ARTIFICIAL INTELLIGENCE EXPLAINED

Artificial Intelligence (AI) has become increasingly popular in recent months due to its accessibility to the public through models like ChatGPT. While various forms of AI have been incorporated into our daily lives for a while now, they are more than just buzzwords and have been instrumental in increasing our productivity both personally and professionally. However, AI applications are not limited to conversation-based chatbots. They extend beyond that.

Artificial intelligence (AI) has been developed to simulate human intelligence through software-coded heuristics. The term can also refer to any computer that displays characteristics of human intelligence, like learning and problem-solving skills. This technology can be utilised in solving issues, developing solutions, answering questions, creating predictions, and offering strategic propositions. Therefore, AI has become a competitive technology in most aspects of our daily and professional lives.

History of Artificial Intelligence

The concept of AI has been around for centuries, with early examples dating back to ancient Greece and China. However, it was not until the 1950s that the field of AI began to take shape. John McCarthy coined the term “artificial intelligence” in 1956, and Allen Newell and Herbert Simon created the first AI program, the Logic Theorist, in the same year.

Throughout the 1960s and 1970s, AI research made significant strides with the development of expert systems, natural language processing, and machine learning algorithms. However, progress stalled in the 1980s and 1990s as researchers struggled to overcome the limitations of existing AI technologies.

It was not until the 21st century that AI began to experience a resurgence, thanks to advances in computing power, big data, and cloud computing. Today, AI is being used in a wide range of applications, from self-driving cars to personalised healthcare.

Artificial Intelligence technology is centred around the idea that a machine could mimic and execute actions like humans. Those actions range from simple to incredibly complex tasks. However, it is important to understand that AI is not just a single computer program or application but an entire scientific field.

The systems utilise an immense amount of data and intelligent processing algorithms to learn from patterns and features in the data they analyse. With each round of data processing, the AI system tests and measures its performance and gains additional expertise. AI systems can work continuously, performing hundreds, thousands, or even millions of tasks at high speeds, allowing them to learn quickly and become proficient at whatever task they are being developed for.

Subfields and components of AI

Artificial Intelligence (AI) is a rapidly evolving scientific field that encompasses a wide range of components or sub-fields. Some of the main subfields of AI include natural language processing, machine learning, computer vision, robotics, and expert systems. Each of these sub-fields has its own unique set of principles, theories, and algorithms designed to enable machines to simulate human-like intelligence and behaviour.

By combining these different fields, AI researchers can create intelligent machines capable of performing complex tasks, learning from data, and adapting to new situations.

1. Machine Learning (ML)

Machine Learning (ML) is a subfield of Artificial Intelligence that empowers computers to learn automatically from data and algorithms. By collecting various types of data, such as photos, numbers, and text, ML prepares the training data for the machine learning model to learn from.

The more data that is used, the more trends and patterns the programs can identify. With the help of ML, AI can uncover insights about trends within the data parameters and produce remarkably accurate results.

There are three subcategories of Machine Learning:

• Supervised Machine Learning model: Machines are trained to forecast results using labelled data.
• Unsupervised Machine Learning model: The model extracts information from the input to find features and patterns and provide an outcome. Machines are trained with unlabelled data.
• Reinforcement Machine Learning model: Machines acquire knowledge through error and feedback-based action.

2. Deep Learning/ Neural Networks

Deep Learning is a subset of Machine Learning that enables AI to develop and learn by analysing data. Deep Learning processes information, identifies relationships between the data, and generates conclusions, or outcomes, based on positive and negative reinforcement using artificial neural networks that resemble biological neural networks in the human brain.

As the machine learns, it is rewarded for its progress through a positive reinforcement process. This continual reinforcement is crucial for the machine to further advance. When a node's output surpasses a predetermined threshold value, the next network layer receives data, allowing the neural network to learn and improve its accuracy. However, to truly master the task at hand, neural networks require ample amounts of training data.

3. Robotics

Also a branch of Artificial Intelligence, robotics focuses on the design, development, and implementation of robots to perform tasks autonomously or semi-autonomously. The goal of developing AI robots is to assist humans in carrying out tasks as needed.

One of the key challenges in developing AI robots is creating machines that can interact with the world safely, efficiently, and effectively. This requires a deep understanding of both the physical and digital worlds as well as the ability to integrate these two domains seamlessly.

Despite the challenges, the benefits of developing AI robots are clear. Robotics is a rapidly growing field that has seen tremendous progress in recent years. With the ability to create robots that can operate independently or with minimal human intervention, the potential for these machines is enormous, ranging from manufacturing and logistics to healthcare, personal assistance, and more.

4. Fuzzy Logic

Fuzzy logic is a method for resolving questions or assertions that can be true or false. This approach mimics human decision-making by taking into account all viable options between digital values of "yes" and "no." In plain terms, it gauges how accurate a hypothesis is.

This area of Artificial Intelligence is used to reason on ambiguous subjects. It is an easy and adaptable way to use machine learning techniques and rationally mimic human cognition.

