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What is AI?

One thing that makes us different and able to stand out from other living beings is intelligence.

There are varying definitions of intelligence. Some consider it the ability to solve problems; others think it’s how people improvise according to the situation or their ability to learn or plan things. But most experts unanimously agree intelligence forms the foundation of human experience. 

Hence, the endeavour to recreate it as Artificial Intelligence (AI). But what is AI? AI today has all the essential traits of human intelligence. It combines problem-solving skills, perception, learning, social adaptability, and creativity. 

You may have seen this technology in restaurants, hospitals, shopping centres — literally everywhere. In fact, many AI-integrated tools are now used to create smart homes. The purpose? Convenience and better accessibility. Let’s explore more about AI in this guide!

AI Explained

AI isn't a new concept. It has been around us since the 1950s when machines started performing tasks that usually required human intelligence. With the advancements and improvements in technology, it evolved and modified into the artificial intelligence systems we have today.

Artificial Intelligence, AI, is a rapidly growing and expanding branch of computer sciences. It is about creating smart machines that can perform tasks without human intervention or intelligence. AI enables devices to mimic and improvise the functioning of the human brain.

There are several branches and areas AI governs, and two fields, i.e., machine learning and deep learning, are making massive impacts, especially in the tech industry.

Everything from automatic transmission cars and robot-run assembly lines to smart assistants like Siri, ChatGPT, and Alexa, AI has become an intrinsic part of our lives.

AI: A Brief History

The history of AI goes a long way back. Starting from the 1950s, each decade saw various milestones achieved and technological breakthroughs over time.

Here’s a short history of AI over the decades:

The Origins

AI started in the 1950s, at the Dartmouth Conference, which made AI’s presence known as a distinct field of its own. A group of researchers gathered around the conference and coined the term ‘Artificial Intelligence.’ They then set on to work on developing intelligent machines.

Some of the notable projects at that time were:

AI Winter

The early years set up pretty high standards for AI. Unfortunately, it could not keep up with the high hopes, so AI faced several challenges during the 1970s. Things got worse when funds and finances were further limited at the time.

Moreover, people grew sceptical of the field and started doubting AI’s ability to achieve human-level intel. Hence, the period is termed ‘AI Winter’, where we saw limited research and progression in the field.

Knowledge-Based AI

AI resurfaced in the 1980s as more expert systems were developed during the decade. These systems employed vast pools of knowledge to procreate human-level expertise in decision-making.

During this phase, AI entered the practical fields and made breakthroughs in healthcare, engineering and finance.

Machine Learning and Neural Networks

The next decade saw a shift towards machine learning, a sub-field of the AI system. New algorithms were developed to make accurate predictions and better learning over time.

Deep Learning

The early 2010s saw an expansion of digital information and computer systems which only pushed AI forward. Deep learning enabled the systems to learn complex patterns, enhancing decision-making and speech and language processing.

Current Applications of AI

Today we see AI in various fields. It's not just limited to medicine and finance only. Business areas like sales, marketing, robotics, etc., are rapidly progressing by embedding AI in their systems.

On the other hand, there are higher ethical concerns about AI. People are more worried about privacy, AI’s impact on work life and society in general, bias, accuracy, etc. All these factors combined are also constraining AI’s ability to progress.

How Does AI Work?

AI enables machines to perform the cognitive functions of the human brain faster and better. Everything from perceiving, using logic and problem-solving, learning, and interacting are modelled in AI.

AI systems are programmed to perform all cognitive functions of the human brain. They can play games, interpret speech, and enhance learning using the most important task of identifying patterns.

AI programs look for patterns as they process large amounts of data. This ability helps them achieve excellence over time in problem-solving and decision-making.

Usually, humans supervise the process to assess the viability of the decisions. They look into the accuracy and feasibility of each decision before reinforcing or withholding them. However, AI systems learn all the patterns over time and can work without human supervision.

Strong and Weak AI

AI can be categorised broadly into general or strong AI and weak AI. 

Strong AI systems can work on problems or impart intelligence on new tasks. Since they have no prior experience or learning through patterns about different scenarios, this form of AI is very close to the working of the human brain.

Strong AI technically doesn't exist today, but you can get an idea about it from sci-fi movies like Star Trek.

