🤖 Hello, this is CLEVI.
We attended the “2025 JBNU Research Fair Week” event hosted by Jeonbuk National University from 20 to 22 January.
This meaningful event showcased Jeonbuk National University’s outstanding research achievements, presented the direction of future technologies linked to industrial and national strategies, and provided a platform for exchange among industry, academia, research institutes and government.
In particular, CLEVI’s CEO, Hwan-ho Lee, was invited as a VIP speaker at the opening ceremony held on the 20th! 👏
Let’s relive the atmosphere of that day!
This is CEO Hwan-ho Lee preparing for his presentation before the event began. You can see him thinking about how to make the presentation more informative and easy to understand.
The event began with the opening ceremony.
The event opened impressively with a live art performance completed on the spot by a student from Jeonbuk National University.
After the first keynote speech, CLEVI’s CEO Hwan-ho Lee delivered the second keynote speech on the topic “Why Does AI Need a Physical Medium?” Shall we take a brief look?
How exactly does AI understand the world,
create sentences, and even take action?
Let’s take a look inside the ‘brain’ of large language models (LLMs), such as ChatGPT and Claude, which we use every day, to understand how they understand and generate sentences.
- Assigning name tags (Token ID): AI first assigns every word a unique number, such as ‘5’ or ‘8’, to manage it.
- Pinning an address on a map (Vector): However, a simple number cannot convey meaning, so each word is given a ‘detailed address (coordinate)’ made up of hundreds of numbers.
- Finding neighbours: Words with similar meanings (for example, ‘the’ and ‘a’) gather very close to one another on this map, like neighbours.
So how does AI predict the next word?
Now that we understand the basic data transformation, let’s look at the entire process of how these numbers become an actual sentence. Let’s assume we have an input such as "Paris is the city..."
Tokenizer (breaking into pieces): As seen earlier, the sentences we enter are converted into numbers (Token IDs) through the tokenizer. "Paris" becomes 1652, "is" becomes 5... and so on.
LLM (a giant calculator): These bundles of numbers enter a giant calculator called an LLM (the Bloom model shown in the image). Here, it understands the context through the 'vector' operations we learned earlier. It realises, 'Ah, we’re talking about the city of Paris right now!'
Logits & Softmax (probability calculation): After completing its calculations, the AI assigns scores to tens of thousands of word candidates that could come next. It then passes them through a filter called 'softmax' to convert them into **'probabilities'** that are easier for us to understand.
Final selection: Look at the graph on the right.
- of → 33.7%
- that → 15.3%
- where → 9.4%
Ultimately, the AI selects **'of'**, which has the highest probability, and continues completing the sentence "Paris is the city of...". It repeats this process tens of thousands of times to create the long answers we see.
In conclusion, for AI, remembering a word does not mean memorising the letters. It is like remembering the 'address' of where that word lives on a vast map of meaning.
LLMs can be expanded into vision, robotics and behavioural learning.
AI that understands meaning, sees and understands, and takes action.
Giving AI arms and legs means expanding intelligence from language to perception, and further to action.
AI is no longer just an idea or a matter of imagination; it has entered the stage of implementation and reality.
CLEVI’s next step
CLEVI plans to continue expanding various opportunities, including on- and off-campus workshops, hands-on projects and experience booths, so that more people can directly experience how AI technology works in the real world.
Just as this lecture at Jeonbuk National University became a valuable bridge connecting the students and us, we will continue to serve as a bridge between technologists and users, and between academia and industry.