re: Is it time to short NVDA ?

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Nvidia hasn't made a large advancement in their GPUs in years now.

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Companies are vying for Nvidia's limited supply of GPUs — used to train and build AI products — as the AI sector booms. Now, the chip giant is using its own AI to make its chips faster in what appears to be an effort to keep up with the demand.

Nvidia has developed an AI system known as ChipNeMo that aims to speed up the production of its GPUs.

Designing GPUs can be labor-intensive. A chip typically takes close to 1,000 people to build, and each person needs to understand how different parts of the design process work together, Bryan Catanzaro, Nvidia's vice president of applied deep learning research, told The Wall Street Journal.

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Artificial intelligence (AI) can be categorized into various forms based on its functionality, approach, and application. Here are some common forms of AI:

Narrow AI (Weak AI): Narrow AI refers to AI systems that are designed and trained for specific tasks or domains. These systems excel at performing particular tasks within a limited context but lack general intelligence and cannot adapt to tasks outside their predefined scope. Examples include virtual assistants (e.g., Siri, Alexa), recommendation systems, and image recognition software.

General AI (Strong AI): General AI, also known as strong AI or AGI (Artificial General Intelligence), refers to AI systems with the ability to understand, learn, and apply intelligence across a wide range of tasks and contexts, similar to human intelligence. Achieving general AI is a long-term goal of AI research but has not been realized yet.

Artificial Superintelligence (ASI): Artificial superintelligence refers to AI systems that surpass human intelligence across all domains and activities. This concept is highly speculative and raises significant ethical, societal, and existential questions. Achieving artificial superintelligence remains a subject of debate and speculation within the AI community.

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