Human intelligence benefits from an interesting duality of arriving at conclusions. On the one hand arriving at conclusions based on perception of patterns, and on the other reaching conclusions based on logical and rational analysis. Both forms are distinct, but complementary. Machine based intelligence also comes in two forms: AI based decision making based on deep learning which interprets patterns in data to arrive at conclusions, mimicking the perception-based intelligence of our brain, and standard rule-based computing (like in a PC) mimicking the rational intelligence of our brain.
If we were to model the brain based on our general observations it would consist of two parts:
- Right – Perception based
- Left – Rational based
Our senses – taste, sight, touch, smell, and hearing – provide patterns to the right part of our brain to generate perceptions. Whereas all our logical interpretations influence the left part and generate a structured and rational understanding of a situation or a problem.
When we study physics or mathematics we are mostly using the rational part of the brain, which is best suited to providing us a logical structure for the subject. However, when we are dealing with patterns created by our senses, we are using the perception part of the brain. Our five senses are the prime sources of patterns for creating perceptions. Since most situations are a mix of logic and patterns, we collaboratively use both rational and perception parts of the brain to arrive at conclusions and make decisions. Both parts, perception and rational, are integral sources of human intelligence.
Both parts of the brain are simultaneously active in all situations. The right part may be busy generating perceptions based on patterns, while simultaneously, for the same situation, the rational part of the brain is busy constructing a rational interpretation of the situation based on some logical structure and comes up with a rational conclusion. Who wins? Right or left-brain? It depends on the situation.
Most AI systems today are based on deep learning, where learning happens through exposing the AI system to tens of thousands of illustrative examples. Deep learning involves absorbing intricate details and subtle nuances in pictures, videos, or sounds into the parameters of the neural network of the AI system. After the training, the AI system is able to perceive the input data based on patterns in images, faces, objects, movements or sounds fed into the system. The AI system’s decision-making is based on the perception of the input data patterns, behaving like the right side of the brain – specializing in perceiving patterns.
The left side is about understanding and dealing with logic of the situation. This works more like the standard computing we know from a personal computer (PC) or a smartphone. This is about coding situations that are clearly structured by rules that can be articulated in “IF-THEN-ELSE” logic. It can be compared to the left side of the brain in our simplistic model. Future PCs and Smartphones are very likely to have AI logic integrated. We want to remind our readers that these simplistic models used here are gross approximations of reality. Their purpose is just to illustrate the working of two processes in the brain and to offer a metaphor to illustrate how two forms of computing work in an easy to understand fashion.
We believe that these two forms of computing for decision making ?—? for processing structured (with standard computing) and unstructured data (with deep learning AI) ?—? can be used collaboratively for a much more balanced decision making. Deep learning AI systems are essentially recognition algorithms that automatically convert unstructured pattern based data into structured information that can then be acted upon using standard logic-based computing. This approach helps in addressing the “black box” issues of AI and increases the transparency of decision making. Let us illustrate this with a couple of examples.
- Surveillance video: Today most public places are monitored with scores of video cameras. Video data from these can be collectively scrutinized for suspicious activities or people using AI. Once AI concludes based on understanding the video feeds that something suspicious is happening or about to happen it creates an alert with its decision on what is happening together with a recommended action. By doing this AI has translated an unstructured situation based on video sequences into a concrete structured information that something critical has happened or is imminent ?—? eg. a terrorist attack or a terrorist has been identified in a public place. With this structured information, the rational intelligence dealing with structured information can kick in and take a specific decision to act (e.g. vacate the public place). This illustrates how both types of computing can work collaboratively.
- Traffic management: Google maps obtains traffic information based on people travelling with smartphones on the road, indicating which segments of roads have normal traffic, slower traffic or traffic jams. Using this information together with other sources of traffic alerts and allows an AI system to predict on how traffic in each segment is likely to evolve over time. Essentially, the AI translates unstructured data of traffic speed data for on various roads into structured information forecasts of how the traffic will be in 15 minutes, in 30 minutes, or in an hour. This forecast information can be used to guide individual drivers via routes that minimize their estimated time of arrival, based on where they are heading.
Both these examples illustrate how AI and standard computing can work hand-in-hand to solve problems in a balanced and collaborative way. AI is essentially used to make sense of the unstructured data patterns and translate it into structured information, which can be transparently and decisively acted upon by standard computing techniques.
This balanced approach can be used to take reliable decisions in complex systems containing mixed sources of unstructured and structured data such as for autonomous vehicles or other robotic solutions. A generic version is illustrated in the following diagram.