[AI Terminology Illustrated Series] #4 5 AI Terms to Understand the Inner Workings of AI | AI x Information Edition

Hello everyone. This is Midnight Latte.

This time, we are covering AI x Information.

In this installment, we will dive a bit deeper and explain five terms related to the "inner workings and behind-the-scenes" of AI.

While these might seem relevant only to engineers, knowing them will help you better understand AI news and how services work.

1. LangChain

This is a toolkit (framework) for developers to build apps using AI.

On its own, an AI like ChatGPT can only "converse and answer." However, in real-world services, you often want the AI to search through documents, perform calculations, or integrate with external services.

LangChain is a tool that bundles the wiring that connects "AI" to "various functions." RAG (a mechanism for searching documents before answering) also becomes much easier to build using LangChain.

It is easy to think of it as a "wiring kit for connecting AI as the brain to external tools as the hands and feet."

2. NPU

Short for "Neural Processing Unit." It is a chip specialized for AI (specifically neural network) calculations.

If the CPU is the brain of a computer, and the GPU is what powers games and video, think of the NPU as an added "brain specialized for AI processing."

AI calculations have clear strengths and weaknesses. The NPU's strength lies in its ability to perform the massive parallel calculations that AI excels at, while consuming less power.

When recent smartphones or PCs are labeled as "AI-ready," it is often because they contain this NPU. The NPU is what supports the era where AI runs on your local device without needing to send data to the cloud.

3. Annotation

This is the process of attaching correct labels to data to teach the AI what it is.

AI learns from massive amounts of data, but just showing it photos isn't enough for it to understand "this is a cat." Humans need to label each image, saying "this is a cat" or "this is a traffic light."

This steady, painstaking work is annotation. The reason self-driving AI can recognize pedestrians and medical AI can detect lesions is because of the vast amount of annotation happening behind the scenes.

It may not be flashy, but it is actually a very important process, as it is said that AI accuracy is determined by the quality of the data.

4. Transformer

This is the name of an AI architecture that appeared in 2017.

The final 'T' in ChatGPT's 'GPT' stands for this Transformer. It is no exaggeration to say that the current generative AI boom is almost entirely built upon this mechanism.

What makes Transformer amazing is its ability to look at the entire text and focus on 'which words are important'. It became possible to determine what 'it' refers to and which parts of the context are important by judging from the entire sentence.

It has nothing to do with robot anime, but in the sense that it has completely changed the world of AI, it is truly a revolutionary existence. Think of it as the heart of the AI you are interacting with now.

5. Open Source AI

This refers to AI whose blueprints and core (models) are made public for free, allowing anyone to use or modify them.

Unlike AI with closed internals like ChatGPT, the internals of models like Meta's Llama are made public. Developers around the world can freely use, improve, and train them for their own purposes.

The advantage is that because it can be run on your own hardware (such as an in-house server), you don't have to send confidential data outside. Another benefit is that it is easier to keep costs down.

On the other hand, knowledge is required to master it, and the risks of misuse are also being debated. The debate over which is better—'AI that is made public for everyone to nurture' or 'AI that is enclosed and refined'—is currently ongoing all over the world.

Summary

✅ LangChain
　　A toolkit for developers that connects AI with external tools

✅ NPU
　　A specialized chip designed for AI processing

✅ Annotation
　　The task of attaching correct labels to training data

✅ Transformer
　　The core mechanism supporting current generative AI

✅ Open Source AI
　　AI whose internals are public, allowing anyone to use or nurture it

Thank you for reading. See you in the next article.

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