Artificial Intelligence Career Future

Artificial intelligence (AI), is the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience. Since the development of the digital computer in the 1940s, it has been demonstrated that computers can be programmed to carry out very complex tasks—as, for example, discovering proofs for mathematical theorems or playing chess—with great proficiency. Still, despite continuing advances in computer processing speed and memory capacity, there are as yet no programs that can match human flexibility over wider domains or in tasks requiring much everyday knowledge. On the other hand, some programs have attained the performance levels of human experts and professionals in performing certain specific tasks, so artificial intelligence in this limited sense is found in applications as diverse as medical diagnosis, computer search engines, and voice or handwriting recognition.

What is intelligence?

All but the simplest human behavior is ascribed to intelligence, while even the most complicated insect behavior is never taken as an indication of intelligence. What is the difference? Consider the behavior of the digger wasp, Sphex ichneumoneus. When the female wasp returns to her burrow with food, she first deposits it on the threshold, checks for intruders inside her burrow, and only then, if the coast is clear, carries her food inside. The real nature of the wasp’s instinctual behavior is revealed if the food is moved a few inches away from the entrance to her burrow while she is inside: on emerging, she will repeat the whole procedure as often as the food is displaced. Intelligence—conspicuously absent in the case of Sphex—must include the ability to adapt to new circumstances.

Psychologists generally do not characterize human intelligence by just one trait but by the combination of many diverse abilities. Research in AI has focused chiefly on the following components of intelligence: learning, reasoning, problem-solving, perception, and using language.

Learning

There are a number of different forms of learning as applied to artificial intelligence. The simplest is learning by trial and error. For example, a simple computer program for solving mate-in-one chess problems might try moves at random until a mate is found. The program might then store the solution with the position so that the next time the computer encountered the same position it would recall the solution. This simple memorizing of individual items and procedures—known as rote learning—is relatively easy to implement on a computer. More challenging is the problem of implementing what is called generalization. Generalization involves applying past experience to analogous new situations. For example, a program that learns the past tense of regular English verbs by rote will not be able to produce the past tense of a word such as jump unless it previously had been presented with jumped, whereas a program that is able to generalize can learn the “add ed” rule and so form the past tense of jump based on experience with similar verbs.

Reasoning

To reason is to draw inferences appropriate to the situation. Inferences are classified as either deductive or inductive. An example of the former is, “Fred must be in either the museum or the café. He is not in the café; therefore he is in the museum,” and of the latter, “Previous accidents of this sort were caused by instrument failure; therefore this accident was caused by instrument failure.” The most significant difference between these forms of reasoning is that in the deductive case the truth of the premises guarantees the truth of the conclusion, whereas in the inductive case the truth of the premise lends support to the conclusion without giving absolute assurance. Inductive reasoning is common in science, where data are collected and tentative models are developed to describe and predict future behavior—until the appearance of anomalous data forces the model to be revised. Deductive reasoning is common in mathematics and logic, where elaborate structures of irrefutable theorems are built up from a small set of basic axioms and rules.

There has been considerable success in programming computers to draw inferences, especially deductive inferences. However, true reasoning involves more than just drawing inferences; it involves drawing inferences relevant to the solution of a particular task or situation. This is one of the hardest problems confronting AI.

You Might Also Read  Types of Descriptive Statistics in AI

Problem-solving

Problem-solving, particularly in artificial intelligence, may be characterized as a systematic search through a range of possible actions in order to reach some predefined goal or solution. Problem-solving methods divide into special purpose and general purpose. A special-purpose method is tailor-made for a particular problem and often exploits very specific features of the situation in which the problem is embedded. In contrast, a general-purpose method is applicable to a wide variety of problems. One general-purpose technique used in AI is means-end analysis—a step-by-step, or incremental, reduction of the difference between the current state and the final goal. The program selects actions from a list of means—in the case of a simple robot this might consist of PICKUP, PUTDOWN, MOVE FORWARD, MOVE BACK, MOVELEFT, and MOVERIGHT—until the goal is reached.

Perception

In perception, the environment is scanned by means of various sensory organs, real or artificial, and the scene is decomposed into separate objects in various spatial relationships. The analysis is complicated by the fact that an object may appear different depending on the angle from which it is viewed, the direction and intensity of illumination in the scene, and how much the object contrasts with the surrounding field.

At present, artificial perception is sufficiently well advanced to enable optical sensors to identify individuals, autonomous vehicles to drive at moderate speeds on the open road, and robots to roam through buildings collecting empty soda cans. One of the earliest systems to integrate perception and action was FREDDY, a stationary robot with a moving television eye and a pincer hand, constructed at the University of Edinburgh, Scotland, during the period 1966–73 under the direction of Donald Michie. FREDDY was able to recognize a variety of objects and could be instructed to assemble simple artifacts, such as a toy car, from a random heap of components.

