Skip to main content

Scope of an Artificial Intelligence

Artificial Intelligence has grown exponentially in the past decade, and so have the career opportunities as an AI expert/specialist. But what exactly does an AI expert do? Also, is becoming an expert the only option while pursuing a career in artificial intelligence?I don’t have any programming/ coding background. Can I still work as an AI expert? And, what specialization or skill set do I need to acquire to get into this field?

Skills Required to Build a Career in Artificial Intelligence

1. Sound Mathematical and Algorithmic Understanding
To be an ideal candidate in AI, you need to have solid knowledge of applied mathematics and a set of algorithms. Having proficiency in problem-solving and analytical abilities will help you in performing tasks in a more efficient way.

You must also have reasonable knowledge of statistics and probability. This helps in understanding various models of AI, like Naive Bayes, Gaussian Mixture Model, etc.

2. Basic Know-How of Programming Languages
Programming languages are to an AI specialist what accountancy is to a Chartered Accountant. Ability to code in C++, Python, Java, R, etc is one of the top skills required to build a career in artificial intelligence. Each language has its own uses in this field. While R is used to develop statistical software and conduct data analysis, Python is used to develop complex algorithms easily.

3. Robotics
We are moving towards a more automated world. And robotics is going to play a major part in the automation process. You should be well versed with robotics, as it is the outcome of development in AI.

4. Efficiency in Data Science
You should have a fair knowledge of data science as well, as AI experts use it to deal with large amounts of datasets (big data) and derive useful information in a fast and systematic manner.
5. Good Knowledge of Unix Tools
Unix tools like awk, cut, tr, find, etc are responsible for most of the processing in AI that happens in Linux based machines. So, a programmer must know how these tools function.

6. Well-Versed with Machine Learning
Machine Learning, as we all know, is a subset of AI. You should be acquainted with ML, as it is applied in cybersecurity and online fraud detection, rapid analysis and prediction, etc.

7. You should be Inquisitive and Creative
Being creative and curious is going to help you in finding solutions to specific problems, as you will be able to crack them by using unconventional methods.

8. Keeping up with the Latest Trends
AI is a field that is experiencing growth with every passing second. An AI professional should be regularly in touch with all the current updates of the industry. To enhance his/her skills, one must be a part of tech communities, and forums, and actively participate in those. They can also learn from online courses or tutorials.

Top Artificial Intelligence Job Profiles as an AI Expert

1. AI Data Analyst
As an AI Data Analyst, you will be responsible for data mining, cleaning the data to acquire related data and discard irrelevant information, and interpret results by using statistical tools and techniques. Noticing major trends will help you in identifying future opportunities.

Proficiency in MS Excel is also necessary, apart from knowledge of regression. A data analyst earns approximately ₹3 lacs to 1 million per annum.

2. Big Data Engineer
A big data engineer assists in developing an ecosystem that facilitates communication between business systems. They create a company’s big data and manage it, along with getting results from big data, or huge amounts of data in a faster manner.

To become a big data engineer, you must have a sound knowledge of programming languages, mainly R, Java, Python, and C. You should be able to build applications that work on a large scale, and conduct data warehousing. Possessing Apache Spark skills and SQL skills is also helpful.
As a big data engineer who has good Apache Spark skills, you can earn ₹10 lacs on an average per year, whereas if you have SQL skills, it’s approximately ₹8 lacs per year.


3. Business Intelligence Developer
A business intelligence developer is responsible for improving the profitability of the business, by planning, building, and sustaining business intelligence solutions.

They should be familiar with computer programming and dealing with data sets. BIDs also present the information in a visualized manner. The average salary of an entry-level business intelligence developer starts from ₹5 lacs a year.

4. Data Scientist
Data scientists use machine learning and predictive analytics to collect, analyze and interpret huge chunks of data. You can predict the future on the basis of current and past trends and patterns in data.

Data scientists should be well acquainted with tools like Hadoop, Spark, Hive, etc, as well as languages like Python and SQL. A data scientist beginner earns about ₹ 4.5 to 6 lacs p.a.

5. Machine Learning Engineer
The role of a machine learning engineer mainly revolves around programming and computing. You will build and manage platforms that enhance machine learning.

A machine learning engineer should be able to work with programming languages such as Java, Python, and Scala. As an ML engineer, you will earn approximately ₹7-8 lacs p.a.

6. Product Manager
A product manager in the AI field is majorly responsible for solving problems by strategically acquiring datasets.
To become an AI product manager, you need to have knowledge on how to identify problems, acquire data and interpret and evaluate its impact on business. The average salary of a product manager in India is approximately ₹ 8 lacs p.a.

7. Research Scientist
A research scientist is an expert in applied mathematics, machine learning, and deep learning.  You must have a good amount of knowledge about reinforcement learning and NLP. Most research scientists are expected to have a Ph.D. or at least hold a master’s degree in computer sciences. A research scientist can earn about ₹6-16 lacs p.a.

Which Companies Hire AI Professionals?
Being an AI professional is sure a fancy job, but which companies actually hire for this profile? Here is a list of companies which hire AI professionals:

Accenture
Jaguar Land Rover
NVIDIA
Amazon
Microsoft
Google
Facebook
Labels:

Comments

Post a Comment

Popular posts from this blog

Statistics Interview Questions Part-1

Q1. What is the difference between “long” and “wide” format data? In the  wide-format , a subject’s repeated responses will be in a single row, and each response is in a separate column. In the  long-format , each row is a one-time point per subject. You can recognize data in wide format by the fact that columns generally represent groups. Q2. What do you understand by the term Normal Distribution? Data is usually distributed in different ways with a bias to the left or to the right or it can all be jumbled up. However, there are chances that data is distributed around a central value without any bias to the left or right and reaches normal distribution in the form of a bell-shaped curve. Figure:   Normal distribution in a bell curve The random variables are distributed in the form of a symmetrical, bell-shaped curve. Properties of Normal Distribution are as follows; Unimodal -one mode Symmetrical -left and right halves are mirror image...
Post a Comment
Read more

Data Science Interview Questions -Part 2

1) What are the differences between supervised and unsupervised learning? Supervised Learning Unsupervised Learning Uses known and labeled data as input Supervised learning has a feedback mechanism  Most commonly used supervised learning algorithms are decision trees, logistic regression, and support vector machine Uses unlabeled data as input Unsupervised learning has no feedback mechanism  Most commonly used unsupervised learning algorithms are k-means clustering, hierarchical clustering, and apriori algorithm 2) How is logistic regression done? Logistic regression measures the relationship between the dependent variable (our label of what we want to predict) and one or more independent variables (our features) by estimating probability using its underlying logistic function (sigmoid). The image shown below depicts how logistic regression works: The formula and graph for the sigmoid function is as shown: 3) Explain the steps in making a deci...
Post a Comment
Read more