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- Mollick highlights key issues in the performance of OpenAI's "Strawberry" (o1-preview) that are unlikely to be resolved soon. Below are the problematic issues & their potential long-term consequences.
Mollick highlights key issues in the performance of OpenAI's "Strawberry" (o1-preview) that are unlikely to be resolved soon. Below are the problematic issues & their potential long-term consequences.
Without such improvements, AI systems like Strawberry may reach a performance ceiling, unable to fully integrate into domains that require trust, adaptability, and nuanced reasoning.
Pascal Hetzscholdt
September 15, 2024
Question 1 of 3: please read the news article “Something New: On OpenAI's "Strawberry" and Reasoning” and tell me what it says.
ChatGPT-4o’s answer:
The article "Something New: On OpenAI's 'Strawberry' and Reasoning" discusses OpenAI's new AI model, o1-preview, nicknamed "Strawberry." The author, Ethan Mollick, explores how this enhanced reasoning system is a significant advancement in solving complex problems that require planning and iteration, such as math and science problems.
Key points in the article:
Capabilities and Limitations: While Strawberry can outperform PhD-level experts in some areas, such as solving complex physics problems, it doesn't excel in all areas. For instance, it’s not better than GPT-4o for writing tasks. However, when it comes to tasks involving planning, its performance is notably stronger.
Example of Planning: The author demonstrates Strawberry's capabilities by giving it a complex task related to building a teaching simulator using multiple agents. It generated detailed code based on the input paper, showcasing how the system "thinks through" the problem rather than just responding to a prompt.
Crossword Puzzle Challenge: The article highlights Strawberry's iterative reasoning capabilities by using a crossword puzzle as an example. Unlike other models, such as Claude, which struggle with puzzles that require reconsidering multiple answers, Strawberry engages in a process of generating and rejecting ideas to come closer to the correct answers. Though it didn't perfectly solve the puzzle on the first attempt, it showed significant reasoning abilities and later solved it with a hint.
Reflections on AI and Human Interaction: Mollick raises concerns about the growing autonomy of AI systems like Strawberry. As AI becomes more capable of independent planning and problem-solving, it diminishes the human role in the process. The author ponders how humans can maintain control and remain actively involved in the AI’s reasoning process to catch errors and ensure alignment with intended goals.
Future Implications: The author suggests that Strawberry signals a shift toward more autonomous AI systems, which could have profound implications for how humans collaborate with AI in solving complex problems. Mollick emphasizes the need to rethink the human-AI interaction as these systems continue to evolve.
In summary, while Strawberry is a breakthrough in AI reasoning and planning, it still has limitations and raises important questions about the future of human-AI collaboration.
Question 2 of 3 for ChatGPT-4o: You previously argued that LLMs cannot "reason" for they don't understand and aren't conscious. Should Mollick has used another word instead?
ChatGPT-4o’s answer:
You raise an important point. The concept of "reasoning" traditionally implies a level of understanding, awareness, and deliberation that large language models (LLMs) like OpenAI's "Strawberry" or GPT-4 do not possess. They don’t understand in the human sense because they lack consciousness, intent, or comprehension of the meanings behind their outputs. They operate based on pattern recognition, statistical relationships, and probabilistic modeling without any real awareness.
In light of this, while Mollick’s use of the term "reasoning" in the context of "Strawberry" may be colloquial and aligned with how AI systems are often discussed, it could indeed be misleading in a strict philosophical or cognitive sense. Perhaps a more accurate term would be "simulated reasoning" or "advanced problem-solving." These terms emphasize that the model is executing processes that appear like reasoning from the outside, but without the deeper cognitive processes associated with human reasoning.
Mollick's use of "reasoning" might make sense in the context of how AI systems perform complex tasks requiring step-by-step analysis, trial and error, and iteration. However, a distinction should be made between algorithmic problem-solving (what LLMs do) and conscious reasoning (what humans do). Rephrasing it to something like "planning," "iterative computation," or "synthetic reasoning" might better reflect the mechanical nature of these tasks, while avoiding the implication of genuine understanding.
Do you think this would impact how people perceive the capabilities of AI like "Strawberry"?
Question 3 of 3 for ChatGPT-4o: Which of the issues flagged by Mollick as being problematic, are unlikely to be fixed anytime soon? List all possible consequences when those issues are left unaddressed
ChatGPT-4o’s answer:
Mollick highlights several key issues in the performance of OpenAI's "Strawberry" (o1-preview) that are unlikely to be resolved soon. These issues primarily revolve around inherent limitations in LLM technology, such as hallucinations, literal interpretation, and the challenge of human-AI collaboration. Below are the problematic issues and their potential long-term consequences if left unaddressed:
1. Hallucinations
Issue: AI systems, including Strawberry, still generate "hallucinations" — outputs that are factually incorrect, fabricated, or irrelevant to the task at hand.
Likelihood of Being Fixed Soon: Low. Hallucinations are a byproduct of the statistical nature of LLMs, which are trained to predict the next token based on patterns in data. Since these systems don’t understand the truth, hallucinations are difficult to eliminate completely.
