In today’s digitally enhanced world, automation is lurking in every corner of our lives – from your Alexa speaker at home to driving decisions in the boardroom. As this technology infiltrates every-day life, it is hard not to hear the buzzwords – Artificial intelligence (AI) and Machine Learning (ML), ringing in our ears.
People often use the terms interchangeably – but are they really one and the same thing? Not exactly.
What is Artificial Intelligence?
Artificial Intelligence can be defined as the science of training machines to perform human tasks. AI is the method of developing computer systems to emulate human intelligence and is also the engineering behind giving machines human-like properties.
To successfully implement knowledge engineering, machines or computer software need to have access to a large amount of information to be programmed to act and think like a human. This helps build up the machine brain power needed to problem-solve, to allow analytical reasoning and to wire in common sense into a machine. Artificial intelligence has two main subsets: Machine learning and deep learning.
Artificial intelligence is ubiquitous in the field of tech today, and to better understand the concept, it would be worthwhile to get a grip on what artificial intelligence can do by identifying some of the AI applications that exist today.
- Image Recognition: Have you ever wondered how facebook automatically recognises faces to tag in a photo? This is an ideal example of AI being used for image recognition.
- Recommendations: AI if equipped with the right information can make recommendations for movies, music, products or services, be it through Spotify’s recommended playlists or targeted online advertisements.
- Natural Language Processing: AI has the capability of understanding human communication to respond in a way that is natural. A chatbot is a perfect example for this. They are programmed to interpret a wide array of queries to give you the best answer to your question.
What is Machine Learning?
To give you a comprehensive introduction to Machine learning (ML), it is necessary to first understand that ML is an element embedded into the wider realm of Artificial Intelligence. This means that machine learning is AI, but not all types of AI are machine learning. ML focuses on training systems to improve their ability to learn so they can better perform tasks. This learning is done by feeding data to computer systems so that they can automatically analyse patterns to guide decision making in the future. This process does not require humans to explicitly hard code tasks to be performed, but allows machines to learn on its own.
For machine’s to be exceptionally advanced at ‘doing by learning’, they require large amounts of granular data that covers a sizably diverse area. Much like humans who learn to do certain things from experience, machines are able to do the same but in a matter of seconds.
Machine learning can be segmented into 3 types:
1. Supervised learning
To enable supervised machine learning, systems need to have data with observations as well as the labels and classifications of that data. To give a simple example, a set of data can consist of images of both cats and dogs that are labeled for the machine to interpret. In the future, this knowledge is then used to predict whether an image presented is a cat or dog.
2. Unsupervised learning
In this case machines require a dataset with observations but without the compulsion of the data being classified or labeled. So if a machine was to learn how to cross a road, you wouldn’t teach it to do so with precise rules, but rather show it a set of 10,000 videos of someone crossing safely and another 10,000 showing the opposite. The machine is then left to learn on its own.
3. Reinforcement learning
This kind of learning is evident from the name itself, the machine is penalised or rewarded based on its performance in an attempt to help it learn the right way of doing a task. If the error is low the reward is high, if the error is high then the penalty is high.
To draw a clearer picture of what is machine learning, and to help see how machine learning is more specific as compared to the broader concept of AI, let’s dive into some of the ways machine learning is used today.
Machine Learning Applications:
- Segmenting Data: Machine learning can help carry out targeted online campaigns, as mentioned under the applications of AI, by segmenting the large database of customers to give you segments based on some criteria, who can then be individually targeted.
- Predicting Outcomes: Machine learning is known to assimilate data continually and learn from it to improve predictions. An example of this would be estimating the ETAs for rides with a cab like Uber. The machine learns from experience the time it would take to reach a certain location at a certain time and makes predictions accordingly.
To help clearly Identify the differences between Artificial Intelligence and Machine Learning, here is a concise comparison table of Artificial Intelligence versus Machine Learning:
Artificial Intelligence versus Machine Learning
AI is defined as the science of training machines to perform human tasks.
ML is defined as training systems to improve their ability to learn so they can better perform tasks.
The aim is to simulate human intelligence with the help of neural networks.
The aim is to significantly improve the performance of a machine based on the data provided for a particular given task.
AI has two main subsets: machine learning and deep learning.
Machine learning has a main subset which is deep learning.
Artificial Intelligence focuses on improving the chances of success.
Machine learning focuses on improving accuracy.
AI can be segmented into 3 types based on its capabilities: strong AI, general AI and weak AI.
Machine learning can be segmented into 3 types: supervised learning, unsupervised learning and reinforcement learning.
AI deals with 3 types of data: structured, semi-structured and unstructured.
ML deals with 2 types of data: structured and semi-structured.