Article • reading time: 12 min. | 02. Aug 2024, written by Kobi Cohen
The Science of AI is made up of three major fields of expertise that deal with everything from machine learning to robotics. Yet there is one constant that links every artificial intelligence career path: data.
This is still very much an emerging field of work. The definitions and job titles can change dramatically depending on company size and the industry, but here’s a closer look at the fundamentals:
The job is to build theoretical learning models in a programming environment which prepare a neural network to solve real-life problems. AI data scientists interpret data. They use statistical models and machine learning techniques to measure and improve the intelligence of a certain network. They also use analytical methods to identify trends and patterns for the AI to correlate within the data they feed it.
Similar to the data scientist, a machine learning engineer handles large amounts of data. However, machine learning engineers focus on creating and managing AI systems and predictive models. Machine learning engineers and data scientists will work in close collaboration with each other — and both require sufficient data management skills.
Responsible for interpreting and correlating large sets of statistical data, the big data engineer does it all. From finding relevant patterns to quickly sort through huge databases to performing deep data mining searches. The big data engineer also analyses forecasting data and builds predictive models for future information gathering and interpretation.
As a Computer Vision Researcher, you advance the field by teaching machines to interpret visual data. Your work involves image analysis, object recognition, and scene understanding, impacting areas from autonomous vehicles to healthcare diagnostics.
In AI for Automotive, you design, implement, and optimize AI solutions, enhancing vehicle safety and automation. This role shapes the future of transportation through AI integration.
As an AI FinTech Developer, you create AI-driven solutions for tasks like risk assessment and fraud detection, revolutionizing financial services for increased efficiency and security. Your work shapes the future of banking and investment.
AI engineers are responsible for designing, developing, and optimizing artificial intelligence systems. They create innovative solutions, implement machine learning algorithms, and work on projects ranging from computer vision applications to enhancing automotive safety with AI. Their tasks involve data management, neural network design, and pushing the boundaries of AI to solve complex real-world problems, making them crucial contributors to the future of technology and automation.
If you've ever wondered how to learn AI, you're in the right place. Before delving into the intricacies of AI, let's begin at the very beginning. From understanding the basics to diving into the complexities, this section will guide you through the foundational knowledge and skills you'll need to pave your way into the world of artificial intelligence. Whether you come from a technical background or not, the journey starts with curiosity and a willingness to learn. So, let's explore the fundamentals and set the stage for your AI career path.
If your background involved statistics and computer science, you’ll have a head start. If not, you’ll need to brush up on your matrices, linear algebra and calculus. Knowledge of statistics and probability is a must because you’ll be working with relational and non-relational databases. And you’re only getting started.
You’ll also have to learn the basics of Python: expressions, variables, data structures, functions and packages such as pip. Next, you’ll need to learn some important data-handling libraries such as pandas, NumPy and Matplotlib. And then you’ll have to get your hands dirty with Virtual Environments effectively.
But there’s no need to get discouraged — you didn’t learn your current skill set overnight either. Therefore, rather than looking at it as one giant hurdle, recognise that you can approach it granularly.
Before you figure out which of the three major AI career paths tickles your interest, you’ll first have to learn how to process data. This is the biggest step in figuring out your path and it involves:
Principal component analysis
Dimensionality reduction
Normalisation
Data scrubbing and handling missing values
Unbiased estimators
Features extraction
Denoising and sampling
If you're determined to make your mark in the field of artificial intelligence and seek a structured path to success, pursuing a degree in AI can be a game-changer. At IU, we offer both Bachelor's and Master's degrees tailored to the diverse facets of AI.
Acquiring a recognized degree in artificial intelligence is an investment in your future, providing a solid foundation and invaluable practical experience that can significantly accelerate your career in this high-demand industry. So, if you're eager to shape the future of AI and explore the boundless opportunities it presents, considering a degree in AI might be the pivotal step toward your dream career.
They say experience is the best teacher. What they don’t’ tell you is that the most data-rich point in any scientific endeavour is failure. And that’s why you should always be as hands-on as possible. When it comes to science or learning, failure is a primordial step that cannot and should not be avoided.
