AI, ML and DL — Let’s make it more clear!
Here you will know about how different this three terms (i.e AI, ML and DL) are!
If you’ve been caught in the confusion of differentiating artificial intelligence (AI) vs machine learning (ML) vs deep learning (DL), don’t worry buddy, we will see it!
Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) are three terms people use as they are same things. Although the three terminologies are usually used interchangeably, they are not exactly same.
Let’s make more clear!
As you can see on the above image of three concentric circles, DL is a subset of ML, which is also a subset of AI.
That means, AI is the all-encompassing concept that initially erupted, then followed by ML that thrived later, and lastly DL that is promising to escalate the advances of AI to another level.
Artificial Intelligence (AI)
Artificial Intelligence (AI) ,as name suggest can be interpreted to mean incorporating human intelligence to machines. AI is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and animals. Leading AI textbooks define the field as the study of “intelligent agents”: any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. The term “artificial intelligence” is often used to describe machines (or computers) that mimic “cognitive” functions that humans associate with the human mind, such as “learning” and “problem solving”.
A more elaborate definition characterizes AI as “a system’s ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation.”
AI-powered machines are usually classified into two groups → Wide AI (general) and Narrow AI.
Wide AI is sometimes also referred to as ‘General AI’. Wide AI is a system with cognitive abilities so that when the system is presented with an unfamiliar task, it is intelligent enough to find a solution. Here the system is capable of having intelligent behavior across a variety of tasks — from driving a car to telling a joke. For example, suppose if machines can move and manipulate objects, recognize whether someone has raised the hands, or solve other problems, then those machines can be general artificial intelligence AI machines can intelligently solve problems.
The narrow intelligence AI machines can perform specific tasks very well, sometimes better than humans — though they are limited in scope. Narrow AI is sometimes also referred to as ‘Weak AI’. However, that doesn’t mean that Narrow AI is inefficient or something of that sort. Narrow AI is an Artificial Intelligence System that is designed and trained for one particular task. Virtual assistants such as Amazon’s Alexa and Apple’s Siri use narrow AI. The technology used for classifying images on Pinterest is an example of narrow AI.
Applications of AI
- AI In Marketing
- AI In Banking
- AI In Finance
- AI In Agriculture
- AI In HealthCare
- AI In Gaming
- AI In Space Exploration
- AI In Autonomous Vehicles
- AI In Chatbots
- AI In Artificial Creativity
AI is usually adopted to solve customer service issues, inform people about the latest news along with giving them live traffic updates and weather forecast.
Artificial Intelligence is very useful in all the following domains:
- Automation
- Productivity
- Decision Making
- Solving Complex Problems
- Economy
- Managing Repetitive Tasks
- Personalization
- Global Defense
- Disaster Management
- Lifestyle
Machine Learning (ML)
Machine learning (ML) is the study of computer algorithms that improve automatically through experience. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop conventional algorithms to perform the needed tasks.
Machine learning is closely related to computational statistics, which focuses on making predictions using computers. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a related field of study, focusing on exploratory data analysis through unsupervised learning.In its application across business problems, machine learning is also referred to as predictive analytics.
Machine learning is an application of Artificial Intelligence (AI) generating systems that can learn and improve without being programmed. Contrary to AI, ML concentrates on developing computer programs that access data and use it to learn for themselves.
ML is a subset of artificial intelligence; in fact, it’s simply a technique for realizing AI.
Training in machine learning entails giving a lot of data to the algorithm and allowing it to learn more about the processed information.
For example, here is a table that identifies the type of fruit based on its characteristics:
As you can see on the table above, the fruits are differentiated based on their weight and texture.However, the last row gives only the weight and texture, without the type of fruit. And, a machine learning algorithm can be developed to try to identify whether the fruit is an orange or an apple.After the algorithm is fed with the training data, it will learn the differing characteristics between an orange and an apple.Therefore, if provided with data of weight and texture, it can predict accurately the type of fruit with those characteristics.
Machine Learning types (we will see it in detail later)
Applications of ML
- Traffic Alerts
- Social Media
- Transportation and Commuting
- Products Recommendations
- Virtual Personal Assistants
- Self Driving Cars
- Dynamic Pricing
- Google Translate
- Online Video Streaming
- Fraud Detection
Machine Learning is often used to power recommendation engines that provide suggestions based on past customers’ behaviours.
Deep Learning (DL)
Deep learning (also known as deep structured learning) is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised.
