Burak Duman

AI 104: The Perfect Storm – Why AI Exploded, and What Else is Under the Hood

• Artificial Intelligence
• Feb 18
• 3

In our previous posts, we laid the groundwork: clarifying the Matryoshka doll relationship between AI, ML, and DL, and then dissecting the academic definition of 'intelligence' from a rational agent perspective. Now, it's time to address a critical question: If the idea of AI has been around for decades, why has it suddenly dominated headlines and reshaped industries in the last 10-15 years?

This final part of our Artificial Intelligence Series will explore the confluence of factors that ignited the current AI revolution and then broaden our perspective to understand that AI is far more diverse than just Machine Learning, encompassing a rich tapestry of approaches that continue to be relevant.

Section 1: The Perfect Storm – Why AI is Booming Now The history of AI is marked by "winters" – periods of reduced funding and interest due to unfulfilled promises. What ended the last AI winter and fueled the unprecedented surge we're witnessing today? The answer lies in a powerful synergy of three key advancements:

1) Big Data: The Fuel for Learning Algorithms
Modern Machine Learning algorithms are incredibly data-hungry. Without vast amounts of relevant data, they simply cannot learn effectively. The last two decades have seen an exponential explosion in data generation:
- The Internet and Social Media: User-generated content, web pages, and interaction logs provide immense textual and visual data.
- Smartphones and Mobile Devices: Every photo, video, text message, and location datum contributes to the global data pool.
- Internet of Things (IoT): Billions of connected devices—from smart home appliances to industrial sensors—constantly stream environmental, operational, and behavioral data.
This deluge of data, often unstructured and diverse, became the indispensable fuel that allowed ML and DL models to move from theoretical curiosities to practical powerhouses.

2) Computational Power: The Engine of Deep Learning (GPUs)
Deep Learning models, with their complex, multi-layered neural networks, require immense computational resources. Training these models involves billions of mathematical operations, primarily matrix multiplications and additions.
Historically, general-purpose Central Processing Units (CPUs) were too slow for this. The game-changer was the realization that Graphics Processing Units (GPUs), originally designed to render complex 3D graphics for video games, were exceptionally good at performing these exact types of parallel computations simultaneously.
The adoption of GPUs in AI research significantly reduced training times from months or weeks to days or even hours, enabling rapid experimentation and the development of much larger, more sophisticated models. This hardware advancement was pivotal in making Deep Learning feasible at scale.

3) Algorithmic Advancements & Open-Source Tools: The Blueprint and Infrastructure
While some core neural network ideas have existed for decades, significant algorithmic breakthroughs and optimizations were crucial. Architectures like:
- Convolutional Neural Networks (CNNs): Revolutionized image processing (e.g., AlexNet's 2012 ImageNet victory).
- Recurrent Neural Networks (RNNs) & LSTMs: Essential for sequential data like natural language and time series.
- Transformer Architecture: The breakthrough behind modern large language models (LLMs) like GPT and BERT, allowing for highly efficient parallel processing of sequential data.
Coupled with these, the rise of open-source software libraries and frameworks (TensorFlow, PyTorch, Keras, Scikit-learn) democratized AI development. Researchers and developers worldwide could leverage state-of-the-art tools without rebuilding everything from scratch, accelerating innovation exponentially.
These three factors—data, hardware, and algorithms/tools—formed a virtuous cycle, each feeding and accelerating the others, leading to the current AI renaissance.

Section 2: Beyond the Hype – The Broader Landscape of AI While Machine Learning and Deep Learning dominate current discourse, it's crucial to remember they are powerful subsets within the vast field of Artificial Intelligence. Many other significant and historically important branches of AI continue to solve complex problems where ML might not be the most appropriate or efficient solution. Understanding these broadens our perspective on what "intelligence" in a machine can truly mean.

