Eski Kafayla Yeni Teknoloji | Ömer Çolakoğlu | TEDxAselsan MTAL Youth

Quick Overview

Ömer Çolakoğlu demonstrates how modern AI technologies, particularly Large Language Models (LLMs) orchestrated via protocols like MCP, enable non-smart systems to become intelligent by allowing them to interact with data sources and perform complex tasks like controlling a drone or simulating warehouse logistics, thereby bridging the gap between the 'old mind' and new technology.

Key Points: The presentation focuses on merging 'old minds' (traditional systems/logic) with 'new technology' (AI/LLMs) to create intelligent systems. LLM orchestration via protocols like MCP allows AI agents to interact with databases and perform actions like controlling a drone (demonstrated by the speaker holding a drone). A simulation environment (Unity Engine) was used to showcase energy savings (254.4601 KWH saved, 87.52% saved percentage) by dynamically controlling street lighting based on traffic conditions. The speaker highlights that many tasks previously considered difficult or requiring human intuition (like complex warehouse logistics or drone control) are now achievable through AI. The concept of building 'smart' systems from 'non-smart' components is central, exemplified by teaching a non-smart robot (like the one shown) basic conversational skills and actions. The speaker thanks the audience, including the students and organizers from TEDxYouthAselsanMTAL.

Context: The presentation by Ömer Çolakoğlu at TEDxYouthAselsan MTAL Youth explores the integration of artificial intelligence, specifically Large Language Models (LLMs) and orchestration protocols (MCP), into existing or 'old' technological systems. The speaker uses demonstrations involving a drone and a simulated smart city environment to illustrate how these new AI capabilities can make traditionally 'dumb' systems intelligent, capable of complex reasoning, interaction, and task execution.

Detailed Analysis

Ömer Çolakoğlu argues that the current era, marked by the advent of generative AI like ChatGPT, presents massive opportunities for integrating intelligence into existing infrastructure, contrasting this with the past where such capabilities were science fiction. He emphasizes that the key is not just possessing the technology but orchestrating it using protocols like Model Context Protocol (MCP). He demonstrated this by commanding a drone (held in his hand) using natural language via an agent built on this architecture. A key demonstration involved a simulation environment (likely Unity, visible on screen later) where an AI agent controlled street lights based on traffic, resulting in significant energy savings (254.4601 KWH, 87.52% saved percentage). He also showed how the agent could query a local Walmart database and interact with a physical robot (shown on screen) to perform actions like learning Kung Fu or suggesting gifts. The core message is that LLMs act as the 'new language' for AI systems, enabling even non-smart components to become context-aware and collaborative, thus making complex automation simple to implement without deep technical coding knowledge for every specific task.