Artificial intelligence (AI) and machine learning have become two of the hottest buzzwords in the tech industry. But what are the differences between AI and machine learning?
AI and machine learning are distinct but related concepts. AI refers to advanced software that imitates how humans process and analyse information. Machine learning is a subtype of AI that uses algorithms–or sets of rules–to perform specific tasks.
These technologies have many innovative uses in finance, healthcare, logistics, and other industries. But the number of people with AI and machine learning skills has not kept up with soaring demand. According to a McKinsey & Company survey(opens new window), over 60% of organisations struggled to hire Machine Learning Engineers and AI Data Scientists in 2023.
Expanding your AI and machine learning skills can help you keep up with the evolving tech landscape. We examine the differences between these technologies, applications in the workforce, and more.
What’s the difference between AI and Machine Learning?
AI is a broad term for software that mimics how humans perform complex mental processes. This technology analyses information, learns from its experiences, and solves problems.
Machine learning is one of the most popular branches of AI. This approach uses algorithms–or instructions–to guide decision-making and execute tasks. All machine learning is AI, but not all AI programs use machine learning.
What is AI?
AI refers to machines and software that imitate human cognitive functions. This technology performs advanced processes that traditionally relied on human intelligence. For example, AI software can identify patterns in large datasets, recognize faces in photographs, and give personalised recommendations.
One of the most popular types of AI are large language models (LLMS). Engineers use vast quantities of human-generated content to train OpenAI’s ChatGPT and other LLMs. The models learn context and language from the data and use this knowledge to respond to human input.
Engineers also use AI to create robots that respond to their environments and perform intricate tasks. For instance, AI-powered vacuum cleaners avoid obstacles in their paths, while AI surgical robots assist surgeons with operations.
Additionally, AI enables researchers to develop autonomous systems that operate without human guidance. Autonomous drones and vehicles use algorithms and sensor technologies to make real-time decisions and navigate their environment.
What is Machine Learning?
Machine learning is a subset of AI that uses algorithms to create intelligent systems that learn from datasets. The algorithms detect patterns in data, make predictions based on historical trends, and complete tasks. They refine their performance over time as they receive more data, so humans don’t need to tweak the programming.
There are three main types of machine learning with different applications:
- Supervised learning: An algorithm processes datasets with historical inputs and outputs and identifies their relationships. The software generalises and extrapolates this knowledge to predict future outputs. Organisations use supervised learning to teach algorithms to classify items, detect abnormal data points, and forecast future trends.
- Unsupervised learning: The algorithm looks for connections and patterns between unlabeled data points and generates insights into the dataset’s structure. For instance, an algorithm could analyse website traffic and sort customers into groups based on browsing behaviour.
- Reinforcement learning: The algorithm gains positive or negative reinforcement from its environment and adjusts its behaviour accordingly. AI-powered robots and self-driving cars use reinforcement learning to learn new tasks and optimise performance. The streaming service Spotify also uses reinforcement learning to provide increasingly accurate personalised recommendations.
Machine Learning and AI: How do businesses use them?
Organisations in all industries leverage AI and machine learning to improve their operations. These technologies complete repetitive tasks faster and more accurately than humans, enhancing productivity. Businesses also use AI and machine learning to drive innovation and develop more efficient processes.
Here are four use cases for AI and machine learning in different sectors.
Autonomous threat detection
Cybersecurity professionals use AI and machine learning to detect cyber threats more efficiently. This technology autonomously monitors computer networks and systems for abnormal behaviour and data points. Algorithms analyse these anomalies to determine if they’re caused by cyber attacks and trigger defence mechanisms.
Autonomous threat detection allows companies to respond more quickly to cybersecurity incidents. Algorithms also improve and adapt in response to emerging threats so organisations can stay two steps ahead of cybercriminals.
Diagnostic imaging
AI and machine learning have revolutionised medical imaging(opens new window). Radiologists and other healthcare professionals use this technology to capture and reconstruct diagnostic images. For example, AI software can create synthetic images based on a single image, so patients spend less time in the radiology department.
AI also helps clinicians analyse images for lesions, tumours, brain aneurysms, and other conditions. In some cases, this technology may detect abnormalities missed by human eyes. This increased precision leads to faster and more accurate diagnoses and improves patient outcomes.
Personalised marketing campaigns
Marketers use AI and machine learning to create more effective and targeted marketing campaigns. Machine learning algorithms analyse behaviour, demographics, and other data to gain insights into customers’ preferences. Companies use these findings to provide personalised product recommendations and promotions.
Businesses also use AI to automate time-consuming marketing processes. AI-powered chatbots answer questions from prospective customers, while generative AI tools create articles and other marketing content. These innovations let marketers focus on tasks that require a human touch, like nurturing client relationships and developing the perfect brand voice.
Supply chain optimization
Products often travel through convoluted global supply chains before they reach customers. AI helps organisations streamline and optimise these processes so goods reach their destinations as efficiently as possible.
Sophisticated machine learning algorithms analyse historical data and forecast future trends. These models predict changes in customer demand, the availability of raw materials, and other market dynamics. Businesses leverage this data to anticipate supply chain fluctuations and respond proactively.
Demand for AI and Machine Learning jobs
The widespread adoption of AI and machine learning has opened new career opportunities in every industry. The World Economic Forum’s Global Risk Report 2024(opens new window) predicts that the demand for AI and Machine Learning Specialists will increase by 40% by 2027.
Data science is one of the fastest-growing AI-related professions. Data Scientists use machine learning algorithms to interpret complex datasets and help business leaders make informed decisions.
According to a 2021 study, demand for Data Scientists in the UK is large and growing—with an estimated 178,000 possible vacancies. These professionals also command healthy salaries. Glassdoor data indicates Data Scientists in London earn a median salary of £59,000(opens new window).
Additionally, LinkedIn’s 2024 Jobs on the Rise Report(opens new window) lists Artificial Intelligence Engineer as the tenth-fastest growing career. These experts use programming languages and technical skills to build, train, and maintain AI software. According to Glassdoor, Artificial Intelligence Engineers earn an average salary of £55,000 in the UK.
Future proof your career with in-demand skills
AI will disrupt approximately 40% of jobs worldwide, according to a 2024 report by the IMF(opens new window). This statistic may sound alarming, but this technology will likely change most jobs, not eliminate them. Developing AI and machine learning skills will allow you to adapt to the evolving workforce and fill critical skills gaps.
Immerse yourself in the latest AI and machine learning developments with Multiverse’s free bite-sized AI training(opens new window). These innovative training modules provide fast, actioned-oriented lessons on foundational AI principles, prompt engineering and teach you how to apply AI ethically in your current career.
Ready to become an AI expert? Talk to your employer about our AI for Business Value apprenticeship to start your journey.
