What is Artificial Intelligence, Machine Learning, and Deep Learning?

From ChatGPT and Siri, to movie suggestions on Netflix, artificial intelligence (AI) has rapidly become a part of everyday life. As such, AI evolved from being a buzzword to a very real investment opportunity over the last 12 months. To understand AI’s potential, it is first vital to understand what it is as well as its subcategories machine learning (ML) and deep learning (DL).

Let’s X-Plain:

What are the differences between Artificial Intelligence, Machine Learning, and Deep Learning?
What is AI?
What is Machine Learning?
What is Deep Learning?

What are the Differences between Artificial Intelligence, Machine Learning, and Deep Learning?

While artificial intelligence, machine learning, and deep learning are often used interchangeably, there are several key differences. One way to visualise the relationship is through a series of concentric circles. AI is the macro topic which encompasses the entire field of study, while ML is a subtopic within AI. DL is a further refinement of ML and represents the most cutting edge AI applications that are being used today and will be developed in the future.

What is AI?

At a basic level, artificial intelligence is the concept of machines accomplishing tasks which have historically required human intelligence. AI can be broken down into two distinct fields:

Applied AI: Machines designed to complete very specific tasks like navigating a vehicle, trading stocks, or playing chess – as IBM’s Deep Blue demonstrated in 1996 when it defeated chess grandmaster Gerry Kasparov.
General AI: Machines designed to complete any task which would normally require human intervention. The broad nature of general AI requires machines to “learn” as they encounter new tasks or situations. This need for a learned approach is what gave rise to modern ML.

What is Machine Learning?

In simple terms, machine learning is the process of building machines which can access data, apply algorithms to this data, and then train themselves to deduce valuable insights based on these underlying datasets.

The key difference between ML and AI is that ML does not rely explicitly on the code of its creator. Rather, ML systems use computer code as a starting point and then gather data, information, and inputs which can be studied – similar to how a student might study for an exam. It is this relationship with big data that makes ML and the Internet of Things (connecting regular objects to the internet so they can collect data or be controlled remotely) so closely intertwined.

What are some examples of ML? Think about monotonous, data-driven tasks, well that is where ML can be applied. For instance, autocorrect, recognising faces, voice commands, and objects, as well as translating languages. It has been successfully implemented in chatbots, such as Siri by Apple, Cortana by Microsoft, and Alexa by Amazon. ML is also crucial to social media platforms like TikTok, YouTube, and Instagram as it helps deliver personalised content to users via algorithms.

What is Deep Learning?

Deep learning takes artificial intelligence a step further, by mimicking how the human brain works through the use of artificial neural networks. In an artificial neural network, each neuron is charged with providing a binary (yes/no) response to basic questions about a piece of data. By layering thousands (or millions) of these artificial neural networks, a deep learning machine can generate reliable outputs (recommendations or interactions) without changing the underlying coding.

Consider a very basic artificial neural network which is responsible for determining if a photo contains a banana or an apple. The network has three neurons which are responsible for answering:

Is the object in the picture round?
Is the object in the picture yellow?
Does the object in the picture have a stem?

The network would respond with no, yes, no for the photo of a banana and yes, no, yes for the photo of an apple. Using binary, the network would learn that a banana is 010 and an apple is 101. Extrapolate this concept across thousands of yes/no questions of exponential complexity and you have the bases of artificial neural networks and deep learning.¹

What are some examples of DL? It is commonly used for more complex tasks beyond the limitations of ML. ChatGPT is an apt example of DL as it absorbs enormous amounts of information, and then distils it to answer questions from its users in a conversational way.² It is also used for image and voice recognition algorithms, plus companies are implementing deep learning to predict customer preferences, detect fraud and spam, fight malware, conduct life-saving diagnoses, and recognise handwriting. In many ways, the possibilities for this technology are endless.

AI is Here to Stay

Artificial intelligence, machine learning, and deep learning have embedded themselves in everyday life and their use cases have steadily developed alongside the technologies which power them – presenting a compelling investment opportunity for the entire AI value chain. ETFs which capture this whole opportunity or hone in on a specific part of it can be an accessible way to gain exposure to the AI theme. For more information on investing in AI, read the Global X Artificial Intelligence ETF (ASX: GXAI) investment case here.

Related Funds

GXAI: The Global X Artificial Intelligence ETF (ASX: GXAI) invests in global companies involved in AI development, AI-as-a-service, provide AI compute power, or design and manufacture AI hardware.