Artificial Intelligence has been in existence for a long time. Long before the popular Alexa, Siri and Cortana, as far back as that 1950s. Recently, it has become one of the hottest buzzwords in the tech industry. Technology that was only seen in movies and as fiction is now a reality because of AI.
AI has been predicted to boost economic growth by an average of 1.7% and increase labour productivity by 40% or more by the year 2035. Experts look at artificial intelligence as a factor of production, which has the potential to introduce new sources of growth and change the way work is done across industries. This will double economic growth in 12 developed nations that will, in turn, continue to draw talented and experienced professionals to work in this domain.
Now let’s get into what AI (Artificial Intelligence) is
Artificial intelligence (AI) is a wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence. In simple terms, it is a way to make machines process information the same way humans do, but faster and more efficiently.
AI is the branch of computer science that aims to answer Turing's question: “Can machines think?” in the affirmative. It is an endeavour to replace or recreate human intelligence in machines. Because of the diversity of the field of Artificial Intelligence, it is difficult to restrict it to one definition.
Russell and Peter Norvig approach the question by unifying their work around the theme of intelligent agents in machines. With this in mind, AI is "the study of agents that receive percepts from the environment and perform actions." - (Russel and Norvig viii)
A brief timeline of how AI has evolved in the past six decades;
- 1956: John McCarthy coined the term ‘artificial intelligence’ and had the first AI conference.
- 1969: Shakey was the first general-purpose mobile robot built. It is now able to do things with a purpose vs. just a list of instructions.
- 1997: Supercomputer ‘Deep Blue’ was designed, and it defeated the world champion chess player in a match. It was a massive milestone by IBM to create this large computer.
- 2002: The first commercially successful robotic vacuum cleaner was created.
- 2005 - 2019: Speech recognition, robotic process automation (RPA), a dancing robot, smart homes, and much more.
Reasons to Get an AI Certification
Artificial Intelligence is the next big thing in the tech industry. Organizations are adopting AI and creating a special budget for certified professionals in the field, hence the growing demand for trained and certified AI professionals.
In the wake of all these, here are the main reasons you should get a certification in AI:
- Increasing demand for certified AI professionals: From now on, one in five companies will be using artificial intelligence to make business decisions. It helps companies provide tailored solutions to not just customers but also provide instructions to employees.
- Better interview discussions: Recruiters are looking for professionals who not just understand the industry but know exactly what they’re doing. Having a certification can not only increase your chances of getting an interview but convince employers that you have the skills and expertise needed in the industry that others do not which will make you a valuable candidate.
- Exploring new career paths: AI is predicted to create 2.3 million jobs in the year 2020, according to a recent Gartner report. What’s more, 83% of companies using AI found that it leads to the creation of more jobs.
- Increased earning potential: Many of the top tech enterprises are investing in hiring talent with AI knowledge, according to a Fortune article. On average, an Artificial Intelligence Engineer earns about $135,000 per year, and getting an AI certification is the first step to increasing your earning potential and becoming more marketable.
Due to the increasing popularity of AI, there is an increasing need for professionals with the skill sets required to handle AI roles. This has lead to an increase in job opportunities in the field. Some of these roles include;
- Machine learning researchers: Researchers specifically tasked to find ways to improve machine learning algorithms.
- Artificial Intelligence software development, program management, and testing: Professionals who develop systems and infrastructure that can apply machine learning to an input data set.
- Data mining and analysis: Investigation of massive data sources, often creating and training systems to recognize patterns.
- Machine learning applications: Applying machine learning framework to a specific problem in a different domain. For instance, applying machine learning to gesture recognition, ad analysis, or fraud detection.
How to Get Started in AI
For anyone looking to move into the field of artificial intelligence, there are a number of prerequisites. For someone not in any way familiar with programming, Van Loon suggests that you start with mathematics while taking all kinds of courses in machine learning. It is also required that you have strong computing skills as well as programming skills and an understanding of AI algorithms. Depending on the industry you find yourself in, the skillsets you require might differ. But generally, working in this field requires having exceptional communication skills. This makes it easier to understand, interpret the data.
For programmers who would like to transition into AI, you can go straight into the algorithms and start coding. Proper certifications will still be needed and all education and certifications recommended should be supplemented with a general knowledge of how business works. Getting hands-on training is the best way to fully get into machine learning. This not just gives you theoretical knowledge but also practical knowledge of what you will be doing as well as an in-depth understanding of how AI works.
For scientists and data analysts transitioning into AI, Van Loon insists you must gain programming skills. Crossing the bridge from data scientist to machine learning requires that you know how to prepare data, as well as have good communication skills and business knowledge. A proficiency in model building and visualization is also a requirement. It takes many team members to make AI work, which allows for specializing in any number of areas in the field. Van Loon suggested as a data scientist, you should first figure out what it is you would like to do, and then start by focusing on that area for your machine learning career.
Van Loon says, AI never stops learning, so you can’t stop learning either. Wherever your starting point in AI, the goal is to continue learning and keeping up with the trends and new strides.
Different Artificial Intelligence Certifications
- Introduction to Artificial Intelligence Course
Simplilearn's Introduction to Artificial Intelligence course is designed to help learners decode the mystery of artificial intelligence and its business applications. The course provides an overview of AI concepts and workflows, machine learning and deep learning, and performance metrics. You’ll learn the difference between supervised, unsupervised and reinforcement learning, be exposed to use cases, and see how clustering and classification algorithms help identify AI business applications.
- Machine Learning Certification Course
Simplilearn’s Machine Learning course will make you an expert in machine learning, a form of artificial intelligence that automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming. You'll master machine learning concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modelling to develop algorithms and prepare you for the role of a Machine Learning Engineer.
- MIT’s Online Certification In Artificial Intelligence
This two-course online certificate program brings a hands-on approach to understanding the computational tools used in modern engineering problem-solving. Leveraging the rich experience of the faculty at the MIT Center for Computational Science and Engineering (CCSE), this program connects your science and engineering skills to the principles of machine learning and data science. With an emphasis on the application of these methods, you will put these new skills into practice in real-time.
- Artificial Intelligence Engineer Master’s Program
Simplilearn’s Artificial Intelligence Master’s Program, in collaboration with IBM, gives training on the skills required for a successful career in AI. Throughout this exclusive training program, you'll master Deep Learning, Machine Learning, and the programming languages required to excel in this domain and kick-start your career in Artificial Intelligence.
- Simplilearn’s Artificial Intelligence (AI) Capstone Project
Simplilearn’s Artificial Intelligence (AI) Capstone project will give you an opportunity to implement the skills you learned in the masters of AI. With dedicated mentoring sessions, you’ll know how to solve a real industry-aligned problem. You'll learn various AI-based supervised and unsupervised techniques like Regression, Multinomial Naïve Bayes, SVM, Tree-based algorithms, NLP, etc. The project is the final step in the learning path and will help you to showcase your expertise to employers.
The self-driving car is probably the most popular use of AI in recent times. Maintenance is another aspect of AI, predicting when equipment maintenance will be needed so that it can be done proactively and efficiently, leading to tremendous savings on finance and budget. AI is used in transportation navigation to help drivers navigate routes more effectively. Smart cities use AI to be more energy-efficient: reducing crime and improving safety. The many applications of AI today are countless, and growing in number all the time. Many industries are moving towards AI, some of these industries include medicine, agriculture, transportation, manufacturing, energy financial finance.