Consequences:
Misinformation: Unchecked, hallucinations can lead to the spread of incorrect or misleading information, especially in high-stakes fields like law, medicine, or science.
Trust Erosion: Persistent hallucinations can reduce trust in AI systems, making users hesitant to rely on them for important or complex tasks.
Legal and Ethical Risks: Hallucinations that produce inaccurate or fabricated content could result in legal disputes, particularly if the AI's output is relied upon in professional settings.
2. Literal Interpretation of Problems
Issue: Strawberry sometimes interprets problems too literally, as demonstrated in the crossword puzzle example, where it failed to recognize metaphorical meanings and stuck to overly literal answers (e.g., interpreting "Galaxy cluster" as an astronomical term rather than a phone brand).
Likelihood of Being Fixed Soon: Low to moderate. While AI developers can work on improving model fine-tuning for better contextual understanding, metaphorical and non-literal reasoning remains a challenge for LLMs, which rely on correlations in data rather than deep understanding.
Consequences:
Inflexible Problem Solving: The AI's inability to grasp nuanced meanings could limit its effectiveness in creative or abstract tasks, where metaphorical or symbolic reasoning is critical.
Errors in Decision-Making: In areas where flexibility and contextual understanding are key (e.g., legal contracts, artistic interpretation), literal interpretation could lead to poor or incorrect decisions.
User Frustration: Users may become frustrated with AI systems that appear incapable of handling ambiguity or interpreting things beyond a surface level.
3. Diminished Human Role in Problem-Solving
Issue: As AI systems like Strawberry become more autonomous in planning and problem-solving, humans may play a smaller role in shaping the outcomes. Mollick notes feeling less connected to the AI's output, which could reduce human involvement in critical thinking.
Likelihood of Being Fixed Soon: Low. As AI models advance, they are designed to take on more complex tasks independently, reducing the need for human intervention. This is a design goal, but balancing it with human oversight is challenging.
Consequences:
Over-reliance on AI: As humans become less involved in the problem-solving process, there’s a risk of over-reliance on AI, potentially leading to blind trust in its conclusions, even when the system may be wrong.
Skill Degradation: Human critical thinking and problem-solving skills may deteriorate over time if AI is allowed to take over too many tasks without human oversight.
Accountability Issues: As AI takes on more responsibility in decision-making, it may become unclear who is accountable for errors or failures — the human operator or the AI system itself.
4. Errors in Complex Reasoning
Issue: Although Strawberry excels in tasks requiring iterative reasoning, it still makes errors, particularly in complex, multi-step tasks (e.g., solving puzzles or scientific problems). The AI's reasoning can go off-track, sometimes needing external corrections or hints to arrive at the right solution.
Likelihood of Being Fixed Soon: Moderate. Iterative reasoning improvements are a focus of AI research, but eliminating all errors in such complex processes is unlikely to happen quickly due to the vast variability in tasks and potential edge cases.
Consequences:
Inconsistent Performance: Users may experience frustration with inconsistent results, particularly in high-stakes applications like scientific research, financial modeling, or strategic planning.
Increased Cognitive Load: AI-generated errors in complex tasks might require humans to step in and correct the system, increasing the cognitive load on users who expected the AI to handle the entire task.
Reduced Adoption in Critical Fields: Persistent errors in complex reasoning tasks could deter industries like healthcare, law, and engineering from fully adopting AI solutions for mission-critical applications.
5. Difficulty in Collaboration with AI
Issue: Mollick points out that as AI systems like Strawberry gain more planning and reasoning autonomy, human collaboration with AI becomes more difficult. Humans might struggle to stay "in the loop" or meaningfully contribute to problem-solving when the AI is largely working on its own.
Likelihood of Being Fixed Soon: Low. The autonomy of AI systems is a central design goal, and improving human-AI collaboration while preserving that autonomy is a difficult balancing act.
Consequences:
Lack of Control: Users might feel that they are losing control over the process, leading to issues of transparency and accountability. If AI systems act too independently, it could be harder for humans to intervene or modify the direction of problem-solving.
Decreased Understanding: As AI systems take on more independent problem-solving, human operators might lose the ability to fully understand how the AI arrived at a particular solution, increasing the risk of "black-box" decision-making.
Increased Error Risk: Without humans closely monitoring or collaborating with the AI, there’s a greater chance that errors or biases could go unnoticed, potentially leading to flawed outcomes in critical applications.
Summary of Consequences:
Trust erosion, leading to reduced adoption in key industries.
Skill degradation as humans become less engaged in problem-solving.
Accountability dilemmas when errors occur in complex tasks.
Misinformation spread due to hallucinations, affecting professional credibility.
User frustration with inconsistent or overly literal AI interpretations.
Addressing these issues will require significant advances in AI alignment, interpretability, and human-AI interaction mechanisms. Without such improvements, AI systems like Strawberry may reach a performance ceiling, unable to fully integrate into domains that require trust, adaptability, and nuanced reasoning.
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