Exploring Artificial Intelligence and Machine Learning

In the past few decades, artificial intelligence (AI) and machine learning (ML) have taken the world by storm. There’s no doubt that intelligent computer engineering has transformed our lives, but what is AI and ML, and what is the difference between them?

In this blog, we will explore what artificial intelligence (AI) and machine learning (ML) is, how they are separate, and how they interact with each other to enrich our digital experiences across an array of industries.

So, sit back, relax, and enjoy!

What is artificial intelligence (AI)?

Artificial intelligence (AI) describes the ability of a computer (or programmable entity), to mirror anthropomorphic qualities such as problem-solving and active decision-making.

The system uses arithmetic and logic via an algorithm to simulate the thought processes and patterns of the human brain to help it learn in a proactive (rather than passive) capacity. This means that the system will continue to learn and improve its decision-making and problem-solving skills through actively practicing and running scenarios.

What is machine learning (ML)?

In summary, machine learning (ML) is the function of AI.

Machine leaning is the act of using mathematically determined models (such as algorithms) to help a system understand concepts without instruction.

This essentially means that the system is actively learning through imitation, rather than passively learning by being told how to perform a function. This allows the system to continue to learn and improve its skills via practice from experience.

So, what is the difference between AI and ML?

Although AI and ML are separate entities, they are closely entwined; both are essential to producing a system that can simulate the act of ‘thought’.

The main difference is that an ‘intelligent’ computer or system uses AI to ‘think’ like a person and perform tasks on its own accord without human supervision or instruction, whilst ML is how a computer or system tries to evolve its intelligence.

One of the ways computer engineers can get a system to imitate anthropomorphic thought abilities is to use a neural network. This is a sequence of algorithms that have been developed to mimic the human brain but written in a way that a computer can interpret. AI is then achieved through deep learning, where the system will run scenarios and logic repeatedly.

How is artificial intelligence and machine learning used?

As artificial intelligence and machine learning continue to develop, there is a growing demand for systems that are capable of human thought processes, such as problem-solving and decision-making.

Below, we have included some of the ways AI and ML is currently being used:

• Predictive Analytics
  To calculate trends and behavioural patterns by determining cause and effect relationships in datasets in order to understand probability of results.
• Recommendation Engines
  To recommend goods, services or offer help to human users based on data analysis of behavioural and interaction patterns.
• Speech Recognition and Natural Language
  To pinpoint words and understand how they may be used in context through analysis of spoken and written language from human interactions.
• Image and Video Processing
  To recognise objects, faces, and actions in images and videos by analysing thousands of examples in order to enhance functions, such as visual search.
• Sentiment Analysis
  To identify and categorise neutral, positive and negative opinions expressed in written language in order to moderate or assess response.

Which industries are actively using artificial intelligence and machine learning?

As popularity for intelligent systems continues to grow exponentially, more global and leading industries are switching to using computers capable of human-like thought to enhance their productivity and customer experience.

• Retail
  The retail industry uses AI and ML to optimise their product inventories, create recommendation engines and improve their customers’ experience with features such as visual search.
• Finance
  AI and ML have been helping the finance industry predict risk, detect fraud and fraudulent behaviour, and provide a more proactive approach to financial advice.
• Healthcare
  The medical and healthcare industry’s growing use of AI and ML is helping them to generate predictive analytics for genomics research, as well as run various theoretical tests for disease and cancer solutions.
• Sales and Marketing
  AI and ML are now an instrumental tool for sales and marketing, helping to interpret data to create bespoke offers, optimised campaigns, sentiment analysis and assessment of the customer lifecycle.
• Manufacturing and Transportation
  The manufacturing and transportation industries have begun to use AI and ML in prognostic maintenance, helping to keep their procedures efficient by scheduling maintenance in advance of areas that need attention. Transportation is also using intelligent route analysis to calculate the most fuel-efficient routes and predict traffic.

So, what are the benefits of AI and ML?

All sectors and industries have ultimately benefitted from artificial intelligence and machine learning in some way or another. Whether that’s through bringing better customer service offerings, such as chatbots and cognitive search systems to help employees and consumers find what they need, or making processes more efficient by using machines to perform menial tasks and free-up staff resource.

AI and ML also bring a significant contribution in keeping our data and digital assets safe. Intelligent systems can now safeguard themselves, by monitoring, detecting, and dealing with malware or irregularities that may otherwise cause harm or faults.

With artificial intelligence and machine learning growing in complexity at an exponential rate, the capabilities of intelligent systems and their uses are growing. AI and ML now provide valuable insights into large, unstructured volumes of data, interpreting and categorising it so that we can benefit from the findings directly. For businesses, using machine learning is an excellent way to improve the speed and efficiency of functions and simultaneously reduce the risk of human error.

Ultimately, intelligent computer systems will help us to increase machine automation, reduce costs, free-up time and resources.