Big Data
Data collection, also known as big data, is all the rage. We’re in a seemingly endless search for meaning, trying to make sense of all the information flying at us. Maybe it’s an effort to take control or get an edge over competitors and peers, or maybe it’s to gain some new insight. Whatever the case, we collect the data, scrub it, and crunch the numbers. The result? Sadly, the return on this investment isn’t good. In fact, it’s downright lousy. Research indicates that, on average, big data projects return pennies on the dollar. Rather than a multiple of what’s invested, they result in a mere fraction. That is what you call a poor return on investment. It’s like putting $100 in the bank and getting just a few dollars out, which is definitely not something you’d want to be doing at all.
And yet, the pursuit of big data (or more broadly referred to as artificial intelligence, or ‘AI’) continues unabated. Context surely applies here. Some big data activities are beneficial, and big data is specifically useful in closed systems, which typically involve tasks that are defined by rigid rules. An example of this would be machine learning, where AI is given boundaries and develops an intelligent system after being ‘trained’ through examples. In banking, this could mean analyzing thousands of bank transactions and their features to determine if a new one is fraudulent or legitimate. However, big data is not so good in open systems, or what we can call ‘wicked’ problems. Humans are often much better at those, though granted this is taking into account our current technological capabilities, not what will be possible in the future. In these instances, we can connect the dots and use our abilities to detect patterns. This would include situations with constantly changing conditions, such as those that involve a deep understanding of the real world and context. This is exactly why you see social media companies often scrutinized for algorithms that takedown posts for seemingly no good reason, as that task currently requires some level of human intervention.
Two Schools of Thought
That brings up two schools of thought. One is that it’s only a matter of time before machines are as intelligent as humans, or that a computer or machine will soon be able to pass the so-called ‘Turing Test.’ According to its namesake Alan Turing, a computer passes the test as soon as a human can’t tell whether they are interacting with a human or a machine. It’s the domain of general artificial intelligence (AGI), which occurs when machines can think in similar ways to us humans. Proponents of this school of thought suggest that the thing that makes us distinctly human, our consciousness (or call it our soul), is just a matter of possessing enough computing power, along with figuring out the right algorithms and code. The other school of thought is that computers will never possess the same level of intelligence as humans, or at least not anytime soon. And even if they do, they won’t have the qualia sense of experience or consciousness, like us humans. We’re not going to settle that debate here, that’s for sure. Even agreeing on the definition of such things as consciousness is a challenge.
But what all of this brings up is that maybe there’s a more reliable alternative to collecting and crunching massive amounts of numbers. So, is there a better way? Enter human intention. Call it tapping our consciousness or intuition, based on our individual and collective know-how and experience. Intuition, or our ‘gut,’ is possibly nothing more than a collection of prior experiences that are called upon when needed to solve a particular problem. When brought about in specific circumstances (under specific conditions or constraints), it appears almost magic, as if we already knew what to do. So, we can define it as the deliberate application of our knowledge and sentiment in certain circumstances (under certain conditions). You see, one of the significant challenges with big data is that at its source, it’s collecting data that’s not contextualized. It’s like the old saying about garbage, garbage in, garbage out. Another way of calling it is data with lots of baggage and noise, and the process of cleaning up that data can be costly while also removing valuable information.
Human Intention
Instead, let’s capture the context of the information that’s gathered. In other words, we’re capturing the human intention behind the data. Let’s define human intention as the deliberate application of our intelligence under certain conditions. Capturing intention or intelligence requires defining rules — or the conditions and circumstances under which intention is applied. If we would attach conditions around the data that’s being generated, then lo and behold, the data collected becomes rich with signal instead of noise. This is because we can compare the results of the data to the conditions under which that generated that data. If we don’t know the conditions or context under which that data was generated, how can we really understand the data? There’s a simple way of looking at this context issue. Suppose a data set is being analyzed and a pattern is detected with the number ‘7.’ We really have no idea what that number means without context. It could be days in a week, the number of items sold, or the number of dollars a stock increased over a given time.
But what about computers that beat humans at chess or Go? That must surely be a sign of the dominance of computers in the war of intelligence. Not necessarily. That actually was raw computing power at work, not the delicate nuance that’s required for solving more intractable, intricate, and open problems. Not to mention, the resource requirements of these machines are massive. The human brain requires an estimated 15–20 watts of power (the entire body requires around 70–100 watts), which for comparison’s sake is around a third of the power required for a typical 60-watt household lightbulb. DeepMind, the computer that won at Go over its human opponents (with the AlphaGo routine), required an astonishing 200 watts of power…per chip. With over 5,000 chips! Now, imagine the collective power of an equivalent number of human brains utilizing a similar amount of resources (by the way, that’s as many as 66,667humans). Let’s be fair to us humans, after all. It argues for the power of the collective human intelligence and what we might accomplish if we work together, acting with the intention to solve our most wicked of problems.
Want to experience the benefits of collective and collaborative human intention in action? Check out macrovo.com
