What is Artificial Intelligence? - AI Guide for 2024!

Struggling to understand what artificial intelligence is? Here’s a layman’s guide to help you understand the nitty-gritty of the technology from multiple directions.

Artificial intelligence or AI is the most radical technology to become a part of the IT ecosystem. Right now, there are so many consumer applications and enterprise software that leverage AI. It makes them capable of automating redundant tasks, streamlining operations, gathering insights, and a lot more. 

With AI putting so many capabilities on the table, a lot of software vendors and app development companies often ponder the question “What is artificial intelligence?”.

To answer that, let’s start dissecting the topic layer by layer with this artificial intelligence guide…

Introduction of Artificial Intelligence

Let’s start with the introduction of artificial intelligence. Artificial Intelligence is the branch of computer science that deals with synthesizing, perceiving, and inferring information mimicking human cognitive functions & abilities in an artificial machine. 

What is Artificial Intelligence in Simple Words?

In simple words, artificial intelligence is a technology that allows any computer to behave like a human to perform human operations. There are multiple tasks that an AI system can perform. Below, we have mentioned the list of the most common ones.

• It can understand and translate language, analyze the sentiment, summarize text, etc
• It has the capability to detect objects such as recognizing faces and objects, tracking and classifying objects, understanding videos, etc
• It can understand speech and perform functions based on it
• It enables different applications such as voice assistants, transcription of language, and control of systems via voice command
• It can be used for creating autonomous vehicles and self-driving cars
• Creating recommendation systems for generating sales
• Detection of fraud in multiple industries such as banking, finances, insurance, cybersecurity, etc
• Making predictions by analyzing historical data
• Create virtual assistants for multiple tasks such as setting reminders, setting up alarms, controlling devices, etc.
• Identifying patterns and correlations between data

Benefits of Artificial Intelligence

To fully understand the scope of  artificial intelligence, it is important to understand its benefits too. There are several potential benefits of artificial intelligence that are mentioned below:

• Enhanced decision-making via insights, and intelligent learning systems
• Automation of redundant and repetitive tasks
• Personalization of content and recommendations
• Capability to produce innovative products and services
• Better safety and security against cyber intrusion
• Higher operational efficiency and streamlined processes

History of Artificial Intelligence

To gain a better understanding of understanding what is AI technology? we need to go through the history of artificial intelligence.

Essentially the journey of the development of AI started with the Turing test by Alan Turing in the year 1950. The test dealt with determining the difference between a computer system and human operators, and the capability of humans to determine which is artificial and which is natural intelligence.

Then there was Isaac Asimov who gave the three rules of robotics. As we travel through the timeline of artificial intelligence in a chronological sequence, there were several inventions that took place.

Inventions such as Logical Theorist (the first AI program), Arthur Samuel’s AI game, the establishment of AI MIT lab, to the recent revolutionary development of ChatGPT. The AI of today has undergone multiple iterations to reach this level of accuracy and capability of automation. It won’t be wrong to say that the developments we’ve had in the realm of AI is a journey that started almost 70 years ago.

Types of AI

There are several types of AI or sub-domains of AI technology that are in use today. Let’s go through them one by one.

1. Reactive AI

A reactive AI stays in the present and reacts primarily to the present inputs provided to it. It doesn’t have the capability to make out inferences from the data provided in the past and isn’t capable of predicting the future.

2. Theory of Mind

If turned into reality, the theory of mind will be an AI type that will be very advance in its nature. This type of AI will be able to detect the identity of the entity, it is interacting with. It will be able to analyze the sentiment of the beings and understand their emotions.

3. Self Aware

It is a hypothetical concept. An AI that is self-aware will be sentient in nature. It can be equal to or maybe more intelligent than human beings. 

4. Artificial Narrow Intelligence

The best example of Narrow AI would be systems like Siri, Google Assistant, Cortana, etc. These systems have a narrow focus on a certain task or a use case. However, they are pretty smart in that aspect.

