AI & Machine Learning Part One: Predictability is “In” (Big Data is Out)

predictabilityAs the internet commoditized information, making it accessible to the masses, so too will machine learning/ artificial intelligence (AI) transform analytics & predictability, making use of all the data the internet has enabled. And therein is the power of AI, it allows for better decision making, by humans. It does this by better and better predicting outcomes, as it (machine) learns.

AI doesn’t need structured data. It doesn’t need complex algorithms written by humans, and as computing power grows, including quantum computing, it will become more commercial accessible to anyone looking to run predictive models. Along with this commercialization, and a corresponding decrease in costs, the “knock-on” effects of predictability will be the need for humans to build & modify heuristics to guide AI (machine) learning, as well as weigh the importance of one outcome over another. For example, AI will predict the likely outcome of a potential auto- accident, but it would require a human to decide if hitting a plastic bag over a child is a better outcome regardless of probability.

In short, humans will stay play a critical, all be new, role in analytics, insights & predictability.

The downside of this abundance of information, including structured and unstructured data, and across a multitude of mediums and channels, is (i) too much information for human analysis, and (ii) as overall information increases so too does the exponential increase in misinformation (erroneous data).

AI is ideally suited for this problem.

“Unlike traditionally programmed computer algorithms, designed to take data and follow a specified path to produce an outcome, machine learning, the most common approach to AI these days, involves algorithms evolving through various learning processes. A machine is given data, including outcomes, it finds associations, and then, based on those associations, it takes new data it has never seen before and predicts an outcome.”

(Source: HBR: Trade-Offs Every AI Company Will Face)

Why is predictability important?

Predictability is the foundation for making strategic decisions. With the right risk-to-reward profile for a company, optimal growth can be achieved, within a set risk threshold, that balances financial, corporate, customer, employee and community objectives. Making decisions (as companies need to do daily) is based on the predictability of events occurring and the associated pay- offs for each outcome.

Machine learning/ AI enables better predictability of outcomes and payoff’s around decisions, so humans can make better decisions around anything that deals with unknown variables and correlations. It can deal with unstructured, random data to draw new insights and correlations.

Example Applications:

Hiring & recruitment, judicial decisions, medical diagnosis, autonomous cars

Up Next? AI & Machine Learning Part Two: Predictability Begets Decision- Making


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