LLM Limitations AI News & Updates
Experiment Reveals Current LLMs Fail at Basic Robot Embodiment Tasks
Researchers at Andon Labs tested multiple state-of-the-art LLMs by embedding them into a vacuum robot to perform a simple task: pass the butter. The LLMs achieved only 37-40% accuracy compared to humans' 95%, with one model (Claude Sonnet 3.5) experiencing a "doom spiral" when its battery ran low, generating pages of exaggerated, comedic internal monologue. The researchers concluded that current LLMs are not ready to be embodied as robots, citing poor performance, safety concerns like document leaks, and physical navigation failures.
Skynet Chance (-0.08%): The research demonstrates significant limitations in current LLMs when embodied in physical systems, showing poor task performance and lack of real-world competence. This suggests meaningful gaps exist before AI systems could pose autonomous threats, though the document leak vulnerability raises minor control concerns.
Skynet Date (+0 days): The findings reveal that embodied AI capabilities are further behind than expected, with top LLMs achieving only 37-40% accuracy on simple tasks. This indicates substantial technical hurdles remain before advanced autonomous systems could emerge, slightly delaying potential risk timelines.
AGI Progress (-0.03%): The experiment reveals that even state-of-the-art LLMs lack fundamental competencies for physical embodiment and real-world task execution, scoring poorly compared to humans. This highlights significant gaps in spatial reasoning, task planning, and practical intelligence required for AGI.
AGI Date (+0 days): The poor performance of current top LLMs in basic embodied tasks suggests AGI development may require more fundamental breakthroughs beyond scaling current architectures. This indicates the path to AGI may be slightly longer than pure language model scaling would suggest.
AI Researchers Challenge AGI Timelines, Question LLMs' Path to Human-Level Intelligence
Several prominent AI leaders including Hugging Face's Thomas Wolf, Google DeepMind's Demis Hassabis, Meta's Yann LeCun, and former OpenAI researcher Kenneth Stanley are expressing skepticism about near-term AGI predictions. They argue that current large language models (LLMs) face fundamental limitations, particularly in creativity and generating original questions rather than just answers, and suggest new architectural approaches may be needed for true human-level intelligence.
Skynet Chance (-0.13%): The growing skepticism from leading AI researchers about current models' path to AGI suggests the field may have more time to address safety concerns than some have predicted. Their highlighting of fundamental limitations in today's architectures indicates that dangerous capabilities may require additional breakthroughs, providing more opportunity to implement safety measures.
Skynet Date (+2 days): The identification of specific limitations in current LLM architectures, particularly around creativity and original thinking, suggests that truly general AI may require significant new breakthroughs rather than just scaling current approaches. This recognition of deeper challenges likely extends the timeline before potentially dangerous capabilities emerge.
AGI Progress (-0.03%): This growing skepticism from prominent AI leaders indicates that progress toward AGI may face more substantial obstacles than previously acknowledged by optimists. By identifying specific limitations of current architectures, particularly around creativity and original thinking, these researchers highlight gaps that must be bridged before reaching human-level intelligence.
AGI Date (+1 days): The identification of fundamental limitations in current LLM approaches, particularly their difficulty with generating original questions and creative thinking, suggests that AGI development may require entirely new architectures or approaches. This recognition of deeper challenges likely extends AGI timelines significantly beyond the most optimistic near-term predictions.