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A simple guide to Artificial intelligence and machine learning in 2023

Artificial Intelligence and Machine Learning have recently become popular topics in the technology industry. But, perhaps more than in our daily lives, Artificial Intelligence (AI) impacts the business world. In 2014, about $300 million in venture capital was invested in AI startups, a 300 per cent increase over the previous year.

Artificial Intelligence is present everywhere, from gaming stations to managing complex data at work. Engineers and scientists are working hard to say intelligent beheviour in machines, allowing them to think and respond in real-time situations. As a result, AI moves from a research topic to the early stages of enterprise adoption. Google and Facebook have made significant investments in Artificial Intelligence and Machine Learning and are already incorporating them into their products. However, this is only the beginning. Over the next few years, we may see AI creep into one product after another.

According to John McCarthy, a Stanford researcher, “Artificial Intelligence is the science and engineering of creating intelligent machines, particularly intelligent computer programes.” Artificial Intelligence is related to the similar task of using computers to understand human intelligence, but AI does not have to limit itself to biologically observable methods.”
Simply put, the goal of AI is to make computers/computer programmes intelligent enough to mimic human mind behaviour. Therefore, knowledge Engineering is a critical component of AI research. Machines and programmes require a wealth of information about the world to act and react like humans. Therefore, AI must access properties, categories, objects, and relations to implement knowledge engineering. In addition, AI instils common sense, problem-solving abilities, and analytical reasoning in machines, which is difficult and time-consuming.


So what exactly is Vertical AI?

These services focus on a single task, such as scheduling meetings or automating repetitive tasks. Vertical AI Bots only do one thing for you, but they do it so well that we might mistake them for humans.

What exactly is Horizontal AI?

These services are designed to handle a variety of tasks. There is no single task to be completed. Horizontal AI is demonstrated by Cortana, Siri, and Alexa. These services operate on a larger scale as questions and answer settings, such as “What is the temperature in New York?” or “Call Alex.” They are useful for a variety of tasks rather than just one.
AI is achieved by analysing how the human brain works while solving an issue and then using analytical problem-solving techniques to build complex algorithms to perform similar tasks. AI is an automated decision-making system that continuously learns, adapts, suggests and takes actions automatically. At the core, they require algorithms that can learn from their experience. This is where Machine Learning comes into the picture.


Artificial Intelligence and Machine Learning are two popular but often misunderstood terms. Artificial Intelligence (AI) is a subset of Machine Learning (ML). ML is the science of designing and implementing algorithms to learn from previous cases. If a certain behaviour has occurred in the past, you can predict whether it will occur again. However, there can be no prediction if there are no previous cases.
ML can solve difficult problems such as credit card fraud detection, self-driving cars, and face detection and recognition. ML employs complex algorithms that iterate over large data sets indefinitely, analysing patterns in data and allowing machines to respond to situations for which they were not explicitly programmed. The machines learn from history to produce reliable results. The ML algorithms use Computer Science and Statistics to predict rational outputs.


1. Supervised Learning – In supervised learning, the system is given training datasets. Data is analysed by supervised learning algorithms, which produce an inferred function. The resulting correct solution can be used to map new examples. One example of a Supervised Learning algorithm is credit card fraud detection.

2. Unsupervised Learning – Unsupervised Learning algorithms are much more difficult to implement because the data to be fed is unclustered rather than in the form of datasets. The machine’s goal is to learn independently, without any supervision. There is no correct solution to any problem. Instead, the algorithm discovers patterns in the data. Recommendation engines, which are present on all e-commerce sites, are examples of supervised learning, as are Facebook friend request suggestion mechanisms.

3. Reinforcement Learning – This type of Machine Learning algorithm enables software agents and machines to automatically determine the best behaviour in a given context to maximise their performance. Reinforcement learning is defined by defining a learning problem rather than learning methods. We consider the reinforcement learning method to be any method that is well suited to solving the problem. Reinforcement learning presumes that a software agent, such as a robot, computer programme, or bot interacts with a dynamic environment to achieve a specific goal. This technique chooses the action that will produce the desired result in the shortest amount of time.


Artificial intelligence has been around for a long time – Greek myths tell of mechanical men who mimic our behaviour. Early European computers were conceived of as “logical machines,” Engineers saw their job as fundamentally attempting to create mechanical brains by reproducing basic arithmetic and memory capabilities. As technology and, more importantly, our understanding of how our minds work has advanced, so has our understanding of what constitutes AI. Rather than increasingly complex calculations, Artificial intelligence research has focused on mimicking human decision-making processes and carrying out tasks in increasingly human-like ways.
Artificial intelligence – devices designed to act intelligently – is frequently divided into two broad categories: applied and general. Applied AI is far more common – systems designed to trade stocks and shares or intelligently manoeuvre an autonomous vehicle fall into this category.
Generalised AIs – systems or devices that can, in theory, handle any task – are less common, but they are where some of the most exciting advances are currently taking place. It is also the field that has given rise to Machine Learning. So it’s more accurate to think of it as the current state-of-the-art rather than a subset of AI.