Deep learning is a class of machine learning algorithms that uses multiple layers to progressively extract higher level features from the raw input. For example, in image processing, lower layers may identify edges, while higher layers may identify the concepts relevant to a human such as digits or letters or faces.
As earlier mentioned, DL is a subset of ML; in fact, it’s simply a technique for realizing machine learning. In other words, DL is the next evolution of machine learning.
DL algorithms are roughly inspired by the information processing patterns found in the human brain. Just like we use our brains to identify patterns and classify various types of information, deep learning algorithms can be taught to accomplish the same tasks for machines. The brain usually tries to decipher the information it receives. It achieves this through labelling and assigning the items into various categories. Whenever we receive a new information, the brain tries to compare it to a known item before making sense of it — which is the same concept deep learning algorithms employ.
For example, artificial neural networks (ANNs) are a type of algorithms that aim to imitate the way our brains make decisions.
Comparing deep learning vs machine learning can assist you to understand their subtle differences. For example, while DL can automatically discover the features to be used for classification, ML requires these features to be provided manually.
Furthermore, in contrast to ML, DL needs high-end machines and considerably big amounts of training data to deliver accurate results.
Applications of DL
Deep learning architectures such as deep neural networks, deep belief networks, recurrent neural networks and convolutional neural networks have been applied to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design, medical image analysis, material inspection and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance.
Some major DL application domains are:
- Automatic speech recognition
- Image recognition
- Visual art processing
- Natural language processing
- Drug discovery and toxicology
- Customer relationship management
- Recommendation systems
- Bioinformatics
- Medical Image Analysis
- Mobile advertising
- Image restoration
- Financial fraud detection
- Military
Deep Learning is used to develop highly automated systems such as self-driving cars. Through their sensors and onboard analytics, these cars can reorganise obstacle and facilitate situational awareness.
How do machine and deep learning differ?
DL is a subfield of ML. In this sense, ML and DL share many characteristics, including their ability to detect patterns from data and learn how to perform a specific task with improving performance over time given further inputs.
There are however many differences between machine learning vs deep learning both in means and application.
The main differences that distinguish machine learning vs deep learning are fourfold:
- data consumption;
- dedicated hardware;
- feature extraction.
01. Data Consumption
DL requires a vast number of labelled samples for it to succeed. For this reason, the explosion of data over the past few decades has enabled DL as a viable technique (along with cheaper and more powerful hardware, of which see below).
However, the quantity of data isn’t in and of itself enough: it needs to be of the right quality, i.e. labelled. Not all data collected is labelled, labelled correctly or in a manner suitable for DL. Nor is such data always publicly accessible.
Where this is the case you (or someone on your behalf) must undertake a data labelling exercise, which is costly in terms of time and money and often requires a defined and rigorous set of procedures, quality controls and domain expertise.
Unfortunately this fact, and its impact on DL’s utility for real-world problems, is often downplayed in discussions concerning DL (and to a similar degree in discussions regarding ML).
02. Dedicated Hardware
The training phase of DL systems typically requires dedicated hardware such as Graphics Processing Units (GPUs) to reduce execution time to something manageable, i.e. hours, days or weeks vs. years. These systems, although increasingly cheaper, are still expensive vs. the needs of simpler ML set-ups.
03. Feature Extraction
Feature extraction (aka feature engineering) is the process of putting domain knowledge into the creation of feature extractors to reduce the complexity of the data and make patterns more visible to learning algorithms.
This process is difficult and expensive in terms of time and expertise.
This is best explained via an example:
- Assume you are building a system that will learn to classify images as either Car or Not Car.
- In classical ML, the algorithmic approach will use data to learn whether the image is Car or Not Car. To help this along, it might have been possible for a human to label constituent features indicative of Car (e.g. wheels) in the images thereby providing extra features with which the system can assess.
- By contrast, a DL solution will also attempt to determine which parts of the image make up the car, e.g. wheels, wing mirrors, headlamps, windscreen etc.
- As a result, DL can reduce the amount of hard coding humans have to apply to define features in datasets. This is the difference between having to label the image as Car vs. Not Car and having to do that plus label other data about the image such as wheels, windscreen, wing mirror etc that indicate Car or Not Car.
This diagram is a useful summary of the core distinction between machine learning vs deep learning:
In conclusion, Artificial Intelligence, Machine Learning and Deep Learning are three different terms that need to be fully understood and used separately.
In upcoming articles we will see AI, ML and DL in more detail. Feel free to ask any doubt regarding to this article. Have a great learning!
