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What is (AI) Artificial Intelligence?

Uncover the potential of AI. Learn the definition of AI, how it works, and educational/career opportunities.

3D interpretation of artificial intelligence

A recurring theme in science fiction, artificial intelligence (AI) has captured our collective imagination and enthralled audiences for over a century. From the early days of science fiction literature to the captivating narratives of iconic movies, the concept of intelligent machines has been a source of fascination and speculation.

Artificial Intelligence (AI) enables machines to learn from experience, adapt to new inputs, and execute tasks resembling human capabilities. By leveraging AI technologies, computers can undergo training to perform particular tasks through the analysis of extensive data sets and the identification of patterns within the data.

What is the definition of Artificial Intelligence?

Artificial intelligence represents a branch of computer science that aims to create machines capable of performing tasks that typically require human intelligence. These tasks include learning from experience (machine learning), understanding natural language, recognizing patterns, solving problems, and making decisions. From self-driving cars to virtual personal assistants, AI is reshaping various aspects of our daily lives, and its significance continues to grow.

“Computer science is about building recipes to achieve different goals and objectives,” said Dr. Ian Kash, Associate Professor for the University of Illinois Chicago’s online Master of Engineering with a focus area in AI and Machine Learning (MEng) program. “In many areas of computer science, we can build things that are guaranteed to do what we want. However, there are a lot of extremely difficult problems in the world. So, to me, the field of AI is a set of techniques and tools that have been developed to solve these hard problems even when we can’t get a fully satisfying ‘just follow this recipe’ solution.”

One pivotal moment in the exploration of AI came in 1950 with the visionary work of British polymath, Alan Turing. In his paper, “Computing Machinery and Intelligence,” Turing introduced the Turing test and explored the mathematical possibilities of AI and questioned why machines couldn’t leverage available information, just as humans do, to solve problems and make decisions. This marked a crucial step in the journey from speculative fiction to tangible innovation.

Unlike traditional computer programs that follow predetermined instructions, AI systems can learn and adapt from data, allowing them to improve their performance over time. This ability to learn and evolve is a key characteristic that sets AI apart from conventional computing.

How does artificial intelligence work?

Artificial Intelligence (AI) works by simulating human intelligence through the use of algorithms, data, and computational power. The goal is to enable machines or software to perform tasks that typically require human intelligence, such as learning, reasoning, problem-solving, perception, and language understanding.

AI Subsets 

Artificial Intelligence comprises various subsets or subfields, each focusing on specific aspects of replicating human intelligence or solving particular types of problems. Although AI subsets often overlap and interdisciplinary approaches are common, below are some of the major subsets of artificial intelligence:

Machine Learning (ML): The ML subset focuses on the development of algorithms and statistical models that enable computer systems to perform tasks without explicit programming. The primary goal of machine learning is to allow machines to learn patterns and make decisions based on data.

Neural Network(s): This subset focuses on AI models that are inspired by the structure of the human brain. These networks consist of layers of interconnected nodes, each layer contributing to the model’s ability to understand increasingly complex features in the data. Deep learning is also a class of neural networks with multiple layers. Deep learning has been particularly successful in tasks like image recognition, natural language processing, and playing strategic games.

Natural Language Processing (NLP): This subset focuses on enabling machines to understand, interpret, and generate human language. This subset is crucial for applications like chatbots, language translation, sentiment analysis, and voice recognition.

Game Playing: This subset focuses on AI systems that are designed for game playing involve creating algorithms that can play strategic games, such as chess at a high level.

Weak AI vs. Strong AI 

There are two different types of artificial intelligence capabilities, particularly in terms of mimicking human intelligence. These concepts help distinguish the extent to which AI systems can replicate cognitive functions and exhibit intelligence.

Weak AI (or Narrow AI): AI systems that are designed and trained for a specific task or a narrow set of tasks and are most of the AI that we see today. It enables some very robust applications, such as Amazon’s Alexa and Tesla’s self-driving vehicles.

Strong AI (or General AI): AI systems with the capacity to comprehend, learn, and apply knowledge across a diverse spectrum of tasks at a level equivalent to human intelligence. It’s a theoretical form of AI where a machine would have an intelligence equal to humans. Current examples only exist in movies, such as WALL-E and Her.

Jobs in AI 

The field of AI is expected to grow explosively as it becomes capable of accomplishing more tasks thus leading to a demand for professionals with expertise in various domains. The U.S. Bureau of Labor Statistics expects employment of computer and information technology occupations to grow 13% from 2020 to 2030 and predicts the field of data scientists to grow 35% between 2022 and 2032.

Here are some potential roles in AI: 

  • Machine Learning Engineer ($160,000 average salary) 
  • Data Engineer ($125,000 average salary) 
  • Natural Language Processing (NLP) Engineer ($111,000 average salary) 
  • Computer Vision Engineer ($135,000 average salary) 
  • Robotics Engineer ($109,000 average salary) 
  • Data Scientist ($125,000 average salary) 
  • AI Software Developer ($129,000 average salary) 
  • AI Consultant ($105,000 average salary) 
  • AI Product Manager ($135,000 average salary)

Education in AI

Individuals looking to enter the field of AI should consider pursuing an advanced degree. A Master of Engineering (MEng) degree can open a wide range of career opportunities in various industries where AI and machine learning are playing an increasingly important role.

UIC’s online Master of Engineering with a focus area in AI and Machine Learning program offers a unique opportunity to dive headfirst into the cutting-edge world of artificial intelligence. The online program’s core courses help students develop their understanding of the fundamental math of AI and ML, as well as AI and ML theories, techniques and tools. They will apply this knowledge more deeply in the courses of Image Analysis and Computer Vision, Deep Neural Networks, and Natural Language Processing.

Teaching the online MEng program’s CS 411: Artificial Intelligence course, Dr. Kash believes AI is a powerful set of tools that can help individuals in all careers solve complex problems. “Whether you’re building the AI tools themselves or thinking about how you can integrate AI tools within your company, there’s a fundamental understanding you’ll get in the online MEng program that is extremely valuable.”

What are the current trends in AI?

Staying on top of current AI trends is imperative to understanding the transformative developments shaping our future. There are several notable trends that are influencing the trajectory of this field. Dr. Kash is intrigued by the possibility of witnessing AI techniques that will address substantial, real-world challenges. Although we have seen AI techniques work well in small scale settings, Dr. Kash says we have not seen many tackle important engineering challenges.

“An AI trend that I’m observing is the integration of classic AI techniques with modern deep learning methods and figuring out the engineering solutions to make those two things work seamlessly together,” said Dr. Kash.

Here are some other noteworthy AI trends to keep a close eye on: 

AI Governance and Regulation: While the field of AI advances rapidly, governments and organizations are working together to establish guidelines, regulations, and frameworks to ensure AI technologies are developed and deployed responsibly.

Generative AI: As we have seen with ChatGPT, generative models are producing remarkably realistic content. These models have applications in content creation, art, and media.

Ethical AI: With the emergence of AI technologies, the field has the potential to be disruptive and it’s essential that these new technologies are ethical. We will continue to see organizations address any legal or ethical issues associated with AI to mitigate any potential problems.

AI in Healthcare: While we watch AI/ML transform many parts of the economy, AI will be creating significant opportunities in healthcare services, life sciences tools and diagnostics, and medical technology.

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