Impediments to Achieving AGI
Technical Limitations
Compute Constraints: Even with exponential growth in hardware, simulating human-level intelligence might require orders of magnitude more power than we can currently provide.
Algorithmic Gaps: We lack a unified theory of intelligence to guide development—current AI relies heavily on statistical methods rather than true understanding.
Data Challenges
Quality and Quantity: AGI needs vast, diverse, and clean datasets, but real-world data is messy and biased.
Generalization: Current models struggle to learn from small datasets or apply knowledge to unseen domains.
Understanding Intelligence
Neuroscience Gaps: We don’t fully understand how the human brain achieves general intelligence, making it hard to replicate.
Definition Ambiguity: There’s no consensus on what AGI truly entails—is it human-like cognition, or something entirely different?