Understanding the structure of Artificial Intelligence (AI) can be tough as terms like machine learning and deep learning may create confusion. However, it is important to understand the differences between the two.
Machine learning enables the computer to learn without the need of being explicitly programmed. Machine learning means the computer performs tasks by learning from data with the help of algorithms. Instead of programming, the computer recognises patterns in data and makes predictions. The learning process of these algorithms can be either supervised or unsupervised. It depends on the data used to feed the algorithms.
Home-assistance devices such as Alexa use deep learning technology to respond to our voice and understand our preferences.
Deep learning is a more complex process compared to machine learning. Deep learning describes algorithms that analyse data with a logical structure just like human beings would draw conclusions. For this, deep learning applications use Artificial Neural Networks (ANN), a layered structure of algorithms. The design of ANN was inspired by the biological network of the human brain. Deep learning algorithms are widely regarded as a sophisticated and mathematically complex evolution of machine learning algorithms. Recent developments in the field of deep learning have led to results that were thought to be impossible before.
Deep learning is used in many fields today – for example, in automated driving to detect traffic signs and pedestrians. Deep learning is used by the military to identify objects from satellites. Home-assistance devices such as Alexa use this technology to respond to our voice and understand our preferences.
With its self-automated learning feature, AI needs little human intervention. This shows the huge potential offered by deep learning. However, the lack of data availability and computing power restrain deep learning from achieving its true potential. Deep learning requires a vast amount of data and substantial computing power. Recent advancements in cloud computing and the emergence of high-performance GPUs help reduce the time needed to train deep learning networks.
Differences between machine learning and deep learning
Traditional machine learning algorithms have a simple structure compared to deep learning algorithms. While traditional machine learning algorithms are based on linear regression or decision trees, deep learning algorithms are based on an artificial neural network. An artificial neural network is complex and intertwined just like the human brain. Deep learning algorithms require less human intervention compared to machine learning.
Fields like economics, neuroscience, psychology, linguistics, electrical engineering, mathematics, and philosophy have also contributed to the development of AI in various ways.
Deep learning algorithms require much more data than the traditional machine learning algorithms to work properly. While machine learning algorithm works with a thousand set data points, deep learning algorithm need millions of data points to avoid fluctuations and to make high-quality interpretations.
Artificial Intelligence is not just about computer science or mathematics. Fields like economics, neuroscience, psychology, linguistics, electrical engineering, mathematics, and philosophy have also contributed to the development of AI in various ways.
There are mainly two types of AI – weak and strong. Strong AI is where machines become self-aware whereas in weak, AI machines are designed to do specific tasks. Though AI is in its weaker stage now, it will continue to grow and change our world in the future.
Now put on your thinking hats and think about the following questions for a couple of minutes.
As a teacher, how would you describe the term "deep learning" to your students?
Can you think of the ways in which deep learning differ from traditional machine learning?
Can you think of the reasons that restrain deep learning from attaining its full potential?
Write down your thoughts and discuss them with your students, children and your colleagues. Listen to their views and compare them with your own. As you listen to others, note how similar or different your views are to others’.
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