Artificial intelligence has changed the way we live with innovative technologies. AI has taken a storm in every industry and has a profound impact on every sector of society. The term Artificial intelligence terms were first coined in 1956 at a conference. The discussion of the conference led to interdisciplinary information tech natural language generationnology. The advent of the internet helped technology to progress exponentially. Artificial intelligence technology was a stand-alone technology for thirty years, but now the applications are widespread in every sphere of life. Artificial intelligence is known by the AL acronym and is the process of recreating human intelligence in machines.
Many new and emerging technologies are embedded in artificial intelligence. Start-ups to gigantic organizations are in a rat race to implement artificial intelligence for operational excellence, data mining, etc. Let us discuss the Ten Latest Artificial Intelligence Technologies.
| Table of contents: Top 10 Artificial Intelligence Technologies |
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Latest Artificial Intelligence Technologies
1. Natural language generation
Machines process and communicate in a different way than the human brain. Natural language generation
is a trendy technology that converts structured data into the native
language. The machines are programmed with algorithms to convert the
data into a desirable format for the user. Natural language is a subset
of artificial intelligence that helps content developers to automate
content and deliver in the desired format. The content developers can
use the automated content to promote on various social media platforms,
and other media platforms to reach the targeted audience. Human
intervention will significantly reduce as data will be converted into
desired formats. The data can be visualized in the form of charts,
graphs, etc.
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2. Speech recognition
Speech
recognition is another important subset of artificial intelligence that
converts human speech into a useful and understandable format by
computers. Speech recognition is a bridge between human and computer
interactions. The technology recognizes and converts human speech in
several languages. Siri of iPhone is a classic example of speech
recognition.
3. Virtual agents
Virtual
agents have become valuable tools for instructional designers. A
virtual agent is a computer application that interacts with humans. Web
and mobile applications provide chatbots
as their customer service agents to interact with humans to answer
their queries. Google Assistant helps to organize meetings, and Alexia
from Amazon helps to make your shopping easy. A virtual assistant also
acts like a language assistant, which picks cues from your choice and
preference. The IBM Watson understands the typical customer service
queries which are asked in several ways. Virtual agents act as
software-as-a-service too.
4. Decision management
Modern organizations are implementing decision management systems for data conversion and interpretation into predictive models. Enterprise-level applications implement decision management systems to receive up-to-date information to perform business data analysis to aid in organizational decision-making. Decision management helps in making quick decisions, avoidance of risks, and in automation the process. The decision management system is widely implemented in the financial sector, the healthcare sector, trading, the insurance sector, e-commerce, etc.
5. Biometrics
Deep learning is another branch of artificial intelligence that functions based on artificial neural networks.
This technique teaches computers and machines to learn by example just
the way humans do. The term “deep” is coined because it has hidden
layers in neural networks. Typically, a neural network has 2-3 hidden
layers and can have a maximum of 150 hidden layers. Deep learning is
effective on huge data to train a model and a graphic processing unit.
The algorithms work in a hierarchy to automate predictive analytics.
Deep learning has spread its wings in many domains like aerospace and
military to detect objects from satellites, help in improving worker
safety by identifying risk incidents when a worker gets close to a
machine, help to detect cancer cells, etc.
6. Machine learning
Machine learning
is a division of artificial intelligence that empowers machines to make
sense of data sets without being actually programmed. Machine learning
technique helps businesses to make informed decisions with data
analytics performed using algorithms and statistical models. Enterprises
are investing heavily in machine learning to reap the benefits of its
application in diverse domains. Healthcare and the medical profession
need machine learning techniques to analyze patient data for the
prediction of diseases and effective treatment. The banking and
financial sector needs machine learning for customer data analysis to
identify and suggest investment options to customers and for risk and
fraud prevention. Retailers utilize machine learning for predicting
changing customer preferences, and consumer behavior, by analyzing
customer data.
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7. Robotic process automation
Robotic process automation
is an application of artificial intelligence that configures a robot
(software application) to interpret, communicate and analyze data. This
discipline of artificial intelligence helps to automate partially or
fully manual operations that are repetitive and rule-based.
8. Peer-to-peer network
The
peer-to-peer network helps to connect different systems and computers
for data sharing without the data transmitting via a server.
Peer-to-peer networks have the ability to solve the most complex
problems. This technology is used in cryptocurrencies. The
implementation is cost-effective as individual workstations are
connected and servers are not installed.
9. Deep learning platforms
Deep
learning another branch of artificial intelligence that functions based
on artificial neural networks. This technique teaches computers and
machines to learn by example just the way humans do. The term “deep” is
coined because it has hidden layers in neural networks. Typically, a
neural network has 2-3 hidden layers and can have a maximum of 150
hidden layers. Deep learning is effective on huge data to train a model
and a graphic processing unit. The algorithms work in a hierarchy to
automate predictive analytics. Deep learning has spread its wings in
many domains like aerospace and military to detect objects from
satellites, helps in improving worker safety by identifying risk
incidents when a worker gets close to a machine, helps to detect cancer
cells, etc.
10. AL-optimized hardware
Artificial
intelligence software has a high demand in the business world. As the
attention for the software increased, a need for the hardware that
supports the software also arise. A conventional chip cannot support
artificial intelligence models. A new generation of artificial
intelligence chips is being developed for neural networks, deep
learning, and computer vision. The AL hardware includes CPUs to handle
scalable workloads, special purpose built-in silicon for neural
networks, neuromorphic chips, etc. Organizations like Nvidia, and
Qualcomm. AMD is creating chips that can perform complex AI
calculations. Healthcare and automobile may be the industries that will
benefit from these chips.
Conclusion
To conclude, Artificial Intelligence represents computational models of intelligence. Intelligence can be described as structures, models, and operational functions that can be programmed for problem-solving, inferences, language processing, etc. The benefits of using artificial intelligence are already reaped in many sectors. Organizations adopting artificial intelligence should run prerelease trials to eliminate biases and errors. The design, models, should be robust. After releasing artificial systems, enterprises should monitor continuously in different scenarios. Organizations should create and maintain standards and hire experts from various disciplines for better decision-making. The objective and future goals of artificial intelligence are to automate all complex human activities and eliminate errors and biases.
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