Let’s Talk: Machine Learning, Deep Learning and Artificial Intelligence

The last decade has seen a revolution in technology. From smartphones to social media, to the rise of artificial intelligence, there’s been a lot of change in the world. Artificial Intelligence(AI) is a big player in this revolution. It has already changed the way we communicate and has even come to be a part of our daily lives such as Siri, Alexa and Google Assistant.

But then, we are also seeing the rise of other technical buzzwords such as Machine Learning (ML) and Deep Learning (DL). Generally, this can be confusing and leave us wondering about their distinct meaning. How do they work in the real world? And how are they related to each other? In the article, we’ll explore these terms (AI, ML and DL) and take a closer look at each of them.

What is Artificial Intelligence?

Artificial intelligence simply refers to machines performing tasks typically requiring human intelligence. John McCarthy first coined the term “Artificial Intelligence” and he described it as “the science and engineering of making intelligent machines, especially intelligent computer programs”.

AI is an umbrella term that encompasses different fields such as Machine Learning, Natural Language Processing (NLP), Pattern Recognition, Computer Vision, Speech Recognition etc. The field was created with the goal of making computers behave more like humans. AI may be used to teach a computer how to recognize objects or faces in pictures, translate text or voice from one language to another, make predictions about future events based on previous occurrences, and analyze large amounts of data.

With this new type of technology, things that were previously impossible or too time-consuming to do are now being solved quickly and efficiently. From problem-solving to data processing to decision making, AI can help improve the efficiency and accuracy of a lot of tasks.

The image below explains it better;

Types of Artificial Intelligence

1. Artificial Narrow Intelligence (ANI)
Another name for ANI is Weak AI and it’s the only type present in our world today. ANI is goal-oriented and рrоgrаmmed tо рerfоrm а single tаsk. It’s extremely intelligent in completing the sресifiс tаsk that it is рrоgrаmmed tо dо. Siri, Google assistants, chatbots, and self-driving cars are great examples.

2. Artificial General Intelligence (AGI)
When it comes to AGI, machines have the ability to learn, comprehend, and act in a manner that is indistinguishable from that of a human in a particular situation. This means that they are conscious machines that are driven by emotions and self-awareness. They are also known as strong AI and do not currently exist. 

3. Artificial Super Intelligence (ASI)
In ASI, machines will have more problem-solving and decision-making capabilities than humans. This means that the machine will be capable of displaying intelligence that surpasses that of the brightest human and this will have a significant impact on humanity.

What is Machine Learning?

Machine learning (ML) is a subset of artificial intelligence (AI) in which machines learn to make predictions based on data. ML gives computers the ability to learn without being explicitly programmed. It provides a way for programmers to write algorithms so that the computer can automatically generate a pattern or a rule, which it can then use to make predictions about new data.

Machine learning is one of the most intriguing and revolutionary technologies today. It is changing our world in many ways. Machine learning can be used in all sorts of industries and for many purposes including classification, prediction, forecasting, data mining, pattern detection, automation and much more.

It enables systems to make data-driven decisions without any human intervention – using analytics to predict outcomes without relying on time-consuming human expertise; automating tasks; adapting systems; and helping people make better decisions.

There are three main types of machine learning algorithms:

1. Supervised Learning – When a model trains using supervised techniques, it learns from labeled data sets. This means that both the input datasets and output have to be known and used to train the model. 

Let’s use a classic example: Assume we have some fruit pictures and we need to create a model that’ll recognize these fruits and classify them appropriately, this is where supervised learning comes in. In order to create this type of model, we’ll provide both input and output data, which means that the model will be trained based on the form, size, color, and taste of each fruit. After the training, we’ll put the model to the test by feeding it a new batch of fruits. The model will recognize the fruit and, using a suitable algorithm, predict the outcome.

2. Unsupervised Learning – Unsupervised machine learning is used on data that contains no labels or there are too few labels to train the algorithm. The computer learns on its own how to identify patterns in the data.

It’s called unsupervised because the algorithms are left to sort the unsorted input on their own by looking for similarities, differences, and patterns in the data. 

Here’s a related example: We’ll make use of the same example above, but in contrast to supervised learning, we shall not give any supervision to the model in this case. All we need to do here is to feed the model with the input dataset and let it detect patterns in the data. The model will train itself and separate the fruits into distinct groups based on the most comparable attributes between them, using a suitable method.

3. Reinforcement Learning

This is a kind of machine learning algorithm in which an agent learns from an interactive environment in a triаl and error-prone manner by continuously utilizing feedbаck from its previous actions and experiences. As usual, we look at an example. In reinforcement learning, we have an agent and a reward but there are numerous obstacles in the way. The agent’s job is to find the most efficient route to the reward. 

Say we have a robot (agent) trying to get a fruit (reward), there will be some obstacles so the robot’s objective is to obtain the fruit prize while avoiding the obstacles. The robot learns by attempting all feasible courses and then selecting the one that provides the best reward with the fewest obstacles. Each correct step will reward the robot, while each incorrect step will deduct the robot’s payout. When the robot approaches the final prize, the fruit, the entire reward will be computed.

Deep Learning

Deep learning is another subset of AI and a subfield of machine learning which focuses on training neural networks with huge amounts of data in order to extract patterns and make inferences from them. Image and voice recognition are two typical examples of deep learning applications.

Deep Learning architectures are called artificial neural networks because they function like the human brain’s network of neurons and synapses. They operate by passing information between nodes in succession using weighted connections.

Deep Learning focuses on neural networks or artificial neurons that are loosely based on the organic ones in our brain, where the connections are strengthened through repeated use of an input pattern. The more connections made between these artificial neurons, the more accurate they become at solving problems similar to those encountered during training.

Summary of Artificial Intelligence, Machine Learning and Deep Learning

Let’s do a quick recap. Machine Learning and Deep Learning are two types of Artificial Intelligence. Machine learning is a branch that provides a system with the ability to automatically learn and improve from experience without being explicitly programmed. It mainly consists of these two categories: supervised learning and unsupervised learning, where supervised learning is when the data provided has labels so the machine can learn by comparing it with the labels, and unsupervised learning is when there are no labels. 

Deep Learning on the other hand refers to a set of algorithms that employs large neural networks with many layers of processing units to discover patterns in vast amounts of data, taking advantage of advances in computer power and training methods.

By now, I’m pretty sure you’ve gotten a better understanding of these terms. These terms are generalized definitions and contain a lot of concepts that need to be furthermore explored. Astrolabs offers a Data Science and Machine Learning Course Bootcamp where you get to learn data science, machine learning, deep learning, and artificial intelligence (AI).

This course bootcamp will literally take you from zero to hero in the field of DS, ML, AI and DL. You get to learn about the fundamental concepts of machine learning and deep learning such as classification, clustering and regression, ANNs, Tensorflow, Keras e.t.c

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