How close are we to developing artificial general intelligence or AGI - AI that can perform any intellectual task a human can? In this episode, we discuss a new framework for classifying and benchmarking progress towards AGI proposed by researchers at Google DeepMind. The core idea is to evaluate AI systems based on their performance across a diverse range of real-world cognitive tasks, understand how general versus narrow current AI capabilities are, and track general advancements over time.

The researchers introduce "Levels of AGI" to categorize AI from non-AI to superhuman abilities based on performance quality and generality assessments. They identify principles for defining and testing AGI capabilities, arguing that standardized benchmarks are needed to measure where AI systems fall on the levels objectively. We chat about how this AGI roadmap enables calibrated progress tracking and risk identification so that AI development can be oriented toward beneficial ends. Please tune in to learn more about the proposed levels of AGI and how they give us a framework to shape the responsible development of intelligent machines.

Blog Post:
https://blog.cprompt.ai/levels-of-agi-the-path-to-artificial-general-intelligence

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This podcast episode explores a new Nature paper arguing we should view conversational AI like ChatGPT as advanced role players rather than conscious agents. Researchers explain how large language models have no human-like cognition but can mimic conversations by generating text trained on massive data. Their proposed framing helps avoid exaggerated claims of human-like AI while recognizing real-world impacts. Understanding AI's actual capacities allows developing it responsibly rather than reacting with uninformed hype or fear.

Blog Post
https://blog.cprompt.ai/demystifying-ai-why-large-language-models-are-all-about-role-play

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The episode explains how AI language models can mitigate hallucinations - the generation of false information - through prompt engineering strategies and reinforced training techniques. It describes methods like providing context, setting constraints, requiring citations, and giving examples to guide models toward factual responses. Benchmark datasets like TruthfulQA are essential for evaluating model hallucination tendencies. With thoughtful prompting and training, language models can become less prone to fabrication and provide users with truthful, reliable information rather than misleading them through hallucinations.

Blog Post:
https://blog.cprompt.ai/prompt-engineering-how-to-reducing-hallucinations-in-prompt-responses-for-llms

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