Thursday, February 20, 2020

Artificial Intelligence for a Middle Schooler

A few days back my middle schooler asked what Artificial Intelligence is. At that moment I realized, how difficult to express AI like complex subject into something accessible to our young minds. This small write up is an attempt to explain AI to a middle schooler. I hope, you will also enjoy it.

Definition 1: Artificial Intelligence is defined as the capability of a device/system which perceives its environment and takes actions that maximize its chance to successfully achieve its goals.

Definition 2: AI is a system’s ability to interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation.
AI can be classified into three different types of systems:

  • Analytical
  • Human-inspired
  • Humanized artificial intelligence

Analytical AI has only characteristics consistent with cognitive intelligence; generating cognitive representation of the world and using learning based on past experience to inform future decisions.

Human-inspired AI has elements from cognitive and emotional intelligence; understanding human emotions, in addition to cognitive elements, and considering them in their decision making. 

Humanized AI shows characteristics of all types of competencies (i.e., cognitive, emotional, and social intelligence), is able to be self-conscious and is self-aware in interactions.

A typical AI analyzes its environment and takes actions that maximize its chance of success. An AI's intended utility function (or goal) can be simple ("1 if the AI wins a game of Go, 0 otherwise") or complex ("Do mathematically similar actions to the ones succeeded in the past"). Goals can be explicitly defined or induced. 

If the AI is programmed for "reinforcement learning", goals can be implicitly induced by rewarding some types of behavior or punishing others. Alternatively, an evolutionary system can induce goals by using a "fitness function" to mutate and preferentially replicate high-scoring AI systems, similar to how animals evolved to innately desire certain goals such as finding food. 

Some AI systems, such as nearest-neighbor, instead of reason by analogy, these systems are not generally given goals, except to the degree that goals are implicit in their training data. Such systems can still be benchmarked if the non-goal system is framed as a system whose "goal" is to successfully accomplish its narrow classification task

Many AI algorithms are capable of learning from data; they can enhance themselves by learning new heuristics (strategies, or "rules of thumb", that have worked well in the past), or can themselves write other algorithms.

Weak AI, also known as narrow AI, is AI that is focused on one narrow task. In contrast, strong AI (also known as general-purpose AI) is defined as a capability to apply intelligence to any problem, rather than just one specific problem, sometimes considered to require consciousness, sentience, and mind. Many currently existing systems that claim to use "artificial intelligence" are likely operating as a weak AI focused on a narrowly defined specific problem.

Siri/Google Assistant/Alexa is a good example of narrow AI.

Our current AI-based systems are based on Analytical AI and Weak AI.


Machine Learning
Machine learning (ML) is the study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. It is a subset of AI. Machine learning algorithms build a mathematical model based on sample data, known as "training data", to make predictions or decisions without being explicitly programmed to perform the task.

Types of learning algorithms

The types of machine learning algorithms differ in their approach, the type of data they input and output, and the type of task or problem that they are intended to solve.

Supervised learning algorithms build a mathematical model of a set of data that contains both the inputs and the desired outputs. The data is known as training data and consists of a set of training examples. Each training example has one or more inputs and the desired output, also known as a supervisory signal.

Unsupervised learning algorithms take a set of data that contains only inputs, and find structure in the data, like grouping or clustering of data points. The algorithms, therefore, learn from test data that has not been labeled, classified or categorized. Instead of responding to feedback, unsupervised learning algorithms identify commonalities in the data and react based on the presence or absence of such commonalities in each new piece of data.

Reinforcement learning is an area of ML concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward.

Association rule learning is a rule-based ML method for discovering relationships between variables in large databases. It is intended to identify strong rules discovered in databases using some measure of "interestingness". Rule-based machine learning is a general term for any machine learning method that identifies, learns, or evolves "rules" to store, manipulate or apply knowledge. The defining characteristic of a rule-based machine learning algorithm is the identification and utilization of a set of relational rules that collectively represent the knowledge captured by the system.

Deep learning (also known as deep structured learning or hierarchical learning) is a class of ML algorithms that uses multiple layers of artificial neural network to progressively extract higher-level features from the raw input. Learning can be supervised, semi-supervised or unsupervised. For example, in image processing, lower layers may identify edges, while higher layers may identify the concepts relevant to a human such as digits or faces.

Note: In this article, I have collected material from various sources and at some places simplified the language.