What is AI? A Complete Primer on Artificial Intelligence
In this post you’ll learn how AI works, what today’s AI can and cannot do and some of the pros and cons of this transformative technology. Let’s dive in!
The time when we thought Artificial Intelligence (AI) was just a sci-fi dream (or perhaps something reserved for Spielberg’s movie) is long gone. Experts predict that by 2030 up to 70% of companies will have adopted at least one type of AI. This expansion is a testament to the real-world impact AI is having on various sectors, from programming and finance to transportation and healthcare, reshaping the way we live, work, and interact with the world around us.
As AI technologies become increasingly embedded in our daily routines, it’s essential to grasp the fundamentals behind their operation. Knowing how these systems learn, process data, and make decisions empowers you to harness their potential responsibly and ethically.
In this post you’ll learn how AI works, what today’s AI can and cannot do and some of the pros and cons of this transformative technology. Let’s dive in!
Table of Contents
- What is AI?
- Building Blocks of AI
- Types of AI
- Advantages of Using AI
- Disadvantages of Using AI
- Parting Thoughts
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What is AI?
AI refers to a device’s capability to function like human intelligence and carry out tasks that humans do, such as recognizing patterns in data and making decisions based on information. It does so through the use of complex algorithms (aka fancy math rules) and data processing techniques.
AI has a long history, starting in the 1950s when scientists first talked about creating intelligent “thinking” machines that would resemble humans. The initial progress slowed down in the 1970s and 1980s, known as the “AI winter,” when interest in these technologies decreased and AI capabilities remained low due to the lack of computing power. Things changed in the 1990s when more digital AI programs emerged.
Now with the recent reemergent interest in AI, that winter is over — for good.
AI has become part of our daily lives and keeps getting better with new technologies and breakthroughs. AI’s prevalence in today’s world has brought a new question to the table – how exactly does AI work?
Kate Crawford, a principal researcher at Microsoft Research and author of the book Atlas AI said on the Pivot podcast that many believe AI is “the set of mathematical, sometimes magical functions in the cloud, but actually, these are profoundly material systems that have very real impact, that use lots of human labor, lots of natural resources, and of course, a gigantic amount of data.” She thinks the best way to explain AI is to look behind the “superintelligence hype” that’s been going on lately and that’s what we are going to do.
Most AI today works with input and output, just like a conversation. And the process is the following:
- AI starts by receiving input — a picture, a voice command, a question, or simply text.
- Once AI gets the input, it uses its algorithms to understand and analyze the information. It tries to find patterns and connections in the data to figure out what it means.
- After processing the input, AI gives us the output — a decision, a suggestion, a prediction, or even creating something new like a piece of music or art.
For example, when you ask ChatGPT to write the code for calculating percentages in Python, the prompt acts as an input. The system then processes that input, predicts what you want, and provides the code as an output.
Giving AI systems more input serves a very important purpose. You know how we humans can learn from our mistakes and get better at something over time? Well, AI can do the same! It learns from lots of input, data, and examples. For instance, it can learn to recognize your face in photos, translate languages, play games, or even suggest what Netflix movie you might like to watch next based on your preferences. Driving from feedback you give, AI can adjust its actions and improve its performance over time.
Some of the everyday examples of AI include:
- Virtual assistants that use voice recognition such as Siri and Alexa
- AI-powered chatbots used for customer service
- Writing assistants like ChatGPT or Grammarly
- Face recognition on your phone
- Targeted advertising on Instagram that learns what you’re interested in
While these and other AI can be incredibly powerful and versatile, despite its name, it cannot act as a replacement for human intelligence. AI may be able to process massive amounts of data quickly, but it lacks the deep emotional understanding and empathy that humans possess. It can analyze patterns and make predictions based on data, but it might not fully comprehend the nuances and context that human intelligence effortlessly grasps. As Kate Crawford said, “They don’t understand the language, they are much more interested in predicting things, predicting the next word in the sentence, predicting the type of image you might be requesting, and it’s not coming from understanding all language. It’s a very different form of interpretive ability.”
Building Blocks of AI
Imagine that you go to your local toy store and pick up a robot-building kit. This kit includes everything you need to build a robot: arms, legs, a head, a body, screws, maybe even a circuit board so your robot can walk. Similarly, AI has its own components or “building blocks” that when combined and configured properly, create a functional and intelligent system. The most talked-about components in the world of AI are natural language processing (NLP), machine learning, and deep learning.
NLP enables us to interact with virtual assistants, chatbots, and other AI-powered systems using natural language, just like we would with a human, as opposed to how we normally speak to a computer which is via a computer language. For example, when you ask Siri or Alexa a question, NLP helps them figure out what you want and then respond with the right information or action.
Machine learning, which is a subset of artificial intelligence, helps automate tasks effortlessly. Instead of explicitly programming computers for each task, machine learning enables them to learn from various experiences and examples. It involves training AI models on large amounts of data, and then the AI can make predictions or decisions based on what it learned from that data. If you’ve ever used Gmail, you’ve probably noticed this; when you write “best” at the end of an email, it usually gives a suggestion to add “wishes” – a prediction based on previous data.
Deep learning is a powerful subset of machine learning that can process vast amounts of data and learn complex patterns with less human involvement. Most aspects of today’s AI that seem human-like, such as computer vision and the ability to remember past interactions, are powered by deep learning. An example of deep learning you’re likely to have encountered is the Face ID feature on newer models of the iPhone.