Fuzzy Logic architecture has four parts:

• Rule Base: This contains all of the rules and the if-then conditions to control the decision-making system.
• Fuzzification: This helps to convert inputs into fuzzy sets. The crisp inputs come from sensors or any input data into the control system for further processing.
• Inference Engine: This helps determine the matching degree between fuzzy input and the rules and decide which rules need to be implemented accordingly.
• Defuzzification: This converts fuzzy sets into crisp values.

5. Natural Language Processing

Natural Language Processing (NLP) is a subfield of Artificial Intelligence (AI) that focuses on helping computers understand human language.

NLP is a multi-disciplinary field that combines machine learning, computational linguistics, and deep learning models to enable machines to understand the meaning, context, and sentiment of human speech or text data.

With NLP, virtual chatbots can converse with humans in a natural language, spam detection algorithms can filter out unwanted emails, and sentiment analysis can help businesses understand their customers' opinions and emotions. NLP has the potential to revolutionize the way we interact with machines, making them more human-like in their understanding of language.

6. Computer Vision

Computer Vision focuses on empowering machines to recognise and comprehend objects and individuals in images and videos. Algorithmic models enable computers to learn the contexts of visual input, so if enough data passes, computers can learn to identify one image from another.

By training machines to analyse and interpret visual data, we can unlock new insights, automate processes, and even create entirely new experiences. From self-driving cars that navigate busy streets to facial recognition software that can unlock our phones, the applications of Computer Vision are virtually limitless.

One of the key challenges in Computer Vision is enabling machines to understand the context of visual input. This involves teaching them to recognise shapes, colours, patterns, and other visual cues that help us distinguish one object from another. Machine learning models can be used to teach computers these patterns, allowing them to learn and adapt to new environments over time.

7. Expert System

An expert system is an advanced form of Artificial Intelligence that aims to replicate the decision-making capabilities of a human expert in a particular field. These systems are designed to address complex problems that require specialized knowledge and expertise. They use a knowledge base supplied with data to create inference rules that help them make decisions based on the information they have.

These systems are designed to address complex problems that require specialised knowledge and expertise. They use a knowledge base supplied with data to create inference rules that help them make decisions based on the information they have.

Expert systems are incredibly versatile and can be used in a wide range of industries, from finance to healthcare. They can help businesses manage information more efficiently, identify and prevent viruses, and even assist in loan analysis. By using a series of if-then statements, expert systems can analyse data and provide solutions to complex problems that would otherwise be difficult to solve.

Undoubtedly, the benefits of using AI are numerous. One of the most significant benefits of AI is its ability to improve efficiency. By automating redundant tasks, AI frees up time for employees to focus on more important ones, which increases productivity and profits.

Another advantage of AI is its 24/7 availability. Unlike human workers, AI can operate tirelessly without interruption or break. This makes AI an excellent tool for businesses that need to provide round-the-clock services.

AI also facilitates a faster and less biased decision-making process. By analysing vast amounts of data in real time, AI can provide insights humans might miss. This helps businesses make informed decisions quickly without the risk of human error or bias.

Finally, AI has mass market potential, making it a valuable tool in different industries. Whether it is healthcare, finance, or transportation, AI can be applied to different sectors to improve efficiency, reduce costs, and improve outcomes.

Although Artificial Intelligence brings many positive opportunities and promising applications, it also has its drawbacks. One of the main challenges of AI is that it is only as good as the data it is trained on. Therefore, if the data used to train AI models is limited and inaccurate, it can result in biased or inaccurate predictions.

In addition, developing or maintaining AI is expensive and requires considerable time and resources. To operate at its full potential, AI needs the latest hardware and software. With the constant improvement of AI, it is crucial that the system keep up with the latest trends to ensure it is effective and up-to-date.

Other challenges and limitations include:

• Lack of Transparency: Many AI models are complex and difficult to interpret, making it challenging to understand how they make decisions. This can result in a lack of trust and accountability, which is especially problematic in high-stakes applications like healthcare and criminal justice.
• Overreliance on Data: AI algorithms require large amounts of data to train effectively, which can be difficult and expensive to obtain. In some cases, there may not be enough data available to train models for specific applications, limiting the effectiveness of AI.
• Security Concerns: As AI becomes more prevalent, it also becomes a more attractive target for cyberattacks. Malicious actors could potentially exploit vulnerabilities in AI systems to gain access to sensitive data or manipulate the decisions made by the AI.
• Ethical Concerns: AI raises a range of ethical questions around issues like privacy, autonomy, and accountability. For example, who is responsible when an AI system makes a harmful decision? How do we ensure that AI is used in ways that are beneficial to society as a whole?

Despite its challenges, AI is transforming industries and improving our lives in many ways. As we continue to develop and improve AI, we need to acknowledge its limitations and work towards addressing them. With a better understanding of the drawbacks, we can continue to harness the power of AI to create a better future for all.

ChatGPT, an AI-powered chatbot, has transformed how we communicate with machines. With its natural, human-like conversation, it is based on OpenAI's Generative Pre-trained Transformer (GPT) language model architecture. The GPT used by ChatGPT is a refined version of a model in the GPT-3.5 series, according to OpenAI.

