In the age of AI and automation, maths and data reign supreme.
AI (or artificial intelligence if you want to be formal!) is all about developing computer systems that use data to perform “intelligent” tasks like visual perception, understanding natural language, reasoning and decision making. Machine learning (ML) is one way of building these systems, where you provide a computer with examples of what it should do, then it learns how to do it. Behind all of this is not magic, but maths!
Nope! Automation is when machines are programmed to perform human tasks. AI needs the machines to do the whole ‘think like a human’ part too.
Agriculture: provides real-time data on crops, water supply and areas that might need fertilisation or treatment.
Education: makes classrooms accessible to students who speak different languages, or those with visual or hearing impairments.
Health: screens for cancer, diagnoses COVID-19 and monitors patients over video telehealth systems.
Logistics: detects fatigue in truck drivers and provide alerts to reduce accidents.
Utilities: analyses video footage from pipe inspections to find blockages.
If AI, automation or ML sound like something you want to get into, it’s time to become best mates with maths. Knowing your stuff in the following areas will definitely give you the edge.
Algorithms
Calculus
Game theory
Linear algebra
Probability
Statistics.
The demand for ML is huge! Employees at the LinkedIn Top Companies (which include big names like Amazon, IBM and Apple) grew their skills in this area by 23% in 2021.
Q: What does Siri, a driverless car and a manufacturing robot have in common?
A: They’re all examples of everyday AI!
Head of Enablement – AI/ML and Data at Blackbook.ai
CarrerswithStem: Hey Brooke! Why is maths so important in AI and automation?
Brooke: Mathematics is all about creatively solving problems using technical frameworks, and working in AI and automation is quite similar! Just like in mathematics, being able to break down a big AI or automation problem into smaller and more manageable chunks means you can build on what you know already, even if you’re the first one to attack a given problem. Obviously there’s lots of mathematics behind the scenes in AI/ML and data science, but the creative problem solving aspect is really important too!
CwS: What’s your top tip for students who want a career in this area?
B: Make the most of the opportunities around you! There are so many resources and programs out there for people wanting to learn to code, or to participate in a hackathon, or attend a workshop. Have a look around for what’s available near you, and don’t be afraid to have a go!
There are so many opportunities in emerging tech fields, which can make it a bit tricky to work out what your path will look like. The best thing to help with this is meeting people in your local community who are working on things that interest you!
● Bachelor of Artificial Intelligence, Deakin University
● Bachelor of Mathematical and Computer Science (Artificial Intelligence), University of Adelaide
● Bachelor of Mathematics/Bachelor of Computer Science, University of Wollongong/
● Automation engineer: $57k - $120k
● Machine learning engineer: $56k - $128k
● Software engineer: $56k - $118k.
(Source: salaries according to payscale.com)
Louise Meers
First published on CareerswithSTEM.com
