Artificial Intelligence
Artificial intelligence (AI) is the simulation of human intelligence
processes by machines, especially computer systems. These processes include
learning (the acquisition of information and rules for using the information),
reasoning (using rules to reach approximate or definite conclusions) and
self-correction. Particular applications of AI include expert systems, speech recognition and machine vision.
AI can be categorized as either weak or strong. Weak AI, also known as narrow AI, is an AI system that is
designed and trained for a particular task. Virtual personal assistants, such
as Apple's Siri, are a form of weak AI. Strong AI, also known as artificial
general intelligence, is an AI system with generalized human cognitive
abilities. When presented with an unfamiliar task, a strong AI system is able
to find a solution without human intervention.
Because
hardware, software and staffing costs for AI can be expensive, many vendors are
including AI components in their standard offerings, as well as access to
Artificial Intelligence as a Service (AIaaS)
platforms. AI as a Service allows individuals and companies to experiment with
AI for various business purposes and sample multiple platforms before making a
commitment. Popular AI cloud offerings include Amazon AI services, IBM Watson
Assistant, Microsoft
Cognitive Services and Google AI services.
While AI tools present a range of new functionality
for businesses ,the use
of artificial intelligence raises ethical questions. This is because deep
learning algorithms, which underpin many of the most advanced AI tools, are
only as smart as the data they are given in training. Because a human selects
what data should be used for training an AI program, the potential for human
bias is inherent and must be monitored closely.
Some industry experts believe that the term artificial
intelligence is too closely linked to popular culture, causing the general public
to have unrealistic fears about artificial intelligence and improbable
expectations about how it will change the workplace and life in general.
Researchers and marketers hope the label augmented
intelligence, which has a more neutral connotation, will help people
understand that AI will simply improve products and services, not replace the
humans that use them.
Types of artificial intelligence
Arend Hintze, an assistant professor of integrative
biology and computer science and engineering at Michigan State University,
categorizes AI into four types, from the kind of AI systems that exist today to
sentient systems, which do not yet exist. His categories are as follows:
·
Type
1: Reactive machines. An example is Deep Blue, the IBM chess program
that beat Garry Kasparov in the 1990s. Deep Blue can identify pieces on the
chess board and make predictions, but it has no memory and cannot use past
experiences to inform future ones. It analyzes possible moves
-- its own and its opponent -- and chooses the most
strategic move. Deep Blue and Google's AlphaGO were designed
for narrow purposes and cannot easily be applied to another situation.
·
Type
2: Limited memory. These AI systems can use past experiences to
inform future decisions. Some of the decision-making functions in self-driving cars are
designed this way. Observations inform actions happening in the not-so-distant
future, such as a car changing lanes. These observations are not stored
permanently.
·
Type
3: Theory of mind. This psychology term refers to the understanding
that others have their own beliefs, desires and intentions that
impact the decisions they make. This kind of AI does not yet exist.
·
Type 4: Self-awareness. In
this category, AI systems have a sense of self, have consciousness. Machines
with self-awareness understand their current state and can use the information
to infer what others are feeling. This type of AI does not yet exist .
Examples of
AI technology
AI is incorporated into a variety of different types
of technology. Here are seven examples.
·
Automation: What makes a system or process function
automatically. For example, robotic process automation (RPA)
can be programmed to perform high-volume, repeatable tasks that humans normally
performed. RPA is different from IT automation in that it can adapt to changing
circumstances.
·
Machine learning: The science of getting a
computer to act without programming . Deeplearning is a
subset of machine learning that, in very simple terms, can be thought of as the
automation of predictive analytics. There are three types of machine learning
algorithms:
o Supervised learning: Data
sets are labeled so that patterns can be detected and used to label new data
sets
o Unsupervised learning:
Data sets aren't labeled and are sorted according to similarities or
differences
o Reinforcement learning:
Data sets aren't labeled but, after performing an action or several actions,
the AI system is given feedback
·
Machine vision: The science of allowing computers
to see. This technology captures and analyzes visual information using a
camera, analog-to-digital conversion and digital signal processing.
It is often compared to human eyesight, but machine vision isn't bound by
biology and can be programmed to see through walls, for example. It is used in
a range of applications from signature identification to medical image
analysis. Computer vision, which is focused on machine-based image processing,
is often conflated with machine vision.
·
Natural language processing (NLP): The
processing of human -- and not computer -- language by a computer
program. One of the older and best known examples of NLP is spam
detection, which looks at the subject line and the text of an email and decides
if it's junk. Current approaches to NLP are based on machine learning. NLP
tasks include text translation, sentiment analysis and speech
recognition.
·
Robotics: A field of engineering focused on the
design and manufacturing of robots. Robots are often used to perform tasks that
are difficult for humans to perform or perform consistently. They are used in
assembly lines for car production or by NASA to move large objects in space.
Researchers are also using machine learning to build robots that can interact
in social settings.
·
Self-driving cars: These use a combination of
computer vision, image recognition anddeep
learning to build automated skill at piloting a vehicle while staying in a
given lane and avoiding unexpected obstructions, such as pedestrians.
AI
applications
Artificial intelligence has made its way into a number
of areas. Here are six examples.
·
AI in healthcare. The biggest bets are on improving
patient outcomes and reducing costs. Companies are applying machine learning to
make better and faster diagnoses than humans. One of the best
known healthcare technologies is IBM Watson. It understands
natural language and is capable of responding to questions asked of it. The
system mines patient data and other available data sources to form a
hypothesis, which it then presents with a confidence scoring schema. Other AI
applications include chatbots, a computer
program used online to answer questions and assist customers, to help schedule
follow-up appointments or aid patients through the billing process, and virtual
health assistants that provide basic medical feedback.
·
AI in business. Robotic process automation is being
applied to highly repetitive tasks normally performed by humans. Machine
learning algorithms are being integrated into analytics and CRM platforms to uncover information
on how to better serve customers. Chatbots have been incorporated into websites
to provide immediate service to customers. Automation of job positions has also
become a talking point among academics and IT analysts.
·
AI in education. AI can automate grading, giving
educators more time. AI can assess students and adapt to their needs, helping
them work at their own pace. AI tutors can provide additional support to
students, ensuring they stay on track. AI could change where and how students
learn, perhaps even replacing some teachers.
·
AI in finance. AI in personal finance applications,
such as Mint or Turbo Tax, is disrupting financial institutions. Applications
such as these collect personal data and provide financial advice. Other
programs, such as IBM Watson, have been applied to the process of buying a
home. Today, software performs
much of the trading on Wall Street.
·
AI in law. The discovery process, sifting
through of documents, in law is often overwhelming for humans.
Automating this process is a more efficient use of time. Startups are also
building question-and-answer computer assistants that can sift
programmed-to-answer questions by examining the taxonomy and ontology associated
with a database.
·
AI in manufacturing. This is an area that has been at
the forefront of incorporating robots into the workflow. Industrial
robots used to perform single tasks and were separated from human workers, but
as the technology advanced that changed .
Security and ethical concerns
The application of AI in the realm of self-driving
cars raises security as well as ethical concerns. Cars can be hacked, and when
an autonomous vehicle is involved in an accident, liability is unclear.
Autonomous vehicles may also be put in a position where an accident is
unavoidable, forcing the programming to make an ethical decision about how to
minimize damage.
Another major concern is the potential for abuse of AI
tools. Hackers are
starting to use sophisticated machine learning tools to gain access to
sensitive systems, complicating the issue of security beyond its current state.
Deep learning-based video and audio generation tools
also present bad actors with the tools necessary to create so-called deepfakes ,
convincingly fabricated videos of public figures saying or doing things that
never took place .
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