The Scientific Principle: How Sure Can You Be About The Future?

How sure can we really be about the future? With artificial intelligence (AI), we can be more sure than ever before.

Data science has become a staple in many businesses’ operations and future-planning strategies in recent years. Underpinning these companies’ efforts is the role of AI and machine learning (ML) to better map the road ahead. In retail, this is no different, with a growing number of large organizations putting their faith in AI solutions that can help predict optimum inventory decisions — primarily for the avoidance of excess waste or running out of stock.

However, many decision-makers are still wary of the role that AI will play in their future supply chains. And the reason, more often than it should be, comes down to that word: science.

It’s human instinct to believe that science in its truest sense leads to certainty — to definitiveness and to surety. We take so much of the world around us as absolute givens, thanks to the science around it. Gravity. Motion. The very planet we exist on. We have accepted them as scientific principles that give us a stable platform on which to live.

What we fail to acknowledge, however, is that each of these certainties has only been reached as a result of millions of observations, scientific hypotheses, counter-declarations and — eventually — agreed-upon principles.

Simply, the same guarantees can’t be provided with every target area of science. This is much less so in the realm of data science. For some in the C-suite, this is a reason not to trust this science-induced upgrade on the human mind.

Moving forward, we need to realize that the alternative to knowing very little isn’t to know absolutely everything. AI is a route to statistical probability, not scientific surety, and is still very much worth the investment.

Deterministic Versus Probabilistic

Circling back to the above nuances, there is a common misunderstanding surrounding the difference between deterministic and probabilistic science.

It’s an easy mistake to make when you look at scientific ideals.

Ultimately, science does indeed seek a definitive answer. It looks for rules that would make an observation 100% correct — something you can prove, with surety, is a constant. This is deterministic scientific. And examples can be found all around us: with every pendulum swing, every time the sun rises and when our feet touch the ground.

We also then use the formulas surrounding these ideals to help describe other phenomena or the likelihood of certain outcomes. For example, even with the invention of an external influence such as a parachute, we can still be scientifically sure that a person will be brought naturally back down to earth again.

However, the vast majority of science isn’t deterministic. It’s probabilistic. Here, again, we look to engage in as many observations as possible to increase our knowledge of trends, patterns and likelihoods. But in most cases, the end result is still nowhere near 100% accuracy. There are simply too many factors to consider in most strands of life to ever be sure.

Making the distinction between these two scientific principles is a big hurdle for some to overcome. We put so much of our blind faith in the deterministic that we then seek the same comfort from probability. It’s a natural leaning to expect science to be 100% sure of something, even if so little of science reaches that figure. And, upon finding out that 100% can’t be reached, there is a tendency to disregard the notion altogether.

An Upgrade To The Human Mind

The flu shot is one such example. When people learn the solution being presented by science isn’t 100% effective, so many opt out — despite the fact that the shot is a better alternative to getting the flu.

Of course, decision-making in retail has far less stern outcomes, but in a business sense, the upshots of failing to implement ML can still be severe. To not be as accurate as possible with procurement and inventory levels is setting the business up for reputational, environmental and logistical shortfalls, as well as sales — all likely to hit revenues hard while the surrounding competition strengthens those same parameters.

What ML really presents is a reflection of the same scientific principles that help yield probabilities and likelihoods. Each day, as more bespoke observations occur, the algorithm achieves additional statistically significant forecasts. Yet, even at a base level, once historical data is fed into the tool, immediate predictions will still, always, be more accurate than a human — no matter how experienced that person is.

There’s No Time To Wait For A Miracle

An additional misconception is that AI tools must be on their way to achieving an eventual level of surety — that the solutions on offer are still in a nascent stage and it’s worth holding out on investment until they mature or evolve further toward that clichéd perception of science.

But while the framework of the world we live in is determined, its innumerable influences on our everyday experiences in society and business mean that 100% accuracy of predictions will never be achieved. People have free will, trends change, the natural environment around us evolves and economic and political situations ebb and flow.

The point is, while we can barely keep up with this level of change, let alone apply statistically probable forecasts to a specific segment of life in real time, ML, comparatively, can.

Probabilities will still always be nearer to 50% than 100%, but that’s a significant upgrade to what we can reach manually.

If asked, not many of us would profess to believe in miracles or to be able to tell the future. The next step is realizing that AI isn’t a miracle worker in machine form, either. But it does get you a significant step closer to predicting the future.

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