AI Boosts Better Decision Making

AI systems do not have the same limitations of our brain. AI can process millions of inputs, without ignoring a single one. And it can do so extremely fast, without rest, without tiring, without distraction, or becoming emotional. AI is an excellent complement to our brain in decision-making. AI is essentially a prediction machine, providing a fairly accurate prediction of the best outcome for a given input situation. Machine Learning has provided modern AI systems with the ability to self-learn from thousands of cases provided.

Fig. 4.1 Anatomy of data based decision making

Decision making starts with data inputs. Some systems use analytics to improve the understanding of the input before using AI. AI/ML generates a prediction in the form of a probability value for the best answer corresponding to the input. Based on the prediction a judgment has to be made to decide the best action. Judgment is a human skill that takes into account the prediction and any other considerations (like emotions, timing etc.). Action results from the decision. The consequences of the action are fed back into the system for tuning. For example, in a skin cancer diagnosis system, the input consists of medical data in form of medical images, reports, and medical history. Based on these the AI system gives a prediction indicating the probability of cancer and need for an operation (e.g. 87%). Using this a surgeon uses their judgment based on the person’s age and other complications to take a decision to operate or not. The outcome of the operation is fed back into the system to improve its diagnostic skills and recommendations. In real-time systems such as an autonomous vehicle, there is no time for human involvement in decision making and the machine directly takes the decision, provided that the machine has achieved a desired confidence level.

Internet of Things (IoT) is becoming very popular for automatically managing complex control systems. Its front-end consists of a networked array of sensors providing real-time data from sources needed as inputs for the control system, such as temperature, location, pressure, motion or an image. This data, in its digital form, is fed into an AI based decision-making system, which decides the best action based on the provided input data. IoT systems are used in a variety of applications ranging from optimizing cooling in a data center and enabling self-driving cars to deciding which segment of crops to harvest when

Companies like IBM, Microsoft, Amazon and Google are setting up AI IoT Cloud platforms for their customers, who can bring in data from their sensor networks. Customers develop control algorithms within the IoT Cloud. The IoT platform provides AI and additional algorithm libraries that are needed for control and decision-making. IoT platform providers are also acquiring vast amounts of sensor data to make their platforms even more attractive. They will be able to provision almost any sensor data needed by their customers on a subscription basis. As a result, creating IoT systems will become significantly easier and faster.

Data, in essence, is a digital representation of the reality in the world. As humans, we take our daily decisions based on sensing and analyzing the reality around us with our five senses and using our brain to decide and act upon that data. Internet of Things (IoT) systems work very similarly?—?with a notable difference. They can decide based on all the thousands of cases they are trained on, 24×7, undistracted, without breaks or the need to sleep.

Drones have become an increasingly useful sourcing tool for IoT data. Drones can capture high-quality images and videos of specific areas at a very low cost. Images and video are excellent sources of data patterns for AI. Describing the content or the subtle uniqueness in images and video clips is often difficult for humans?—?but not for AI. Machines have become better than humans at pattern recognition and can learn to differentiate subtle patterns in images without human assistance. It is possible to do reliable lip-reading by training AI machines with millions of samples of people talking and the corresponding text. Using image data of vineyards, captured by drones, AI can now predict the best time for harvesting different sections of the vineyard. The color and texture of the grapes provide the patterns for the AI.

As the cost of better decision-making with AI drops steeply, AI will get integrated into almost all products, services, or processes. This will make them a lot smarter and more competitive.

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