flights and in varied conditions. Now, there’s a better view of helicopter crashes and the machine finally learns that, no, helicopters don’t crash because all the helicopters are broken. It took a human to realize that fact first, however.
MIA: Chunks of crucial but hard-to-get real-world data
At a 2019 Microsoft workshop, the powers that be gave tech journalists and industry analysts hands-on experience in programming AI chat bots and a preview of upcoming Microsoft data-related technologies, including AI, quantum computing, and bioinformatics. One topic touched on was the need for synthetic data, although if I recall correctly, Microsoft called it something else at the time. (Virtual data? Augmented data?)
Regardless of what people call it, you might ask why anyone would need to use artificially created data, given the exponential growth of data from the real world. International Data Corporation, a premier global market intelligence firm, has reported that data from the real world expected to be created over the years 2020 to 2023 is growing at a rate that will surpass the amount of data created over the past 30 years. The analysts also say that the world will create more than three times the data over the next five years than it did in the previous five. Statista, another global leader when it comes to market and consumer data, pegs data growth to be more than 181 zettabytes by 2025.
I don’t care how big your data center is, that’s an overwhelming amount of data! So, why on earth would you need to create artificial data on top of what you already have? Well, it comes down to the fact that data sets are by nature incomplete. Furthermore, some real-world data is extremely difficult, impossible, or too dangerous to capture.
You can’t safely send a repairperson to replace the sensor in the middle of a commercial wind turbine farm where the wind coming off the blades of numerous high-powered windmills can be at hurricane force. You can, however, infer data reads based on previous sensor data relative to neighboring wind turbine data in current weather conditions — filling in the missing data with values inferred from previous metrics and/or neighboring devices’ measurements, in other words. For example, one can infer without benefit of actually measuring it again that, since a specific structure measured 6 feet tall yesterday and it does not possess the ability to grow, that it is still 6 feet tall today. A better inference would also note that the structure has not toppled or sunk into the ground.
However, you can also create synthetic data sets based on known laws of physics, wind turbine specs, and other factors to create a simulation resulting in synthetic data that can be safely collected and used in decision-making. Most, but not all, synthetic data is created by simulations.
FIGURE 3-1: How fast are these spinning again?
Another example would be facial recognition data. Many countries regulate how much (if any) facial data can be taken or used without a person’s prior consent. This can significantly limit the amount of facial data available on which to train facial recognition machine learning models. To overcome a data shortage, companies turn to AI-generated faces of people who don’t actually exist. Data from fake faces also helps machine learning know how to determine which faces are real and which are not. The distinction can be useful in many endeavors, including detecting deep fake videos.
Evaluating man-versus-machine in decision-making
Business leaders often formulate a vision for their company or a particular project. This practice is not an attempt to predict the future but rather to aim for a specific future. This person is steering toward a future that they believe to be profitable or advantageous to the company. A vision, then, is a decision with a purpose.
How do you form a business vision? Developing an obtainable vision requires both creative and deductive skills. A business leader must be able to imagine possibilities, recognize opportunities, deduce their value and the probability of success, and shape a new or creative idea — a vision, in other words.
In short, a leader’s vision is part imagination and part information with more than a dash of math. Business acumen is a talent based in large part on pattern recognition and the ability to see connections between heretofore unrelated items or pieces of information.
Given that data analytics and machine learning are particularly adept at discovering patterns and data relationships, why are they not good at identifying new business visions? Part of an answer might be found in W.I.B. Beveridge’s The Art of Scientific Investigation, where the author explores the intuitive side of scientists and speaks of originality as “often consisting in linking up ideas whose connection was not previously suspected.” Interestingly, that book was originally published way back in 1950 by W.W. Norton & Company Inc. Other great visionaries explain the role of imagination expressed in any form — whether it’s art, science, or business — in similar terms. For example, the legendary graphical designer Paul Rand said that the role of the imagination “is to create new meanings and to discover connections that, even if obvious, seem to escape detection.”
What about logic and math and the other hard skills that machines do excel in? Do they not form the lion’s share in importance and worth when it comes to making a decision — particularly in data driven companies? It’s true that machines do surpass human skills in this regard. Machines can do math faster and usually error-free, but humans can do it without even consciously thinking about it.
The human brain runs a significant part of this work in the background, leading to the seemingly out-of-nowhere “Eureka!” moment in a flash of inspiration. It’s called intuitive intelligence — the ability to use the subconscious mind to make faster and more integrated decisions.
Though both human and machine have pros and cons when it comes to decision-making, leveraging the strengths of each leads to decisions that consistently deliver a better value to the organization. This is why disregarding or discounting human instinct, gut feelings, experience, and talent is a grave error —
