What is Artificial Intelligence?
Artificial intelligence (AI) is a set of disciplines that enable computer systems to perform tasks that normally require human intelligence - such as learning, reasoning, problem solving, language understanding and visual perception. The English equivalent of the term, Artificial Intelligence (AI), was first introduced to academic literature by John McCarthy in 1956.
"Artificial intelligence is the branch of science and engineering that enables machines to imitate human intelligence."— John McCarthy, Stanford University, 1956
Brief History: From 1950 Present
The historical development of artificial intelligence has been shaped by cycles of optimism and stagnation referred to as “winters” and “summers.” These fluctuations are both inspiration and lessons for today's researchers.
1950 Turing Test
Alan Turing questioned whether a machine could think or not and defined the Turing Test in his article "Calculating Machines and Intelligence".
1956 Dartmouth Conference - Birth of Artificial Intelligence
John McCarthy This conference, held under his leadership, defined artificial intelligence as an independent academic field.
1970–80First AI Winter
AI research slowed down due to failure to meet expectations, funding cuts, and disappointment. Expert systems achieved limited success.
1997 Deep Blue - Defeats the Chess Champion
IBM's Deep Blue system made a huge public impact by defeating world chess champion Garry Kasparov.
2012 Deep Learning Revolution
AlexNet's victory in the ImageNet competition ushered in the era of deep learning. started it. The computing power of GPUs played a decisive role.
2017 Transformer Architecture
Google's "Attention Is All You Need" article presented the Transformer architecture, which forms the basis of modern large language models (GPT, BERT, etc.).
2022–presentThe Age of Generative AI
ChatGPT, GPT-4, Claude, Gemini and similar models brought artificial intelligence into daily business life. Sectoral transformation has accelerated.
Types of Artificial Intelligence
Classification by Capacity
Narrow Artificial Intelligence (ANI - Artificial Narrow Intelligence):
General Artificial Intelligence (AGI - Artificial General Intelligence):
Super Artificial Intelligence (ASI - Artificial Super Intelligence): Human intelligence It represents a completely speculative future scenario that occurs in all fields.
Classification by Approach
Machine Learning (ML): A set of methods that enable systems to learn from data.
Deep Learning (DL) is a sub-branch of ML that uses multi-layered artificial neural networks. Generative AI (GenAI) covers the latest generation systems that build on these foundations and can produce text, images, code and sound.
Basic Tools and Alternatives for Expertise
Gaining competence in the field of artificial intelligence starts with choosing the right tool ecosystem. The table below summarizes the basic tools, their primary roles, and common alternatives.
Programming Language
Python
The de facto standard of the AI ecosystem. TensorFlow, PyTorch, scikit-learn and Hugging Face libraries are built on Python. It minimizes the learning curve with its simplicity of syntax and extensive community support.
Alternatives
R / Julia / Scala
R: Powerful for statistical analysis and academic research. Julia: Preferred when high-performance numerical computing is required. Scala: Common in big data pipelines with Apache Spark.
Code Editor / IDE
Visual Studio Code
Microsoft's open source editor. It is the industry standard in AI development processes with its Python extension, Jupyter Notebook integration, Git connection and GitHub Copilot support.
Alternatives
PyCharm / Cursor / Neovim
PyCharm (JetBrains): Advanced IDE specific to Python; It is preferred for corporate teams. Cursor: AI-powered code editor; GitHub Copilot competitor. Neovim: For advanced users with a terminal preference.
Version Control
Git + GitHub
Alternatives
GitLab / Bitbucket / DVC
GitLab: For institutions that prefer self-hosted. Bitbucket: It works integrated with the Atlassian ecosystem (Jira, Confluence). DVC (Data Version Control): Open source tool specialized for large data sets and model versioning.
Notebook Environment
Jupyter Notebook / Lab
Interactive code environment for data exploration, prototyping and results presentation. Cell-based runtime structure is ideal for documenting AI experiments and creating reproducible analyses.
Alternatives
Google Colab / Kaggle / Deepnote
Google Colab: Free GPU/TPU access; Perfect for rapid prototyping. Kaggle Kernels: Integrated with the competitive data science community. Deepnote: Cloud notebook environment focused on team collaboration.
