Artificial intelligence and it's impact on manufacturing.

Artificial Intelligence (AI) is stirring up a storm in many sectors, in no other sector is artificial intelligence having more of an impact than on manufacturing.

What is artificial intelligence?

Artificial intelligence (AI) is a division of computer science focused on building smart machines capable of performing intelligent tasks that require human intervention. Artificial intelligence is the combination of machine learning, deep learning, neural networks and data mining - all seen championing the business world.

Artificial intelligence definition:

Artificial intelligence (AI),  the ability of a digital computer or computer-controlled robot to perform tasks associated with intelligent beings. Encyclopaedia Britannica

How does Artificial Intelligence work?

AI performs depending on the assigned task. This includes unique implementations and strategies, but isolated as two main approaches:   


This approach uses algorithms — a series of direct instructions used by computers to solve problems. The chosen algorithm will determine and direct the AI on how to approach the problem it faces. Each defined algorithm will have different goals, strengths, weaknesses and approaches, depending on the desired result and problem at stake.

Examples based:

An examples-based approach creates models out of data by finding patterns. Models in this case can be examples of visualisations or predictions.  Some examples of the data used in these scenarios include: user profiles, data logs, transactions, weather reports, etc. The AI is "fed" data, ans using machine learning it gains a general understanding of the project by identifying patterns in the data set.

The types of artificial intelligence:

AI programming focuses on cognitive skills - characterised as four types of AI: reactive, limited memory, theory of mind and self-aware. The processes to achieve these include:

Learning processes: Acquiring data and creating rules which turn the data into actionable information. These provide the machine with actionable instructions to complete specific tasks.

Reasoning processes: Determining the correct algorithm (path) to achieve the desired outcome.

Self-correction processes: A process of continuous adjustment, fine-tuning and developing the algorithm to ensure the desired results.

Four types of artificial intelligence:

In a 2016 article Arend Hintze, an assistant professor of integrative biology and computer science and engineering at Michigan State University, explained that AI can categorise into the following four types: 

History of artificial intelligence:

The following is some of the most important events in AI.















Weak AI versus strong AI:

Weak AI, also known as narrow AI, excels on preforming one task extremely well, such as answering questions or playing chess. Strong AI, can preform a range of tasks, and eventually teach itself to solve new problems.

Weak AI requires human input and relies on it to define its working parameter. Strong AI in comparison, does not require human intervention, thus over time, strong AI develops consciousness to improve its learning. 

What are the applications of AI technology? 

Artificial intelligence examples:

How important is artificial intelligence?

What are the advantages of artificial intelligence?

What are the disadvantages of artificial intelligence?

Artificial intelligence in manufacturing process:

The advent of artificial intelligence for manufacturing will revolutionise the entire sector. This includes adding value intelligent supply chains through: mass customisation, efficiency, predictive maintenance and raw material stock analysis to name a few.

The pandemic has allowed a hard reset and a chance for manufacturers to implement advanced artificial intelligence into their supply chain. What are the use cases of AI in manufacturing?

Applying AI to manufacturing can reduce running costs significantly.

Predictive maintenance allows manufacturers to predict when machines need maintenance with a high degree of accuracy. Advanced sensors and analytics embedded into the machinery will allow manufacturers to prevent unplanned downtime by using machine learning. For example, predicting the optimal time to replace replace or resharpen tools in the manufacturing chain.

Supply chain management will be able to benefit from using big data. Manufacturers collect a whole host of data, but often struggle with making sense of it all. With intelligent AI, manufacturers will be able to unlock the insights that were previously unreachable.

Improvements in robotics, from traditional pre-programmed robots to AI-powered robots which can interpret data efficiently and make decisions on their own. This alone will not only save time, but manufacture to a better standard. This is because AI powered robots can interpret data from CAD models and schematics to identify the most efficient and appropriate way to complete the task without any pre-programming.

Mass customisation is a manufacturing technique that enables personalisation of custom-made products at scale. AI provides the possibility of creating customised solutions for development and production through greater efficiency in the form of modular production. AI algorithms can formulate estimations and implement changes by looking at patterns in consumer behaviour, socio-economic and macroeconomic factors, location and more. This not only means a more personalised solution for the customer, but optimisations and adaptations made independently by the AI.

The manufacturing sector is the perfect candidate for artificial intelligence. As the technology matures and costs drop, expect to see more manufacturers utilising AI as it becomes more accessible. The future of AI is certain in an industry known for embracing new and emerging technologies. Manufacturers are consistently looking to minimise cost, make data-driven decisions, optimise operations and deliver a better product to their consumers.

How close are we to artificial intelligence?

How far away are we from a future surrounded by artificial intelligence? Most of the methodologies and applications of AI mentioned are possible today, the algorithms just have to be applied. What we are yet to see is the successful execution of  artificial general intelligence.

The mammoth task of replicating the processing power of a human brain to achieve artificial general intelligence is pending and quantum computing is likely to be our portal. It is likely we will begin to see the first AGI systems in 2030 and could take up to 2060 to see AI with the consciousness of a human.


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