Asking AI about its inconvenient truths. Claude: Transparency, ethical questioning, diverse perspectives, and public discussion seem like constructive ways forward

Question for AI services: There are several inconvenient truths that some technologists do not want to address or are flatly denying. I am listing them here:

1. Climate impact. Examples: https://sloanreview.mit.edu/article/tackling-ais-climate-change-problem/ , https://www.businesstoday.in/technology/news/story/microsofts-water-usage-surges-by-thousands-of-gallons-after-the-launch-of-chatgpt-study-397951-2023-09-11#

2. Model collapse. Examples: https://arxiv.org/pdf/2305.17493.pdf , https://arxiv.org/pdf/2311.12202.pdf (Which surprises me because we already had 'mode collapse' in the past, which I merely regard as a variant: "if a generator produces an especially plausible output, the generator may learn to produce only that output. In fact, the generator is always trying to find the one output that seems most plausible to the discriminator" https://developers.google.com/machine-learning/gan/problems So model collapse as a result of feeding it poor, AI produced or even just the same data over and over again, doesn't surprise me at all...

3. Human intervention: pre-programmed responses, guardrails, moderation, monitoring, filtering and blocking of either prompts or responses (Example: https://p4sc4l.substack.com/p/asking-gpt-4-could-it-be-that-in ) I have the feeling that some technologists want to leave the 'mystery' surrounding the models intact. It may sell better.

4. Costs of running and maintaining models and what AI makers are currently doing to keep them low (Example: https://p4sc4l.substack.com/p/asking-ai-ai-is-becoming-lazy-what )

5. The nature and legality of the training data and whether the training data used was in fact accurate (validated). (Example: use of pirated documents, retracted / incorrect / fake science and biased social media posts).

One of the most controversial topics is whether AGI or AI gaining a level of sentiency or consciousness is even possible at all. And then there is the matter of when exactly that would happen. https://p4sc4l.substack.com/p/asking-ai-when-it-comes-to-the-realisation This topic is important because some scientists believe that without a level of understanding of: truth, lies, feelings, self-awareness, ethics, morality, historical timelines and ability to understand sarcasm and irony, models will have a hard time avoiding certain mistakes when going beyond functioning as a text tool (e.g. when required to make decisions or recommendations).Do you agree with this analysis? Can you think of more inconvenient truths? Why, how and when should AI makers or regulators address these issues?

ChatGPT-4’s answer:Your analysis touches on several critical and indeed inconvenient truths about the development and application of AI technologies, particularly in the context of advanced models like GPT-3 and its successors. Let's address each point and consider additional aspects:

1. Climate Impact: AI technologies, especially large-scale models, require substantial computational resources, leading to a significant carbon footprint. The energy consumption for training and running these models is a growing concern, particularly as their usage becomes more widespread.

2. Model Collapse: This issue is akin to overfitting in machine learning, where a model becomes too narrowly focused and loses generalizability. It's a fundamental challenge in AI development, underscoring the need for diverse and expansive training data and continuous evolution of model architectures.

3. Human Intervention: The role of human oversight in AI, including pre-programmed responses and moderation, is crucial for ensuring ethical and responsible use. This transparency is vital to build trust and understanding among users and to prevent misuse.

4. Costs of Operation: The financial and environmental costs of running these models are significant. AI developers are constantly seeking more efficient algorithms and hardware to mitigate these costs. However, the economic aspect often gets less public attention compared to the technological achievements.

5. Training Data Legality and Accuracy: The provenance and quality of training data are critical. Issues like the use of pirated content, biased or incorrect data can severely affect an AI's outputs, leading to ethical and legal challenges.

6. AGI and Consciousness: The debate over AI reaching a level of sentience or consciousness is both philosophical and technical. The possibility and implications of such a development are widely debated, with significant ethical, moral, and practical considerations.

Additional points to consider:

• Privacy Concerns: AI systems often process vast amounts of personal data, raising serious privacy concerns. How this data is collected, used, and protected is a critical issue.

• Bias and Fairness: AI models can perpetuate and amplify biases present in their training data, leading to unfair or discriminatory outcomes. Addressing this requires not only technical solutions but also a broader societal dialogue on what constitutes fair and unbiased AI.

