Artificial intelligence (AI) and machine learning (ML) are often used interchangeably, especially in discussions regarding big data, predictive analytics, and digital transformation. However, it is important to recognise the differences between these two trending technologies. This article will explore the dissimilarities and connections between AI and ML, exploring their scope, applications, and practical implications for organisations today.
What is Artificial Intelligence?
Artificial intelligence is a broad field that involves using technology to develop machines and computers capable of mimicking cognitive functions associated with human intelligence. These functions include visual and language comprehension, data analysis, recommendation generation, and more.
While often considered a standalone system, artificial intelligence encompasses a set of technologies implemented within a system to enable reasoning, learning, and problem-solving.
What is Machine Learning?
Machine learning, on the other hand, is a subset of artificial intelligence that focuses on enabling machines or systems to learn and improve autonomously through experience. Instead of relying on explicit programming, machine learning utilises algorithms to analyse vast amounts of data, gain insights, and make informed decisions.
As machine learning algorithms are exposed to more data, they continuously enhance their performance. The output of machine learning is represented by the models that the program develops from training data. The more data available for training, the better the model becomes.
The connection between AI and ML
AI and ML are closely connected, although they are not synonymous. A simple way to understand their relationship is as follows:
- AI: Enabling machines or systems to sense, reason, act, or adapt like humans.
- ML: An application of AI that allows machines to extract knowledge from data and autonomously learn from it.
To further illustrate the distinction, it is helpful to envision them as umbrella categories. Artificial intelligence serves as the overarching term encompassing various specific approaches and algorithms. Machine learning falls within this umbrella, alongside other subfields like deep learning, robotics, expert systems, and natural language processing.
Differentiating AI and ML
Now that we have established their connection let's examine the main difference between AI and ML. While artificial intelligence focuses on developing machines that can imitate human intelligence, machine learning's goal is not to achieve mimicry. Instead, machine learning aims to teach machines to perform specific tasks and provide accurate results by identifying patterns.
For instance, if you ask your Google Nest device, "How long is my commute today?" the device will provide an estimated time for your drive to the office. In this scenario, the machine's task is to successfully perform the calculation, which would otherwise be done manually. Machine learning is employed to analyse real-time transit and traffic data, forecast traffic volume and density, and maximise performance for that specific task.
On the other hand, artificial intelligence enables machines to simulate human intelligence to solve complex problems. AI systems can reason, learn, and act like humans. They have many applications and can work with different data types, such as structured, semi-structured, and unstructured. AI systems can continuously self-correct and improve by utilising logic, decision trees, and learning algorithms.
Benefits of Using AI and ML Together
Combining the power of AI and ML offers significant advantages to organisations of all sizes. As data grows in size and complexity, automated and intelligent systems play a crucial role in automating tasks, unlocking value, and generating actionable insights for better outcomes. Some of the key benefits include:
- Wider Data Ranges: AI and ML enable analysing and activating a broader range of structured and unstructured data sources.
- Faster Decision-Making: By improving data integrity, accelerating data processing, and reducing human error, AI and ML facilitate more informed and quicker decision-making.
- Efficiency: Implementing AI and ML enhances operational efficiency, leading to cost reductions and streamlined processes.
- Analytic Integration: Integrating predictive analytics and insights into business reporting and applications empowers employees to make data-driven decisions.
Applications of AI and ML
The versatility of artificial intelligence and machine learning allows for their application in numerous domains. Organisations leverage AI and ML to transform operations, optimise processes, and improve customer experiences. Here are some common applications:
- Healthcare and Life Sciences: AI and ML are utilised for patient health record analysis, outcome forecasting, accelerated drug development, diagnostics augmentation, patient monitoring, and information extraction from clinical notes.
- Manufacturing: Applications in manufacturing include production machine monitoring, predictive maintenance, IoT analytics, and operational efficiency.
- E-commerce and Retail: AI and ML are employed for inventory and supply chain optimisation, demand forecasting, visual search, personalised offers and experiences, and recommendation engines.
- Financial Services: In the financial sector, AI and ML find applications in risk assessment and analysis, fraud detection, automated trading, and service processing optimisation.
- Telecommunications: AI and ML enhance telecommunications through intelligent networks and network optimisation, predictive maintenance, business process automation, upgrade planning, and capacity forecasting.
What is the difference between artificial intelligence (AI) and machine learning (ML)?
AI refers to using technology to build machines and computers that mimic cognitive functions associated with human intelligence. It encompasses a broad range of technologies implemented in a system to enable reasoning, learning, and problem-solving.
On the other hand, ML is a subset of AI that focuses on enabling machines to learn and improve from experience autonomously. It uses algorithms to analyse data, gain insights, and make informed decisions.
How are AI and ML connected?
AI and ML are closely connected. AI is a broader concept encompassing various approaches and algorithms, including ML. ML is an application of AI that allows machines to extract knowledge from data and learn from it autonomously. Think of AI as the larger umbrella term and ML as one of its subfields.
Can you provide examples of AI and ML applications?
Certainly! Here are some common applications:
- In healthcare and life sciences, AI and ML are used for patient health record analysis, outcome forecasting, accelerated drug development, diagnostics augmentation, patient monitoring, and information extraction from clinical notes.
- In manufacturing, applications include production machine monitoring, predictive maintenance, IoT analytics, and operational efficiency.
- In e-commerce and retail, AI and ML are employed for inventory and supply chain optimisation, demand forecasting, visual search, personalised offers and experiences, and recommendation engines.
- In financial services, AI and ML find applications in risk assessment and analysis, fraud detection, automated trading, and service processing optimisation.
- In telecommunications, AI and ML enhance networks through intelligent network optimisation, predictive maintenance, business process automation, upgrade planning, and capacity forecasting.
What are the benefits of using AI and ML together?
By combining AI and ML, organisations can unlock powerful benefits, including:
- Analyzing and activating a wider range of structured and unstructured data sources.
- Improving data integrity, accelerating data processing, and reducing human error for faster decision-making.
- Increasing operational efficiency and reducing costs.
- Empowering employees by integrating predictive analytics and insights into business reporting and applications.
Can AI and ML work with different types of data?
Yes, AI systems can work with various data types, including structured, semi-structured, and unstructured data. However, ML primarily relies on structured and semi-structured data for training its algorithms and producing predictive models.