On the other hand, weak, narrow, or specialised AI work under constraints and limitations. Weak AI is limited to performing tasks like speech transcription, curating web content, etc. Since the focus is limited, weak AI can work exceptionally well on a singular task. They operate under many limitations and constraints compared to the human brain.

All intelligent AI assistants like Alexa, Siri, chatbots, Netflix recommendations, etc., fall under the category of weak AI.

Super AI

Based on task performance, there’s another category after the strong and weak AI, i.e., Super AI.

Super AI can profoundly affect and shake the world as we know it. The Super AI system goes above all forms of human intelligence and outperforms the human brain in all tasks and functions. Currently, the system only exists in sci-fi fiction and movies.

Machine Learning vs Deep Learning

Machine learning and deep learning are not just limited to AI courses. Today you'll find the terms used in casual conversations and blogs related to AI.

Machine learning is part of AI, and deep learning is a type of machine learning.

Machine Learning

In machine learning, the data is entered into a computer. With extensive processing and techniques, the AI learns and performs better at a task with time. Historical data is also used to predict future outcomes.

Machine learning uses both supervised and unsupervised learning. In supervised learning, the output is known, whereas, in unsupervised learning, the result is unknown as the data sets aren't labelled.

Machine learning is AI based on data-trained algorithms. These can make accurate predictions based on learning patterns and experiences. Hence the efficacy improves over time due to these experiences and past responses. 

Vast data can be processed via machine learning, which is impossible for the human mind. Practically, machine learning has impacted hi-res weather forecasts and medical imaging.

Deep Learning

Deep learning is a type of machine learning. Here the input runs through a human brain-inspired neural network architecture. Many hidden data processing layers exist, so the machine goes way deep into learning, giving us the best results possible.

Deep learning is ideal for processing wider forms of data, like a combination of text and images. The results are far more precise and do not require human assistance or supervision. 

The technology uses networks (like human brain neurons) that process data through iterations. That way, they learn complex patterns and features that enable the network to make decisions and solve problems based on professional learning.

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Uses and Benefits of Machine Learning and Deep Learning

Several industries and businesses have benefited from machine and deep learning. As per McKinsey, over 400 cases of machine learning alone have been reported, but all industries can benefit from the two.

A few examples of using machine learning and deep learning across industries are as follows:

Predictive Maintenance

All businesses that involve the use or upkeep of equipment perform predictive maintenance. It helps them keep tabs on the equipment to complete maintenance before the machinery breaks down or develops a fault. In turn, that translates to lower operating costs and less downtime.

Logistics Optimisation

AI logistics Optimisation can also help businesses reduce costs. If used for transit, AI can help find the best routes for shorter delivery times and also help companies save up on fuel and other transit costs.

Customer Service

Customer service centres can promote seamless service and efficient complaint processing via AI. Chatbots or helplines can also be placed under the supervisory role of a human. So, in case of a bigger problem, AI can assess the customer's tone and transfer the call to the human manager.

Types of AI

Task complexity and type are the two base factors that determine the type of AI. Generally, experts agree there are four types of AI:

  • Reactive machines

  • Limited memory

  • Theory of mind

  • Self-awareness

Reactive Machines

These simple machines see and react to things based on their perception. They do not have a memory to store past experiences or patterns. So, they can only perform specialised tasks. 

Limited Memory

Limited memory AI can store memory or history. So, it has a lot of experience and data from where it can quickly draw comparisons and predictions. It's not basic but slightly more complex than reactive machines.

The AI is generated when a model is continuously trained and made to analyse and utilise data within an AI environment.

Theory of Mind

This one is just a theoretical model of AI, yet to be developed or achieved.

It is based on the belief that other people, animals and living beings' thoughts and emotions play a significant role in affecting the decision and thought processes of the machine. AI today cannot 'feel' other people's emotions and self-reflect to base the decisions, like a human. 

Self Awareness

Self-awareness is the last stage of AI. This is the step where AI acquires human-like understanding and consciousness. It also means AI could acknowledge and understand people's emotional states, establishing its presence worldwide.

So, instead of just taking verbal commands, it would be able to comprehend the unsaid and communicate and understand the way humans do.