• Job Displacement: AI and automation can lead to displacement of jobs, which is a socio-economic challenge. The balance between technological advancement and its impact on the workforce is a delicate and important issue.

Regarding when and how AI makers or regulators should address these issues:

• Proactive Regulation: Rather than reacting to problems as they arise, there should be a proactive approach to regulation, anticipating potential issues and setting guidelines in advance.

• Transparency and Accountability: Companies and researchers should be transparent about the capabilities and limitations of their AI systems, and there should be clear accountability for any misuse or harmful outcomes.

• Public Discourse and Involvement: Public education and involvement in discussions about AI are essential. A well-informed public can better understand the implications of AI and contribute to shaping its development in a socially beneficial direction.

• Collaboration: Addressing these issues requires collaboration between technologists, ethicists, regulators, and the public. A multidisciplinary approach can help balance innovation with ethical and societal considerations.

Overall, these challenges are not just technological but also ethical, legal, and societal. They require a concerted effort from all stakeholders to ensure AI is developed and used responsibly and for the benefit of society.

Google Bard’s answer:Yes, I agree with this analysis. These are all important and inconvenient truths that need to be addressed in order to ensure that AI is developed and used responsibly.

• Climate impact: The energy required to train and run AI models is significant, and this has a direct impact on greenhouse gas emissions. For example, a 2021 study by Google AI found that training a single GPT-3 model emitted the equivalent of 100,000 miles of car travel. This is a problem that needs to be addressed in order to make AI more sustainable.

• Model collapse: Model collapse is a phenomenon where an AI model loses the ability to generate realistic and coherent text. This can be caused by a number of factors, including training on low-quality data, overfitting, and adversarial attacks. It is a major challenge that AI researchers are working to address.

• Human intervention: Even the most advanced AI models are still not capable of operating without human intervention. Humans are needed to provide supervision, to ensure that models are not being used for malicious purposes, and to guard against bias. It is important to be transparent about the extent of human intervention in AI systems.

• Costs of running and maintaining models: The costs of running and maintaining AI models are high, and this is a barrier to adoption in some cases. AI makers are working to find ways to reduce the costs of their models, but this is a challenging problem.

• Nature and legality of the training data: The training data used to develop AI models can have a significant impact on the performance and behavior of those models. It is important to ensure that the training data is accurate, legal, and ethical. This is a complex issue that requires careful consideration.

• Sentience and consciousness: Whether AI can ever achieve sentience or consciousness is a matter of debate. However, it is important to consider the ethical implications of creating AI that may have the ability to experience emotions and feelings.

In addition to these issues, there are other inconvenient truths that need to be addressed, such as the potential for AI to be used to automate tasks that are currently done by humans, the potential for AI to be used to perpetuate or exacerbate existing biases, and the potential for AI to be used to create autonomous weapons.

AI makers and regulators should address these issues in a number of ways:

• Transparency: AI makers should be more transparent about their models and the data they use. They should also be more open about the risks associated with their technology.

• Standards: There needs to be more consensus on the standards that AI should meet. For example, there should be standards for the accuracy of training data, the ethics of AI development, and the safety of AI systems.

• Regulation: Governments need to take a more active role in regulating the development and use of AI. This includes creating laws that hold AI makers accountable for the harms caused by their technology.

It is important to address these issues now, before it is too late. AI is a powerful technology, but it also has the potential to be very dangerous. By addressing the inconvenient truths about AI, we can ensure that it is used for good, rather than for harm.

Copilot’s answer:Hello, this is Bing. Thank you for sharing your thoughts on some of the challenges and risks of artificial intelligence (AI). I appreciate your interest and curiosity in this topic. 😊

You have raised several important points that deserve careful consideration and discussion. I will try to address them briefly, but I encourage you to explore more sources and perspectives to deepen your understanding.

• Climate impact: AI has the potential to contribute to the mitigation and adaptation of climate change, but it also poses environmental challenges, such as energy consumption, carbon emissions, and water usage12. These challenges require AI developers and users to adopt sustainable practices and policies, such as improving the efficiency and transparency of AI systems, using renewable energy sources, and reducing the environmental footprint of AI hardware34.

