I'm interested in different approaches to study artificial intelligence that would open up career opportunities. What resources are available? What disciplines are required?


What are the paths toward a career in Artificial Intelligence?

I'm interested in different approaches to study artificial intelligence that would open up career opportunities. What resources are available? What disciplines are required?

Since the question stated, "Paths toward a career," the educational path is significant in that it is linked to the career path. Several reading and university selection Q&A already exists, some of which are listed here for convenience.

Where one begins has much to do with the starting place. Does the enthusiast have a degree and work experience already and in what? This answer will try to cover the most common starting conditions and provide a reasonable map of the most common paths, since AI is an umbrella term and there are several smart routes into a career that could be called AI work yet are distinct in the kind of career demand and the preparation to meet those demands.

Certainly mathematics is important across the board, and the perusal of this list may help the reader understand where their strengths exist already and what mathematics study is indicated.

There are a few other existing posts on this site, such as this one.

This set of answers was targeted toward someone coming from a web app development background.

This is a query for reference material for Java or Python.

This one is for those with undergraduate degrees willing to pursue a masters.

that includes research into how intelligence works, control of complex systems, automation of intelligent decision making, development of logical inference, computer vision and hearing, and dozens of other things.

Existing Work That Falls Under the Umbrella of AI

Some of the most trending work today is in these categories. An attempt is made here to capture the primary sub-fields where demand exists and what kinds of requirements are generally indicated in personnel requisitions. They are categorized roughly to provide some understanding of the natural branching under the umbrella of AI. This may be missing various pieces, but an attempt will be made to respond to comments and otherwise maintain the categories and items.

Data Analysis

This is more than just data mining and straightforward data science, which falls largely under machine learning and statistics, for which there are two other sub-sites under the StackExchange.com domain, https://stats.stackexchange.com/ and https://datascience.stackexchange.com/ respectively. There is an unavoidable fuzzy boundary between this sub-site and those.

  • Application of AI to deeper intelligence in the field of Big Data, especially adaptive systems, strategies, and algorithms for data acquisition, hygiene, normalization, feature recognition, aggregation, advanced indexing, automation in modelling, military and business intelligence reporting, and market analysis
  • Advanced data modelling in bioinformatics, including applied genetics and cellular mechanics comprehension — This includes advanced investigation into the mechanisms of human organs including the human brain.
  • Video, image, and audio media processing in a data center environment (distinct from real time in its challenges and requirements)

Advanced Robotics

By advanced is meant beyond the basic strategies of automated control used in factories around the world today. The sectors involved are military, transportation, and consumer.

  • Guidance systems for aircraft, missiles, and other aeronautic vehicles and projectiles, including unmanned vehicle piloting
  • Weapons, ballistic, anti-ballistic and other countermeasure systems
  • Other flight systems
  • Air traffic control — This highly complex system is actually robotic even though personnel are part of the system. It is highly dependent on intelligent prediction in three dimensional motion, like weapons systems. It involves dispatch, predictability, risk reduction, chaos theory, and a variety of other complex applications of advanced theory.
  • Appliance automation, including smart washers, smart HVAC, robotic cooks, and the coveted automated vacuum system, lawn mower, and general purpose surface cleaning robot
  • Smart gift and toy design
  • Industrial robots beyond existing automated manufacturing systems, including robots that can enter toxic or dangerous environments and perform tasks designed for trained human maintenance and construction personnel
  • Automated driving of car, RV, minivan, bus, truck, train, bike, motorcycle, and other pavement and off-road vehicles — This is a highly valuable area of development because licensing criteria for human drivers is too weak to avert injury, fatality, and property damage but cannot be changed easily in the current way modern economies run. The only solution is to remove the riskiest component of the transportation system: People, who may be rushing, talking, texting, drunk, high, or otherwise mentally compromised and unable or unwilling to substantially minimize risk.
  • Automated heavy machinery for construction, upgrade, retrofitting, other modification, and smart demolition
  • Manual (as in of the hand) and tactile transmission over the Internet

Natural Language

It was once believed that language could be guided by science, that the development of systems of grammar could guide natural use. Linguistics has long since turned the corner to realize that natural language will always be driven by factors that cannot be fully controlled by educational systems to that level of detail. Professional writers and people chatting on the phone or Internet will invent linguistic structure and content that rely on the ability of readers and listeners to put together what is meant from unexpected combinations of text or sound.

This places a large burden on natural language computer systems to interact with humans accordingly. Beyond simply getting the words right, linguistic tokens include prefixes, suffixes, conjugations, gender, plurality, colloquialisms, brands, phonic content such as intonation, emphasis, and associated affect. There are over a hundred generally applicable and innate language techniques that do not have nice one-to-one correlations with words. Even once all of that is known, a higher level of abstraction has technique such as interrogativity, imperativity, jest, satire, euphemism, and a dozen more dimensions of semantic context that humans learn from listening, reading, and communicating to others while gauging their responses. This leads to cognition and meaning, which are abstractions above that.

The application of AI to this complex natural language interfacing and processing process is critical to the advent of conversant computer systems and is one of the most difficult problems in computer science and technology. These are a few regions of current work.

