AI is a rapidly evolving field that could revolutionize many aspects of our lives. Artificial intelligence can learn from data, make decisions, and solve problems without being explicitly programmed. Because of this, they’re incredibly powerful tools.
The core of AI is surprisingly simple: conditional statements. These are simple instructions that let software make decisions based on certain inputs. Here’s an example of a basic conditional statement:
if temperature > 30:
print("It's not that hot.")
All AI decisions can be boiled down to intricate webs of “if-else” statements. They let the program react differently to all kinds of scenarios.
If AI is based on such rudimentary logic, how does it accomplish the impressive feats we’ve come to expect? What else do you do? Play chess, create art, drive a car?
AI systems are trained on huge amounts of data and use statistical models to help them learn.
Modern AI systems are trained on massive datasets, sometimes containing millions or billions of data points. This gives them a broad spectrum of scenarios to learn from. This ensures that the “if” conditions in their decision-making algorithms are based on rich, real-world data.
Machine learning, a subset of AI, uses statistical modeling to find patterns in this data. For instance, a neural network (a popular ML model) adjusts its internal parameters over numerous iterations to minimize the difference between its predicted outputs and actual values.
Probabilistic Decision Making
Unlike our basic “if-else” example, many AI systems make decisions based on probabilities. They might weigh thousands of potential factors (each a result of numerous underlying “if-else” statements) to determine the likelihood of a particular outcome.
Bridging Micro to Macro: An example
Consider an AI system tasked with identifying whether a picture contains a cat.
- Micro Level: The AI might start by checking simple conditions, such as, “Is there something in the image with a particular shape or color?”
- Macro Level: By processing thousands of cat images, the AI develops a statistical model of what “cat-ness” looks like. Instead of relying on one or two conditions, the AI examines the image under this model. It calculates the probability of the image contains a cat based on its patterns.
By leveraging massive datasets and sophisticated statistical models, AI systems can achieve remarkable accuracy, often nearing 100% under controlled conditions.
It’s humbling and fascinating to realize AI’s complex world is built on simple logic constructs we learned in our programming classes. Nonetheless, these basic constructs can predict, recognize, and generate results that astonish us. In a world where AI is pushing the boundaries, it’s important to remember the principles that make it work.