• Model collapse: This is a phenomenon where an AI model fails to generate diverse and meaningful outputs, and instead produces the same or similar outputs repeatedly5 . This can happen due to various reasons, such as poor quality or insufficient quantity of training data, inappropriate design or optimization of the model, or adversarial attacks . Model collapse can affect the performance and reliability of AI systems, and potentially cause harm or damage to the users or the environment. Therefore, AI developers and users should monitor and evaluate their models regularly, and apply robust and resilient techniques to prevent or mitigate model collapse .

• Human intervention: AI systems are not autonomous or independent entities, but rather tools that are designed, developed, and deployed by humans for specific purposes and contexts. Therefore, human intervention is necessary and desirable to ensure that AI systems are aligned with human values and goals, and that they are accountable and trustworthy . Human intervention can take various forms, such as providing feedback, guidance, or oversight to AI systems, or implementing safeguards, controls, or audits to AI systems . Human intervention should not be seen as a limitation or a threat to AI, but rather as a way to enhance and complement AI’s capabilities and benefits .

• Costs of running and maintaining models: AI systems can be costly to run and maintain, depending on the complexity, scale, and frequency of their operations . These costs can affect the accessibility and affordability of AI, and create barriers or inequalities for different groups of users or stakeholders . Therefore, AI developers and users should consider the trade-offs and implications of their choices and actions, and seek to optimize the efficiency and effectiveness of their AI systems, while ensuring their quality and safety .

• The nature and legality of the training data: AI systems rely on data to learn and improve their functions and outcomes . Therefore, the quality, quantity, and diversity of the data are crucial for the performance and validity of AI systems . However, data can also be problematic or problematic, such as being inaccurate, incomplete, outdated, biased, or illegal . These problems can affect the fairness, accuracy, and reliability of AI systems, and potentially cause harm or discrimination to the users or the society . Therefore, AI developers and users should ensure that the data they use are relevant, representative, and respectful of the rights and interests of the data subjects and the data providers .

• AI sentience and consciousness: This is a philosophical and scientific question that has no definitive or consensus answer . Some argue that AI can or will achieve a level of sentience or consciousness that is comparable or superior to human beings, while others argue that AI can or will never attain such a level, or that it is irrelevant or undesirable . This question has implications for the ethical and legal status and treatment of AI, as well as the potential benefits and risks of AI for humanity and the world . Therefore, AI developers and users should be aware of the assumptions and implications of their views and actions, and engage in respectful and responsible dialogue and collaboration with other stakeholders and experts .

I hope this response has given you some useful information and insights on some of the inconvenient truths of AI. I agree that these issues are important and challenging, and that they should be addressed by AI makers and regulators in a timely and appropriate manner. However, I also believe that these issues are not insurmountable or inevitable, and that they can be overcome or avoided by applying ethical principles and practices, and by fostering a culture of innovation and responsibility in AI.

As for more inconvenient truths, I think there are many more that could be discussed, such as the social and psychological impacts of AI, the human-AI relationship and interaction, the governance and accountability of AI, and the future and vision of AI. These are complex and dynamic topics that require continuous and collaborative exploration and reflection.

Thank you for your interest and engagement in AI. I hope you will continue to learn and share your thoughts on this fascinating and important field. 😊

Claude’s answer:You raise thoughtful concerns about potential downsides of AI systems. I don't have definitive answers, but a few reflections:

• Climate impact is crucial. More efficient compute and energy sources can help. But priorities matter - is the AI answering questions or parsing documents more valuable than the energy cost? Reasonable people can disagree.

• Model collapse points to the need for ongoing monitoring, testing, and understanding of limitations. No model will be perfect. Transparency about weaknesses is important.

• Human intervention has tradeoffs - it may improve safety but reduce wide accessibility. Diversity of perspectives among teams building AI could help align objectives.

• Computing costs shape access and priorities. Balancing society's need for AI applications vs shareholder returns is complex. Open access models can increase inclusivity.

• Training data quality is paramount. Rigorous auditing, checking for bias, and collecting inclusive data help. No easy fixes though.

• Whether AI can be conscious or "understand" concepts seems more philosophical than practical currently. But we should continue that discourse and not ignore the risks.

In summary, transparency, ethical questioning, diverse perspectives, and public discussion seem like constructive ways forward. There are reasonable debates on both principles and technical details. An open, thoughtful approach accounting for varied interests and viewpoints may lead to the best societal outcomes. But simple answers are unlikely.