  • Text to speech
  • Speech to text
  • Natural language translation
  • Command recognition
  • Search phrase comprehension
  • Hearing aids
  • Reading aids
  • Informative dialog between humans and computers
  • Call center automation
  • Artificial pets
  • Cheering, encouraging, and emotionally supporting systems
  • Gaming character natural language

Gaming and Game Theory

The game industry is engaging in natural language and data analysis to augment the game experience, and games that involve robotics are expected to emerge in greater numbers over time. There are several aspects of AI that are specific to gaming.

Because of Morgenstern and Von Neumann's Theory of Games and Economic Behavior and the mediagenic development triumph of artificial chess and go players, AI has been linked closely in the minds of the public and the technology savvy with game theory. That the most affluent financial organizations and governments have applied game theory to everything from strategic analysis to high speed training has led to further opportunity in this area.

Games that ever approach a virtual reality, not just visually but also in terms of story built into the game are of interest to gaming enthusiasts, game designers, and hardware vendors.

  • Interactive story automation
  • Character language use
  • Character motion intelligence, to add sophistication to fighting, athletics, beauty, and grace in game and story based interactive systems
  • Intelligent, ultra high speed selection of detail rendition — In moving images, realism cannot be a result of ray tracing of every object into every field of view and does not need to be. Digital artifacts in rendered action are a result of choices made by the rendering engine. Intelligent prediction of what would be perceived as a flaw by human visual systems is key to rendering authenticity.


The interest in the nature of consciousness, self-awareness, intuition, belief, creativity, the adaptivity of animal nervous systems, and other capabilities exhibited by brains and its connections to the senses and other parts of bodies is older than modern science. It will not end, and is perhaps at the core of the vision of AI even if such research has few immediate returns on investment. If discovery and being a pioneer is more important that high pay, this is a most interesting career path.

Educational Paths

Every major university that has a mathematics department, computer science department, engineering department, linguistics department, computer art department or any of the other items listed above has an interest in AI. Some have spawned AI specific laboratories or degree programs at graduate or undergraduate levels, and this trend is expected to continue. Depending on the readers past education, test results, and grade average, opportunities for acceptance in these programs vary widely.

Self-education worked for front end, database, and middle tier development as an entry path to direct employment and consulting in information technology over the last thirty years because some self-taught programmers could get the job done faster and better than those with aligned degrees from renown universities. This is true of AI specialists. If the result of self-education is laboratory research in an extra bedroom or corner of the living room and the result of that is winning some public AI contest, that person may have a high school diploma without honors and be hired before a PhD from MIT, the home of one of the first AI labs.

Those who like to download and self-learn are encouraged to do so, but AI is constructed on top of mathematical theory, biology, chemistry, signal theory, control systems theory, and a number of other older disciplines. It is important to study rigorously and read, whether or not the educational path taken to align with one of the above career paths is pursued in formal educational institutions, fast-tracking classes online, via experimentation, or through some other means.

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Since the question is about career path I have given step by step guide hope it will help you:

As artificial intelligence (AI) continues to invade different verticals it has changed the way businesses work. AI over the years have proved that it is here to stay, not to fade. It has created a plethora of job opportunities in the market and the field is not as easy as it may seem. To make your space in the world of AI, you need to acquire certain skill sets that can demonstrate your mastery in artificial intelligence and machine learning. So, it is high time for people who are into AI or want to take a career in AI, should start preparing.

Here are ways to make you kick start your career-

Understand the AI Career Landscape:

While starting a career in any field, you need to have extensive knowledge about your chosen field. Choosing a field without educating yourself or having a basic knowledge will be a blunder. Do your homework on research and know what the pre-requisites for a career in AI. Have a deep understanding of what AI is and what its subsets are.

Educational and Knowledge Prerequisites:

Computer Science


Physics, engineering, and robotics

Cognitive science theory

Take Relevant Courses Strengthen your skills in artificial intelligence by taking the best online course, attending conferences, and following tutorials. If you want to demonstrate your expertise in the field, get a certification and further prove your skill in AI.

• AI & Deep Learning with TensorFlow

• Deep Learning Specialization

Learn Python and Other Related Languages: Python is the most popular language in Artificial intelligence technology and also become an expert in writing codes in languages such as Python, R, Ruby, etc. Polish your skills in C++ and Javascript.

Learn How Data Analysis Works: AI involve a lot of data. If you are aspiring to become AI and ML engineer, acquire complete knowledge of how data analytics work. Understanding of how to work on data will help you build projects for the partners and stakeholders.

Get yourself Certified: Credible certificate brings in a plethora of opportunities and makes you land your dream job in no time. AI certifications are a great way to get recognized by the employers as they provide you credibility and your knowledge in the field.

Consider Artificial Intelligence certification by credible bodies like:

Artificial Intelligence Engineer Certification- By Artificial Intelligence Board of America


Consider job hunting portals:

After acquiring ample knowledge and required certifications consider these best job platforms that would help you land your next job in the field of AI.

• Indeed

• LinkedIn Job Search

• Google For Jobs

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