5. Artificial General Intelligence

Artificial general intelligence is a hypothetical concept. This concept aims to mimic the human mind, aiming to make it indistinguishable from a human in terms of intelligence, perception, and response. They are or can be at par with human but doesn’t surpass their capabilities.

6. Limited Memory

These are the polar opposite of reactive AI. A limited memory AI uses its past data to process a particular request given to it. However, its memory is very short-lived. This type of AI is often used in autonomous systems.

7. Artificial SuperIntelligence

A Super AI is the type of AI that surpasses every human in every way. It will be able to perform any task better than humans, and it will have its own desires, belief system, needs, etc. A great example of this would be super villain “Ultron” from the MCU (Marvel Cinematic Universe) 

Different Branches of AI Technologies

Below are some of the sub-domain of AI technologies. These technologies are commonly in use as of today. Let’s check them out:

1. Machine Learning

Machine learning is the type of AI that makes a computer capable of learning and evolving by using the data provided. This could be the organization's historical data, different datasets, augmented datasets, etc.

2. Natural Language Processing

Natural language processing is the AI technique that helps a computer understand human language. For this, the NLP models are trained on entire dictionaries of data to understand the context and the meaning behind the language. This AI technology is generally used for translation and transcription.

3. Deep Learning

Deep learning is an AI technology that is inspired by the human mind. In this type of AI, an artificially intelligent neural network is created. Based on different learning of the different nodes in this neural network, an AI model is created that is capable of taking the best possible decisions with the input provided to it.

4. Artificial Neural Network

Artificial neural networks are an inferior version of deep learning. However, one could say that the basis of deep learning is artificial neural networks. This as explained earlier tries to mimic the neural network of the human brain. There are multiple interconnected nodes of learning based on which the neural network provides the best possible decision. Basically, it is used with simpler data.

5. Expert System

An expert system tries to emulate the decision-making capabilities of a human by utilizing artificial intelligence. These systems are used for solving complex problems. There are primarily three components of an expert system i.e.

• Knowledge base
• Inference engine
• User interface

6. Computer Vision

This is the field of AI that enables computer systems to see and derive meaning out of it. Some of the common capabilities of a computer vision system are:

• Face detection
• Object detection
• Face recognition
• Understanding of scene

AI Use Cases - Inclusion of AI in Different Industries and with Technologies

Currently, the usage of AI is prominent in a variety of fields leading to the emergence of multiple artificial intelligence development companies. Let’s have a look at them one by one:

1. AI in Healthcare

Healthcare is amongst the most complicated industries is out there. It works 24*7 and requires all the support and assistance it can get. With the inclusion AI in healthcare, the potential of enhancing patient care, reduction in operational cost, and accelerating medical research has actually gotten enhanced.

2. AI in Finance

The finance industry has to undergo a series of repetitive tasks associated with primarily three tasks that are reconciliation, data entry, and review. Each of these tasks gets subdivided into its own use cases such as financial report review, accounts payable processing, compliance, financial review, etc. By utilizing artificial intelligence in finance, the industry is capable of deriving insights that are great for strategic purposes and automating processes.

3. AI in Marketing

Marketing is a tricky industry to be in. It is because the environment in marketing is dynamic in nature. This makes the ROIs generated by marketers' efforts often subjective in nature. To gain a better grasp and ease the process, marketers are required to make data-based decisions. By utilizing AI in marketing, this gap can be easily bridged.

4. AI in Robotics

Humans has been experimenting in the field of robotics for decades now. However, with the inclusion of AI in the field of robotics, robots have the capability to perform even more complex tasks and be useful in multiple challenging environments.

5. AI in Autonomous Vehicles

In simple terms, autonomous vehicles are self-driving cars. However, the degree to which they are capable in terms of self-driving depends on their level of autonomy. Some common examples of companies working on autonomous vehicles are Tesla, Waymo, Nvidia, Zoox, May Mobility, etc.

