What is the difference between ArtificiaI Intelligence, Machine Learning and Deep Learning ?

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Extract of a dialog with Chat GPT, a conversational robot powered by AI

Artificial Intelligence (AI), Deep Learning (DL), and Machine Learning (ML) are some of the most popular, discussed, and rapidly evolving fields in the world of technology.

These technologies have a significant impact on many industries, including robotics, where they are used to develop intelligent systems that can carry out complex tasks.

One of the most exciting areas of application for ArtificiaI intelligence, DL, and ML is with robotic arms projects. In this article, we will explore some examples of how AI, DL, and ML are being used to develop intelligent arm robots that can perform a wide range of tasks.

Although the three are often used interchangeably, they are distinct terms that refer to different aspects of computer systems. Artificial intelligence is the broadest term that encompasses all forms of computer systems that can perform tasks that typically require human intelligence. On the other hand, Machine Learning and Deep Learning are subfields of AI that involve training computers to learn from data, recognize patterns, and make predictions.

For each one, we will first provide an easy yet precise definition, then we will illustrate it with our favorite topic: robotic arm design and engineering.

Artificial intelligence: towards minimal human intervention?

Artificial Intelligence or AI refers to the ability of a machine to perform tasks that would typically require human intelligence. In robotics, AI is used to develop intelligent systems that can perform tasks autonomously or with minimal human intervention. Robotic projects that incorporate AI technologies have shown promising results in various applications, such as manufacturing, healthcare, and space exploration.

One example of AI in robotic arms projects is the use of Reinforcement Learning (RL) algorithms. RL is a subfield of ML that involves training a system to make decisions based on rewards or penalties. For a a robot, RL algorithms can be used to train robots to perform complex tasks, such as grasping and manipulation of objects. A recent study conducted by researchers at Carnegie Mellon University demonstrated the use of RL in robotic arms to perform tasks like sorting and assembling parts in a manufacturing plant.

Deep Learning: algorithms and patterns for real-life uses

Deep Learning or DL is a subfield of Machine Learning that uses artificial neural networks to learn patterns in data. Deep Learning algorithms are used in a wide range of applications, including image and speech recognition, natural language processing, and robotics. DL algorithms are used in robotic arms to train them to perform tasks that require visual perception and recognition like detecting and tracking the edges of metal components.

You’ve certainly heard of using a vision set on a robotic arm, well, that’s an example of Deep Learning!

The development of vision-based arm robots use Deep Learning algorithms to process visual data and identify objects, allowing the robot to grasp and manipulate objects autonomously.

At Niryo, we developed a vision-based collaborative robot that could perform pick-and-place tasks with 99% accuracy. Thanks to Tensorflow (an open-source machine learning tool developed by Google), recognizing multiple objects became possible with Ned2! See the example below.
Presentation of Ned2s camera, aka Vision Set

Machine Learning: a supervised way to develop intelligent systems

Machine Learning or ML is also a field of AI that involves training machines to learn from data. ML algorithms are used in robotics mostly to develop intelligent systems that can perform tasks such as object recognition, path planning, and decision-making.
Courtesy of Stéphane Bonnevay, Polytech Lyon. Stéphane is using Ned, Ned2’s predecessor.

For instance, the use of supervised learning algorithms is one example of Machine Learning. Supervised learning involves training a system using labeled data to predict the outcome of new data. A recent study by researchers at the University of Stuttgart demonstrated the use of supervised learning in arm robots to detect and grasp objects in a cluttered environment.

So yes, AI, DL, and ML are transforming the way we think about the world. These technologies have shown promising results in a wide range of applications, from manufacturing and healthcare to space exploration. With continued research and development, arm robots will continue to become more intelligent, adaptable, and capable of performing complex tasks autonomously, making them an invaluable asset in many industries, as well as in universities and research centers.

As technology continues to advance, we can expect to see even more exciting developments in the field of robotics using AI, DL, and ML, which have the potential to revolutionize the way we approach tasks that were once considered too difficult or dangerous for humans to perform.