AI & Machine Learning

Artificial intelligence (AI) vs. machine learning (ML)

It is not uncommon for people to use the terms artificial intelligence (AI) and machine learning (ML) interchangeably, particularly when talking about big data, predictive analytics, and other topics related to digital transformation. The misconception is understandable given the strong relationship that exists between artificial intelligence and machine learning. Nevertheless, these emerging technologies are distinct from one another in a number of respects, including scope, applications, and others.  

The proliferation of AI and ML solutions can be attributed to the fact that organisations are increasingly using these technologies to process and analyse enormous volumes of data, facilitate improved decision-making, offer recommendations and insights in real time, and produce accurate forecasts and predictions. 

What exactly differentiates machine learning (ML) from artificial intelligence (AI), how are ML and AI related to one another, and what exactly do these concepts signify for businesses and other organisations in the real world today? 

We are going to contrast artificial intelligence with machine learning and investigate the similarities and differences between these two forward-thinking concepts.

What is artificial intelligence?

Artificial intelligence is a broad field that refers to the use of technologies to build machines and computers that have the ability to mimic cognitive functions associated with human intelligence. These functions include the ability to see, understand, and respond to spoken or written language, as well as the ability to analyse data, make recommendations, and a variety of other functions. 

Although artificial intelligence is frequently considered to be a system in and of itself, the term really refers to a collection of technologies that, when integrated into a system, provide that system the ability to learn, reason, and take action to solve difficult problems.

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What is machine learning?

A subfield of artificial intelligence known as machine learning is the process of automatically enabling a machine or system to learn and develop as a result of its previous interactions with data. Machine learning is an alternative to explicit programming that involves the use of algorithms to analyse vast amounts of data, gain insights from that analysis, and then make decisions based on those learnings. 

The performance of machine learning algorithms tends to improve with time as they are trained, or exposed to an increasing amount of data. Following the execution of an algorithm on training data, machine learning models are the output, or what the programme learns from the experience. The more data that are utilised, the more accurate the model will become. 

How are AI and ML connected?

Although AI and ML are not exactly the same thing, there is a strong connection between the two. The following is the simplest explanation of the relationship that exists between AI and ML:  

• The broader idea of artificial intelligence (AI) refers to the ability to teach a computer or system to sense, reason, act, or adapt like a human. 
• Machine learning (ML) is an application of artificial intelligence that enables machines to independently learn from data by extracting knowledge from it.

Imagine that machine learning and artificial intelligence are two different types of umbrella categories. This can be a handy approach to remember the distinction between the two. The umbrella phrase "artificial intelligence" refers to a wide range of distinct methods and algorithms that have been developed in recent years. Learning on a machine is one of the key subfields that fall under this umbrella, along with other significant subfields such as deep learning, robotics, expert systems, and natural language processing.

Differences between AI and ML

What are the primary distinctions between AI and ML now that you have an understanding of how they are related to one another? 

computer learning, on the other hand, does not include the concept of a computer that is capable of imitating human intelligence. Artificial intelligence, on the other hand, does include this concept. The goal of machine learning is to educate a computer or other electronic device how to carry out a particular activity and produce correct results by recognising patterns. 

Suppose you were to ask your Google Nest gadget, "How long is my commute today?" In this scenario, you address a query to a machine, and the response you obtain is an estimation of the amount of time it will take you to travel to your workplace. In this case, the overarching objective is for the gadget to effectively carry out a task, which is something that you would typically have to carry out on your own in a real-world setting (for example, study your commute time). 

In the context of this illustration, the purpose of implementing ML across the board in the system is not to provide it the ability to carry out a specific activity. For example, you may train algorithms to analyse live transit and traffic data in order to make predictions regarding the volume and density of traffic flow. Nevertheless, the scope is restricted to recognising patterns, determining how accurate the forecast was, and learning from the data in order to achieve the highest possible level of performance for that particular endeavour.

Artificial intelligence

• Artificial intelligence (AI) is the ability of a machine to replicate human intelligence in order to solve issues.
• The objective is to design a clever computer programme that can handle difficult responsibilities.
• We develop computer systems that are capable of solving complicated problems in the same way that humans do.
• The potential uses of AI are extremely diverse.
• Artificial intelligence is the application of several technologies within a framework to simulate human decision-making.
• The three categories of data that can be used by AI are structured, semi-structured, and unstructured.
• Artificial intelligence systems learn, reason, and self-correct through the application of logic and decision trees.

Machine learning

• A computer is able to learn on its own based on previous data thanks to ML.
• The objective is to develop machines that are capable of gaining knowledge from data in order to improve the precision of the output.
• We teach machines to carry out certain jobs using data, which enables us to produce reliable results.
• The scope of applications for machine learning is somewhat restricted.
• Machine learning generates prediction models through the use of self-learning algorithms.
• Machine learning can only work with data that is structured or semi-structured.
• Machine learning systems are dependent on statistical models for learning and have the ability to self-correct when given new data.

Benefits of using AI and ML together

Artificial intelligence (AI) and machine learning (ML) deliver significant benefits to organisations of all shapes and sizes, with new opportunities appearing all the time. To be more specific, as the size and complexity of the amount of data continues to expand, automated and intelligent systems are becoming increasingly necessary to assist businesses in automating operations, unlocking value, and generating actionable insights in order to achieve better results. 

The following are some of the advantages that adopting artificial intelligence can bring to businesses:

Wider data ranges

performing analysis on and activating a greater variety of data sources, both organised and unstructured.

Faster decision-making

Improving data integrity, speeding up data processing, and cutting down on human error in order to make decisions more quickly and with better information.

Efficiency

enhancing the effectiveness of daily operations while also cutting costs.

Analytic integration

Providing employees with more autonomy through the incorporation of predictive analytics and insights into the reporting and applications used in businesses.

Applications of AI and ML

The combination of artificial intelligence and machine learning has a wide range of potential applications, one of which is the automation of manual or repetitive business processes by organisations in order to facilitate more informed decision-making.

AI and ML are being put to use in a variety of new ways by businesses across all sectors to revolutionise the way they operate and conduct their operations. Incorporating AI and ML capabilities into their strategies and systems enables businesses to rethink how they use their data and available resources, improve customer and employee experiences, promote productivity and efficiency, and enhance data-driven decision-making through predictive analytics.   

The following are some of the most common uses of artificial intelligence and machine learning: 

Healthcare and life sciences

Analysis and insights of patient health records, outcome predictions and modelling, expedited drug development, improved diagnostics, patient monitoring, and information extraction from clinical notes are all examples of applications of this technology.

Manufacturing

Monitoring of production machinery, preventative maintenance, analyses of Internet of Things data, and improved operational efficiency.

Ecommerce and retail

Optimisation of inventory and supply chains, demand forecasting, visual search, personalised experiences and offers, and recommendation engines are all examples of emerging technologies.

Financial services

Risk assessment and analysis, fraud detection and prevention, automated trading, and the optimisation of service processing are all areas of focus.

Telecommunications

Intelligent networks and network optimisation, predictive maintenance, business process automation, upgrade planning, and capacity forecasting are some of the capabilities that intelligent networks provide.