Artificial Intelligence is one of the biggest technological waves to hit the world of tech in decades. It has not only changed the way we conceptualise technology today, but it forever marks a distinction in our ways of thinking about humanity itself. It is estimated that Artificial Intelligence will create over 2 million jobs within the next couple of years, far outweighing the 1.4 million jobs that it will eliminate due to automation.
More interestingly, it has already started impacting job growth in a major way – and now is the time to capitalise on this trend, because it is not just a trend, after all. It is here to stay, as the demand has already doubled over the last couple of years!
In this article, we will take you through some of the basic concepts surrounding Artificial Intelligence and what the role of an Artificial Intelligence Engineer entails. We will also be providing you a detailed roadmap of how to make sure that you end up getting the job you want, what skills you will require to get there, and what your roles, as well as responsibilities, will be when you do get there. Moreover, to make this guide even more comprehensive, we will also provide salary trends!
Table of Contents
What is Artificial Intelligence, and Who is an Artificial Intelligence Engineer?
Simply put, AI enables a machine to mimic certain aspects of human behaviour. In theory, it provides the base for computers to be able to perform many of the tasks that humans are able to, by simulating certain human skills such as visual perception, decision-making, translating from one language to another, as well as by recognising speech.
It is not that Artificial Intelligence replaces humans, it is merely the formulation of patterns based on human skills and applying them on various situations.
Taking this forward, an Artificial Intelligence Engineer is someone who works with the modes through which these patterns are implemented. These tools are algorithms of various kinds, neural networks, and others, all of which advance Artificial Intelligence in one way or another.
An Artificial Intelligence Engineer can work in any of the multiple industries in which AI plays a prominent role, such as Retail, Healthcare, Manufacturing, Automotive, and more.
The basic definition of an Artificial Intelligence engineer is that work problems that can be solved through on Artificial Intelligence solutions. Depending on the industry and the range of problems that they encounter, the Artificial Intelligence in question can be strong or weak – but it is highly industry-specific and depends on the setup.
That being said, there are many ways, generally speaking, to become an Artificial Intelligence Engineer. Let’s take a look at some of those ways.
Steps to become an AI Engineer
- The first step to become an AI Engineer will be to get a Bachelor’s Degree. In this step, there is a lot of freedom in so far as which course you choose. You can select from any of the following:
- Information Technology
- Computer Science
- After this, you will have to finetune your technical skills. To be an AI Engineer you will need to be good at software development as well, in addition to just being good at programming. You will need good working knowledge, both theory and practice, of the following:
- Deep Learning & Neural Networks
- Software Development Life Cycle
- Design Patterns
- Machine Learning
- Electronics, Robotics, and Instrumentation
- Modularity, OOPS, Classes
- Statistics and Mathematics
- You will also require business skills to become an AI Engineer. As a general tip, this is helpful for any career of your choosing, but especially so in the case of an AI Engineer. You will have to have a working, practical knowledge of:
- Creative Thinking
- Effective Communication
- Analytic Problem-Solving
- Industry Knowledge
Depending on what’s convenient for you, you can either learn these skills through practice or by opting for a PG Diploma in Artificial Intelligence and Machine Learning. A lot of new research is being conducted in the field of AI, given that it is an emerging topic in today’s world, and a PG Diploma will be particularly useful. Consider subjects like Machine Learning, Computer Science, or Data Science – these will prove to be of tremendous value.
Technical And Business Skills Required
You will also need a whole host of technical as well as business skills to make sure that there is nothing lacking in your disposition as an AI Engineer. Let’s take a brief look at what these skills are:
1. Programming Languages
As an AI Engineer, you need to be abreast of the most current coding languages, and you have to make sure that you have a deep understanding of all classes as well as data structures. Simply knowing Python won’t be enough, as often you’ll need to deal intricately with classes, linking, and memory management. Read more about the top programming languages for machine learning and AI.