Language

A language is a system of signs having meaning by convention. In this sense, language need not be confined to the spoken word. Traffic signs, for example, form a mini-language, it being a matter of convention that ⚠ means “hazard ahead” in some countries. It is distinctive of languages that linguistic units possess meaning by convention, and linguistic meaning is very different from what is called natural meaning, exemplified in statements such as “Those clouds mean rain” and “The fall in pressure means the valve is malfunctioning.”

An important characteristic of full-fledged human languages—in contrast to birdcalls and traffic signs—is their productivity. A productive language can formulate an unlimited variety of sentences.

It is relatively easy to write computer programs that seem able, in severely restricted contexts, to respond fluently in a human language to questions and statements. Although none of these programs actually understands language, they may, in principle, reach the point where their command of a language is indistinguishable from that of a normal human. What, then, is involved in genuine understanding, if even a computer that uses language like a native human speaker is not acknowledged to understand? There is no universally agreed-upon answer to this difficult question. According to one theory, whether or not one understands depends not only on one’s behaviour but also on one’s history: in order to be said to understand, one must have learned the language and have been trained to take one’s place in the linguistic community by means of interaction with other language users.

Artificial Intelligence Career Guide

  • Artificial Intelligence (AI) Overview

    This section provides a general overview and gives a brief introduction to the field of AI.

  • AI Industry Update

    The field of AI and related technologies are drastically changing the way business is done across all industry sectors. AI is the hottest buzzword in tech today, and all the major enterprises are using it to improve business. And this section explains how AI is gradually bringing about transformational changes and faster processes across sectors, ranging from the real-estate to ecommerce.

  • Hottest Opportunities in AI

    This section of the career guide includes top job roles in the field of AI as well as the most in-demand job role, that is, AI engineer, and the skills needed to become one. From a birds-eye-view, an AI Engineer develops, manages, and owns AI initiatives within an organization. But is that it? Download the guide to know the day-to-day challenges and responsibilities of an AI engineer.

  • AI Learning Path

    This section of the career guide provides a detailed roadmap (including tools and skills) needed to become an AI engineer. On the other hand, this section also mentions all the top companies that are hiring machine learning pros ranging from Google to Intel, and a personalized AI learning path to reach your dream organization.

  • Starting Your Journey

    This section will provide you several pathways and certifications that will help you either start or fastrack your machine learning career.

The growth of AI has increased the demand for talented minds to help solve pressing business challenges faster and better. However, quitting a full-time job to go back to school isn’t realistic for most people, and this is where Simplilearn comes into play. With our highly-detailed course of study, you’ll master everything you need to know to make a splash in these thriving fields.

Your Next Step to Success

  1. Post Graduate Program in AI and Machine Learning with Purdue University

    As the demand for technologies like AI and machine learning has increased, organizations require professionals with in-and-out knowledge of these growing technologies and hands-on experience. Keeping the innate need in mind, Simplilearn has launched the Post Graduate Program in AI and Machine Learning with Purdue University in collaboration with IBM that will help you gain expertise in various industry skills and technologies from Python, NLP, speech recognition, to advanced deep learning. This Post Graduate program will help you stand in the crowd, learn the right AI learning path, and sharpen your AI, machine learning, and deep learning skills.

  2. Artificial Engineer Master’s Program

    This 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.

Our Artificial Intelligence Career Guide will give you insights into the most trending technologies, the top companies that are hiring, the skills required to jumpstart your career in the thriving field of AI, and offers you a personalized AI learning path to becoming a successful AI expert.

How to Get a Job in Artificial Intelligence

To get a job in artificial intelligence, you’ll want to earn a bachelor’s degree in computer science or a related major. Also, consider pursuing a postgraduate degree in the field and build your experience and portfolio. Learn more about various jobs you can get in AI, the skills and educational requirements needed to enter it, salary information, and possible career paths you can take in AI.