Aside from the different sensors that are used in autonomous vehicles from which the AI models collect data to provide this capability, there are three components of AI in autonomous vehicles. These are:

• Machine learning
• Computer vision
• Natural language processing

6. Artificial Intelligence in Supply Chain Management

Supply chain management is a complex network that enables the transportation of goods from one place to another. By using artificial intelligence in supply chain management, the industry has enjoyed improved efficiency, increased agility, better customer service, 24*7 support, etc.

7. AI in Sports

The sports industry is always looking for some sort of enhancements be it in terms of entertainment or in terms of player skills enhancement. With AI in sports, the industry is able to coach better, provide fans with interactive tools and merchandise, figure out insights, and a lot more. 

8. AI & IoT

IoT stands for Internet of Things. Utilizing this technology, we can control hardware via computers. AI and IoT are two very closely tied streams and are often used together. IoT enables the creation of a network of physical devices that are connected with each other over the internet.

9. AI in Real Estate

Real estate is another industry that is getting rapidly transformed via AI. Here are some of the applications of AI in real estate:

• Evaluation of the property 
• Buying and selling homes
• Managing the network of properties
• Analyzing the investments

10. AI in Education

Right now, the education industry is going through a technological renaissance. It is transforming at a rapid where the classroom is becoming closer to the students and way more interactive. To achieve that AI has been playing a significant role in the education industry.

Some of the ways AI in education is being used are:

• Personalization of learning
• Adjustment of learning to student’s capability to understand and retain.
• Classrooms via VR and AR tech
• Chatbots for clearing doubts
• Gamification for an enhanced learning experience

With the inclusion of AI in education, there have been several benefits which are:

• Better student learning outcomes
• Reduction in operational cost
• Effective remote learning
• Better teacher productivity

11. AI in Agriculture

Farmers majority of the time are on the behest of nature. Adding to this, there are also challenges related to meeting the demands of the market in terms of producing yield. By utilizing AI in agriculture, several farmers around the globe have actually catapulted their yield. It has worked as an effective solution that has aided farmers heavily.

Here are some of the applications of AI in agriculture:

• Precision farming
• Robotics for handling the crops
• Drones to identify the condition of fields with AI technology
• Machine learning for relevant predictions

12. AI in Quantum Computing

Quantum computing is the branch of science that tries to take the advantage of quantum mechanics. Unlike traditional computing which utilizes a ‘bit’ to store, transfer and process information, quantum computers utilize ‘qubits’ that are the fundamental units in quantum computers displaying and making use of quantum properties. This phenomenon is taken advantage for developing specialized hardware.

With the help of AI and Quantum computing, there are several problems that can be solved which are:

• Discovery of new drugs
• Understanding the material science better
• Prediction of climate change
• Assessing the financial market

What is Generative AI?

To define Generative AI,” It is revolutionary AI technology that is capable of creating content in formats like text, images, and music simply as a response to text-based prompts”.

The generative AI models have been trained on large datasets that already exist. By assessing the user prompt, the AI model is able to identify patterns from its database. Based on that identification it provides a relevant answer that is very human-like. 

This technology has a lot of potential and has become very popular in a very short period of time. Here are some of the existing technologies that utilize generative AI. These are:

• Dall-E
• GPT-3
• DeepDream

Comparison of Common AI Technologies - For Better Understanding!

There are several technologies in the AI domain that are commonly misunderstood in comparison to each other. To provide our users with a better understanding, we have extrapolated the differences between these technologies on a basic level.

Therefore, let’s start comparing them one by one…

Machine Learning vs Deep Learning

Machine learning vs deep learning is one of the most commonly searched topics on the internet. Essentially, both of these technologies are the two facets of AI technologies. Both of these technologies aim to enhance the capabilities of computer systems. However, there is a reasonable difference between these two technologies.

Machine learning, in general terms, is for any AI that learns from the data and evolves. It encompasses different types of algorithms, from simple models that classify data to much more complex models that help in tasks such as drug discovery. The technology enables AI models to make predictions and take the best course of action possible. Right now, the technology of machine learning has evolved a lot from its initial stage and is capable of developing much more complex models.

