Artificial intelligence (AI) refers to computer systems capable of performing complex tasks that historically only a human could do, such as reasoning, making decisions, or solving problems.
Today, the term “AI” describes a wide range of technologies that power many of the services and goods we use every day – from apps that recommend tv shows to chatbots that provide customer support in real time. But do all of these really constitute artificial intelligence as most of us envision it? And if not, then why do we use the term so often?
In this article, you’ll learn more about artificial intelligence, what it actually does, and different types of it. In the end, you’ll also learn about some of its benefits and dangers and explore flexible courses that can help you expand your knowledge of AI even further.
What is artificial intelligence?
Artificial intelligence (AI) is the theory and development of computer systems capable of performing tasks that historically required human intelligence, such as recognizing speech, making decisions, and identifying patterns. AI is an umbrella term that encompasses a wide variety of technologies, including machine learning, deep learning, and natural language processing (NLP).
Although the term is commonly used to describe a range of different technologies in use today, many disagree on whether these actually constitute artificial intelligence. Instead, some argue that much of the technology used in the real world today actually constitutes highly advanced machine learning that is simply a first step towards true artificial intelligence, or “general artificial intelligence” (GAI).
Yet, despite the many philosophical disagreements over whether “true” intelligent machines actually exist, when most people use the term AI today, they’re referring to a suite of machine learning-powered technologies, such as Chat GPT or computer vision, that enable machines to perform tasks that previously only humans can do like generating written content, steering a car, or analyzing data.
Artificial intelligence examples
Though the humanoid robots often associated with AI (think Star Trek: The Next Generation’s Data or Terminator’s T-800) don’t exist yet, you’ve likely interacted with machine learning-powered services or devices many times before.
At the simplest level, machine learning uses algorithms trained on data sets to create machine learning models that allow computer systems to perform tasks like making song recommendations, identifying the fastest way to travel to a destination, or translating text from one language to another.
Some of the most common examples of AI in use today include:
ChatGPT: Uses large language models (LLMs) to generate text in response to questions or comments posed to it.
Google Translate: Uses deep learning algorithms to translate text from one language to another.
Netflix: Uses machine learning algorithms to create personalized recommendation engines for users based on their previous viewing history.
Tesla: Uses computer vision to power self-driving features on their cars.
AI in the workforce
Artificial intelligence is prevalent across many industries. Automating tasks that don’t require human intervention saves money and time, and can reduce the risk of human error. Here are a couple of ways AI could be employed in different industries:
Finance industry. Fraud detection is a notable use case for AI in the finance industry. AI’s capability to analyze large amounts of data enables it to detect anomalies or patterns that signal fraudulent behavior.
Health care industry. AI-powered robotics could support surgeries close to highly delicate organs or tissue to mitigate blood loss or risk of infection.
What is artificial general intelligence (AGI)?
Artificial general intelligence (AGI) refers to a theoretical state in which computer systems will be able to achieve or exceed human intelligence. In other words, AGI is “true” artificial intelligence as depicted in countless science fiction novels, television shows, movies, and comics.
As for the precise meaning of “AI” itself, researchers don’t quite agree on how we would recognize “true” artificial general intelligence when it appears. However, the most famous approach to identifying whether a machine is intelligent or not is known as the Turing Test or Imitation Game, an experiment that was first outlined by influential mathematician, computer scientist, and cryptanalyst Alan Turing in a 1950 paper on computer intelligence. There, Turing described a three-player game in which a human “interrogator” is asked to communicate via text with another human and a machine and judge who composed each response. If the interrogator cannot reliably identify the human, then Turing says the machine can be said to be intelligent.
To complicate matters, researchers and philosophers also can’t quite agree whether we’re beginning to achieve AGI, if it’s still far off, or just totally impossible. For example, while a recent paper from Microsoft Research and OpenAI argues that Chat GPT-4 is an early form of AGI, many other researchers are skeptical of these claims and argue that they were just made for publicity.
Regardless of how far we are from achieving AGI, you can assume that when someone uses the term artificial general intelligence, they’re referring to the kind of sentient computer programs and machines that are commonly found in popular science fiction.
Artificial Intelligence in Education: Are we ready?
Today technology is so advanced we can store thousands of books in a few gigabytes of memory. An enormous amount of information is also available on the Internet. But greater access to a massive amount of information is only worthwhile if we read the relevant material. That is why we must underline developing reading habits for our students. Students also need to comprehend and critically think about what they read from the books and other resources. Real learning ensues only then. When the students become good learners and remain life-long learners, that reflects the quality of education provided to them. Quality in Education is accomplished when students are motivated to accumulate adequate knowledge and skills and can proficiently utilize these tools to find pragmatic solutions to pressing challenges in their lives and careers.
