Understanding the Basics of Artificial Intelligence

Gain insights into the basics of AI, history, applications, and future in this quick AI overview.

AI generated illustration

Disclaimer: This article was written by me, a human, with assistance from an AI writing tool for editing purposes. I am responsible for the final content and its accuracy.

This is not meant to be an exhaustive overview.

Although I am pretty new here on Substack, if you have been following my work, you know that I am a communicator, (check out Understanding AI Without the Jargon), and that I am interested in AI literacy for everyone, and that I write to raise awareness about the risks of Artificial Intelligence (AI). You also might know that I believe that if we don’t know what AI is, we can’t use it intentionally, question it, and hold big developers accountable.

This is not an exhaustive overview whatsoever and that is why I created this newsletter, because there is so much to talk about when it comes to AI, so we will have more opportunities to expand on many topics in future newsletters.

I am going to try to avoid anthropomorphic language, but it’s challenging when we’re describing tools that are made to simulate human behaviors.

Why this article, you might ask. Well, many of my friends and community members are asking about what AI really is, so I wanted to go back to the basics before moving forward with other AI topics.

---

AI mimic human intelligence, enabling systems to perform tasks that typically require human intellect. From recognizing speech to making decisions, AI is reshaping industries and our daily life. It is worth noting that there is no general consensus on the definition of intelligence.

Understanding AI is crucial as it becomes more integrated into our lives, it certainly offers potential for innovation and efficiency. However, it also raises important ethical and societal questions that need careful consideration.

This article aims to provide a clear and brief overview of AI’s basics. We’ll explore its history, workings, applications, and future.

What is Artificial Intelligence?

Artificial Intelligence refers to systems capable of performing tasks that ordinarily require human intelligence. These tasks include visual perception, speech recognition, and even decision-making.

Generally speaking, AI can be divided into two categories, narrow AI and general AI.

• Narrow AI focuses on specific tasks, such as voice recognition.

• General AI aspires to perform any intellectual task a human can do. We are not there yet, although we could say that Gemini, ChatGPT, etc. are emerging Artificial General AI (AGI).

AI relies on huge amounts of data and algorithms to work and it is supported by advancements in computing power.

A Condensed History of AI

My favorite part, the history of it all, how did it all start?

1936–1940s: Computing Begins

• Turing defines modern computation (1936).

• Mid-1940s → first computers (Colossus, ENIAC, ACE).

1950: Turing’s Intelligence Test

• Proposes the Imitation Game for evaluating machine behavior.

1956: AI Is Born

• Dartmouth Conference names “artificial intelligence.”

1950s–60s: Early AI

• Symbolic logic + rule-based systems (Good Old Fashioned AI, GOFAI).

• Perceptron (1958) → early neural network ideas.

• ELIZA (1966) → first conversational program.

1974–1980: First AI Winter

• Funding and optimism collapse.

1980–1987: Expert Systems Boom

• Knowledge-engineered systems thrive briefly.

1987–1995: Second AI Winter

• Expert systems stall; industry contracts.

1995–2012: Modern Machine Learning Rises

• Internet + big data + stronger computers.

• 1997: Deep Blue beats Kasparov.

• Growth of probabilistic graphical models, ensemble methods, and support vector machines.

• Graphics Processing Units (GPUs) unlock large-scale model training.

2012–2022: Deep Learning (DL) Era

• AlexNet’s ImageNet win ignites DL revolution.

• Rapid progress in vision, speech, and language.

• 2016: AlphaGo’s landmark victory.

• 2018: Transformers → birth of Large Language Models (LLMs).

• 2022: ChatGPT brings AI to the masses.

Source: Introduction to AI Safety, Ethics, and Society developed by

Dan Hendrycks

, Director of the

Center for AI Safety

.

How Does AI Work?

AI systems improve through exposure to huge amounts of data and are able to identify patterns and generate outputs. The data serves as the training material which quality and quantity are extremely important for the responses AI will give us.

• Machine Learning (ML): This is a type of AI where systems learn from data to identify patterns and make predictions without direct programming.

• Deep Learning (DL): A subfield of ML, deep learning uses artificial neural networks with many layers to learn from data. These networks are inspired by the structure of the human brain.

• Natural Language Processing (NLP): NLP enables computers to interpret and generate human language. Voice assistants like Siri and Alexa are NLPs.

• Computer Vision: This area allows computers to interpret visual information like images and videos. Facial recognition to self-driving cars use this technology.

Source: Google Cloud

Key Applications of AI Today

AI applications are vast, transforming numerous fields.

Some applications include:

• Chatbots & Virtual Assistants: E.g., ChatGPT, Siri, Alexa

• Healthcare: Diagnostics, drug discovery, personalized treatment

• Finance: Fraud detection, customer insights

• Computer Vision: Face recognition, autonomous vehicles, medical imaging

• Creative & Content Generation: Text, art, music, video generation

• Robotics & Automation: Manufacturing, logistics,

• Decision Support & Analytics: Predictive modeling, optimization, data insights

• Military: Autonomous drones, intelligence analysis
  

As you can tell some of these applications can be more high-risk than others. To learn more about high-risk AI check out 10 Lesson From The EU AI Act.

Benefits and Challenges of AI

AI offers some benefits but challenges accompany these advancements.

• Ethical Concerns: Addressing bias and fairness.

• Privacy Issues: Handling sensitive data responsibly.

• Job Impact: Labor rights and unemployment.

• Environmental Issues: Availability of resources.

Navigating these challenges requires thoughtful governance and regulatory frameworks. Responsible use and continued research will shape AI’s role in society, maximizing its potential while minimizing drawbacks.

The Future of Artificial Intelligence

AI will probably move toward greater autonomy and deeper integration into every part of our lives. As these systems grow more powerful and more present, the ethical questions surrounding them grow equally urgent.

Strong governance, thoughtful regulation, and a commitment to transparency will be essential to guide this next phase.

When we balance progress with accountability, we create a future where AI evolves in ways that serve and benefit humanity.