AI
Artificial Intelligence is a field of science concerned with building computers and machines that can reason, learn, and act in such a way that would normally require human intelligence or that involves data whose scale exceeds what humans can analyze.
Understanding AI: From Ancient Dreams to Modern Reality
Many people think that Artificial Intelligence is something new that started in the 2020s. But that’s not true! Like so many innovations, the idea of thinking machines came from ancient civilizations.
Even in Homer’s Iliad from the 8th century BC, we find descriptions of artificial beings with intelligence. Homer describes golden attendants created by the god Hephaestus: “These are golden, and in appearance like living young women. There is intelligence in their hearts, and there is speech in them and strength.”
In ancient Egypt, engineers built huge mechanical robots that acted like gods, operated by priests to amaze worshippers.
What is Artifical Intelligence?
Artificiall Intelligence (AI) can be defined as the simulation of human intelligence by machines. Think of AI as a skilled actor that plays a human character on stage.
We can think about AI as a large toolbox containing other technologies like machine learning, deep learning and natural language processing.
How does an AI system work?
- ingesting large amounts of data
- analyzing this data for correlations and patterns
- finally making predictions (generates the next word, gives answers, creates a nice picture…)
Two types of AI-systems:
- Narrow or weak AI
designed for particular task, and it isn’t able to operate without human interactions
For example: digital assistants like Siri, Alexa; generative AI-tools like ChatGPT, Gemini; self-driving cars - Strong AI
has human cognitive abilities, thus it could solve tasks without human intervention. Recent AIs haven’t been in this level, yet.
Key Milestones in Artificial Intelligence History
1950: Alan Turing published “Computing Machinery and Intelligence” (Turing and his team had previously cracked the German ENIGMA codes during World War II, helping the Allies win the war)
1956: John McCarthy coined the term “Artificial Intelligence”
1997: Chess champion Garry Kasparov was defeated by IBM’s Deep Blue computer
2011: IBM’s Watson won at Jeopardy! against human champions
2016: Go champion Lee Sedol was beaten in a five-game match by DeepMind’s AlphaGo program
2022: OpenAI’s ChatGPT became publicly available
Main Components of AI Systems
Data: Large quantities of information that AI systems learn from
Algorithms: Sets of rules that AI systems use to process data and make decisions. Think of them like a series of “if-then” questions: “If the weather forecast predicts rain, I should bring an umbrella, but if the afternoon will be sunny, I need sunglasses too.”
Computing Power: Processing and running the previous two components requires immense computational power. This means huge data centers, AI “mega-factories,” and significant amounts of electricity and water for cooling the devices. This is not only expensive but also environmentally challenging.
Timeline of AI Development
PAST: Reactive Machines
IBM’s Deep Blue, which beat chess champion Garry Kasparov in 1997, is an example of reactive AI. These systems have no memory and cannot learn from past experiences.
PRESENT: Limited Memory
Most modern AI falls into this category. These systems have memory, but it’s limited—what we call a “context window.” This ranges from about 4,000 to 100 million tokens on average, which is huge but still has boundaries.
FUTURE: Theoretical AI Types
Theory of Mind AI: This type doesn’t currently exist, but researchers are exploring its possibilities. It would describe AI that can understand the human mind and has decision-making capabilities equal to humans, including recognizing and remembering emotions and reacting appropriately in social situations.
Self-Aware AI: A step beyond theory of mind AI, self-aware AI describes a hypothetical machine that is aware of its own existence and has the intellectual and emotional capabilities of a human. Like theory of mind AI, this doesn’t currently exist.
Finding the Right Balance
Since we created our first tools, we’ve been relying on them. They make us more effective and productive, but also increasingly dependent. We need to find the right balance when using AI-based systems. How much “brain-outsourcing” is helpful without being harmful?
We’re still in the early stages—enthusiasm is high (some might call it hype), but we’re already seeing symptoms of overreliance. Some people worry about “brain rot” from depending too heavily on AI.
AI systems are powerful tools when used appropriately for the right tasks. The key is learning how to use them wisely.