AI Technology
WHAT IS ARTIFICIAL INTELLIGENCE?
Artificial intelligence
(AI) is a wide-ranging branch of computer science concerned with building smart
machines capable of performing tasks that typically require human intelligence.
WHAT ARE THE
FOUR TYPES OF ARTIFICIAL INTELLIGENCE?
1. Reactive
Machines
2. Limited
Memory
3. Theory of
Mind
4.
Self-Awareness
WHAT ARE
EXAMPLES OF ARTIFICIAL INTELLIGENCE?
Siri, Alexa
and other smart assistants
Self-driving
cars
Robo-advisors
Conversational
bots
Email spam
filters
Netflix's
recommendation
How Does
Artificial Intelligence Work?
AI
Approaches and Concepts:
Less than a
decade after breaking the Nazi encryption machine Enigma and helping the Allied
Forces win World War II, mathematician Alan Turing changed history a second
time with a simple question: "Can machines think?"
Turing's
paper "Computing Machinery and Intelligence" (1950), and its
subsequent Turing Test, established the fundamental goal and vision of
artificial intelligence.
At its core,
AI is the branch of computer science that aims to answer Turing's question in
the affirmative. It is the endeavor to replicate or simulate human intelligence
in machines.
The
expansive goal of artificial intelligence has given rise to many questions and
debates. So much so, that no singular definition of the field is universally
accepted.
The major limitation in defining AI as simply "building machines that are
intelligent" is that it doesn't actually explain what artificial
intelligence is? What makes a machine intelligent? AI is an interdisciplinary
science with multiple approaches, but advancements in machine learning and deep
learning are creating a paradigm shift in virtually every sector of the tech
industry.
In their
groundbreaking textbook Artificial Intelligence: A Modern Approach, authors
Stuart Russell and Peter Norvig approach the question by unifying their work
around the theme of intelligent agents in machines. With this in mind, AI is
"the study of agents that receive percepts from the environment and
perform actions." (Russel and Norvig viii)
The Four
Types of Artificial Intelligence
Reactive
Machines:
A reactive
machine follows the most basic of AI principles and, as its name implies, is
capable of only using its intelligence to perceive and react to the world in
front of it. A reactive machine cannot store a memory and as a result cannot
rely on past experiences to inform decision making in real-time.
Perceiving
the world directly means that reactive machines are designed to complete only a
limited number of specialized duties. Intentionally narrowing a reactive
machine’s worldview is not any sort of cost-cutting measure, however, and
instead means that this type of AI will be more trustworthy and reliable — it
will react the same way to the same stimuli every time.
A famous example of a reactive machine is Deep Blue, which was designed by IBM in the 1990’s as a chess-playing supercomputer and defeated international grandmaster Gary Kasparov in a game. Deep Blue was only capable of identifying the pieces on a chess board and knowing how each moves based on the rules of chess, acknowledging each piece’s present position, and determining what the most logical move would be at that moment. The computer was not pursuing future potential moves by its opponent or trying to put its own pieces in better position. Every turn was viewed as its own reality, separate from any other movement that was made beforehand.
Another example of a game-playing reactive machine is Google’s AlphaGo. AlphaGo is also incapable of evaluating future moves but relies on its own neural network to evaluate developments of the present game, giving it an edge over Deep Blue in a more complex game. AlphaGo also bested world-class competitors of the game, defeating champion Go player Lee Sedol in 2016.
Though limited in scope and not easily altered, reactive machine artificial intelligence can attain a level of complexity, and offers reliability when created to fulfill repeatable tasks.
Limited
Memory
Limited
memory artificial intelligence has the ability to store previous data and
predictions when gathering information and weighing potential decisions —
essentially looking into the past for clues on what may come next. Limited
memory artificial intelligence is more complex and presents greater
possibilities than reactive machines.
Limited memory AI is created when a team continuously trains a model in how to analyze and utilize new data or an AI environment is built so models can be automatically trained and renewed. When utilizing limited memory AI in machine learning, six steps must be followed: Training data must be created, the machine learning model must be created, the model must be able to make predictions, the model must be able to receive human or environmental feedback, that feedback must be stored as data, and these these steps must be reiterated as a cycle.
There are
three major machine learning models that utilize limited memory artificial
intelligence:
Reinforcement
learning, which learns to make better predictions through repeated
trial-and-error.
