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AI Goals: A Brief Introduction

In previous blog posts, we discussed AI in practice as well as ethical considerations that need to be taken under account when designing systems with AI capability. In this blog post, we will focus on the goals of AI. These goals, describe tasks commonly associated with intelligent beings such as humans [1].

Automated Reasoning: Reasoning is the process of thinking about something in a logical way in order to form a conclusion or judgment [2]. For example, consider the following statements

“Every Winter, it has been snowing in Northern Sweden. Therefore, it will also snow in the coming Winter.”

The above statement is known as inductive reasoning, which uses examples and observations to reach a conclusion (i.e. from specific to general). Consider also the following example:

“It will be snowing in Northern Europe during the coming winter. Sweden is part of Northern Europe. Therefore, it will snow in Sweden in the coming winter.”

The above statement is known as deductive reasoning, which applies facts to make a logical argument (i.e. from general to specific). Automated reasoning, in context of computer science, is concerned with the process of building computer systems that automate the reasoning process [3].

Machine Learning: Machine learning allows computers to learn and improve from experience without being explicitly programmed to do so. Given a set of sample data (known as “training data”), machine learning algorithms build mathematical models through a process known as training. These models can then make decisions or predictions based on new input data not seen before. Such decisions or predictions are probabilistic in nature. Their accuracy depends on the dataset used to train the model (for example if it does not contain biased data or too few data), as well as the machine learning algorithm used. Depending on the type of training data, and its availability, there are different machine learning approaches used.

As of 2020, deep learning has emerged as the predominant tool for machine learning. Artificial neural networks, which mimic biological functions of the human brain, play a central role in deep learning. Deep learning has been used to produce models for visual object detection applications (e.g. autonomous vehicles and face recognition), fraud detection, chatbots and virtual assistants, natural language processing, etc.

AI Planning and Scheduling: Given some pre-stated objectives (“goals”), AI Planning and Scheduling algorithms create a set of actions out of a larger set of possible actions, that if executed in sequence will achieve these objectives. Examples of AI Planning applications include autonomous navigation of robots in remote environments where it is difficult for human operators to carry out navigational tasks (e.g. on Mars), scheduling of production lines in a manufacturing plant, scheduling of bus schedules in a city based on spatiotemporal demand, etc.

Knowledge Representation refers to the ability to represent data in a machine-readable format that later other AI methods, such as the aforementioned reasoning and planning, can be used efficiently and effectively. In context of this challenge, semantic web technologies such as linked data and ontologies are used extensively.

Machine Perception tries to mimic human senses (sight, hearing, taste, etc.), insofar as taking sensory inputs and interpreting those inputs in a human-like way. The long term goal is not only for machines to interpret environment stimuli as a human would, but also be able to explain their actions/decisions to humans in an understandable way. Deep learning technologies play a major role in interpreting sensory input, however machine perception is a multi-disciplinary field that also involves so-called “soft-sciences” such as psychology.

The reader should note, that when building AI systems, these techniques are not used in isolation, but are rather complementary of each other, and oftentimes overlapping. For example, consider the case of a simple virtual assistant app guiding the user through an e-commerce website. This application may use knowledge representation for inventory and vocabulary/grammar definition, but also reasoning and deep learning for interpreting user queries verbally and processing using stored logic. Lastly, perception may be used for communicating to users the result of their query in a human-understandable format (e.g. as speech).



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