Data Privacy vs Cheap value vs Compromises

How your data is bought at Low costs and leveraged to build ML Models !!!

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LLMs - Production vs Prototype

 #LLM is #Cool! #PromptEngineering is great. Demonstrating a prototype is beneficial, but for a production application, preparing data and consistently getting Top K results are essential. The ability to handle diverse queries, manage large datasets vs. remembering minimal key data are significant trade-offs and design choices that come with domain knowledge. Building a working solution is easy, but achieving predictable, low-latency, and consistent behavior requires constant iteration and evolution. #Perspectives and #AppliedLearning are key. #AI #MachineLearning #DataScience #Prototypes #ProductionReady #DataPrep #Latency #Consistency #Iteration #

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Why big companies failed to build LLMs

 

How Alexa dropped the ball on being the top conversational system on the planet
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A few weeks ago OpenAI released GPT-4o ushering in a new standard for multimodal, conversational experiences with sophisticated reasoning capabilities.

Several days later, my good friends at PolyAI…

— Mihail Eric (@mihail_eric) June 11, 2024

• Data was poorly annotated. Documentation was either nonexistent or stale.
• Experiments had to be run in resource-limited compute environments. 
• That meant for months our internal annotation team had been mislabeling thousands of data points every single day
• antagonistic mid-managers that had little interest in collaborating for the greater good
• Duplicated efforts due to no shared common ground 

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LLM Perspectives - Familiarity vs Randomness

 

There's a big difference between solving a problem from first principles vs applying a solution template you previously memorized. It's like the difference between a senior software engineer and a script kiddie that can't code.

A script kiddie that has a gigantic bank of scripts… https://t.co/l2W8FUnwj8

— François Chollet (@fchollet) June 10, 2024

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