Access to affordable education results in better opportunities and social and economic mobility for the individual student and the entire family. Building sound educational systems has the prospect of meeting the aspirations of the students by improving their ability to be good learners.
The pillars of any sound educational system are well-trained and scholarly teachers. While they should be continuously trained to imbibe the changing pedagogical approaches, introducing AI-based technologies in Education can lead to student-centric teaching-learning processes.
Carl Gustav Jung, a Swiss psychiatrist and psychoanalyst, said: The shoe that fits one person pinches another; there is no recipe for living that suits all cases. The same is valid for Education. Our educational system has become too rigid for too long, and we imparted Education to our students without worrying about the distinctions in their cognitive abilities and necessities.
The number of students who want to access high-quality Education is increasing. On the other hand, the need to provide personalized instruction to have better equity is becoming even more critical. Both seem contradictory as with a larger number of students in a classroom, can a teacher provide personalized attention?
However, there will be a paradigm shift in the teaching-learning processes if AI systems are used. We will move from a teacher-directed, more generic approach to personalized student-centric learning.
As the necessity to educate a large number of students increases, we ought to develop AI tools and deploy them to aid the learning needs of our students. AI in Education must be a part of a strategic approach toward providing quality education.
A common question is whether AI-driven tools will replace real teachers and professors in the classroom. That is very unlikely. Human teachers are irreplaceable. However, AI in Education can enhance learning outcomes and provide more engaging learning experiences. AI tools can be handy in recurring processes such as evaluation, management, and operations in Education.
There are three ways in which AI tools can assist students in making them better learners.
AI-directed Learning: An AI-driven machine can be loaded with predetermined expertise just as a teacher brings prior knowledge to the classroom. This AI-machined can then impart knowledge to the student just as a teacher does in a school. A real teacher may give certain learning activities and evaluate the student to see if the student has acquired the required learning outcomes. In AI-directed learning, the AI-driven machine, as a teacher does in the classroom, will also ensure that the student is guided on a predetermined learning trail to attaining the desired learning goals. Such a system can act as a tutor to the student during self-study after class hours. This mode of teaching-learning is called AI-directed learning, where the student remains a mere recipient of knowledge and follows a predetermined learning pathway. If AI-directed learning becomes popular, the need for home tutors may disappear. Mere imitation of a tutor using AI technology does not realize the full potential of AI. There are better ways of using AI to improve learning.
AI-supported Learning: The student collaborates with the AI machine in an improved version of machine-assisted Education, known as AI-supported learning. In AI-supported learning, the machine continuously collects data from the student while interacting with the student. This data is then used as incremental feedback to adjust the learning process since the AI machine can estimate the progress made by the student. Through this back-and-forth process, the AI machine adaptively guides the student to improve the learning process. Here, the student is the focus of learning. The student’s performance nudges the AI machine to alter the difficulty level leading to a perfect personalized learning experience for the student. Both students and AI machines are collaborating.
AI-empowered Learning: The exciting aspect of AI-empowered Education is that there are multiple players here – students and teachers. Numerous learners collaborate to solve complex problems. The teachers provide practical feedback and advice, providing an improved teaching-learning environment. Since it is a collaborative effort, learners should acquire effective communication abilities and need to be creative.
Undoubtedly, AI will continue to be an essential component of Education, with new AI applications being developed rapidly for customized and personalized learning. There are short-term and long-term benefits to introducing AI in Education. However, as educators, we also need to ponder the purpose of Education – is it not to enhance human agency, i.e., the capacity to reflect, analyze and act with consciousness? Only this capacity can help the students navigate through their lives successfully. Therefore, the ethical aspect of AI in Education is an area that should not be taken as trivial as we make our beginnings in adopting AI in Education.
Education is much beyond mere data collection and access to facts or providing succinct and brief answers. Therefore, AI machines can best function as teaching assistants or tutors rather than professors.
Since AI machines collect data from students on their learning paths, how this data is collected and protected can also cause concern to students and educators. In domains other than Education, we have already seen how privacy issues and ethical use of data are taking center stage of our concerns. AI machines work on models and data, which AI experts essentially determine. Their biases and worldview can decide how AI machines interact with their users in Education and, consequently, their learning experiences. The real teacher may become a mute spectator while the AI machines shape the future of students’ learning and their future. Suitable policy formulation and evolving new strategies at the institutional and government levels are crucial to distinguishing between opportunities and challenges and minimizing AI-induced education risks.