1) Symbolic AI (GOFAI - Good Old-Fashioned AI): This approach, dominant in the 1970s and 80s, focuses on explicit knowledge representation and logical reasoning. Instead of learning from data, systems are programmed with rules, facts, and logical inference mechanisms.
- Expert Systems: These were prominent, encoding human expert knowledge (e.g., medical diagnosis rules, financial advice) into IF-THEN statements. They provided clear, explainable decisions.
- Knowledge Representation: Logic-based systems used formal languages (like First-Order Logic) to represent knowledge and deduce new facts.
Why it's still relevant: For tasks requiring strict logical consistency, traceability, and explainability (e.g., legal reasoning, formal verification), symbolic AI still offers advantages. Modern research in Neuro-Symbolic AI attempts to combine its reasoning power with ML's pattern recognition.

2) Search and Planning Algorithms: These are foundational to AI, particularly for problem-solving and decision-making in discrete spaces.
- Pathfinding Algorithms (e.g., A* search): Used in GPS, video games (NPC movement), and logistics to find the most efficient path between two points.
- Game Playing (e.g., Minimax, Alpha-Beta Pruning): Algorithms that explore future moves in games like chess to find the optimal strategy. AlphaGo, while using Deep Learning, still heavily relies on tree search.
- Automated Planning: Creating a sequence of actions to achieve a specific goal (e.g., a robot assembling a product, an AI controlling spacecraft maneuvers).
Why it's still relevant: These algorithms are essential for structured problem-solving where objectives are clear and actions are discrete.

3) Evolutionary Computation: Inspired by biological evolution and natural selection, these algorithms search for optimal solutions by iteratively evolving a population of candidates.
- Genetic Algorithms: Mimic processes like mutation, crossover, and selection to find solutions to optimization and search problems.
- Genetic Programming: Extends genetic algorithms to evolve computer programs themselves.
Why it's still relevant: Excellent for complex optimization problems, design automation, and situations where the search space is too vast for exhaustive search or gradient-based methods.

4) Fuzzy Logic: Unlike classical Boolean logic (true/false, 0/1), fuzzy logic deals with degrees of truth, allowing for concepts like "somewhat hot" or "partially true."
Why it's still relevant: Ideal for control systems that need to make decisions based on imprecise or subjective inputs, often found in consumer electronics (washing machines, air conditioners) and industrial control.

Conclusion: The End of the AI Series, The Beginning of Your Journey
This exploration has revealed that the current AI boom is not merely a sudden stroke of genius but the culmination of decades of research converging with unprecedented data and computational power. It also reminds us that "AI" is a rich and diverse academic field, with many powerful paradigms beyond just the currently dominant Machine Learning approaches. Each method has its domain of applicability and continues to contribute to the grand goal of artificial intelligence.

We've covered the foundational concepts of Artificial Intelligence, from its core definitions and philosophical underpinnings to the technological forces driving its current surge, and the broader landscape of its diverse subfields. My hope is that this series has provided you with a robust, academic, yet engaging foundation to understand AI more deeply.

This concludes our dedicated "Artificial Intelligence Series." Thank you for joining me on this journey!

Announcement: I plan to create an external Blogger site and write my blog posts there. Of course, the link will be in my personal site's menu. Since I find the site's built-in Blogger clunky and cumbersome, I'll be switching to Blogger.

AI 103: The Academic Corner: What is "Intelligence"?

• Artificial Intelligence
• Feb 07
• 1

Pop culture equates "intelligence" with consciousness, emotions, and human-like thought. However, the academic definition in the field of AI is far more pragmatic and, frankly, "boring."

Turing's Imitation Game: In his seminal 1950 paper, "Computing Machinery and Intelligence," Alan Turing asked the famous question: "Can machines think?" To escape this philosophical swamp, he proposed the "Imitation Game" (now known as the Turing Test): A human interrogator communicates via text with both a human and a machine, without seeing either. If the interrogator cannot reliably tell which is the machine, the machine is said to have passed the test.

The Turing Test isn't concerned with "consciousness" or "true understanding"; it is concerned only with convincing imitation.

