Artificial intelligence (AI) has seen remarkable advances in recent years, from self-driving cars to defeating humans at games like chess and Go. However, leading AI expert Melanie Mitchell argues that achieving truly human-like "general" AI is much harder than many experts predict. Here are the key takeaways from her paper for business leaders:
- Narrow AI versus general AI: Success in specialized applications like chess or image recognition does not necessarily translate to broader capabilities. We cannot assume today's AI systems are on a steady path to human-level intelligence.
- "Easy" things are hard: Basic human skills like perceiving the world and carrying on a conversation have proven very difficult to replicate in machines. Conversely, AI can excel at things that are quite hard for humans.
- Wishful vocabulary: Terms like "learn," "understand," and "think" are often applied to today's AI, but these systems do not have the same underlying capabilities as humans. Using human vocabulary can misleadingly imply advanced intelligence.
- Intelligence is embodied: Human intelligence relies heavily on our experiences and interactions using our entire bodies, not just abstract reasoning. Attempts to achieve human-level intelligence must consider embracing human-like bodies and environments.
- Common sense is key: To operate successfully in the real world, AI needs the vast background knowledge humans accumulate about how the world works. We still do not understand how to enable machines to acquire this "common sense."
Key implications:
- Avoid overconfidence about timelines for achieving human-level AI based on hype and narrow successes. True general intelligence likely remains far off.
- Focus investment on applications of existing AI capabilities, not attempts to replicate human thinking. Manage expectations of near-term outcomes.
- Monitor advances in embodied AI and research on common sense reasoning as indicators of progress toward general AI.
- Ensure AI systems have transparent workings, clear objectives, and human oversight. The orthogonality thesis that intelligence can be coupled to any goals does not hold for human-like general intelligence.
The path to human-level AI is long with much still unknown. By avoiding unfounded assumptions and acknowledging the challenges ahead, business leaders can make wise strategic decisions about how to apply AI technology today and anticipate what may come tomorrow.
Sources:
Why AI is Harder Than We Think
Melanie Mitchell
