I’m excited to discuss a fascinating topic! Artificial intelligence… We hear about it everywhere nowadays, right? Sometimes thinking about it, I wonder how 20 years ago, computers were just like calculators, and now they can perform the most complex tasks. Just the other day, I realized that our smart vacuum cleaner maps out the house and knows the rooms better than my children do. Honestly, this surprised and somewhat scared me. I couldn’t help but think about where this technology is heading.
For me, this journey started with a somewhat old memory. During university years, or even when I was younger, we worked on embedded systems projects. We tried to move a robotic arm, and back then, the biggest achievement was connecting two points with a simple ‘if-else’ loop. Now, I think about it, those efforts seem so trivial compared to today’s AI. But at that time, it felt like changing the world. Once, due to a tiny mistake in the code, our robot arm caused some chaos. Luckily, nobody got hurt, but we experienced quite a panic. That day, I realized how fast technology progresses and how much history is behind it.
Anyway, let’s get to the topic. Artificial intelligence didn’t just appear overnight. It has a long history, perhaps one of humanity’s earliest dreams. Think about the robots and mechanical designs in Ancient Greek mythology… These seem like the first seeds of ‘artificial intelligence.’
The foundations of AI began to be laid in the 1940s and 1950s. During this period, scientists pondered whether machines could think or learn. The famous ‘Alan Turing’ incident comes to mind, which coincides with this era. He proposed a test to determine whether machines can behave like humans. It is called the ‘Turing Test,’ and the idea originated then. You can check out here for a better understanding.
In 1956, the Dartmouth Conference took place, often considered the ‘birthday’ of AI. John McCarthy and others first used the term ‘artificial intelligence’ there. Everyone was very excited, almost like a magic wand had been waved. They believed machines could do anything. Of course, computers weren’t as powerful back then, so they couldn’t do everything we imagine, but the ideas were grand.
But things didn’t always go smoothly. AI research sometimes entered ‘AI winters,’ periods where high expectations, investments, and breakthroughs disappeared, leading to a slowdown in progress. This is normal with such technologies, just like how projects sometimes stagnate in other fields.
Within AI’s history, there are pivotal milestones. For example, in the 1970s, ‘expert systems’ emerged that mimicked specialists’ knowledge in specific fields like medicine. These were revolutionary at the time. Later, in the 1980s, machine learning gained prominence, involving systems learning from datasets. The idea of machines learning automatically became more serious then.
And of course, the recent rise of deep learning is a major breakthrough. Using neural networks, machines now recognize complex patterns. It revolutionized fields like image recognition, voice assistants, and translation. Those assistants on our phones, for example, owe their intelligence largely to deep learning. There are many great explanations about this on YouTube.
Many prominent names have contributed to these developments—Turing, McCarthy, Minsky, Simon, Newell, and others. Later, Geoffrey Hinton, Yann LeCun, Yoshua Bengio are recognized as pioneers of deep learning. Many bright minds stand behind this vast structure.
Now, onto the practical side—coding. When we think of AI, ‘machine learning’ immediately comes to mind. Let me show a simple example. Suppose we have a dataset, and we want to classify data points, like identifying a flower type based on petal length and width. Initially, we can think of complex algorithms.
But in languages like Python, libraries simplify this. For example, Scikit-learn allows us to easily build machine learning models. Sometimes, through trial and error, we can see which algorithm performs best.
Here’s an example. Suppose there’s a classification problem. The first thing might be manual rule creation, but that becomes impossible as data grows. That’s where a simple ‘Logistic Regression’ in Scikit-learn comes in handy. Here’s the code:
import pandas as pd from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from sklearn.metrics import accuracy_score# Let's create a simple dataset (petal length, width) data = {'yaprak_uzunlugu': [5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0, 4.4, 4.9, 5.4, 7.0, 6.4, 6.9, 5.5, 6.5, 5.7, 6.3, 4.9, 6.2], 'yaprak_genisligi': [3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, 3.1, 3.7, 3.2, 3.2, 3.1, 2.3, 2.8, 2.8, 3.3, 2.4, 2.9], 'tur': ['A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B']} df = pd.DataFrame(data)
# Separate data into features (X) and target (y) X = df[[‘yaprak_uzunlugu’, ‘yaprak_genisligi’]] y = df[‘tur’]
# Split data into training and test sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
# Create and train logistic regression model model = LogisticRegression() model.fit(X_train, y_train)
# Predict on test data y_pred = model.predict(X_test)
# Calculate accuracy score accuracy = accuracy_score(y_test, y_pred) print(f”Model Accuracy: {accuracy:.2f}”)
# Make prediction for a new flower new_flower = pd.DataFrame({‘yaprak_uzunlugu’: [6.0], ‘yaprak_genisligi’: [2.8]}) prediction = model.predict(new_flower) print(f”Predicted Type: {prediction[0]}”)
What did we do in this code? We first created a simple dataset, then split it for training and testing. We used a Logistic Regression model and saw the results. The model’s accuracy is pretty good, around 100%. That’s a satisfying result for a basic dataset. It’s like a small initial success that makes you happy. This shows that even with simple libraries, we can work with such algorithms. Of course, this is just the beginning; the depths of AI are much broader.
In conclusion, the short history of AI reflects humankind’s endless curiosity and problem-solving desire. Filled with milestones, key figures, ups and downs, this journey will become even more exciting. We must adapt to this technology or even contribute to the field. Remember, today’s simple ‘if-else’ could be the foundation of a whole new revolution tomorrow.