Decoding AI jargons with Chai

Well, there are a lot of AI jargons that we need to explore. You know the best way to understand the jargons ?

By connecting them with real life scenarios!

Artificial Intelligence (AI) is transforming the world, but the technical jargon can feel overwhelming. Let’s simplify some key AI terms by connecting them to real-life scenarios, all while sipping a cup of chai.

Now, to learn AI jargons - Let’s think of our favourite drink ‘chai’ as our scenario.

1. Transformer

A Transformer is like the chaiwala who remembers your chai preferences. It’s an AI model that understands relationships between words in a sentence, helping it grasp context and meaning. For example, when you type "best chai near me" on Google, a Transformer helps interpret your query and provide relevant results.

2. Encoder and Decoder

Think of the Encoder as the friend who listens carefully to your chai order and the Decoder as the one who prepares it exactly how you like it. In AI, the encoder processes input (like text or speech), and the decoder generates meaningful output, such as translating "chai" into "tea" for an English speaker.

3. Vectors

Imagine describing chai using numbers - sweetness (3/5), spiciness (4/5), and temperature (5/5). These numbers form a vector, which represents data in AI. Vectors help machines understand relationships, like how "chai" is closer to "tea" than "coffee" in meaning.

4. Embeddings

Embeddings are like chai recipes written in a universal language. They convert words into mathematical forms that capture their essence. For instance, "chai" and "tea" may have similar embeddings because they’re often used interchangeably.

5. Positional Encoding

When making chai, the order matters—boil water first, then add tea leaves and milk. Similarly, Positional Encoding helps AI understand word order in a sentence so it knows whether “The cat sat on the mat” or “The mat sat on the cat” makes sense.

6. Semantic Meaning

This is about understanding context. If someone says, “I want chai,” AI uses semantic meaning to infer they’re asking for tea, not just listing random words. It’s what makes chatbots sound more human when helping you order online. another example is ‘The river bank’ vs ‘HDFC Bank’ - One is financial Bank which deals with finances and another is a bank of the river - where we go for picnic or fishing.

7. Self-Attention

Imagine you’re making chai for multiple people with different preferences. You focus on each person’s request while keeping track of others'. Self-Attention allows AI to weigh the importance of each word in a sentence to understand its overall meaning better.

8. Softmax

When deciding which chai flavor is most popular—masala, ginger, or cardamom—you might assign probabilities to each choice (e.g., 70% masala). Softmax does this in AI by converting raw scores into probabilities that sum up to 1.

9. Multi-Head Attention

If you’re multitasking - making chai while chatting and taking orders—you focus on different tasks simultaneously. Similarly, Multi-Head Attention lets AI analyze multiple aspects of a sentence at once for deeper understanding.

10. Temperature

In chai-making, adjusting the flame controls how strong or mild it tastes. In AI, temperature controls randomness in text generation - low values make responses predictable, while high values make them creative.

11. Knowledge Cutoff

Imagine your favorite chaiwala moved away last year; your knowledge about his recipes stops there. Similarly, an AI model’s knowledge cutoff is the point after which it doesn’t know new information.

12. Tokenization

Breaking down “masala chai” into “masala” and “chai” is like tokenization. It splits text into smaller parts (tokens) so machines can process them easily.

13. Vocab Size

This refers to how many words or tokens an AI model knows - like how many ingredients you have for making different types of chai.

By understanding these jargons with relatable examples, we hope you feel more comfortable navigating the world of AI - one sip at a time!

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