You must have heard the buzz about the self-driving cars recently and have wondered what all is this about? Have you wondered how the game of chess when you play against the computer plans its moves? Or how a robot on shop floor executes the tasks seamlessly? Or how Siri and Cortana understand what you say and give such relevant answers? These things, which nobody even thought about a few years ago, are a reality now. And the reason behind this shift is Artificial intelligence. It is one of the most coveted fields for technology enthusiasts today. And if you want to make a career in AI, but don’t know what it is. And from where to build up your career, you have come to the right place, my friend! This blog will help you understand how to choose a career in Artificial Intelligence?
What is AI?
Artificial intelligence is defined as the theory and development of computer systems able to perform tasks normally requiring human intelligence. It include visual perception, speech recognition, decision-making, and translation between languages. But artificial intelligence is huge and the development we are seeing is just a small bit. Artificial intelligence is a science and technology based on disciplines like Computer Science, Psychology, Mathematics, Biology, Linguistics and Engineering.
What is intelligence and how it can be artificial?
Defined intelligence as the ability to gain and apply knowledge and skills. Intelligence is intangible. It has the following major characteristics:
- Reasoning: It is the set of processes that enables us to provide the basis for judgment, making decisions, and prediction
- Learning: It is the act of gaining knowledge or skill through studying, practicing, being taught, or experiencing something
- Problem-solving: It is the procedure in which one perceives and tries to arrive at an expected solution from a present situation. One take a path blocked by hurdles
- Perception: It is the process of acquiring, interpreting, selecting, and organizing sensory information
Hence Artificial intelligence is all about inducing the above characteristics into machines.
Broad research areas in AI
- Natural language processing: It is concerned with the interactions between computers and human (natural) languages. It is about human-computer interaction. For example, any personal assistant systems like Siri, Cortana, Google Now. Any system which does speech/voice recognition uses NLP.
- Neural network: The Neural network is a system of hardware and/or software patterned after the operation of neurons in the human brain. They are a variety of deep learning technologies. Commercial applications of these technologies focus on solving complex signal processing or pattern recognition problems. Examples include recognition systems such as face recognition, handwriting recognition
- Expert systems: An expert system is a computer system that imitates the decision-making ability of a human expert. Expert systems are aimed to solve complex situations by reasoning about knowledge, represented mainly as if-then rules rather than through conventional procedural code. Examples include systems like flight tracking systems, clinical systems.
- Robotics: It is the branch of technology that deals with the design, construction, operation, and application of robots. Examples include Industrial robots for moving, spraying, painting, precision checking, drilling, cleaning, coating, and carving.
- Fuzzy Logic: Fuzzy logic is a method to compute based on “degrees of truth” rather than the usual “true or false” (1 or 0) boolean logic on which the computer is based. Examples include consumer electronics, automobiles.
Goals of AI
- To Create Expert Systems− The systems which exhibit intelligent behaviour, learn, show, explain and advise its users.
- To induce Human Intelligence in Machines− Crafting systems that think, understand, learn, and behave like humans.
How is AI helping us?
The primary style to AI now is a machine learning, in which programs are trained to pick out and respond to patterns in huge amounts of data, such as identifying a face, object, nature in a picture or choosing a winning move in the board game of chess. Numerous sorts of problems are solved using ML.
AI is the wave which will push this generation towards the next industrial revolution. Industries across the healthcare, transportation, finance, retail, IT and manufacturing sectors are witnessing its hugely positive impact. Artificial intelligence is also being used to analyze vast amounts of molecular information looking for potential new drug candidates–a process that would take humans too long to be worth doing. And this is just the beginning for artificial intelligence. As technology advances, so will the number of innovations and applications.
Risks of AI
There are two schools of thoughts related to this. Some who think there is an existential risk because of AI and other experts say that there is zero risk. But, rather than worrying about a future AI takeover over humans, the real risk is that we can put too much trust in the smart machines we are building. The problem is because a system is only as good as the data it learns from.
Talent demand-supply gap
The rapid adoption of the machine learning technology in the finance, advertising & media, and retail sectors along with the increase in the usage of natural language processing techniques are back to the progress of the artificial intelligence market. Also, artificial intelligence in a closely integrated with big data. Big data and AI have the inherent capability of changing the form and function of the industries.
Fields like analyzing bid data already incorporates AI. Big data is currently in the exploration stage, it is to see on how AI will grow as a career option. Thousands of openings in artificial intelligence and machine learning posted on job boards are going unfilled. Since it is a budding field, the demand-supply gap of talent is huge. Usually the industry experts opine that the demand-supply gap of talent is one grey area that needs to address first. The right skills and expertise are required to make a career in the field.
Skills and Abilities Helpful to Careers in AI
The most prosperous AI professionals often share common features that enable them to succeed and make their careers. Working with artificial intelligence requires:
- An analytical thought process: Your thought process should be analytical and creative. The narrow and restricted thought process is a barrier to utilization of AI.
- The ability to solve problems with cost-effective, efficient solutions: Now even if you have an analytical thought process, the solution you find should be cost-effective and do more good than harm.
- Foresight about technological innovations: AI is one of the most recent and budding fields. And to make a career in it you need to can foresee technological innovations that translate to state-of-the-art programs that allow businesses to remain competitive
- Technical skills to design, maintain and repair technology and software programs: Although analytical skills are important, a major part of AI falls into the technical domain. And thus having an understanding of the technical aspect is very important
- How to translate highly technical information in ways that others can understand to carry out their jobs: You might need to make people understand what you are doing. People may include non -technical staff, board or investors. Thus you must have the ability to convert the technical information into more communicable and understandable forms.
- Good communication and the ability to work with colleagues on a team.
Educational Requirements for Careers in Artificial Intelligence
Basics of computer technology and math backgrounds form the backbone of most artificial intelligence programs. Entry-level positions require at least a bachelor’s degree while positions entailing greater responsibilities, leadership or administrative roles frequently require master’s or doctoral degrees.
Typical coursework involves a study of:
- Various level of math, including probability, statistics, algebra, calculus, logic, and algorithms.
- Bayesian networking or graphical modelling, including neural nets.
- Engineering, physics, and robotics.
- Computer science, programming languages, and coding.
- Cognitive science theory.
Now as you know everything about a career in AI, prepare yourself and enhance your skills to bring new wonders of technology to this world.