The Modern Definition: The Rational Agent

The definition that forms the foundation of modern AI textbooks (most notably Stuart Russell and Peter Norvig's Artificial Intelligence: A Modern Approach) is much clearer: AI is the study and design of rational agents.

What does this mean?

- An Agent: Is anything that perceives its environment through sensors (a camera, microphone, keyboard) and acts upon that environment through actuators (a robotic arm, a screen, a motor). (Even a thermostat is a simple agent).

- Rational: Means "doing the right thing." More technically, it means selecting an action that is expected to maximize a performance measure, given the available information.

Consider an AI that plays chess:

- Environment: The 8x8 chessboard.

- Sensors: The code that reads the current state of the board.

- Actuators: The code that outputs the next move.

- Performance Measure: The probability of winning the game.

Whether this AI is "conscious," "enjoys chess," or "thinks" is academically irrelevant. What matters is whether it successfully computes the function f(board_state) → move that maximizes its chance of winning.

Strong AI vs. Weak AI (AGI vs. ANI)
This distinction is also critical:

Weak AI (ANI - Artificial Narrow Intelligence): This is AI designed for a specific, narrow task. It only plays chess, it only filters spam, it only recognizes faces. ALL AI systems you use today are Weak AI.

Strong AI (AGI - Artificial General Intelligence): This is a hypothetical type of AI that, like a human, can learn and apply its intelligence to any intellectual task. This is the AI of science fiction (The Terminator, HAL 9000). We are not there yet.

AI 102: The Matryoshka Dolls (AI vs. ML vs. DL)

• Artificial Intelligence
• Feb 04
• 3

Let's begin with the most fundamental and crucial distinction. These three terms are often used interchangeably, but in reality, they are subsets of one another. The easiest way to visualize this is with a set of Matryoshka (Russian) dolls.

1) Artificial Intelligence (AI): This is the largest doll; the all-encompassing umbrella term for the entire field. Coined in the 1950s, AI refers to the broad, philosophical goal of "machines that can mimic human intelligence." Any system that demonstrates cognitive abilities like seeing, speaking, decision-making, or learning falls under the AI umbrella. If a program exhibits any intelligent behavior, it's AI.

2) Machine Learning (ML): This is the middle doll; the most popular and powerful subfield of AI. ML is based on the idea of "giving machines the ability to learn without being explicitly programmed." In traditional programming, a developer manually writes rules (e.g., IF-THEN statements). In Machine Learning, we instead feed the system data and let the algorithm discover the rules (patterns) on its own.

3) Deep Learning (DL): This is the innermost doll; it is a subfield of Machine Learning. Deep Learning uses complex, multi-layered structures inspired by the human brain (neurons) called "Artificial Neural Networks" (ANNs). The word "deep" refers to the high number of layers in this neural network. Nearly all of the most impressive AI achievements today (image recognition, natural language processing, etc.) are powered by Deep Learning.

In short: All Deep Learning is Machine Learning, and all Machine Learning is AI. But not all AI is ML (for example, older rule-based expert systems).

AI 101: From the Dream of Thinking Machines to Academic Reality

• Artificial Intelligence
• Feb 03
• 3

If you're here, you've likely heard the term "Artificial Intelligence" (AI) everywhere—at tech conferences, in news headlines, or just in a coffee chat. From ChatGPT to DALL-E, from autonomous vehicles to medical diagnostics, AI has evolved from a science fiction concept into an integral part of our daily lives.

But this popularity has brought with it a significant amount of conceptual confusion. What is Artificial Intelligence? Is it the same as "Machine Learning" (ML)? And where does "Deep Learning" (DL) fit into all of this?

That is the precise goal of this blog series. We won't just scratch the surface; we will dive deep. If you know nothing about AI, this series will give you a solid foundation. If you are already knowledgeable, I aim to push you beyond your current understanding, right down to the mathematics and philosophy of the field.

Buckle up. We're starting an engaging journey into the academic world of AI.