Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems. Specific applications of AI include expert systems, natural language processing, speech recognition and machine vision.
As the hype around AI has accelerated, vendors have been scrambling to promote how their
products and services use AI. Often what they refer to as AI is simply one component of AI, such as
machine learning. AI requires a foundation of specialized hardware and software for writing and training
machine learning algorithms. No one programming language is synonymous with AI, but a few, including
Python, R and Java
In general, AI systems work by ingesting large amounts of labeled training data, analyzing the data for
correlations and patterns, and using these patterns to make predictions about future states. In this
way, a chatbot that is fed examples of text chats can learn to produce lifelike exchanges with people,
or an image recognition tool can learn to identify and describe objects in images by reviewing millions
of
AI programming focuses on three cognitive skills: learning, reasoning and self-correction.
Learning processes. This aspect of AI programming focuses on acquiring data and creating rules
for how
to turn the data into actionable information. The rules, which are called algorithms, provide computing
devices with step-by-step instructions for how to complete a specific task.
Reasoning processes. This aspect of AI programming focuses on choosing the right algorithm to
reach a
desired outcome.
Self-correction processes. This aspect of AI programming is designed to continually fine-tune
algorithms
and ensure they provide the most accurate results possible.
AI is important because it can give enterprises insights into their operations that they
may not have been aware of previously and because, in some cases, AI can perform tasks better than
humans. Particularly when it comes to repetitive, detail-oriented tasks like analyzing large numbers of
legal documents to ensure relevant fields are filled in properly, AI tools often complete jobs quickly
and with relatively few errors.
This has helped fuel an explosion in efficiency and opened the door to entirely new business
opportunities for some larger enterprises. Prior to the current wave of AI, it would have been hard to
imagine using computer software to connect riders to taxis, but today Uber has become one of the largest
companies in the world by doing just that. It utilizes sophisticated machine learning algorithms to
predict when people are likely to need rides in certain areas, which helps proactively get drivers on
the road before they're needed. As another example, Google has become one of the largest players for a
range of online services by using machine learning to understand how people use their services and then
improving them. In 2017, the company's CEO, Sundar Pichai, pronounced that Google would operate as an
"AI first" company.
Today's largest and most successful enterprises have used AI to improve their operations and gain
advantage on their competitors.
Artificial neural networks and deep learning artificial intelligence technologies are
quickly evolving, primarily because AI processes large amounts of data much faster and makes predictions
more accurately than humanly possible.
While the huge volume of data being created on a daily basis would bury a human researcher, AI
applications that use machine learning can take that data and quickly turn it into actionable
information. As of this writing, the primary disadvantage of using AI is that it is expensive to process
the large amounts of data that AI programming requires.
1. Good at detail-oriented
2. Reduced time for data-heavy tasks.
3. Delivers consistent results. and
4. AI-powered virtual agents are always available.
1. Expensive.
2. Requires deep technical expertise.
3. Limited supply of qualified workers to build AI tools.
4. Only knows what it's been shown; and
5. Lack of ability to generalize from one task to another.