Adding to it, deep learning is a subset of artificial intelligence. It utilizes artificial neural networks to learn from the data fed to it. The concept of deep learning is inspired by the human mind.

AI and Natural Language Processing

AI and Natural Language Processing are complementary technologies. On one hand, where AI tech provides artificial systems to reason, learn, and act autonomously. Adding to it, NLP is a subfield of AI that deals with linguistics and makes the interaction between humans and computers possible. It is capable of understanding the context of human language, transcript it, and translating it.

AI and Cybersecurity

Artificial intelligence and cybersecurity, if used together, these can act as powerful tools to reinforce the security of your connected devices. Cybersecurity essentially is the protection of the computer systems that are connected to the internet. It is important to consider it because the number of intrusions has increased with the increase in internet usage. With artificial intelligence, the cybersecurity landscape is quickly transforming as it enables organizations to protect themselves in a much more effective way.

Strong AI vs Weak AI

The argument between Strong AI vs Weak AI has been a long ongoing conversation. These are essentially two different approaches to the artificial intelligence (AI) domain. 

Let’s define them…

A Strong AI is an ideology that aims to create machines that can be intellectual like humans and responds like a human. On the other hand, a weak AI is an ideology of creating goal-oriented AI systems that can perform specific tasks better than humans.

Here’s a basic comparison of the two technologies i.e. Strong AI vs Weak AI

Features	Strong AI	Weak AI
Objective	Machines with human intellect and responses	Machines capable of performing specific tasks better than humans
Development Stage	Hypothetical	Is implemented in every to some degree
Examples	None	Recommendation Engine, Data Analytics insights, Autonomous cars, generative AI, etc.

 

 

Challenges and Limitations of AI

Despite AI technology being an absolute saver in many ways, there are still multiple challenges of AI that one needs to handle. These are:

• These AI systems require a large amount of data to learn and evolve. It is tricky considering the fact that clean and accurate data is hard to find and expensive at times.
• AI systems are trained over user data. Therefore, if the data is biased, the AI model will likely also show bias.
• AI systems are complex to understand and therefore are often difficult to work with.
• AI systems are often vulnerable to attack.
• AI can be used for malicious purposes.

Future of Artificial Intelligence

The future of artificial intelligence has always been a popular topic. There are multiple experts that speculate what the future holds for AI. However, the closest thing that people often imagine is artificial general AI. AI that is embodying the human experience within itself blurring the lines between humans and machines. However, that reality is far-fetched.

Yet, there are certainly multiple AI trends that are taking center stage and will be reducing human efforts and pushing us toward operational excellence. 

Below are some AI trends and technologies that are either gaining prominence or bound to become prominent in the future:

• Growth to autonomous market by AI technology
• Game characters and NPCs becoming smarter
• Enhanced customer support and service that responds like humans
• Reinforced algorithms for a much more personalized experience
• AI based pets for emotional support
• Healthcare monitoring with possible treatment via AI

Cost of AI Software Development

The AI software development cost is a subjective topic. It is considering the fact that each and every application has its own SDLC (standard development life cycle). Also, the length of this development cycle depends on various factors such as:

• Complexity of the project
• Size of the team required
• Expertise required
• Total cost of hardware and software for the development process

Adding to it, software development cost charged by artificial intelligence companies in Canada and everywhere else can range from $5000 to more than $50,000 depending upon the complexity from simple to complex.

Conclusion

AI has been transforming multiple industries at a rapid pace. It is already an inherent part of our daily life without us even noticing it. From applications like Netflix to your favorite music recommendation, and customer offer that we receive, AI is playing in the background. With our guide, we have tried to cover every facet of this technology. However, there is a lot more that hasn’t been diiscussed. Till then, Stay tuned to MobileAppDaily for similar amazing articles around technology and its impact.