Previously we touched base on how AI works and AI’s capabilities but have not gone into detail. One of the main reasons modern AI is possible is due to machine learning. In this blog, we will cover the basics of machine learning, as well as deep learning, neural networks, fuzzy logic, and Natural Language Processing (NLP).
Machine learning is a pattern-based subset of AI. Machines are given data and create rules based on patterns detected within. Through these set rules, AI can understand and predict outcomes from certain information, then makes actions to avoid or obtain said outcomes. Several tasks in the simulation industry can be benefited from the use of machine learning. For Example, It can help train pilots’ capabilities through Semi-Automated Forces (SAF).
Machine Learning in Simulation
The pentagon recently held a simulated dogfight between two F-16s. One is controlled by a human, and the other by AI. The AI won five to nothing. This win for machine-kind came only a year after DARPA said that no AI could beat a Human in a dogfight. What makes this possible is that the AI performs millions, if not billions, of iterations of trial and error predicting all the possibilities of what could occur.
The AI predicts the pilot’s decisions and adjusts its decision-making accordingly. Once it has enough data, AI can make rapid decisions faster and more accurately than any human.
Machine learning was turned off for this bout. However, if it was turned on and the human pilot got the better of it even once, the AI could learn from its mistakes and use the information gained to perform better on the next go.
Limitations of Machine learning
From chess boards to dogfights AI has come a long way as it triumphs in rules-based quick decision-making tasks. Machine learning does have its limitations, however. It lacks cooperation. Modern machine learning AI is difficult to link together to have, say, a fleet of AI piloted jets working in unison.
Currently, AI is used in drone technology to prevent collisions. The drone will take control if there is an interruption between the drone and the manned controller. For example, the up-and-coming Loyal Wingman Drone will be manned by a fighter pilot. However, they will simply direct the drone’s AI, allowing the AI to perform specific tasks. AI drones are also much cheaper than F-35 manned stealth fighters.
With all this said, however, one could ask to go a step deeper. We explained that machine learning is what makes modern AI work. Next, we will explain how neural networks make machine learning work.
Neural networks simulate the neurons within the brain using artificial neurons or nodes. It is used in machine learning to simulate the human brain making connections through trial and error rather than simple data sets. It is basically a computer system that mimics the brain by classifying the information and detecting patterns using probabilities and if-then statements.
Neural networks base statements, predictions, decisions, and actions on a degree of likelihood created by recent patterns found within the experience. In other words, the network makes an action, analyses the outcome, and determines if that was the right or wrong thing to do based on the data sets and the performed analysis. But the learning does not stop there.
Deep learning is a subset of machine learning. It is composed of larger neural networks. Like machine learning, Deep Learning starts with giving a system plenty of data to sort through to allow it to make decisions on different subjects. The output is then sent through the large neural networks which ask binary questions, extract numerical values, and classify answers.
With these massive neural networks set in place, more data can be collected, analyzed, and used to optimize performance. Rather than basic machine learning which uses smaller neural networks. The larger the neural networks, the deeper the learning. Yet still, the question remains, how do neural networks and deep learning collect information and analyze it? With fuzzy logic, of course!
Fuzzy logic measures truth values and is the contrast to Boolean logic. In Boolean logic, the variables of truth values can only be 1 or 0, true or false. However, fuzzy logic accepts the possibility of partial truth and therefore may be any real number between 1 and 0.
While Boolean logic accurately represents how computers “think”, fuzzy logic more so represents how humans think. Fuzzy logic understands that people make decisions imprecisely and with non-numerical information. Through fuzzy logic, it becomes easier to explain and replicate human thought through computers. A prevalent example of how fuzzy logic and AI are used today is Natural Language Processing (NLP)
Natural Language Processing
NLP is a computer system’s attempt at understanding verbal human communication/language. Software systems try to understand human communication through rules and patterns. These rules and patterns are created from large amounts of inputted data on language and written excerpts.
As useful as it is, NLP is only one example of the seemingly limitless possibilities of AI. But with all the power AI has in almost every facet of our lives it raises questions of concern. Therefore, in our next blog, we will discuss the history of AI and the ethical questions that have arisen around the subject.
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