Diversifying AI usage in collective intelligence

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Recent discourse in the field of AI-driven collective intelligence has raised an interesting point for consideration. Namely that the maturing of the discipline has brought with it an increasingly narrow gaze in its approach to using artificial intelligence to further its goals.

This is both a valid concern and one that has serious ramifications if it not addressed in any reasonable amount of time.

At first, it may seem odd to pin diversity as a core problem in this field. Indeed, the ‘collective’ within the name collective intelligence implies a distinct opposition to homogeneity.

After all, drawing on a wide range of thoughts, ideas, and expertise is what allows collectives who set out to solve a problem or achieve a goal to thrive and push for real change.

But as has been made clear in the ongoing conversations around this topic, it is a lack of diversity in the AI practices used to assist collective intelligence that is a cause for concern.

The cited problem is an overreliance on certain types of AI methods.

If a majority of collective intelligence outlets could be gathered and then asked how they utilise AI within their knowledge generation and problem-solving tasks, the answer would be distinctly similar.

AI is used in most cases to order and make sense of vast datasets, with machine learning acting as the tool by which structure is forged from chaos.

The reason for this is obvious, to take tasks humans aren’t capable of and relegate them to an AI.

What is also obvious is why such a proliferation of this tactic has occurred in recent years.

As an increased number of exterior industries have begun embracing the potential for machine learning to assist in them furthering their own goals.

And within this expansion lies the catalyst for fading diversity in the use of AI within collective intelligence.

As collective intelligence has further entrenched itself in more and more industries, it has naturally become beholden to the direction and intent of the sphere it now operates within.

At first, this doesn’t seem like an issue, as assimilation is a key to success when generating actionable information and achieving collective goals.

However, where once collective intelligence found itself as the bridge between a problem and a solution, it's intertwining with industries other than itself has morphed its intent.

This is not to say that AI-driven collective intelligence is now engrossed in an end-solution dominated mindset. Instead of that, it has begun to shift away from its original role as a middleman and its course must be corrected before it begins to stagnate.

To effect this change, the narrative around the usage of AI within collectives must shift.

It is a fair concern that it may be difficult to take even a single step back from the data dominated problem-solving tasks that AI currently undertakes. However, the potential for other streams of growth using AI could more than fill the gap created by such a change.

Take for example the process of using artificial intelligence as a means of improving the way that information is shared, rather than generated.

Our own work at Mindhive stands as an example of this more novel approach to AI usage. One that has proven it can reap rewards previously unknown to an industry such as ours.

Our efforts have also proven that AI can fill different roles within a collective, while also being just as effective at reaching the same end goal that data-crunching AI would be traditionally employed to fill.

The key to success lies in finding appropriate channels to enhance with AI usage, and then scaling and adapting these AI-driven processes to fit the needs of each collective.

For this to work, however, it must be an industry-wide undertaking.

Smaller entities within the AI-driven collective intelligence field are readily revisiting how different types of machine learning can assist them in reaching their goals.

To push this change further, however, there must be a concerted effort to bring larger players into the fold and begin to build out the processes by which we apply AI practices to collective intelligence over the long term.

The risk that is run by not adopting a new approach to AI utilisation is not just stagnation of industry growth, but the possibility of stunting further growth in artificial intelligence as well.

Want to see how we do AI-driven collective intelligence differently? Join the conversation at Mindhive and help us shape a new future for problem-solving using the power of many.

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Written by

Mindhive | ex — Eidos, Boilerhouse, Basement, Margaret Marr | Speaker, Author | Bringing the shared economy to problem-solving #collectiveintelligence

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