Common Examples of AI

Some popular use cases of AI in this digital world are:


An AI chatbot making rounds on the internet is the latest ChatGPT (GPT: Generative Pre-trained Transformer.) The bot can translate and curate content, create natural language and respond to queries. Since it's easily accessible by all, it's becoming one of the most popular AI tools.

GPT-3, for one, was the largest language model ever launched. Today we have GPT-4, which has an even broader scale of parameters.

Self Driving Cars

Self-driving cars use machine learning via sensors and cameras to make sense of their surroundings to drive safely.

For instance, Waymo, a driverless car, drives like a taxi. Currently, the service is limited to the US only. But companies in other parts of the world are also following suit and progressing rapidly in self-driving vehicles.

Google Maps

Google Maps use location data, road accidents, construction breaks etc., and assess the traffic flow to give you the best route to your desired destination.

Medical Wearable Devices

Wearable devices like blood pressure equipment, heart rate and oximeters have specialised sensors that help assess the general health of an individual.

Many of these devices also come with memory functions and store the previous record of the patient to make critical medical assessments.


AI may not have achieved the super AI level seen in sci-fi movies like The Terminator, but it has made advancements in robotics. Robots can use AI to navigate and respond to stimuli and environmental factors.

Types of Machine Learning

There are two main categories of machine learning.


Supervised learning is used for teaching AI systems by example. The annotated, labelled data is fed to the system to learn the pattern and characteristics of the data. This training enables the system to perceive things based on experience.

Unsupervised Learning

In unsupervised learning, AI looks for patterns or similarities to categorise data.

An example of unsupervised learning is the large language models. These models look at books, articles, web pages, etc., and process the data to make sense of the patterns and relationships. 

This enables them to curate human-like responses and decision-making—for example, GPT 3.5 and GPT-4.

What is Reinforcement Learning?

In this type of learning, the AI system looks for the best outcome for a reward. It's a kind of trial-and-error learning, which enables the system to master learning over time.

Conversational AI Systems

Conversational AI systems like chatbots are programmed to have human-like conversations with people. They are trained and experienced in listening to input so they can respond or provide output to the user.

Since the tone is conversational, the AI uses natural language to understand the user queries and to respond to them in similar tones.

Generative AI and its Uses in the Real World

The generative AI model is programmed to respond to prompts. Tools like ChatGPT are becoming essential for businesses as they can produce creatives and writings within seconds. 

Most companies that use AI systems use machine learning AI systems. Machine learning is widely popular, followed by data mining and AI-based robotic process automation software.

Plus, with more instructions, these responses are further customised and personalised for every user. Industries like software houses, IT, etc., can benefit from their instantaneous responses. 

Companies can also use task-specific Generative AI for specific jobs or actions. That way, instead of wasting valuable resources, time and cost in these aspects, they can focus on creating more value for the users.

However, the downside is that Generative AI can be highly inaccurate in its predictions or responses. Since the learning is based on internet training, the output is often plagiarised and biassed. Here are a few examples of generative AI in different fields:


Technical fields, like medicine, can also use technology to produce high-quality and high-resolution imaging.

Marketing and Sales

Generative AI can easily be used to create personalised messages for marketing on social media platforms. You can add images and videos or rely on introductory text to attract your target audience with personalised CTAs.


AI can generate and document codes in the IT sector.

It can answer complex queries from vast legal documents and drafts. Later, it reviews those documents and reports for data extraction and records.

Benefits of AI

AI has vast benefits and uses across various domains. From medicine, marketing, and finance to banking and IT, AI can mitigate risks and improve productivity at a fraction of the cost. Let's look at a few key benefits of AI and see how it's changing the business world.


Most financial entities already use AI as part of their business functions to assess risks and generate more revenue. They can use AI systems for fraud detection, anti-money laundering compliance, offering personalised banking services, etc.

The involvement of AI in banking will lead to billions of dollars of savings that can later be used for improvising other areas.


AI can play a role in diagnostics. While other areas are tricky and still need a lot of human intervention, AI can reduce the time and effort required in diagnosing diseases and designing better health policies for individual patients.

Employment Opportunities

While AI's widely believed to take over most human jobs, it will also create more jobs. Jobs like AI support and maintenance, AI development, ethics and regulations, new industrial setups etc., are on the horizon already.