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Types of AI
While the building blocks of AI refer to the foundational components that make up the infrastructure of AI systems, the types of AI refer to the level of intelligence it exhibits (called capabilities) and the types of tasks it performs (called functionalities).
In terms of capabilities, Forbes says there are three types of AI: Narrow AI, General AI, and Superintelligence.
Narrow AI is like a specialist AI that’s really good at doing one specific thing, but it’s not like a super-smart all-knowing tool. For example, Siri is great at answering your questions, setting reminders, and playing music, but it can’t summarize a 200-page document in 100 words.
Both General AI and Superintelligence remain hypothetical at this point. General AI would possess human-like intelligence and understand, learn, and perform tasks across various domains. Superintelligence would not only replicate but surpass human intelligence in all aspects.
Now in terms of functionalities, Forbes names four types of AI: Reactive AI, Limited Memory AI, Theory of Mind AI, and Self-aware AI.
Reactive AI doesn’t have memory or learning capabilities; it operates in the present based solely on the current input. The best example is Chess-playing AI, such as IBM’s Deep Blue, which analyzes the board and makes the best move without considering past games.
Limited Memory AI can learn from past data and experiences, allowing it to make decisions based on historical information, such as self-driving cars or ChatGPT.
Theory of Mind AI and Self-aware AI are both hypothetical and remain decades, if not centuries away from being developed. These would both function by exhibiting their own human-like emotions and needs.
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Advantages of Using AI
One of the best things about AI is that it can analyze vast volumes of data at incredible speeds, far beyond what humans can handle. This ability allows AI to uncover patterns, correlations, and insights that might otherwise remain hidden. It has become a game-changer for businesses and professionals seeking to streamline their operations and make data-driven decisions. If you have a business, AI can analyze all the data and come up with smarter ways to do things, like scheduling employees or managing inventory more efficiently.
On top of data analysis, AI can also become something like your personal assistant and handle all those repetitive tasks that might take an unnecessarily long time to complete, like sorting through emails or doing data entry. For example, AI-powered tools can automatically generate code snippets or even entire functions based on natural language descriptions or specific requirements. This helps developers speed up their coding process and reduces the chances of errors. A significant 64% of businesses believe that artificial intelligence will help increase their overall productivity, as revealed in a Forbes Advisor survey.
Kevin Roose, a technology columnist for the New York Times is one of the most prominent AI writers and a co-host of the Times tech podcast Hard Fork. He’s been actively using ChatGPT as a daily assistant and has come up with a list of tasks where large language models (LLMs) excel, including code writing.
“These models weren’t designed to write software. But when they were trained on vast amounts of internet text, including the contents of coding sites like GitHub and Stack Overflow, they learned how to code — a phenomenon known as emergent behavior. Now, if you type in a prompt like “Build me a Chrome extension that translates the text of any website to pirate-speak,” you might get back the code for an app that actually works.” Of course, you’d have to know how to code yourself to implement the code and debug any issues that come up!
Meanwhile, AI-powered creative tools, such as generative art, music composition algorithms, and even AI-driven storytelling, are redefining the boundaries of human imagination. These systems can analyze vast amounts of existing creative works and generate new and innovative pieces.
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Disadvantages of Using AI
When new technology develops at the speed of light (like AI or iPhones back in the day), there is a tendency to overhype its capabilities. Kate Crawford thinks AI has “a perspectival shift that can feel like magic. But I can assure you, it is not magic, and that’s where it gets interesting.”
On the one hand, AI is great at tasks like analyzing data and recognizing patterns, such as in medical diagnoses from images. But if it encounters an image taken from an unusual angle, its accuracy can drop significantly.
The issue is not only the type of data, but how trustworthy it is to start with. Cade Metz is a technology reporter for The New York Times and the author of the book Genius Makers: The Mavericks Who Brought A.I. to Google, Facebook, and The World. He says inaccuracy in AI’s response comes from the vast amount of false information available on the Internet nowadays.
“And because of the surprising way they [AI systems] mix and match what they’ve learned to generate entirely new text, they often create convincing language that is flat-out wrong or does not exist in their training data. A.I. researchers call this tendency to make stuff up a “hallucination,” which can include irrelevant, nonsensical, or factually incorrect answers.”
On top of misinformation, the rise of AI also brings ethical concerns. For instance, according to Harvard University, facial recognition software has shown higher error rates for people of color, leading to concerns about racial bias. Also, if misused or not properly regulated, face recognition AI could compromise people’s privacy and lead to invasive surveillance.
Devashree Madhugiri, data analyst and technical content writer thinks while AI can enhance our lives in many ways, overreliance on it might diminish our ability to perform tasks independently. For instance, relying heavily on AI-powered code auto-completion tools might hinder your ability to write code from scratch without assistance.
AI might be incredibly intelligent, but it lacks emotions and human empathy. In areas like customer service, automated chatbots struggle to provide the same level of understanding and support as human agents.
Parting Thoughts
AI is an exciting field that has the potential to revolutionize various industries and improve our lives significantly. When we have a clear picture of AI’s limits and capabilities, we can set realistic expectations and avoid getting caught up in any false ideas about what it can achieve.
AI may be capable of performing tasks that require human intelligence, but it’s still a machine that operates based on algorithms and data. While it can do amazing stuff, we should keep in mind that it’s all about processing data and following instructions. No feelings or consciousness there, just cool computer “magic”!
This understanding helps us make the most out of AI’s potential and find the right areas where it can actually be helpful. It’s like knowing the rules of the game, so we can play it well and win!
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