Artificial Intelligence vs Machine Learning vs Deep Learning

Artificial Intelligence, Machine Learning and Deep Learning, still confused?
Artificial intelligence (AI) and machine learning (ML) are topics of interest to business, technology, and researchers worldwide. Most descriptions of artificial intelligence and machine learning oversimplify the real relationship between the two. This article lays the groundwork for your understanding of artificial intelligence, explains how today’s artificial intelligence is based on machine learning.

Artificial Intelligence, Machine Learning and Deep Learning

In 2016, AlphaGo defeated the Korean Go champion Lee Sedol. In media reports, the concept of ” deep learning ” was mentioned many times. The new version of AlphaGoZero makes full use of the deep learning method. It no longer starts training from the previous records of human chess players, but relies entirely on its own learning algorithm to learn how to play chess through self-play. After a period of self-learning, it beat the version of AlphaGo that beat Lee Sedol and Ke Jie.

It can be seen that the machine has indeed begun to have some kind of learning ability. What it gets in training is no longer just rules and object information, but also the possible conditions for the appearance of objects. In other words, it has been able to “feel” and capture possibilities, not just the ready-made. This kind of learning is a non-linear, probabilistic, feedback-adjusted quasi-occurrence process that is deepened and formed layer by layer and time by time. This is an acquisition process with some real time history.

If a machine can make its own decisions, the intelligence here includes these three aspects: artificial intelligence, machine learning and deep learning. What is the connection between them?Today, let’s explain in detail the relationship and difference between artificial intelligence, machine learning and deep learning.


0.1 Artificial Intelligence, Machine Learning and Deep Learning

Artificial Intelligence: Probably the most talked about concept since 1956. By 2015, widespread use of GPUs has made parallel processing faster, more powerful, and cheaper. And increasingly cheap storage can store big data at scale (from plain text to images, maps, etc.). This created a need for data analysis, more commonly known as data science leading to the development of machine learning as the method to achieve artificial intelligence.

AI and ML



Machine Learning: It is one of the branches of artificial intelligence and is at the core. As the name suggests, the research of machine learning aims to make computers learn to learn, to simulate human learning behavior, to build learning ability, and to realize recognition and judgment. Machine learning uses algorithms to analyze massive amounts of data, find patterns from them, and complete learning, using the learned thinking model to make decisions and predict real events. This approach is also called “training“.
Deep learning: It is an implementation technology of machine learning, which was first proposed by Hinton et al. in 2006. Deep learning follows bionics and originates from the research of neurons and neural networks. It can imitate the way human neural networks transmit and receive signals, and then achieve the purpose of learning the way of thinking of humans [2].
In short, machine learning is a method to achieve artificial intelligence, deep learning is a technology to achieve machine learning, and generative confrontation network is a classification in deep learning . The relationship between them can be shown in the figure below.

The relationship between artificial intelligence, machine learning and deep learning

The relationship between artificial intelligence, machine learning and deep learning



0.2 Machine Learning – The Approach to Artificial Intelligence.


At its most basic, machine learning is the use of algorithms to parse data, learn from it, and then make decisions and predictions about real-world events. Unlike traditional software programs that are hard-coded to solve specific tasks, machine learning is “trained” with large amounts of data, and various algorithms learn from the data how to complete tasks.
Machine learning comes directly from the early field of artificial intelligence. Traditional algorithms include decision tree learning, inferential logical programming, clustering, reinforcement learning, and Bayesian networks, among others. As we all know, we have not achieved strong artificial intelligence. Early machine learning methods couldn’t even achieve weak AI.

The most successful application area of ​​machine learning is computer vision, although it still requires a lot of hand coding to get the job done. People need to manually write classifiers, edge detection filters so that the program can recognize where the object starts and ends; write a shape detection program to determine whether the detected object has eight sides; write a classifier to recognize the letters “ST-OP “. Using these hand-written classifiers, one can finally develop algorithms to perceive an image and determine whether it is a stop sign or not.