Plus, many jobs do not exist today, so it’s hard to comprehend where AI can take us in the future.

Limitations of AI

There are a few known as well as unknown risks of AI.

For instance, the outcomes may look human-like and precise but, in reality, could be entirely wrong. Since all the information is based on the internet, it may also be biassed or manipulated. 

For instance, algorithms like ChatGPT will not give you the correct response to some confidential questions. But if you twist the query to make it sound like an emergency, it will instantly respond. So, all kinds of information can be extracted by manipulating the system easily.

Then, there are legal issues that need to be looked into. Relying solely on the system may lead to publishing biassed or copyrighted content. Bing and Midjourney can also be used to copy voices and videos to create fake videos or even for identity theft. 

One thing we can do to combat these risks is to invest in smaller specialised systems to get focused outcomes or have a human supervise AI. They should be assigned to view all content and assess critical decisions before implementation.

Furthermore, a significant chunk of the population is still opposed to AI. They still feel unsafe sitting in self-driving cars and worry that AI may oust them of their jobs. Furthermore, some companies are also concerned about the high development costs of the systems.

AI - Future Outlook

Over the last six years, the number of businesses and companies that have adopted AI models in their businesses has doubled. It's not just limited to IT and technological setups anymore. 

But product development, marketing, sales, corporate finance, etc. Sectors have also had high adoption rates of the technology.

AI has become the USP for many companies. It is now one of the prime factors that drive growth, particularly with the system currently involved in hiring, upskilling and faster scaling.

The demand is ever on the rise in the Irish market too. As per the National Standards Authority, 40% of companies already use AI technology, and over 50% are likely to adopt it by the coming year.


What is AI?

AI is a vast term that covers techniques machines use to copy human intelligence. Just like the working of the human brain, which perceives certain stimuli as inputs present around them in the environment, machines take input from the data presented to them. Then, they make sense of the data to make informed decisions.

What Is the Difference Between Artificial Intelligence, Machine Learning, and Deep Learning?

Machine learning is a type of AI, and deep learning is a type of machine learning. Machine learning is when a device can make decisions and reason without being taught or programmed. It allows devices to learn from the data to achieve excellence over time and make informed decisions as they earn experience. Deep learning uses neural networks to draw high-grade input data features.

What is an AI Assistant?

AI assistants are programs backed by machine learning to respond to your queries. The assistants give you the necessary information, determine your wants and perform small user tasks.

The smart assistants are typical of smartphones and devices, like smart home appliances, cars, speakers, cameras, locks, XR glasses, etc.

What Are the Major Improvements Happening in AI?

AI today has access to advanced tools and hardware along with better networks. So, it's becoming more accurate, especially in regular tasks with combined efforts in the AI research community, via workshops, courses, etc. It is helping people and companies apply AI to different devices and systems. 

Also, the efforts are increasing personalisation and learning efficiency, thus enhancing the user experience with AI.

How Can We Eliminate Bias From AI?

Biassed input data will result in biassed output. One way to ensure the bias doesn't creep in, affecting the results, is to enable the model to collect diverse data. The data should be collected from different locations comprising different demographics.

How Capable Is AI Today?

Let's understand the capabilities of AI through examples: 

  • Royce uses AI for predictive maintenance and service of engines.

  • Google Duplex can call and make dentist and salon appointments for users.

  • Companies like Baidu (China) are working on self-driving vehicles as a mode of city transit.

  • Using AI, Netflix and Amazon make user recommendations based on their choices and past behaviours.

  • Chatbots can respond to all types of human queries.

  • Google DeepMind, another AI system, beat professional gamers in a global StarCraft game.

  • Fintech uses AI to combine data sets and analyse more data efficiently.

  • Then in the field of healthcare AI gathers information for preventive medicines.


AI is all set to change the way we view the world. It is already impacting our work, way of living, health and privacy. From getting basic amenities like transport assistance and Siri on your phone, AI tools also affect our work proficiency and productivity.

Now is the right time to gain as much knowledge and enhance the usability of AI systems. The better you can master the algorithm, the more opportunities will open up.

Enrol in a Professional AI course at UCD Professional Academy to learn all the advanced skills and techniques to master AI to the best of your advantage. This course will give you a headstart in today's throat-cutting business employment market.