At a symposium, the term “artificial intelligence” was initially used in 1956. That means that artificial intelligence has been a word for over fifty years. However, in the past 20 years, people have started to realise how enormous its potential is. For thirty years, artificial intelligence technology operated independently, but today it is widely used in all aspects of life.
It can be used in many different fields, including robotics, speech recognition, natural language processing, and simulation. Our goal in writing this piece is to educate you on the different applications of artificial intelligence. So let’s get started right away.
Table of contents
What does Artificial Intelligence mean?
How does AI work?
History of AI
Top 10 Artificial Intelligence Technologies
Natural language generation
Speech recognition
Virtual agents
Decision management
Biometrics
Machine learning
Robotic process automation
Peer-to-peer network
Deep learning platforms
AL-optimized hardware
Advantages of AI
Disadvantages of AI
Frequently Asked Questions
What does Artificial Intelligence mean?
Artificial intelligence is, to put it simply, the imitation of human intelligence. It entails providing a large amount of data to the machines. They can now make decisions on their own without assistance from humans. This implies that computers are capable of intelligence-based tasks like pattern recognition and image identification, in addition to basic maths.
Therefore, the major goal of AI is to enable machines to learn from data and mimic human thought processes. Furthermore, the field of artificial intelligence is a subset of data science that focusses on leveraging data to give robots intelligence akin to that of humans. Machine learning and deep learning, which use more sophisticated frameworks like sci-kit-learn and TensorFlow to train the machine, are also included in artificial intelligence. Below is a display of this relation:
How does AI work?
Let’s now examine how AI functions. It uses sophisticated algorithms to analyse enormous volumes of data in order to find patterns and make judgements. AI therefore depends on machine learning algorithms to carry out tasks without the need for explicit programming by learning from data. These algorithms analyse data, identify features, and create predictions or classifications using mathematical models.
Let’s use an example to better grasp this. Let’s say you purchase ten items in a single day. The machines are provided this transaction data so they can analyse it. With knowledge about your preferences and past purchases, the computer can forecast the things that you will most likely buy. The computers’ ability to learn and gauge their correctness is the foundation of the entire AI process.
You may be wondering now if this is where it all began.
History of AI
AI has come a long way in the past. To determine the best approach for imbuing the machines with intelligence, extensive global research was undertaken. Below are the dates for the advancements in the field of artificial intelligence:
In 1846, mathematician Charles Babbage of Cambridge University and Augusta Ada Byron, the Countess of Lovelace, contributed the designs and hypothesised that programmable machines might be feasible.
Princeton mathematician John Von Neumann designed the stored program computer’s architecture in the 1940s. Additionally, McCulloch and Pitts created the first neural network mathematical model during this decade.
At the Dartmouth Conference in 1956, the term “artificial intelligence” was first used. This signified the formal beginning of AI research.
1957–1966: The earliest artificial intelligence
The 1970s saw a sharp decline in AI. Because funding and interest fell due to disappointed expectations, it was dubbed the “AI Winter”.
The 1980s saw the beginning of the rebirth of AI research with the creation of symbolic AI and expert systems.
In 1997, IBM’s Deep Blue overcame Garry Kasparov, the reigning global chess champion. This demonstrated AI’s capacity for making sound strategic decisions.
The years 2000–2010 saw the emergence of statistical and machine learning methods in artificial intelligence. The frameworks for computer vision and speech recognition entered the picture. Additionally, image identification, driverless cars, and natural language processing all benefited from the development of deep learning and neural networks.IBM began developing Watson in 2011. On Jeopardy, it overcame two previous champions.
From 2020 to the present: AI is developing continuously. AI applications are being utilised to streamline complicated procedures in a variety of industries, including healthcare, banking, and transportation.
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Top 10 Artificial Intelligence Technologies
1. Natural language generation
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 developers automate content and deliver it 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.
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 into several languages. Siri on the iPhone is a classic example of speech recognition.
Additionally, speech recognition technology is increasingly being integrated into a wide range of applications and devices, including smartphones, smart speakers, automotive systems, and healthcare solutions
3. Virtual agents
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. 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
Decision management systems are being implemented by overseas organisations in order to convert and understand data into prediction models. Decision management systems are used by enterprise-level apps to get the most recent information needed to analyse business data and support organisational decision-making.
Making quick judgements, avoiding risks, and automating processes are all made easier with the aid of decision management. The financial, healthcare, trading, insurance, and e-commerce sectors are just a few of the industries that heavily utilise decision management systems.
5. Biometrics
Biometrics in AI involves the use of biological characteristics to authenticate and identify individuals. This technology relies on capturing and analyzing unique physical or behavioral traits such as fingerprints, facial features, iris patterns, voice prints, and even gait. The process typically begins with the acquisition of biometric data through specialized sensors or devices, which is then processed using AI algorithms. These algorithms extract distinctive features from the biometric data and convert them into mathematical representations known as templates or biometric signatures.
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.
For example, 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. Also, Retailers utilize machine learning to predict changing customer preferences, and consumer behavior, by analyzing customer data.
7.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. The process begins with identifying tasks suitable for automation, such as data entry, form filling, and routine data manipulation.
The RPA software then records the steps involved in completing these tasks, creating a set of instructions or a “robotic script.” These scripts are typically created using drag-and-drop interfaces or scripting languages. Thus, non-technical users can automate processes without extensive programming knowledge.
Robotic process automation