AI, more than a buzzword?

20 June 2024 | 3 minutes of reading time

Comparing AI and Machine Learning

What is Artificial Intelligence (AI)? Ask a random person on the street, and they might mention Tesla's self-driving cars or Iron Man's J.A.R.V.I.S.. IT specialists, on the other hand, often offer a more grounded response. Many dismiss AI as merely a flashy term coined by marketing departments to describe Machine Learning. Yet, the increasing prominence of chatbots like ChatGPT and image generators such as DALL-E are causing the term AI to become mainstream. Companies and academics alike are increasingly favoring the use of the term Artificial Intelligence over Machine Learning. This trend raises a question: is AI really just a fancy buzzword?

What is Machine Learning?

Machine learning (ML) involves developing algorithms that enable computers to learn from and make predictions based on data. The concept dates back to the mid-20th century when early researchers began exploring ways to teach computers to learn from data patterns. Arthur Samuel popularized the term machine learning in the 1950s when he created one of the first self learning models, a program that could play checkers. In subsequent decades, techniques such as neural networks were created, leading to significant advancements in the 2000s and 2010s with the rise of big data and powerful computational resources. Today, machine learning drives innovations across various fields, from personalized recommendations in healthcare to predicting the weather.

What is Artificial Intelligence?

Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think and learn like humans, performing tasks that typically require human cognitive abilities such as visual perception, speech recognition, decision-making, and language translation. AI encompasses various machine learning approaches, from rule-based systems to more advanced neural networks and deep learning algorithms. The concept of AI dates back to classical times, with myths and stories about artificial beings. However, the modern era of AI began in the mid-20th century with the development of machine learning. As machines like Arthur Samuel’s checkers-playing program emerged, they inspired many to explore the full extent of what computers could achieve in human-like tasks. Today, AI is rapidly advancing. 

An example of this is ChatGPT, a large language model (LLM). LLMs are a subset of AI models designed to understand and generate human-like text based on vast amounts of data. They use deep learning techniques to process and generate natural language, making them capable of engaging in conversations, answering questions, and performing a variety of language-related tasks.

Rethinking AI and Machine Learning

While AI encompasses the broader concept of creating ‘intelligent’ machines, ML is a specific approach to achieving AI by allowing machines to learn from data. Thus, AI represents the overarching objective, with ML serving as the methodology to attain this goal. Therefore, it could be argued that AI is more than a fancy buzzword for machine learning.

However, the term AI can still be debated. After all, is it really artificial and intelligent? Calling a model such as ChatGPT "artificial" seems to downplay the human creativity and effort that underpins its development. Generative AI, like ChatGPT, is more of a synthesis of human knowledge and creativity, drawing on the work of artists, musicians, and writers. Additionally, while AI excels at pattern-matching and processing vast amounts of data, true intelligence encompasses more than just these capabilities. Intelligence involves understanding context, having a sense of history and emotion—qualities that machines currently lack. Therefore, a more accurate term for ‘artificial intelligence’ as we use it today might have been ‘Synthetic cognition’.

(Read ‘i-spark and AI/ML’ for more information on how i-spark uses machine learning and artificial intelligence)