Long Short
Term Memory (LSTM), which utilizes past data to help predict the next item in a
sequence. LTSMs view more recent information as most important when
making predictions and discounts data from further in the past, though still
utilizing it to form conclusions
Evolutionary Generative Adversarial Networks (E-GAN), which evolves over time, growing to explore slightly modified paths based off of previous experiences with every new decision. This model is constantly in pursuit of a better path and utilizes simulations and statistics, or chance, to predict outcomes throughout its evolutionary mutation cycle.
Theory of
Mind:
Theory of
Mind is just that — theoretical. We have not yet achieved the technological and
scientific capabilities necessary to reach this next level of artificial
intelligence.
The concept
is based on the psychological premise of understanding that other living things
have thoughts and emotions that affect the behavior of one’s self. In terms of
AI machines, this would mean that AI could comprehend how humans, animals and
other machines feel and make decisions through self-reflection and
determination, and then will utilize that information to make decisions of
their own. Essentially, machines would have to be able to grasp and process the
concept of “mind,” the fluctuations of emotions in decision making and a litany
of other psychological concepts in real time, creating a two-way relationship
between people and artificial intelligence.
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WHAT IF
AI BECAME SELF-AWARE? BY ALLTIME10S
Self-awareness:
Once Theory
of Mind can be established in artificial intelligence, sometime well into the
future, the final step will be for AI to become self-aware. This kind of
artificial intelligence possesses human-level consciousness and understands its
own existence in the world, as well as the presence and emotional state of
others. It would be able to understand what others may need based on not just
what they communicate to them but how they communicate it.
Self-awareness
in artificial intelligence relies both on human researchers understanding the
premise of consciousness and then learning how to replicate that so it can be
built into machines.
How is AI
Used?
While
addressing a crowd at the Japan AI Experience in 2017, Data Robot CEO
Jeremy Achin began his speech by offering the following definition of how AI is
used today:
"AI is
a computer system able to perform tasks that ordinarily require human
intelligence... Many of these artificial intelligence systems are powered by
machine learning, some of them are powered by deep learning and some of them
are powered by very boring things like rules."
Artificial
intelligence generally falls under two broad categories:
Narrow
AI: Sometimes
referred to as "Weak AI," this kind of artificial intelligence
operates within a limited context and is a simulation of human intelligence.
Narrow AI is often focused on performing a single task extremely well and while
these machines may seem intelligent, they are operating under far more
constraints and limitations than even the most basic human intelligence.
Artificial
General Intelligence (AGI): AGI, sometimes referred to as "Strong AI," is the
kind of artificial intelligence we see in the movies, like the robots from
Westworld or Data from Star Trek: The Next Generation. AGI is a machine with
general intelligence and, much like a human being; it can apply that
intelligence to solve any problem.
Narrow
Artificial Intelligence
Narrow AI is
all around us and is easily the most successful realization of artificial
intelligence to date. With its focus on performing specific tasks, Narrow AI
has experienced numerous breakthroughs in the last decade that have had
"significant societal benefits and have contributed to the economic
vitality of the nation," according to "Preparing for the Future of
Artificial Intelligence," a 2016 report released by the Obama
Administration.
A few
examples of Narrow AI include:
1. Google
search
2. Image
recognition software
3. Siri,
Alexa and other personal assistants
4.
Self-driving cars
5. IBM's
Watson
Machine
Learning & Deep Learning:
Much of
Narrow AI is powered by breakthroughs in machine learning and deep learning.
Understanding the difference between artificial intelligence, machine learning
and deep learning can be confusing. Venture capitalist Frank Chen provides a
good overview of how to distinguish between them, noting: Simply put,
machine learning feeds a computer data and uses statistical techniques to help
it "learn" how to get progressively better at a task, without having
been specifically programmed for that task, eliminating the need for millions
of lines of written code. Machine learning consists of both supervised learning
(using labeled data sets) and unsupervised learning (using unlabeled data
sets).
Deep
learning is a type of machine learning that runs inputs through a
biologically-inspired neural network architecture. The neural networks contain
a number of hidden layers through which the data is processed, allowing the
machine to go "deep" in its learning, making connections and
weighting input for the best results.
Artificial
General Intelligence:
The creation
of a machine with human-level intelligence that can be applied to any task is
the Holy Grail for many AI researchers, but the quest for AGI has been fraught
with difficulty.
The search
for a "universal algorithm for learning and acting in any environment,"
(Russel and Norvig 27) isn't new, but time hasn't eased the difficulty of
essentially creating a machine with a full set of cognitive abilities.
AGI has long
been the muse of dystopian science fiction, in which super-intelligent robots
overrun humanity, but experts agree it's not something we need to worry about
anytime soon.
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