Artificial Intelligence Specialists
Exploring this Job
There are many ways to learn more about AI. First read books such as Artificial Intelligence For Dummies, by John Mueller and Luca Massaron, and review the terms in AI glossaries (such as those at https://www.analyticsinsight.net/understanding-artificial-intelligence-a-comprehensive-glossary-of-terms-and-definitions). Additionally, the data analytics software company, SAS, provides an overview of AI—including how it works, how it is used in a variety of industries, and its pros and cons—at https://www.sas.com/en_us/insights/analytics/what-is-artificial-intelligence.html.
Take classes in AI and related subject areas via online learning platforms such as Udacity, Coursera, and edX. Udacity offers a free Introduction to Artificial Intelligence class that provides a good primer on the field. It also offers fee-based classes that explore AI and other topics—such as natural language processing, machine learning, and computer vision—in greater detail.
Students between the ages of four and 20 can participate in the World Artificial Intelligence Competition for Youth, which challenges them to learn and use AI technology to solve real world problems. The competition has a new theme each year. One recent theme was “How to Make the World Better with AI.” Visit http://www.readyai.org/waicy to learn more about the competition.
One of the best ways to prepare for this career is to learn how to code. The following online learning platforms offer free or low-cost classes in coding: Codeacademy (https://www.codecademy.com), edX (https://www.edx.org), Coursera (https://www.coursera.org), and Khan Academy (https://www.khanacademy.org).
Participate in open data science, programming, and related competitions for high school, students, college students, and professionals. Here are a few competitions to check out:
- Kaggle: https://www.kaggle.com
- American Statistical Association (ASA) DataFest: http://ww2.amstat.org/education/datafest
- Association for Computing Machinery Special Interest Group on Management of Data Student Research Competition: https://www.acm.org/education/student-research-competition
Siri. Nest. Smart robots. E-mail spam blockers. Automated stock trades. Language translation services such as Google Translation. Auto-pilot systems on aircraft. Drones. Speech recognition systems. These are just a few examples of products, services, and systems that incorporate artificial intelligence (AI) to help users save time and money. Companies such as Google, Netflix, Amazon, General Motors, and Boeing; government agencies (including intelligence agencies) and the U.S. military; and nonprofit organizations are using AI to analyze large data sets (called Big Data), increase worker efficiency (and even replace workers), train robots to think, and perform many other tasks.
Artificial intelligence is technology that can be programmed to perform functions and tasks in a “smart” manner that mimics and learns from human decision-making processes. A variety of workers can be categorized as AI specialists. For example, AI development often begins with the need to analyze and use large sets of data. Data analysts study various data sets to provide answers to questions posed by their employers. Data engineers build pipelines that transform data into formats that data scientists and other professionals can use.
Once the data is analyzed and a data pipeline is created, a plan is developed that uses algorithms to make a useful product or service. Algorithm developers, scientists, and engineers (titles are sometimes interchangeably in the AI industry) use this data and conduct additional research to develop algorithms that tell software and hardware what to do. Developers of basic algorithms have a background in software development or programming, while those who create complex algorithms are also experts in a particular field. For example, a developer who is creating a complex financial trading algorithm will have a background in trading and sometimes a Ph.D. in mathematics or computational finance. At some companies, algorithm quality assurance professionals ensure that algorithms work as designed.
Other career paths include user experience professionals, who work with AI-enabled products to ensure that users can easily understand how to use them and that the physical and digital components of the product are user-friendly. Artificial intelligence research scientists conduct scientific research in order to make new discoveries in the use of AI, rather than develop AI for consumer and industrial uses. Applied AI research scientists seek to use AI to help better understand and solve real-world problems, such as pandemics and global climate change.
There are many sub-areas of AI such as machine learning, computer vision, and natural language processing.
Machine learning is a method of data analysis that incorporates AI to help computers study data, identify patterns or other strategic goals, and make decisions with minimal or no intervention from humans. Machine learning scientists and engineers develop algorithms that allow computer programs to learn and automatically improve their performance as they acquire more experience on a certain task. Then they build and maintain scalable machine-learning solutions, monitor the process to ensure that the software works correctly and logically, and develop processes and tools to monitor and assess model performance and data accuracy.
In computer vision, huge neural networks with many layers of processing units are used to teach machines how to view and interpret the world around them by using data collected by cameras and other methods. One example of how computer vision is being used is in the development of self-driving vehicles. Engineers and scientists at BMW, Tesla, Volvo, and other car manufacturers have developed self-driving vehicles with multiple cameras, RADAR, LIDAR, and advanced sensors that gather and process visual information as the car navigates the road. The computer uses the information gathered through these sources to avoid pedestrians, other vehicles, and obstructions in the road. As the “brain” of the vehicle obtains more information, it is theoretically better able to navigate safely. Other examples include smartphones that are programmed to unlock by scanning our faces, and scanning technology that has been found to be better at finding cancerous growths on mammograms than radiologists. Computer vision scientists and engineers design and develop image processing algorithms and optical systems for 2–D and 3–D applications.
Natural language processing (NLP) is an AI subdiscipline that aims to teach computers to understand, interpret, and manipulate spoken and written human language. One example of NLP is found in online and telephone customer service. NLP-powered software allows customers to receive assistance without ever talking to an actual human being. The use of NLP reduces the number of customer service professionals a company has to employ and allows it to assist more customers at one time. Natural language processing scientists and engineers use their knowledge of technology and human language to develop NLP models that train computer systems to communicate intelligently with humans.