FEED Summer 2024 Web

to learn from data – either labelled or unlabelled – without requiring explicit programming for specific conditions by human engineers. In contrast, AI is a broader term encompassing systems mimicking human intelligence, including ML and explicitly programmed software. Deep learning – a subset of ML – structures its computational models similarly to how neurons are interconnected in the human brain. The precise distinctions among these terms have become quite blurred recently, particularly to the general public. This is because the recent advancements in deep learning have led to its predominance within the field of ML, and consequently, some people started using the terms interchangeably. Nowadays, the term AI seems to be used more in the marketing space to attract general attention, while ML is used more in the technology space to specifically describe the type of ML algorithms being used.

The precise distinctions among AI and ML are blurred

Simply put, if the algorithm didn’t have any examples of the word you intend to use coming after your previous word, it could never suggest your intended word. That is the key difference between ML and AI. AI is intelligent and capable of understanding. It is a metaphysical concept, as it doesn’t need a previous example to know what comes next; it can invent and create. The lines have become progressively blurred with further developments of ML. As the complexity of the algorithm grows, it begins to combine seemingly unconnected elements, demonstrating the perception of intelligence. BRIAN KENWORTHY: Although ML and AI are closely related concepts, they are still distinct in their own way. AI is a broader category which uses machines to automate processes or enable machines with intelligent behaviour. ML, on the other hand, is a specialised branch within AI which focuses on systems to learn from data without explicit programming. While AI aims at automating tasks and decision-making, ML specifically focuses on pattern recognition and predictive modelling through data analysis. HARRY BLOXHAM: AI is the umbrella term for the designing of systems to mimic human intelligence. It is broken down into subsets including ML and deep learning. With ML, the target is to create a simulation of human learning which allows an application to adapt to uncertain or unexpected conditions. Think of unstructured data, like applying metadata to clips or facial recognition. To perform this task, ML relies on algorithms to analyse huge datasets and perform predictive analytics faster than any human can. It uses various techniques including statistical analysis, finding analogies in data, using logic and identifying symbols. In contrast, deep learning processes data using computing units called neurons, arranged into ordered sections known as layers. This technique – at the foundation of deep learning – is called a neural network and is intended to mimic how the human brain learns. It is this deep-learning technology that is commonly (perhaps incorrectly) called AI today. TIM JUNG: ML comprises algorithms and methodologies which enable software programs


HARRY BLOXHAM: ML algorithms and the productivity tools they spawned have led to automation of many intricate tasks which were once time-consuming, and these have been in applications for a while. Adobe Sensei introduced tools like content intelligence and face-aware editing, while the Neural Engine was added to DaVinci Resolve 16 with upscaling and auto colour. These meant that even independent artists and creatives could benefit from simpler and slicker workflows – the iterative process is still


Powered by