Investigate jobs in AI

The first step to getting started is to research which jobs within the field of AI you’d like to pursue so that you can tailor your education and build your skills as needed. Indeed reports the top 10 jobs involving AI skills as:

  • Director of analytics: Directs the data analytics and data warehousing departments and is in charge of research, development, and implementation of relevant data systems
  • Principal scientist: Designs, executes, and documents research experiments in many fields and industries as part of a research team
  • Machine learning engineer: Generates programs that enable machines to take actions without being specifically directed to perform various tasks
  • Computer vision engineer: Uses computer vision and machine learning research to solve real-world problems in real-time
  • Data scientist: Utilizes coding and other computer programming to collate and store data efficiently
  • Data engineer: Finds ways to improve data quality and reliability, combining new information to create formats that machines can read through and understand
  • Algorithm engineer: ​​Assists clients in understanding more prominent data trends and reporting on these trend
  • Computer scientist: Designs innovative uses for new and existing computing technology, solving computing problems in various industries
  • Statistician: Creates or uses different mathematical or statistical theories and methods to gather and explain the numerical data findings for a given project
  • Research engineer: Utilizes educated research findings to create a reliable answer for problems at hand

Develop skills needed in AI

You can expect to hone several skills when preparing to work in artificial intelligence. There are many branches of AI, but most have some core commonalities. You can build many of these skills through self-guided practice, learn via online courses or bootcamps, or develop through coursework when earning a degree.

Learn technical skills

You’ll notice many jobs in AI lean on proficiency in programming languages and coding. In fact, coding is one of the very first skills many people interested in this field learn. Expect to also work with a variety of computer systems. A few essential technical skills to build upon include the following:

  • General-purpose languages: Python and C/C++
  • Database management: Apache Cassandra, Couchbase, DynamoDB
  • Data analysis and statistics: MATLAB, R, Pandas
  • AI platforms: Microsoft Azure AI, Google Cloud AI, IBM Watson
  • Data acquisition systems: Physical sensors and wireless sensors
  • Digital marketing goals and strategies
  • Industry knowledge

Build workplace skills

Workplace skills aren’t always something you can learn through courses but rather an experience. Consider working on these human skills when thinking about pursuing a career in artificial intelligence:

  • Communication skills
  • Effective collaboration
  • Analytical skills
  • Problem-solving skills
  • Management and leadership skills

Educational requirements

Educational requirements are one of the first requirements you’ll want to meet when breaking into artificial intelligence. These requirements will vary by job type, whether the position is entry-level or higher, and the industry.

Understand the degree type

Expect most jobs in AI to require a bachelor’s degree or higher. For some entry-level positions, you may only need an associate degree or no degree, but that’s not too common. If you want to build a career for yourself in the field of artificial intelligence, it’s probably a good idea to earn at least your bachelor’s degree. As for what field of study to focus on, most individuals working in AI obtain undergraduate degrees in computer science, mathematics, or a related field.

Gain certifications

If you already have your undergraduate degree in a field related to AI, consider enrolling in courses to learn some of the technical skills you’ll need working in artificial intelligence. Even if you don’t have your degree, certifications show potential employers that you’re serious about your career goals and make you a more attractive candidate. Some AI certifications to consider include:

  • MIT: Artificial Intelligence: Implications for Business Strategy
  • USAII:
    • Certified Artificial Intelligence Engineer
    • Certified Artificial Intelligence Consultant
    • Certified Artificial Intelligence Scientist
  • ARTIBA: Artificial Intelligence Engineer

Salary range and job outlook

The outlook is quite bright for artificial intelligence jobs, good news for anyone working in the growing field of AI. In fact, machine learning engineers and data scientists have made the top of the list on Indeed’s Best Jobs for the past few years [123].

The US Bureau of Labor Statistics (BLS) groups careers in artificial intelligence with computer and information research scientists and notes the employment of computer and information research scientists is projected to grow 22 percent from 2020 to 2030 [4]. This is much faster than average. In 2021, the median yearly wage for this job was $131,490, according to the BLS.

Glassdoor reports salaries, which include base pay plus compensation, for the following AI jobs in the US as of June 2022:

  • Artificial intelligence engineer:$120,017 [5]
  • Machine learning engineer: $125,087 [6]
  • Robotics engineer: $107,771[7]
  • Data analyst: $97,329 [8]
  • Computer vision engineer: $123,665 [9]
  • Data scientist: $121,275 [10]

Explore possible career paths in AI

Four of the most common occupations in the AI field are machine learning engineer, robotics engineer, computer vision engineer, and data scientist. Getting on a career path to lead to one of these jobs is a smart move if you’re planning a long-term career in artificial intelligence.

One possible career path to becoming a machine learning engineer, for example, would be to get hired into an artificial intelligence entry-level job like a software engineer, software programmer, or software developer. You’ll need a bachelor’s degree for these entry-level jobs.

Your next step is to earn your master’s degree in data science, computer science, software engineering, or similar. You may also want to work on gaining some certifications, building your skills, and creating your portfolio. Finally, apply for the machine learning engineer position, an upper-level position that pays very well. In fact, it’s one of the highest-paying positions in the field of AI.