Artificial intelligence and machine learning are terms that are often used synonymously. On a broader level, we can look at these two trending technologies and note a fundamental difference. Artificial intelligence is a vast concept. In AI, the creation of machines that imitate
human behaviour is carried out. On the other hand, in machine learning, those intelligent machines, thus created, are enabled to learn from the input of data without any explicit programming.
Introduction to Artificial Intelligence As the word suggests, artificial intelligence is a human-made thinking power technology. It is defined as the kind of technology which creates intelligent systems simulating human intelligence. These intelligent systems need to possess a pre-existing algorithm. Instead, algorithms that have the power to work on their own intelligence are preferred.
Since machine learning is an application of artificial intelligence, the algorithms used in machine learning, such as deep learning, neural networks, reinforcement learning algorithms, prediction of modelling and natural language processing algorithms, are implemented in AI applications. E.g., virtual assistants like Siri or Google Assistant.
On the basis of merit and capabilities, artificial intelligence is classified into three categories:
- Weak AI
- General AI
- Strong AI
Introduction to Machine Learning
This subfield of artificial intelligence does not use any program to enable the machine to learn from the pre-existing data. Machine learning works in certain domains only. For example, machine learning helps in establishing geological relations between different pictures of earth, taken from various places. If you provide new data about something other than pictures of Earth, the algorithm will not respond. Machine learning is used in online recommendation systems like Google Search algorithms, Facebook Auto-friend tagging suggestions, Email spam filters, voice searches, etc.
There are three types of machine learning categories:
- Supervised learning
- Reinforced learning
- Unsupervised learning
The Difference between AI and ML
- The goal of AI is to make smart computer systems that are characterised by simulated human intelligence.
- The goal of ML is to let machines study data automatically and give precise output.
2. Tasks and Scopes
- AI has a broader scope than ML.
- In AI, the machine can perform tasks as a human would. In ML, machines perform a specific task only.
3. Core difference
- AI is concerned with maximising success and optimising time.
- ML is concerned about accuracy and patterns.
4. Primary Activities
- AI includes tasks involving learning, reasoning and self-correction. All the data is structured, semi-structured or completely unstructured.
- ML tasks include learning and correcting themselves after input of new data.
5. Applications and Subsets
- Machine learning and deep learning are significant subsets of AI. The primary applications include online gaming, Siri, customer support using catboats, etc.
- Deep learning is the principal subset of ML. Applications include online recommendation services and Google search algorithms.
Artificial intelligence and machine learning are two separate categories in computer science, although they are used interchangeably. Machine learning is a mere subset of AI. AI includes machine learning, among many other techniques, to create human capabilities and human behaviour simulations. If you wish to know more about these technologies and their future potential, click here to find out more information about online courses for AI and machine learning.