Decoding Data & AI: Machine Learning, Deep Learning, Artificial Intelligence

In our Decoding Data & AI series, we provide you with key insights for successful data & AI projects in a clear and easily understandable format, empowering your business to thrive by facilitating integration into your corporate strategy. Part 1 of this series delves into the topics of Machine Learning (ML), Deep Learning (DL), and Artificial Intelligence (AI).

It’s become widely acknowledged that AI is progressing rapidly and can complete many tasks, often rivaling or surpassing human capabilities, and certainly with greater efficiency. Moreover, there are continued discussions surrounding the impact of machine learning and deep learning on the tech world over the last decade(s), including an overhaul of advertising, media ecosystems, and shopping experiences. To fully understand the transformative potential of these technologies, it's important to dive into their fundamental characteristics and origins. So, what defines ML, DL, and AI, and what is the difference between them?

Is artificial intelligence (AI) a new development or something that has been around for a while?

Both are right. Current AI developments (large language models, image generation, and so on) are part of a long-established research program to use existing data to predict new things. At its core, it consists of filtering through very large amounts of data, attempting to discern patterns within it, and predicting something based on these patterns. Although these predictions can be employed for familiar uses such as predicting demand, AI’s capabilities extend far beyond that, enabling predictions across a diverse range of scenarios - behind many machine learning and AI use cases, there is a prediction task, such as:

• The correct version of a misspelled word (autocorrect)
• Whether a customer will churn soon (churn prediction)
• Which product someone is likely to buy (personalized advertising)
• Which movie someone will enjoy (recommendation engines)
• Words a user may wish to continue a text with (text generation)

What is machine learning?

Throughout history, people’s endeavors to forecast future events have been characterized by all sorts of methods, from mystical to scientific. The latter entails the creation of models – frequently employed by economists, for example, to simulate and forecast changes in the economy. These different models are heavily influenced by theories about how the economy works, which is why there is such a large variety of them, and why they often contradict one another. Business decisions are very often shaped by existing models, as they help explain what is expected to happen and why a certain decision might be better than another.

But could we not have models without theories? The answer is yes: with enough data, you can let a fast computer conceive the best-fitting model by itself. This, in a nutshell, is machine learning. By giving a machine learning algorithm a target and feeding it with large amounts of data, it will generate a model that can then be used to generate the desired predictions (and hence also the desired output).

As an example, assume that a company wants to predict which products a user is likely to buy based on their interactions on social media. In this scenario, an extensive dataset comprising Instagram engagement metrics alongside consumer purchase histories would be fed to a machine learning model, which would learn which view patterns lead to buying which product. While some associations appear straightforward, such as an inclination to buy athletic wear after engaging with fitness-related content, others present more nuanced connections. For instance, watching certain comedic content might unexpectedly correlate with a propensity to purchase vegan cuisine, language learning resources, or customized footwear. The machine learning algorithm systematically checks all possible connections, without any prejudices.

Interestingly enough, there is now a growing research area in neurobiology that argues that this is also how the human brain itself works and learns: "Predictive coding." 
 

What are the key components necessary for successful machine learning output?

A key factor needed to make machine learning work is a large amount of data – the more, the better. This explains why established companies with extensive data pools can create such efficient predictive models, and also why so many parties are scrambling to obtain more and more high-quality text data to feed into their Generative AI (GenAI) models.

The second factor is the machine learning algorithm itself. There are many different ones, often with curious and mystical-sounding names, and an especially noteworthy method is deep learning.

Deep learning essentially uses large, powerful computers to simulate a human brain. It creates millions or billions of artificial synapses, which take in one small signal, process it, and then pass it on to many others – known as an artificial neural net. As it turns out, graphics processing units (computer components that are specially designed to run video games) are perfectly suited for creating these brain simulations, explaining how Nvidia went from a company mostly familiar to gamers to the third-highest valued company in the world.

Upon providing the deep learning algorithm with a target and vast amounts of data, it will be trained to correctly predict the target based on inputs. If an algorithm is trained with human-generated text, it will gradually learn how to respond to text prompts with useful text (i.e., how to predict what the user wants to read) and become a large language model (LLM).

The model learns this by gradually adjusting what happens within each of the billions of synapses over many iterations. This requires a significant amount of time and computing power and is also the reason these models are often described as "black boxes" - what happens in these adjustments and why is now far beyond human understanding. Models created with deep learning can easily beat all humans at chess, Go, or Super Mario Kart, as well as generate text, audio, and video content that is as good or better than what humans can create.
 

What is the difference between all these terms?

Machine learning constitutes building a model to predict something, by letting an algorithm train itself by discerning patterns within a large amount of data.

Deep learning is a type of machine learning which uses an artificial neural net. This method came to be extremely effective, but the internal workings of those nets can generally no longer be understood or explained by humans.

Finally, artificial intelligence, as it is used today, refers to the practice of using deep learning to generate text, images, or video content that mimics what a human does, or can do. For this to work well, both enormous amounts of data and computing power are needed.

One might consider categorizing the entire area of machine learning under the umbrella of "artificial intelligence" – and many people do – as it creates highly accurate predictions that frequently surpass human capabilities, requiring minimal human input.

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