Individuals and businesses can benefit from this language model by increasing their efficiency in handling redundant tasks and providing personalised user experiences for customer service and content creation. Users receive prompt and insightful answers to their questions by typing prompts, with clear and detailed instructions yielding more insightful responses.

Apart from answering questions, ChatGPT can perform a variety of functions, including writing essays and codes, summarising and checking grammar, creating to-do lists, and drafting travel plans. Its versatility makes it an invaluable tool for modern-day communication.

Should your business be afraid of ChatGPT and similar solutions? Check out our blog article here and learn more.

AI in Manufacturing

AI plays an important role in the Manufacturing industry, especially in the planning and execution. AI can predict future problems and provide solutions. They can also offer many generative design options for the product and forecast the raw materials needed to produce it.

On the other hand, robotics could be used to produce goods of consistently high quality since the AI system could spot products with flaws based on the desired quality established in quality assurance. Moreover, AI can also help analyse the analytical aspects of the manufacturers and provide the necessary insights.

In addition to the benefits mentioned above, AI can also enhance the safety of workers by detecting potential hazards and alerting them in real time. This is especially important in hazardous environments, where human error can lead to serious accidents. Moreover, with the help of AI-powered sensors, machines can detect when they need maintenance, reducing downtime and increasing productivity.

AI can also be used in supply chain management to track inventory levels and predict demand, thus enabling manufacturers to optimise their production schedules and reduce waste. This not only saves costs but also benefits the environment by reducing the amount of resources used in production.

AI in Hospitality

In Hospitality, Artificial Intelligence is implemented to enhance customer satisfaction and staff productivity. By automating tedious and repetitive tasks, such as booking confirmations and check-ins, AI-powered chatbots have freed up staff to focus on more complex tasks, such as managing guest complaints and providing personalised recommendations.

There are countless AI applications available on the market today. Hoteliers can leverage well-developed solutions, such as AI chatbots and virtual assistance. In addition to chatbots, AI can also be used for data analysis, allowing hoteliers to understand guest preferences, spending habits, and travel patterns to enhance their overall experience. This data can be used to personalise promotions and messages to guests, helping to increase loyalty and repeat business.

Furthermore, AI can optimise various hotel processes, such as lighting and temperature control, to improve guest comfort while reducing energy consumption. This not only saves money but also helps to minimise the hotel's environmental impact.

AI in Financial Services

• Personal Finance - Artificial Intelligence enables individuals to be more aware of their financial health and how to properly manage it. AI offers 24/7 chatbots that can provide financial guidance and provide solutions on a variety of wealth management topics.
• Consumer Finance – Artificial Intelligent can help prevent fraud and cyberattacks by inspecting and detecting irregularities in transactions or financial activities.
• Banking and Corporate Finance – Many financial institutions are utilising AI technology to calculate risks before making financial decisions. For instance, the implementation of Robo Advisor provides individuals and corporations with guidance and solutions for investing, portfolio options for halal investing, socially responsible investing, and other types of investments to suit everyone’s preferences.

AI in Oil and Gas

AI can allow Oil and Gas companies to optimise their asset management, leading to better decision-making processes and reduced carbon emissions. Predictive asset maintenance can also be significantly improved, making asset maintenance more efficient and inventory management more streamlined, all thanks to AI and robotics.

One of the most significant benefits of AI in the Oil and Gas industry is that it can help locate the optimal drilling location. By analysing data from multiple sources, AI can pinpoint the best location for drilling, maximising production while minimising environmental impact. Furthermore, the integration of robotics in the industry has allowed for safe machine usage in hazardous environments, reducing the risk of accidents and injuries.

AI has also made it possible for Oil and Gas companies to make data-driven decisions. By analysing large volumes of data, companies can identify patterns and make more informed decisions. They can also monitor operations in real time, allowing for quick and effective interventions when necessary.

Infor Coleman can be a game-changer for businesses looking to leverage the power of Artificial Intelligence without the cumbersome implementation process and steep learning curve. This AI tool is specifically designed to cater to the unique needs of business users by offering unparalleled assistance with task execution, recommending the best sales offers, and even predicting maintenance issues that could impact production schedules.

What sets Infor Coleman apart is its ability to deliver tangible results with unprecedented clarity and speed. The ROI on AI projects with Coleman is immediately visible, and businesses can reap the benefits of automation, integration, and productivity almost instantly. You do not need to have advanced technical skills or engage in unpredictable service engagements to work with Coleman. It is built on the Infor OS technology platform, making complex technologies such as natural language processing, intelligent automation, machine learning, and voice user experience much more accessible.

The components of Coleman are designed to help you, the users, understand and trust the AI tool as you integrate it into your business operations. This level of transparency and usability ensures that your team can add value to your business as they work with Coleman.

Moreover, Infor Coleman is designed to integrate seamlessly into your existing ERP ecosystem, providing your business with a competitive advantage by offering a lower adoption cost and deployment time. With Infor Coleman, you don't have to sacrifice flexibility or functionality to leverage the power of AI. It is an all-in-one solution that can take your business to the next level.