a vast range of fields, we’d never have been able to refine our artificial intelligence practices to reach the point we have today.
What we’ve noticed, is that those with no background in artificial intelligence have been empowered to learn more about its inner workings, to better contribute in the future.
This is now the cycle of growth that underpins our work in the collective intelligence field. To bring more professionals into the system and show them not only how AI can augment their ability to create knowledge, but also how they, in turn, can contribute to it becoming better and better over time.
Consider this cycle as a test case for wider change.
The more uptake we have, the more varied and diverse the input into the direction of our AI becomes. Something we think might be a future worth pursuing for artificial intelligence.
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