Both human as well as machine learning generate knowledge — but there’s a big difference between the two.
Learning is the act of acquiring new or reinforcing existing knowledge, behaviors, skills or values. Humans have the ability to learn, however with the progress in artificial intelligence, machine learning has become a resource which can augment or even replace human learning, says engineer and psychologist Peter Rudin in Singularity2030.
Both human as well as machine learning generate knowledge, one residing in the brain the other residing in the machine, Rudin suggests. But is this really the only difference between the two? And, more importantly, how do we apply what kind of knowledge and how do we balance these knowledge resources for optimal results?
Machine learning has become a rapidly growing subset of artificial intelligence research. The application of so-called neural network software, mimicking functions of the human brain coupled with the availability of low-cost massive computational hardware resources provides opportunities to solve problems which so far have relied on human brain-power. Huge data pools (Big Data) consisting of medical or financial information, picture libraries or information about customer behavior to name just a few are processed with various types of highly complex algorithms to produce digital knowledge without conventional programming.
The human brain is not like a computer, nor is a computer like a human brain. In spite of the fact that computers can perform “neural network” processes, they are inspired by the neurons of the brain but are not self-organizing and adaptive. Furthermore, machine learning, which teaches computers to act in ways they are not explicitly programmed to perform, cannot replace human learning.
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It is proven that machine based knowledge far exceeds the capacity of the human brain as far as memorizing knowledge, understanding and comprehending are concerned. Consequently humans tend to increasingly rely on machine based knowledge with the added advantage that there is no retention problem as this knowledge is always accessible ‘on-line’.
As soon as we step it up to more challenging abilities such as applying of knowledge, abstracting and problem analyzing the combination of human and machine learning knowledge represents the latest in various business segments.
An interesting application of machine learning in big data analysis was developed by a startup named BehaviourExchange. They used billions of recorded online interactions to create millions of user profiles that allow, for instance, e-marketers to adjust a website’s content to the specific user’s interests in real time. Their system is able to understand a web visitor’s demographic and psychographic characteristics, as well as their short-term and long-term sentiments. Without any doubt this a state-of-the-art combination of machine learning, big data and human brainpower that gives us a hint of the digital future to come.
