How pigeons and rats gave the idea of ML
Pegion Superstition
The term "pigeon superstition" actually refers to a famous psychology experiment conducted by B. F. Skinner on operant conditioning. Here's the gist:
Skinner's Experiment: Pigeons were placed in cages and given food at random intervals. Interestingly, the pigeons developed repetitive behaviors (e.g., circling, bobbing their heads) that seemed to coincide with the food delivery.
The Catch: These behaviors didn't actually cause the pigeons to get food any faster. It was purely coincidental. However, since the food sometimes appeared right after they performed a specific action, the pigeons mistakenly believed their behavior influenced the outcome.
Superstitious Behavior: This is where the term "pigeon superstition" comes from. It highlights how animals (and sometimes humans) can associate random events with their actions and develop rituals or routines based on that mistaken belief.
In essence, it's not that pigeons are truly superstitious, but their behavior demonstrates a basic learning principle: operant conditioning, where actions followed by rewards are more likely to be repeated.
Bait Shyness
Bait shyness, also sometimes called conditioned food aversion, is an evolutionary adaptation seen in many animals, particularly rodents. It describes the avoidance of a food source that has been associated with a negative experience in the past.
Here's a breakdown of bait shyness:
Learning from Experience: Animals, especially intelligent ones like rats and mice, are good at learning from experience. If they eat something and then get sick, they'll likely avoid that food source in the future.
Taste and Smell: Animals rely heavily on taste and smell to identify food sources. When they experience illness after consuming something, they associate the taste and smell with the negative outcome.
Survival Benefit: Bait shyness helps animals avoid poisonous or harmful foods in the wild, increasing their chances of survival.
Inductive Bias
Imagine you're playing with building blocks. You've never seen a tower before, but you keep seeing grown-ups putting blocks on top of each other.
Learning from Examples: You start to guess how towers work. You might think, "Towers are always made of blocks, and bigger blocks usually go on the bottom." This is like learning from examples, which is what computers do with inductive bias.
Guessing Based on What You Know: Inductive bias is like having a favorite type of block you like to use first. Maybe you always start with the red block because that's what you see most often. This "favorite block" idea is the computer's guess about how things work based on what it's seen before.
Not Always Right: Just like sometimes a tower needs a smaller block on the bottom to be strong, the computer's guess might not always be right. It needs to see more examples and adjust its ideas as it learns new things.
So, inductive bias is like a computer's way of making a guess about how things work based on what it's already learned, even though the guess might need to change later!
Pegion, rats and inductive bias
Imagine you're playing outside and see a yummy-looking cookie on the ground. You reach for it, but just before you take a bite, a big gust of wind blows your hat away! You run to get your hat and come back... the cookie is gone! Uh oh!
Pigeon Superstition: This is like a pigeon thinking, "The wind blew my hat away, then the cookie disappeared. Maybe the wind took my cookie!" Even though the wind didn't really take the cookie, the pigeon makes a guess (induction) based on what it already experienced. This guess is the pigeon's inductive bias.
Bait Shyness: Now imagine you see some tasty-looking seeds on the ground. You eat a few, but then you feel yucky and sick! You avoid those seeds from then on. This is like bait shyness. You learned (induction) that those seeds make you sick, so you avoid them next time.
Both these examples show inductive bias. It's like a guess an animal (or even a computer sometimes!) makes based on what it's already experienced. It's not always right, just like the pigeon and the cookie, but it helps them learn and avoid getting hurt.
How is Inductive Bias important for ML
Machine learning (ML) algorithms are like super students who can learn from data. But unlike us, they don't have all the background knowledge or common sense we take for granted. This is where inductive bias comes in!
Inductive Bias as a "Learning Preference":
Imagine you're teaching your friend a new card game. You might explain the basic rules, but you probably won't mention every single situation that could come up. Your friend will use their own ideas (inductive bias) to fill in the gaps based on what they've learned so far.
Similarly, an ML algorithm has an inductive bias that reflects its basic assumptions about the data it's learning from. These assumptions guide the algorithm towards certain types of solutions over others.
Pigeon Superstition and Inductive Bias in ML:
Remember the pigeon who thought the wind stole its cookie? That's a funny example of inductive bias. The pigeon made a connection (induction) between the wind blowing and the cookie disappearing, even though there was no real connection.
In machine learning, the algorithm might have an inductive bias that assumes data points that are close together are more likely to be similar. This can be helpful, but it can also lead to mistakes if the data doesn't actually follow that pattern.
Benefits of Inductive Bias:
Just like a little common sense helps your friend learn the card game, inductive bias helps ML algorithms learn faster and more efficiently. It allows them to focus on the most likely possibilities instead of considering every single option, which would be overwhelming.
For example, an algorithm with an inductive bias for recognizing handwritten digits might assume that slightly messy versions of a "7" are still likely to be "7s." This helps it make accurate predictions even with imperfect data.
Challenges of Inductive Bias:
The downside of inductive bias is that it can lead to blind spots if the assumptions aren't right. Imagine your friend stubbornly thinks the highest card always wins in the card game! They'll miss out on important strategies.
In ML, if the inductive bias doesn't match the actual data patterns, the algorithm might make poor predictions. For example, an algorithm trained on mostly sunny weather data might not handle rainy days well because it has a bias towards sunny weather.