2. Statistics / Linear Algebra / Calculus
Without mathematics, you will not have about half of the technical skills required to become an AI Engineer. Statistics is basically the backbone of a lot of data, and you will also require a deep understanding of integrals and derivatives. You need to be familiar with means, standard distributions, as well as Gaussian distributions of sets of data. With Probability, you will be able to comprehend models such as:
- Hidden Markov Models
- Naive Bayes
- Gaussian Mixture Models
And more. You will also require a firm understanding of algorithm theory as well as the practical functioning of these algorithms. Partial Differential equations, Quadratic Programming, Lagrange, Convex Optimization, Gradient Descent, and Summations – you will need to have a firm handle over all of these concepts.
Even if you feel a little overwhelmed by all the maths, do not worry about it. All of this mathematics is a part of what makes AI so engaging in the first place. Without these models there would not be a focal determining point for most algorithms – and algorithms are what make AI tick. With time, you will be able to make sense of these things.
- Audio, Video and Language Processing
You need to have workable knowledge of some libraries to be able to achieve language processing. Some of these libraries are Gensim and NLTK, as well as techniques like Sentimental Analysis, Summarization, and word2vec. Natural Language Processing mixes up both linguistics as well as computer science and often it deals with either audio or video – so there is a chance you’ll be working with them in the length of your career as an AI Engineer.
- Neural Network Architectures
Although usually, the understanding is that artificial intelligence models replicate human understanding, they can also be leveraged for tasks that are far beyond the capabilities of humans. This is arguably where Artificial Intelligence shines the most, and this is where neural networks come into the picture.
Through the help of neural networks, several tasks have become simplified, including translation, image classification, as well as speech recognition, all of which play a significant role when it comes to Artificial Intelligence.
AI Engineer Salary Trends
We have seen now that AI Engineers need a lot of education to be able to do their jobs well. This, to a sufficient extent, makes their job well paid. Moreover, in addition to this, there is also a huge demand for AI Engineers, and this certainly makes them even better disposed to getting higher pay.
According to various websites, the Average Salary of an Artificial Intelligence Engineer is around 8 lakhs per Annum. The minimum salary of an AI Engineer is usually around 7.5 lakhs, while their maximum salary goes as high up as 10 lakhs per annum. Read more about Artificial Intelligence Salary in India.
Conclusion: Start on your Journey to becoming an AI Engineer
There is no wrong time to get started on your dreams! If you meet some of the basic criteria to become an AI engineer, you should take hold of the chance to become one as well! The world is currently undergoing a technological revolution, and now would be the ideal time to own up to it, since the foreseeable future is completely within your grasp.
The salary is quite handsome and the scope for growth – granted, that has not been covered in this article – is also quite significant. So don’t wait around anymore, get started on your dream to become an AI Engineer today!
How is neuroscience used in Artificial Intelligence?
The introduction of Deep Learning has revolutionised Artificial Intelligence to great lengths. Artificial Intelligence essentially tries to modulate systems that understand humans. The scientific field of neuroscience fits perfectly with Artificial Intelligence and Deep Learning requirements. In return, AI provides valuable insights into human behaviour, automating systems work, adapting to problems, and deriving human solutions. Formulating algorithms, updating them, and moulding them to mirror the human brain helps AI create efficient models.
What are the upcoming trends in Artificial Intelligence?
Artificial Intelligence has immense potential to transform digital systems, healthcare systems, and programming models. It has developed automated programs in self-driving cars, visual and auditory recognition software, and robots for human service. AI has been assisting humans and is predicted to work one-on-one with them in developing new technologies in the healthcare industry, managing analytics in marketing. AI is also creating safer, efficient systems in cybersecurity. The latest trend is the announcement of the Metaverse, where humans will reside in the virtual world and use the algorithms to build their virtual life.
Is Data Science a part of Artificial Intelligence?
Data Science is a field that integrates statistics, machine learning, and analytics to analyse data points, get a perspective on raw data, and predict outcomes of data sets. Artificial Intelligence focuses on human behavioral processes and Deep Learning methods to produce better algorithms. Artificial Intelligence is a part of Data Science, but Data Science builds on studying data sets and delivering profitable insights. Although, without Artificial Intelligence, Data Science would have subpar growth as AI constantly updates the program features and creates better models for training.