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Magic behind ChatGPT
Magic behind ChatGPT

March 13, 2023

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๐‚๐ก๐š๐ญ๐†๐๐“ has been making waves in the tech industry lately and everyone is in awe of how is it able to generate human-like responses to natural language inputs. Letโ€™s learn about it in brief:

1. What is working behind the scenes to create this magic?
Its ๐‹๐‹๐Œ (๐‹๐š๐ซ๐ ๐ž ๐‹๐š๐ง๐ ๐ฎ๐š๐ ๐ž ๐Œ๐จ๐๐ž๐ฅ)
Itโ€™s a ๐๐ž๐ž๐ฉ ๐ฅ๐ž๐š๐ซ๐ง๐ข๐ง๐  ๐š๐ฅ๐ ๐จ๐ซ๐ข๐ญ๐ก๐ฆ that can recognize, summarize, translate, predict, and generate text and other content based on knowledge gained from massive datasets. It is based on the ๐ญ๐ซ๐š๐ง๐ฌ๐Ÿ๐จ๐ซ๐ฆ๐ž๐ซ ๐ฆ๐จ๐๐ž๐ฅ which we will discuss in a later post.

2. But how are ๐‹๐‹๐Œ๐ฌ made?
LLMs are trained on datasets large enough to include nearly everything thatโ€™s on the internet over a large period.
A huge amount of text is fed into the AI algorithm using ๐ฎ๐ง๐ฌ๐ฎ๐ฉ๐ž๐ซ๐ฏ๐ข๐ฌ๐ž๐ ๐ฅ๐ž๐š๐ซ๐ง๐ข๐ง๐ . Through this LLM learns words and the relationships between and concepts behind them.

๐‚๐ก๐š๐ญ๐†๐๐“ is trained on a massive amount of text data, such as books, articles, and web pages.

3. What are LLMs ๐š๐ฉ๐ฉ๐ฅ๐ข๐œ๐š๐ญ๐ข๐จ๐ง๐ฌ?
LLMs can be customized for specific use cases, using techniques like ๐Ÿ๐ข๐ง๐ž-๐ญ๐ฎ๐ง๐ข๐ง๐  or ๐ฉ๐ซ๐จ๐ฆ๐ฉ๐ญ-๐ญ๐ฎ๐ง๐ข๐ง๐ , through which they can be trained for a specific application. Applications are endless. A few applications are:
=> ๐‚๐ฎ๐ฌ๐ญ๐จ๐ฆ๐ž๐ซ ๐ฌ๐ž๐ซ๐ฏ๐ข๐œ๐ž: Create chatbots to handle queries and complaints and reduce workload
=> ๐๐ž๐ซ๐ฌ๐จ๐ง๐š๐ฅ๐ข๐ณ๐š๐ญ๐ข๐จ๐ง: Analyze user behavior and their preferences thus enabling companies to provide personalized recommendations
=> ๐‘๐ž๐ฌ๐ž๐š๐ซ๐œ๐ก: Analyze large datasets of text, enabling researchers to gain insights into language use and cultural trends.
=> ๐‡๐ž๐š๐ฅ๐ญ๐ก๐œ๐š๐ซ๐ž: LLMs can analyze electronic medical records and assist healthcare professionals in making diagnoses and treatment recommendations.
and many moreโ€ฆ

4. Lots of praise. Are there any ๐œ๐ก๐š๐ฅ๐ฅ๐ž๐ง๐ ๐ž๐ฌ with LLMs?
Challenges do exist. A few of them are:
=> ๐๐ข๐š๐ฌ: Biases present in the data they are trained on can result in biased or unfair outputs. This can have negative implications, such as perpetuating stereotypes or discriminating against certain groups.
=> ๐„๐ง๐ž๐ซ๐ ๐ฒ ๐œ๐จ๐ง๐ฌ๐ฎ๐ฆ๐ฉ๐ญ๐ข๐จ๐ง: Training and running them require significant computational resources, which can lead to high energy consumption and environmental impact
=> ๐ƒ๐š๐ญ๐š ๐ช๐ฎ๐š๐ฅ๐ข๐ญ๐ฒ: Massive amount of high-quality training data to function effectively. However, the quality of data can vary widely, which can affect the quality of the modelโ€™s output

๐๐ฎ๐ญ with rapid innovation and new tech being introduced, these challenges shall soon fade away and customers and enterprises of all sizes will reap benefits from this amazing technology.

#technologyย #aiย #chatGPTย #largelanguagemodelsย #learningย #innovationย #tech


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