Deep learning: differences with machine learning and traditional AI

Share this post:

Deep learning (DL) is a branch of machine learning and one of the most promising technologies in the field of artificial intelligence (AI), but how does it differ from machine learning (ML) and traditional AI?

In this article, we will try to understand what deep learning is and how it differs between the various types of AI, offering some practical examples.

Continue reading.

Table of contents

What is artificial intelligence?

First of all, what is artificial intelligence? AI is the science of creating computer systems capable of performing tasks that normally require human intelligence, such as object recognition, natural language understanding, problem solving and learning.

In other words, AI is a branch of computer science that studies the design of intelligent agents, i.e. systems capable of perceiving their environment and acting in pursuit of specific goals.

AI, therefore, is the ability of machines to simulate human cognitive abilities, such as reasoning, planning and creativity.

AI falls into 3 categories.

1. Weak artificial intelligence

Weak artificial intelligence refers to those systems that perform only one type of task, such as winning a chess game or identifying a person within a photo. This is the most widespread and realised form of AI today.

2. General artificial intelligence

General artificial intelligence, instead, concerns more complex systems capable of reaching the same level of intelligence as a human being with an average IQ and performing tasks such as understanding context, humour and emotions. This is the most ambitious and challenging form of AI, still under development.2

3. Superior artificial intelligence

A superior artificial intelligence does not exist today; if it did, it would surpass the intelligence of any human being and could perform tasks that are not available to him, such as creating new life forms or colonising other planets. It’s probably the most speculative and controversial form of AI, which could have unpredictable outcomes for mankind.

What is machine learning?

Machine learning is a branch of artificial intelligence concerning the ability of complex systems to learn automatically from clusters of data in order to make predictions or decisions.

Machine learning is based on mathematical algorithms that analyse data, identify patterns and build predictive models.

There are 2 types of ML.

1. Supervised machine learning

Supervised machine learning involves systems learning of labelled data, i.e. data containing both input and output information. For example, a supervised machine learning system can classify e-mails into spam or non-spam, using a set of e-mails already labelled as such.

2. Unsupervised machine learning

Unsupervised machine learning uses, differently, unlabelled data learning systems, i.e. data that do not contain both input and output information, but only the former. In this case, an unsupervised machine learning system knows how to group images according to their similarity, without knowing a priori to which categories they belong.

deep learning

What is deep learning?

Deep learning is a further sub-branch of machine learning; it deals with creating complex artificial intelligence systems based on artificial neural networks.

Artificial neural networks are computational models inspired by the functioning of the human brain, consisting of several layers of units (usually two or three), called neurons. Each neuron receives input from the neurons of the previous layers, processes them using a complex mathematical function and produces an output for the neurons of the next layer, and so on.

Deep learning owes its name to the use of deep neural networks, i.e. with many layers of neurons, capable of learning complex and abstract representations of data that would otherwise be impossible to achieve with traditional machine learning methods.

The success of deep learning today is due to the great availability of big data and, on the hardware side, the advent of powerful central processing units (CPU) and graphics (GPU), which accelerate learning.

The results are surprising and find application in several areas, such as:

What are the differences between deep learning, machine learning and traditional AI?

As we have seen, deep learning, machine learning and traditional AI are related concepts, but with significant differences. We can summarise them as follows:

Deep learning vs machine learning

Deep learning algorithms, compared to those used by machine learning, are more “malleable”, i.e. have a more dynamic code, and are used for analysing large amounts of data.

Moreover, deep learning

Deep learning vs traditional AI

Traditional AI, also called symbolic AI, uses predefined rules and formal logic to solve problems. Deep learning, by contrast, learns from experiences (or rather from data, even raw data as we have seen) rather than from rules.

Traditional AI tends to work well in domains that are already defined and limited, but has difficulty handling unexpected situations or scaling large amounts of data. Deep learning, instead, excels in dynamic and complex environments, while requiring huge amounts of data and computing power.

The 5 (possible) best uses of deep learning

According to the multinational Oracle, which sells database software and technologies, the five best possible uses of deep learning are as follows.

Social media

Deep learning can be used to analyse large numbers of images (such as those on social media) to target users.


Artificial neural networks can predict corporate values, identify threats and/or develop negotiation strategies.


Deep learning can help to understand the behaviour of patients, allowing the best treatment for them to be identified.

IT security

Deep learning algorithms are not only able to detect threats, but also offer effective solutions against viruses, spyware and malware.

Digital assistants

Natural language processing (NLP) allows us to have digital assistants such as Siri, Cortana, Google Assistant, Alexa, etc. that use conversational artificial intelligence to offer us the best answers.

Most important challenges

Although the implementation of deep learning within companies and PA is in the making, there are some important limitations and challenges to be faced, such as:

Despite the aforementioned obstacles, data scientists are getting closer and closer to creating deep learning models that are highly accurate, precise and capable of learning without external supervision.

Deep learning in short

In summary, deep learning creates models that can make autonomous decisions on the basis of examples, without the need for pre-programmed rules. In this sense, deep learning represents a crucial step forward in the development of AI.

It’s the approach that most closely mimics the functioning of the human brain, learning directly from data and its combination, rather than following rigid rules.

A “behaviour”, in conclusion, that allows deep learning to perform better than machine learning and traditional AI, especially in tasks such as voice and image recognition and machine translation.

Choose PMF Research to develop deep learning projects

PMF Reserach is a research and development (R&D) centre established in 2003 and part of the JO Group cluster of companies; it deals with ICT, virtual reality, artificial intelligence and big data.

If you found this article interesting, would like to learn more and, why not, realise an entrepreneurial project involving deep learning, please contact us by filling in the contact form below or by calling +390957225331. A team of experts is here to answer all your questions.

Looking for ICT project partners? Ask PMF Research by filling out the Contact Form
This site uses cookies to improve users' browsing experience and to collect information on the use of the site.