The AI Glossary
Machine learning? Artificial intelligence? Automation? What does this all mean?
It’s all so confusing, especially since these terms are often used interchangeably. But there are differences and similarities, and sometimes, the definitions overlap.
It is worth taking a look, though, because an AI survey by technology manufacturer, Arm, has found that 25% of Siri users did not know that it is AI- powered, and even less people were aware of AI-style tech driving other popular apps like Facebook, Netflix and even Spotify.
Setting them apart
Artificial intelligence is often seen as the broader concept. Some would think of it as an umbrella term. It refers to the whole idea that one day, computers, robots and software will replicate most of human life. Dave Evans, the managing director of Access Planit- a company that provides all-in-one, cloud-based learning management systems for businesses- said in his article: “Artificial intelligence is designed to simulate human thinking.”
It is split into two: general and applied.
Applied AI are the various high-tech systems we see being used in this day, designed to replicate some form human intelligence, such as categorising data and making sense of it. Examples of this are intelligent trading programs or automated vehicles. Certain automation technologies would be included here, more on that later.
Generalised AI are agreeably the more exciting part of AI, with a lot of creative energy currently being put into furthering this field. These are the programs or devices that can theoretically handle any task. It’s the type of AI that comes to mind when you see the word “singularity”- the possibility that a machine would eventually surpass human intelligence. However, this field of AI remains…elusive, to keep it simple.
The AGI Society defines artificial general intelligence as “an emerging field aiming at the building of ‘thinking machines’: General-purpose systems with intelligence comparable to that of the human mind which may very well lead to surpassing human intelligence”, although at the moment, that is still widely-debated, and purely speculation. If you’ve been keeping up with the times, and scrolling the Internet in the past year, you would have probably heard of the controversial, yet equally fascinating Sophia the Robot. This is one of the iconic developments in this exciting field.
The wonderful, but rather terrifying field of Generalised AI aims to create machines designed to mimic or even surpass human intelligence. It has largely contributed to the developments in Machine Learning. Image by: Pixabay.
Machine Learning is the current state of AI, aptly named; as it refers to machines learning by themselves as it receives more data.
Machine Learning, learns. It is designed to interpret the information given to it, store it in its memory, and overtime, provide more sophisticated interpretations. A good case to look at is Spotify’s use of AI ; it learns a particular user’s music tastes, recommending new tracks to them in their Discover Weekly feature.
Automation is generally referred to as software or machines that are designed to do things automatically, relying heavily on human programming. It makes businesses and factories more cost-efficient and productive. Image by: Pixabay.
Automation, as the name implies, refer to systems that automate, based on pre-programming done by humans. You have to set your automation technologies to work the way you want it to work. The main purpose is to let machines perform repetitive, monotonous tasks, leaving more complex, insightful tasks, requiring a “human touch”, to people. The end result is a cost-effective business and a more productive workforce. And it’s so common nowadays; you would find some kind of automation nearly everywhere you look.
However, some automation technologies and software are driven by artificial intelligence: some software will do as you say, but will learn how to do certain things by itself as it goes along. A good example of this is automation in the journalism industry, which are being used for data-heavy, repetitive articles.
Watch the video below for a little more about automation in journalism:
An introduction about automation in journalism. Video by: Ainaa Mashrique. Soundtrack by: Bensound.com. DISCLAIMER: all pictures and clips used within this video belong to their respective owners- CNN, Caspian Report, CNBC, Expedia, The Tonight Show, The Globe and Mail and The Jakarta Post. This is a transformative work, which constitutes "fair-dealing" of copyright material, allowed under section 30 of the Copyright, Design and Patents Act 1998.
Compartmentalising them together
All of these technologies require some amount of data. And that is the caveat. It needs data to interpret, learn and work. Data is to artificial intelligence what atoms are to literally everything in existence.
A brief recap
Artificial intelligence is the broad concept. The end-goal is to create an actual intelligence- one that can learn, extrapolate and grow, and do everything a human can. They are meant to be conscious (somewhat). A manmade brain, if you will.
The current state of AI is Machine Learning, where a software or a machine is able to learn based on the data it receives. This is the first huge leap towards true Artificial Intelligence.
Automation simply automates, with human instruction. It may sometimes use artificial intelligence technology, but not always. It is mostly just a program that does what you tell it to do.
All of them are (or will be) important innovations in science and tech, and will certainly drive most, if not all industries, to greater heights. And here at AI Today, we embrace this revolution.