0.3 Deep Learning – Techniques That Enable Machine Learning

Artificial neural network is an important algorithm in early machine learning. The principle of neural networks is inspired by the physical structure of our brains – neurons that are interconnected with each other. But unlike a neuron in the brain that can connect to any neuron within a certain distance, artificial neural networks have discrete layers, connections, and directions in which data travels.
For example, we can divide an image into image patches and input them to the first layer of a neural network. Every neuron in the first layer passes data to the second layer. The neurons in the second layer do a similar job, passing the data to the third layer, and so on, until the last layer, and then generating the result.
Each neuron assigns weights to its inputs, and the correctness of this weight is directly related to the task it performs. The final output is determined by summing these weights.
Let’s take the Stop sign as an example. All the elements of a stop sign image are smashed and “examined” with neurons: octagonal shape, firetruck-like red color, bold letters, typical dimensions of a traffic sign, and still motion features and more. The task of the neural network is to come up with a conclusion whether it is a stop sign or not. Based on all the weights, the neural network will come up with a well-thought-out guess — a “probability vector.”
In this example, the system may give the following results: 86% may be a stop sign; 7% may be a speed limit sign; 5% may be a kite hanging from a tree and so on. The network structure then tells the neural network whether its conclusions are correct.
In fact, in the early days of artificial intelligence, neural networks already existed, but the contribution of neural networks to “intelligence” was minimal. The main problem is that even the most basic neural networks are computationally intensive. The computing requirements of neural network algorithms are difficult to be met.

Now, image recognition trained with deep learning can even do better than humans in some scenarios: from identifying cats, to identifying early components of cancer in blood, to identifying tumors in MRIs. Google’s AlphaGo first learned how to play Go and then trained it playing Go against itself. The way it trains its neural network is to keep playing chess with itself, repeatedly, without stopping.

4.0 Key Differences Between Machine Learning and Deep Learning

Both deep learning and machine learning provide ways to train models and classify data, so what exactly is the difference between the two?
Using standard machine learning methods, we need to manually select the relevant features of the image to train the machine learning model. The model then references these features when analyzing and classifying new objects.
Through a deep learning workflow, relevant features can be automatically extracted from images. In addition, deep learning is an end-to-end learning, the network is given raw data and tasks such as classification, and can be done automatically.
Another key difference is that deep learning algorithms scale with data, while shallow learning data converges. Shallow learning refers to the way machine learning can achieve platform-level performance at a certain level of performance as users add more examples and training data to the network.
If you need to make a choice between deep learning and machine learning, users need to know whether they have a high-performance GPU and a large amount of labeled data. If the user does not have a high-performance GPU and labeled data, then machine learning has advantages over deep learning. This is because deep learning is usually complex and in the case of images may require thousands of images to get reliable results. A high-performance GPU can help users spend less time modeling and analyzing all images.
If users choose machine learning, they can choose to train the model on a variety of different classifiers, and can also know which features can extract the best results. Also, with machine learning, we have the flexibility to choose a combination of modalities, using different classifiers and features to see which permutation works best for the data.
So, in general, deep learning is more computationally intensive, while machine learning techniques are usually easier to use .

ai vs ml


Summary: AI is fundamentally about intelligence, and machine learning is about deploying AI-enabled computing methods. And deep learning enables several practical applications of machine learning by breaking down tasks in a way that makes all types of machine assistance seem possible. With the help of deep learning, artificial intelligence may even achieve the kind of sci-fi state humans have long imagined.

The main differences between AI and ML are:

artificial intelligence machine learning
AI stands for Artificial Intelligence where intelligence is defined as knowledge acquisition Intelligence is defined as the ability to acquire and apply knowledge. ML stands for machine learning and it is defined as the acquisition of knowledge or skills
The aim is to increase the chance of success rather than accuracy. The goal is to improve accuracy, but not concerned with success
It works as a computer program that works intelligently Here, machines take data and learn from it.
The goal is to simulate natural intelligence to solve complex problems. The goal is to learn from data for a specific task in order to maximize performance on that task.
AI is decision making. Machine learning allows systems to learn new things from data.
It is developing a system that mimics human problem solving. It involves creating self-learning algorithms.
AI will find the best solution. Whether optimal or not, machine learning seeks solutions.
Artificial intelligence leads to wisdom. Machine learning leads to knowledge.
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