The Construct: Railtown AI’s Artificial General Intelligence and Large Action Model

Artificial General Intelligence (AGI) in AI’s field focuses on technology’s ability to replicate human intelligence is a theoretical pursuit, the time of its creation and the ability to continuously learn through Large Action Models. The current rise of LAMs complements AGI’s goal because it understands complex human goals that are expressed in natural language. It understands human intention through prompts and the combination of these technologies allows Railtown AI’s Conductor to be trainable, directly learning from human interfaces with complex reasoning through prompts.

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Railtown AI channels true artificial general intelligence because it channels human-like creativity, problem-solving, learning, natural language comprehension, continuous learning, and reasoning. Our all-in-one tool is a game-changer for software development stakeholders. Railtown AI can detect intention through natural language prompts and return information-based and actionable insights taking team efficiency and performance to the next level.

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The conductor is one of Railtown’s pillars that transforms data buried in Jira or Azure We tried using SRED as a use case to explore our AGI platform. The Conductor wasn’t able to return a response initially because the prompt is considered jargon - concepts, and expressions that are used by a particular group or profession and are typically hard to understand. This is a common limitation because AI is usually trained on a limited dataset.

BEFORE

In a similar example from above, after the user trains the Conductor about SRED and uses the same prompt, it now returns a detailed and contextual response from the prompt.

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AGI surpasses the limitation mentioned for AI. Artificial general intelligence allows AI to be a trainable engine to provide better user value and for the impact to be transformative for the user. This potential opens up opportunities for teams to have their work elevated in terms of efficiency and performance.

AFTER

Prompts heavily influence the output a user receives - the more descriptive and contextual your prompt is, the higher the likelihood that it will return the response you’re looking for. Ask the right questions to get the right answers, they say.

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