Video Summary - Felsefe ve Yapay Zekâ - 3 | Prof. Dr. Enis Doko

Main ideas & lessons

AI philosophy is becoming mainstream in universities worldwide
- Many major philosophy departments began offering courses on AI ethics and on the philosophy of AI even before AI-related degree programs expanded broadly.
- The emphasis includes both:
  - Ethics of AI: ethical boundaries, what AI ethics should be, and which AI models should be used in which contexts.
  - Philosophical/ontological questions about AI: e.g., whether AI can have a mind, whether it could have free will, and how that would relate to the human mind (similarities and differences).
Philosophy matters most when technology causes large societal change
- Philosophy is framed as a practice of stepping back and reflecting on fundamental issues—especially when established habits are disrupted.
- AI is described as potentially a revolution bigger than the internet, and possibly comparable to or larger than the industrial revolution, creating urgency for philosophical reflection.
Ethical understanding of AI is widely underdeveloped
- There is little practical experience with the ethical consequences of AI, so society needs experts trained to think this through.
- As a result, undergraduate and graduate programs are being created to train specialists in ethics and AI-related philosophy—particularly noted in England.

Create interdisciplinary undergraduate programs, such as:
- Computer Science & Artificial Intelligence
- Computer Science & Philosophy
Teach students both technical AI foundations and philosophical tools, aiming to produce graduates who can operate across both domains.
- Philosophy curriculum components
  - Philosophical thinking skills
  - Philosophical writing
  - Intellectual history
  - Argumentation
  - Strong training in logic
- AI/computing curriculum components
  - Very strong AI training (including how AI models are produced)
  - Understanding how training enables models to generate outputs
  - Close-to-engineering-level insight into how computers/AI systems work (described as similar to a minor in engineering)
Program goal: produce experts knowledgeable in both fields, even if they don’t become professional philosophers.

Master’s programs focus on bridging ethics and technical competence, such as:
- Algorithms, Data, and Ethics (example institutions: Cambridge, Edinburgh)
Primary purpose: give engineers a foundation in:
- Ethical thinking
- The methods used in ethical reasoning
- How to think ethically about technical consequences

Traditional engineering often allows ethics to be deferred (e.g., “let governments and philosophers think about it”).
In contrast, AI today is becoming general-purpose and widely used—not confined to a special domain.
Because AI is accessible to “everyone” (e.g., chat-based use of AI algorithms), engineers cannot rely on others to handle ethics later—so serious ethical training is needed.

The number of such programs is expected to increase further, driven by:
- Market demand for AI/ethics expertise
- The need for specialists and writers/contributors to the field

AI is not only an ethics topic—it is conceptually rooted in philosophy
- The origin of AI is described as stemming from the idea that human thought can be expressed artificially through algorithms.
- Key intellectual foundations linked to:
  - Aristotle: logic as formal methods for thinking
  - Al-Khwarizmi: algorithmic thinking (noted through algebra)
  - Turing: “Can machines think?” and the famous paper “Mind”/“Mind”, referenced as appearing in a philosophy journal
Philosophy topics that influence AI research
- Logic: formalizing thought, testing correctness
- Justification: when ideas are reasonable vs. not
- Language and meaning
  - Theories of meaning and how language acquires meaning
  - Philosophy of language influencing AI approaches
- The speaker argues that developing new AI models requires philosophical ways of thinking, especially about language structure and the nature of thinking.

Programs connecting AI, philosophy, and cognitive science are described as combining:
- Cognitive science, including psychology and neurobiology
Rationale:
- One approach to creating AI is to understand the human mind and replicate aspects of it in machines.
This strengthens the relationship between philosophy and AI (and computer science more broadly).

The speaker claims that in many countries (including Türkiye), the gap between philosophy and technical sciences remains wide.
Philosophy is described as frequently miscategorized (e.g., incorrectly called a “social science” rather than treated as a distinct humanities/philosophical discipline).
The speaker argues the separation should be reduced (“rapprochement re-established”) to enable further progress—hence the creation and growth of these interdisciplinary programs.

Prof. Dr. Enis Doko (speaker throughout)
Aristotle
Al-Khwarizmi (al-Khwārizmī)
Alan Turing
Unnamed philosophy/logic and philosophy-of-language works (referenced generally, not cited by specific titles beyond Turing’s paper)