A Step-by-Step Guide to Pursuing Artificial Intelligence Engineering
A decade ago, the term Artificial Intelligence would make most people think of futuristic, sci-fi machines or sentient, self-aware robots. AI today is one of the most sought-after areas in business and industries across the world. As the amount the data created continues to rise globally, AI forms the crux of the ongoing digital transformation happening in all aspects of our professional and personal lives. From aiding self-driving cars and predicting legal outcomes to providing media and shopping suggestions, improving the video gaming experience, and advancing space exploration, AI is playing a more significant and diverse role in our lives than ever before. With more and more companies getting on the AI bandwagon, they are facing an increased need to acquire skilled talent who can help them capitalize on these technologies. This is opening up new career paths, not just in AI, but also in AI-adjacent fields such as computer science, math, and statistics.
AI has touched practically every industry, and an increasing number of Artificial intelligence engineering colleges are joining the race to employ AI-related skills in some form or other which makes it an excellent time for career aspirants and opportunists to upskill in the technologies that drive the future of tech.
History of AI
This range of time was when the interest in AI really came to a head. Alan Turing published his work Computer Machinery and Intelligence which eventually became The Turing Test, which experts used to measure computer intelligence. The term artificial intelligence was coined and came into popular use.
The time between when the phrase “artificial intelligence” was created, and the 1980s was a period of both rapid growth and struggle for AI research. The late 1950s through the 1960s was a time of creation. From programming languages that are still in use to this day to books and films that explore the idea of robots, AI became a mainstream idea quickly.
The 1970s showed similar improvements, such as the first anthropomorphic robot being built in Japan, to the first example of an autonomous vehicle being built by an engineering grad student. However, it was also a time of struggle for AI research, as the U.S. government showed little interest in continuing to fund AI research.
AI engineer skill requirement
A good artificial intelligence engineering colleges teaches their students a particular skill set for the future. Some of them include-
Technical skill
Programming skill
The first skill required to become an AI engineer is programming. To become well-versed in AI engineering, it’s crucial to learn programming languages, such as Python, R, Java, and C++ to build and implement models.
Linear Algebra, Probability, and Statistics
To understand and implement different AI models—such as Hidden Markov models, Naive Bayes, Gaussian mixture models, and linear discriminant analysis—students must have detailed knowledge of linear algebra, probability, and statistics.
Algorithms and Frameworks
Understanding how machine learning algorithms like linear regression, KNN, Naive Bayes, Support Vector Machine, and others work will help implement machine learning models with ease. Additionally, to build AI models with unstructured data, learners should understand deep learning algorithms and implement them using a framework. Some of the frameworks used in artificial intelligence are PyTorch, Theano, TensorFlow, and Caffe.
People Skills
The difference between successful engineers and those who struggle is rooted in their soft skills. Although being an AI engineer is primarily a technical job, the ability to effectively communicate with others, deal with problems, organize one's time effectively, and work in collaboration with others are the key factors that determine whether or not a project is successfully completed and delivered.
Communication Skills
There is a broad range of people with different levels of competence that artificial intelligence engineers have to talk to. Suppose that any company asks students to create and deliver a new artificial intelligence model to every division inside the company. If anyone wants to convey complicated thoughts and concepts to a wide audience, they probably want to brush up on their written and spoken communication abilities.
Problem-solving Abilities
To be a successful data scientist or software engineer, students must be able to think creatively and solve problems. Because artificial intelligence seeks to address problems as they emerge in real time, it necessitates the development of problem-solving skills that are both critical and creative.
Conclusion
AI has touched practically every industry, and an increasing number of organizations are joining the race to employ AI-related skills in some form or other which makes it an excellent time for career aspirants and opportunists to upskill in the technologies that drive the future of tech. Artificial intelligence engineering colleges give training on the skills required for a successful career in the AI field.