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.
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
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.
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