AI / ML Framework
PyTorch
The dynamic computational graph structure developed by Meta is the most common choice for research and production development. It has become a standard in NLP projects with its deep integration with Hugging Face libraries.
Alternatives
TensorFlow / JAX / scikit-learn
TensorFlow/Keras: Google supported, powerful for production deployment. JAX: Google's next-generation framework for high-performance research. scikit-learn: The industry standard for classic ML algorithms.
Model Management & MLOps
MLflow
Alternatives
Weights & Biases / Neptune / Vertex AI
W&B (Wandb): Real-time experiment visualization; Popular among researchers. Neptune: Comprehensive for enterprise MLOps. Vertex AI (Google): Fully managed cloud ML platform.
// Getting Started Toolkit Recommendation
Python 3.11+ → VS Code + Python Ext. → Git + GitHub
Jupyter Lab → scikit-learn → PyTorch → Hugging Face
Google Colab (free starter for GPU access)
Dictionary of Basic Artificial Intelligence Terms
The following terms describe the most frequently encountered concepts in the artificial intelligence literature, with their English originals and Turkish equivalents. explains.
Algorithm(Algorithm)
A set of step-by-step instructions designed to solve a specific problem. It is the general name of the mathematical structures that manage the learning process of AI models.
Machine Learning (ML) (Machine Learning)
A sub-branch of artificial intelligence that enables systems to learn from data without explicit programming and improve their performance with experience.
Deep Learning (DL) (Deep Learning)
Machine learning technique using multi-layer artificial neural networks. It exhibits superior performance in complex tasks such as image recognition, sound processing and natural language understanding.
Neural Network(Artificial Neural Network)
A computational model consisting of interconnected processing units inspired by the neuron structure of the human brain. It is the basic building block of deep learning.
Large Language Model (LLM)(Large Language Model)
Transformer-based model trained with billions of parameters and huge text data, capable of natural language generation and understanding. GPT-4, Claude and Gemini are examples of this category.
Prompt Engineering(Command Engineering)
The discipline of systematically designing and optimizing input text (prompt) to obtain the desired output from large language models. It is a critical competency in enterprise AI applications.
Token(Token)
The smallest meaningful unit in which language models process text. It corresponds to approximately ¾ word. API costs and context window limits are calculated by the number of tokens.
Roadmap to Becoming an Expert
1
Mathematical Foundation: Linear Algebra, Probability, Calculus
Concepts such as gradient descent, matrix multiplication and probability distributions are indispensable for a deep understanding of AI models. 3Blue1Brown and Khan Academy are good starting resources.
2
Python Programming Proficiency
Object-oriented programming, data structures and NumPy/Pandas libraries. "Python for Everybody" (Coursera) or "CS50P" (Harvard) are among the recommended courses.
3
Classical Machine Learning Algorithms
Linear regression, decision trees, random forests, SVM and k-means clustering. Kaggle competitions are the most effective way to bring theoretical knowledge into practice.
4
Deep Learning and PyTorch
CNN, RNN, LSTM and Transformer architectures. fast.ai courses are practically focused, while Andrew Ng's Deep Learning Specialization (Coursera) is recommended for theoretical depth.
5
Big Language Models and Generative AI
Hugging Face libraries, OpenAI and Anthropic APIs, RAG architecture and prompt engineering. LangChain and LlamaIndex are the key tools of this phase.
6
MLOps and Production Deployment
Docker, Kubernetes, MLflow and cloud platforms (AWS SageMaker, Google Vertex AI, Azure ML). This stage is the critical bridge that turns AI projects into real business value.
7
Domain Expertise and Ethics
Acquiring domain knowledge about the selected sector (finance, health, retail, etc.) and internalizing responsible AI principles. It is the last layer that transforms technical competence into business value.
Conclusion
Artificial intelligence has long ceased to be a technical field that only data scientists or software engineers are interested in. As of today, it has turned into a discipline that directly affects every corporate function such as strategy, operations, marketing and customer experience.
Artificial intelligence will not put you out of business. But someone who uses artificial intelligence can.— Frequently quoted practical warning in the industry
Metin Tiryaki · metin@metintiryaki.com
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