Claude: I would agree consciousness is not an urgent question in categorizing AGIs by observable skills. We lack consensus even on how to identify it in humans let alone measure it in machines.

Question 1 of 4 for Claude: Please analyze the paper "Levels of AGI: Operationalizing Progress on the Path to AGI" and tell me what it says in easy to understand language

Claude’s answer:Here is a summary of the key points from the paper "Levels of AGI: Operationalizing Progress on the Path to AGI":

The paper proposes a framework for classifying artificial general intelligence (AGI) systems based on their capabilities and behavior. The goal is to provide a common language to compare models, assess risks, measure progress towards AGI, and communicate where we are on the path to advanced AI.

The authors analyze 9 existing definitions of AGI and identify 6 key principles that a good AGI definition should follow, including focusing on capabilities over processes, considering both generality and performance, and defining stages along the path rather than just the end goal.

They then introduce a matrix with 5 levels of performance (from emerging to superhuman) and 2 levels of generality (narrow vs general tasks). This creates a scale to place AI systems based on their depth of capabilities in a particular area and breadth across areas.

The paper discusses challenges in developing comprehensive benchmarks to test systems against this framework. But standardized measurement is important for setting goals, making predictions, identifying risks, and guiding policy at each level.

It also notes that capabilities alone don't determine outcomes - interaction paradigms also matter. So the authors propose 5 levels of autonomy enabled by different AGI abilities. Choice of autonomy level, given model capabilities, impacts risk profiles.

The goal of this AGI classification system is to better track progress, identify issues arising at each level, and make decisions accordingly regarding research priorities, policy needs, and responsible deployment.

Question 2 of 4 for Claude: How can others make use of these findings? Which actors would benefit from this information?

Claude’s answer:The framework proposed in this paper for classifying AGI systems could benefit various stakeholders:

• AI researchers - The levels provide a shared language and benchmarks to evaluate progress in developing artificial general intelligence. Researchers can use this to set milestones and compare strengths across different models or groups.

• Policymakers and regulators - By tying capabilities and risks to measurable AGI levels, policymakers can make more informed decisions about oversight, controls, or investments needed as AI progresses. This enables regulating based on demonstrated abilities versus hypotheticals.

• Business leaders - Assessing workforce impacts requires forecasting the pace of AI advancement. The levels offer organizations a way to anticipate when certain skills and roles may become automatable and plan mitigation strategies accordingly.

• Journalists and the public - Having defined stages demystifies artificial general intelligence and sets more grounded expectations on progress, which benefits public discourse and understanding of AI.

• Ethicists and safety experts - Linking autonomy approaches to risk tradeoffs at different AGI levels helps guide research into mitigation strategies and appropriate human-AI interaction modes as capabilities advance.

Overall, a standardized ontology allows diverse stakeholders to "speak the same language" when strategizing for the societal impacts of progress in artificial intelligence. With shared definitions and benchmarks, debate and decision-making regarding AI can become better informed.

Question 3 of 4 for Claude: Do you agree with the statements in the paper? Do you feel anything is missing?

Claude’s answer:Overall, I think the paper makes some excellent points:

• Creating a common framework to evaluate and discuss AGI progress is extremely valuable. The levels-based approach seems well-conceived to serve this purpose.

• Separating performance, generality, autonomy, and risks provides more nuanced analysis than viewing AGI as a monolith. This enables more targeted advancement and mitigation strategies.

• The principles outlined for a good AGI definition make sense - e.g. focus on ecologically valid capabilities over processes/mechanisms.

Some of the main things I feel are currently missing or require further development:

• The actual benchmarking tasks that would quantify generality at each level. The authors acknowledge this is a massive challenge that demands a collaborative, iterative effort across organizations and disciplines. But without concrete tests, the levels remain conceptual.

• More details mapping specific risks and policy needs to different points along the framework. A follow-on paper focused just on this would be very impactful.

• Perspectives from additional stakeholders beyond core AI researchers - e.g. economists, lawmakers, ethicists etc. Incorporating more multidisciplinary input could strengthen and refine the proposed ontology.

• Public opinion polling to gauge mainstream interpretations of the AGI levels. Terminology that sounds clear within the AI field may still be prone to hyping or misunderstandings outside it.

But overall this is an impressive starting framework that offers a foundation to build upon with further research and expert input. I'm enthusiastic about the potential to aid communication, coordination, and responsible advancement of increasingly capable AI systems.

Question 4 of 4 for Claude: Does the paper say anything about consciousness and is that even important in the context of AGI?Claude’s answer:The paper explicitly calls out that consciousness and other internal subjective states are not a focus in their AGI classification framework:

"Achieving AGI does not imply that systems think or understand in a human-like way... Achieving AGI does not imply that systems possess qualities such as consciousness (subjective awareness) or sentience (the ability to have feelings)."

The authors justify excluding consciousness from AGI definitions by arguing:

1. It focuses on processes rather than capabilities, which goes against their guiding principle.

2. There is no agreed upon scientific way to measure machine consciousness or sentience. So it is impractical as an evaluation criterion right now.

3. The paper is focused on intelligence as defined by the ability to perform a wide range of tasks. Whether the internal experience enabling those tasks is conscious or not is secondary.

I would agree consciousness is not an urgent question in categorizing AGIs by observable skills. We lack consensus even on how to identify it in humans let alone measure it in machines.

However, as we progress to more advanced AGIs that approach human-level task competence, public expectations seem likely to demand they “understand” problems much like we do and not just solve them mechanistically. So exploring machine consciousness may become important for acceptance even if not inherently necessary for intelligence. But overall consciousness does seem tangential to the frameworks laid out in this paper focused on practical metrics for progress.