How to Become an Artificial Intelligence Engineer?
How to Become an Artificial Intelligence Engineer?
To become an Artificial Intelligence Engineer, you must meet the minimum criteria described in this article. But first, you must know if this is the career for you. It can be seen that the global artificial intelligence market is experiencing a huge growth of 154%. So what sparked this revolution? There were 3 main factors influencing this exponential growth.
- Improvement in machine learning
- The wide use of cloud-based transactions
- Advances in data collection, storage, and processing methods
Since Artificial Intelligence is a new subject, there are many areas that are still not used. Before the “how” part, let’s briefly focus on the “why” part. Gartner’s predicted trends in Artificial Intelligence are as follows:
- Artificial Intelligence will create a business volume of 3.9 trillion dollars by 2022.
- Artificial Intelligence is expected to be the most determinant technology in processing power, capacity, speed, and data variety and processes on deep neural networks in the next decade.
- Decision automation systems (tasks such as business processes that will provide an advantage to the automation of Artificial Intelligence or assistant voice, classification of data that is very difficult to compartmentalize with ordinary methods, etc.) will expand to 16% with a sudden jump of 14% in the next 4-5 years.
Who is an Artificial Intelligence Engineer?
Artificial Intelligence Engineers are people who create, test, and try to solve the encountered problems by applying models to the Artificial Intelligence infrastructure. They progress with the combination of traditional software models and machine learning. The demand for these IT professionals has increased with the development of machine learning and artificial intelligence in different areas of the Information Technology (IT) industry.
How to become an Artificial Intelligence Engineer?
There are a few prerequisites you have to face and a few Artificial Intelligence skills you must learn to become an Artificial Intelligence Engineer. Here are these:
Education Prerequisites:
- IT, Computer Science, Statistics, Data Science, Finance etc.
- Computer Science, Mathematics, Cognitive Science, Data Science, etc.
- Data Science, Machine Learning etc. Certifications
SEE ALSO: How to Make an Artificial Cell?
Technical Artificial Intelligence Engineer Mastery Skills
To become an Artificial Intelligence Engineer, you must have a few technical and non-technical skills. Let’s take a closer look at these skills:
Programming languages
To be a successful Artificial Intelligence Engineer, you must be proficient in several programming languages. You need to choose one or more languages that will help you discover and apply the capabilities of Artificial Intelligence. Some of the many languages related to Artificial Intelligence:
- Python
- Java
- C ++
- R
Each language meets the different needs of Artificial Intelligence. While you may not learn everything, you can start with a language that fits your learning curve and helps you get started using AI. Now you have to wonder, which one is the best? Obviously, Python is a favorite of developers and Artificial Intelligence Engineers for its simplicity, clear syntax, versatility, and huge library.
You can also start in Java or C ++, but you’ll run into portability and multitasking issues. Also, Python will not be enough, so we recommend starting with Python or R and eventually moving around to learn algorithms.
Statistics / Mathematics / Algebra
Statistics is the backbone of any algorithm. In fact, your entire Artificial Intelligence program will depend on the type of algorithm you use. Now, to work on an algorithm, you will need substantial knowledge of statistics, math, or algebra, and the possibility of upgrading your AI program. Also, in your Artificial Intelligence journey, you will find Naïve Bayes, Hidden Markov etc. which require a precise understanding of probability. You will come across Machine Learning models such as.
Algorithms and Applied Mathematics
To build a model or use an existing model, you must have a good knowledge of algorithms. Also, to understand algorithm concepts, you must have a solid understanding of applied mathematics. Here are the areas where you will use your algorithms and applied mathematics knowledge frequently:
Quadratic equations
Partial different equations
- Graduated Descent
Lagrange
- Convex Optimization etc.
Natural Language Processing
Natural Language Processing (NLP) is all about bringing together computer science, information engineering, linguistics, and artificial intelligence, and programming the system to process and analyze large data sets. Therefore, as an Artificial Intelligence Engineer, you must work extensively on NLP, which includes language, audio, and video processing, leveraging various NLP libraries and tools, including:
- NTLK
- Emotion Analysis
- Gensim
- TextBlob
CoreNLP
- PyNLPI, etc.
Neural networks
A neural network is a system (software or hardware) that functions similarly to the human brain. The concept of artificial neural networks is developed according to the neural functionality of a human brain. As an Artificial Intelligence Engineer, you will learn about object recognition, face recognition, handwriting recognition, etc. There are many types of trade and business applications that you will solve complex problems in their fields.
Non-Technical AI Engineer Mastery Skills
Communication and Collaboration Skills
As an Artificial Intelligence Engineer, you will work intensely on data. Of course, your stakeholders will depend on you to solve their critical problems. To do this, you must communicate your findings effectively. Also, having good communication skills will allow you to build a good relationship with your stakeholders and get somewhere.
Analytical Skill
You have to shift up quickly to check the numbers and data. This analytical skill requires thinking. You should also ask questions to the data analysis team to determine the feasibility of the data and to brainstorm with key stakeholders.
Business mind
The most successful, but also widespread, Artificial Intelligence project involves working up front and resolving vulnerable pain points. So no matter what industry you are in, you have to be aware of how the market, customers and core business are run. Having these technical skills will be less helpful if you don’t have the business acumen to turn your technical ideas into a successful business model.
Repetition of ideas (Rapid Prototyping)
To find a valid idea, you must consider a number of ideas as soon as possible. This means choosing the right model, A / B testing, NLP libraries etc. Applies to. You should use a variety of techniques to quickly create realistic-scale models of solid parts or assemblies using 3D computer-aided designs, especially when using 3D models.
Artificial Intelligence Engineer Salary
USA: The average annual salary of entry-level Artificial Intelligence Engineers is $ 71,600, and the average annual salary of experienced Artificial Intelligence professionals is $ 248,625.
You now know the profile of an Artificial Intelligence Engineer and what path you should choose to pursue a career in Artificial Intelligence.
Resource: https://ocw.mit.edu/