Two significant breakthroughs resulted in the emergence of Machine Learning as the vehicle propelling AI development forward at the current rate.
One of these was the realisation, attributed to Arthur Samuel in 1959 that rather than teaching computers everything they need to know about the world and how to perform tasks, it might be possible to teach them to learn for themselves.
The second, more recent event was the advent of the internet, which resulted in a massive increase in the amount of digital information generated, stored, and made available for analysis.
Engineers realised that rather than teaching computers and machines how to do everything, it would be far more efficient to code them to think like humans and then plug them into the internet to give them access to all of the world’s information once these innovations were in place.


The development of neural networks has been critical in teaching computers to think and understand the world in the same way we do while retaining the inherent advantages that computers have over us, such as speed, accuracy, and lack of bias. A Neural Network is a computer system that categorises information in the same way that the human brain does. It can be taught to recognise images and classify them based on the elements they contain, for example.
It essentially operates on a probability system – based on data fed to it, and it can make statements, decisions, or predictions with a high degree of certainty. The addition of a feedback loop allows for “learning” – by sensing or being told whether its decisions are correct or incorrect, it modifies its approach in the future.
Machine Learning applications can read the text and determine whether the author is complaining or congratulating themselves. They can also listen to music, determine whether it is likely to make someone happy or sad, and then find other music to match the mood. In some cases, they can even compose music that expresses the same themes as the original piece, knowing that fans of the original piece will appreciate it.
These are possibilities provided by systems based on machine learning and neural networks. However, thanks largely to science fiction, the notion that we should be able to communicate and interact with electronic devices and digital information as naturally as we would with another human being has emerged. To that end, another branch of AI, Natural Language Processing (NLP), has emerged as a source of tremendously exciting innovation in recent years and is heavily reliant on ML.
NLP applications attempt to comprehend natural human communication, whether written or spoken and communicate with us in a similar, natural language. ML is used in this context to assist machines in understanding the vast nuances of human language and learning to respond in a way that a specific audience is likely to understand.


When determining the distinction between artificial intelligence and machine learning, it is useful to consider how they interact due to their close relationship. This is how Artificial intelligence and machine learning interact:

Step 1: Machine learning and other techniques build an Artificial intelligence system. 

Step 2: Machine learning models are created by analysing data patterns. 

Step 3: Data scientists optimise machine learning models based on data patterns. 

Step 4: The process is repeated and refined until the models’ accuracy is sufficient for the tasks at hand. 


Almost every industry is discovering new opportunities due to the connection between Artificial intelligence and machine learning. These are just a few of the capabilities that have proven useful in assisting businesses in transforming their processes and products:

  • Analytics Predictive – This capability assists businesses in predicting trends and behavioural patterns by identifying cause-and-effect relationships in data. 
  • Engines of Recommendation – Companies use recommendation engines to recommend products that someone might be interested in based on data analysis. 
  • Natural Language Understanding and Speech Recognition – Natural language understanding recognises meaning in written or spoken language, whereas speech recognition enables a computer system to identify words in spoken language. 
  • Processing of Images and Videos – These capabilities enable the recognition of faces, objects, and actions in images and videos and the implementation of functionalities such as visual search. 
  • Analysis of Sentiment – A computer system uses sentiment analysis to identify and categorise positive, neutral, and negative attitudes expressed in text. 


The connection between artificial intelligence and machine learning provides significant benefits to businesses in almost every industry, with new possibilities emerging regularly. These are just a few of the many advantages that businesses have already realised:

  • Additional Data Input Sources – AI and machine learning enable businesses to gain valuable insights from a broader range of structured and unstructured data sources.  
  • Improved and Faster Decision-Making – Companies use machine learning to improve data integrity and artificial intelligence (AI) to reduce human error, resulting in better decisions based on better data. 
  • Enhanced Operational Effectiveness – Companies become more efficient through process automation with AI and machine learning, which reduces costs and frees up time and resources for other priorities. 


Companies from various industries are developing applications that take advantage of the relationship between artificial intelligence and machine learning. These are just a few examples of how AI and machine learning are assisting businesses in transforming their processes and products:

  • Retail – Retailers use artificial intelligence and machine learning to optimise their inventories, build recommendation engines, and improve the customer experience with visual search. 
  • Healthcare – Health organisations use AI and machine learning in applications such as image processing for improved cancer detection and predictive analytics for genomics research. 
  • Finance and Banking – AI and machine learning are useful tools in financial contexts for detecting fraud, predicting risk, and providing more proactive financial advice.   
  • Sales and Marketing – Sales and marketing teams, use AI and machine learning for personalised offers, campaign optimisation, sales forecasting, sentiment analysis, and customer churn prediction. 
  • Cybersecurity – AI and machine learning are potent cybersecurity weapons, assisting organisations in protecting themselves and their customers by detecting anomalies. 
  • Customer Care – Chatbots and cognitive search are used by businesses across industries to answer questions, gauge customer intent, and provide virtual assistance. 
  • Transportation – AI and machine learning are useful in transportation applications because they help companies improve the efficiency of their routes and use predictive analytics for things like traffic forecasting. 

Manufacturing – Manufacturing firms use AI and machine learning to predict maintenance and make their operations more efficient than ever before.