But you can’t fail if you don’t try stuff. You can’t see what works and what doesn’t if you don’t play around with the programming yourself.
So don’t wait until you graduate to start working on your own projects. It’s only by trying to implement what you learn that you figure out where you went wrong. Failure can be frustrating, but if you shift your perspective, failure — and the process of learning — can be one of the most rewarding parts of your day.
How to get an AI certification might not be a question you want to bother answering. And if you can prove that you can do the work, getting a job in the industry won’t be too difficult.
However, it’s hard to deny that a degree in artificial intelligence looks good on a résumé. It shows potential employers that you’ve got a firm grasp of the basics and that you’re ready to start. Getting certified could also fast-track your career because all the practical experience you get results in projects you can create a portfolio with.
So how long does it take to become an AI specialist? It can take considerably more without a well-structured learning programme.
While demand for AI specialists far exceeds the number of people in the field, your success necessitates some creativity. Sure, your degree and project portfolio will do a lot of the talk, you’ll still have to walk the walk yourself. And in order to be noticed, you’ll first need to build your crowd.
If you’re like most people, you probably don’t have a lot of AI specialists in your circles. And this is where online platforms come in handy.
You can search for people who work in AI on LinkedIn. Don't feel awkward about just adding them to your network out of the blue — people with similar interests tend to support each other.
From here on out it's all about being active in your community — make posts about your questions on AI, comment on other people's posts and don't forget to smash that like button!
Success is always built with people. You'll learn a lot faster too if you put yourself out there and ask for feedback on your projects. You never know, you might just impress the right person and land an internship!
How does artificial intelligence work to make life better and what does the future hold?
We’re not quite ready to bring Ultron or Vision into the world, but as with all technology, advancements are exponential. It didn’t take long to go from rudimentary lane-keeping assistance to self-driving cars. And that technology is now speeding up our agriculture with drones and self-driving tractors.
As AI is one of the fastest growing and best-paid sectors today. And you’ll need to keep developing your digital skill set to move your career forward. But on the flip side, your curiosities and passions can lead you to create technologies nobody ever dreamt of — tech that everyone might start using in the next 10 years.
Now you know what a career in AI looks like, you know the artificial intelligence career paths and you understand how AI works. You’ve got a roadmap and know how you can start your career in artificial intelligence. Excited?
Is a career in AI worth it? Here’s a quick summary of why you should be looking forward to making this decision and sticking with it.
Simply put, this is the bleeding edge of computer science. For some, this technology will make their jobs a thing of the past. And, as with any situation, your perspective gives meaning to it. Some will choose to see this as a bad thing, to think that AI is their enemy.
But you can choose to see AI as an opportunity to free up more of your time so you can do less tedious and more interesting things. There’s no denying that intelligent machines are changing everything and creating countless job opportunities worldwide. You can be part of that change.
We've already spoken at length about the market's demand for AI specialists. Data runs everything today. Thus, AI specialists have a direct impact on decisions and the way companies are managed.
Because AI is seeing eye-watering adoption speed in pretty much every industry sector, salaries are pretty impressive too.
In fact, the average pay for an AI specialist is around €100.000 per year. And if you're highly qualified and have a lot of experience under your belt, you can expect to make well above €250.000.
Countless opportunities and abundant income are staples of AI work. But what does the work really look like? Well, it’s as mundane or as exciting as you’re willing to make it.
AI’s been at the forefront of medical advancement — from mapping the human genome to the fight against COVID and revolutionising medical imaging analysis — and it’s directly changing outcomes. In Japan, there are already home robots that help the elderly with their daily tasks.
There isn’t any other limit to AI applications. How AI will change our cities and home appliances, and how it will free up more of our time hinges on your imagination as a specialist. And if you keep learning, it will to.
Considering a career in tech, but have no idea where to start and how to get there? Not sure what are the differences between computer science, software development and software engineering? This guide has you covered!
Embark on the path to an exciting career in artificial intelligence. Discover the steps, skills, and education required to become an AI engineer. Dive into the world of cutting-edge technology and innovation where opportunities abound.
