Would you trust an AI that can diagnose disease but doesn’t know that fire burns?
In 2023, Meta launched CICERO, an AI agent that could beat most humans at the board game Diplomacy. Unlike chess or Go, Diplomacy isn’t just about strategy. It requires negotiation, reading social cues, and making promises. CICERO succeeded because it didn’t just compute moves, it inferred motivations. Yet, it still couldn’t grasp that a glass of water spills if tipped over. That’s the puzzle: AI systems can master complex games or code generation, but struggle with basic, common-sense reasoning.
Despite reaching milestones in image generation, natural language, and predictive modeling, AI is still failing at one essential trait humans take for granted: knowing what is obvious.
Why Common Sense Is Still an Unsolved Problem in AI
Modern AI systems excel in pattern recognition and prediction. GPT-4 can write novels, Midjourney can create artwork, and autonomous drones can navigate dense terrain. But even the most sophisticated AI often falters in scenarios that require intuitive, cause-and-effect understanding.
Common sense is messy. It’s learned over years of experience, socialization, trial and error. We aren’t taught that a dropped egg will crack. We just know. For AI to truly mirror human thinking, it must also understand physical properties, social contexts, and abstract cause-and-effect relationships.
This limitation is not just theoretical. It’s an urgent concern for industries pushing AI into critical applications:
- Self-driving cars need to anticipate pedestrian behavior.
- Virtual assistants must respond in socially appropriate ways.
- AI doctors must avoid treatments that defy basic human logic.
The race to solve common sense in AI is more than academic. It’s the foundation for making machines genuinely useful and safe.
The High Stakes and Higher Hurdles
Building AI with common sense faces technical, philosophical, and ethical challenges:
- Lack of structured data: Most training datasets are not designed to reflect everyday reasoning or consequences.
- Contextual gaps: AI can misinterpret intentions or consequences when context is subtle or implied.
- Symbol grounding problem: Machines struggle to tie words or concepts to real-world experiences. For example, “ice melts” is just a string unless grounded in sensory understanding.
- Evaluation complexity: Unlike benchmarks for translation or vision, testing common sense lacks universal metrics.
- Risk of overgeneralization: AI might apply a rule universally, missing exceptions that are obvious to humans.
Yet, the benefits are transformative:
- Smarter AI copilots in enterprise and code generation
- Better social robotics for healthcare and education
- Enhanced safety in autonomous vehicles
- More nuanced content moderation and AI policy enforcement
Real Progress: Where the Industry Stands Today
Despite the complexity, there is real momentum.
- OpenAI’s GPT-4 integrates chain-of-thought reasoning to improve its logical deductions. It can walk through a problem step-by-step, improving its common sense judgment in some tasks.
- Anthropic’s Claude is trained with constitutional AI principles, designed to align more closely with human intent and reasoning.
- Allen Institute for AI is behind COMET, a neural model trained on the ATOMIC-2020 dataset to predict everyday causal and social consequences. It attempts to answer questions like: “What might someone do after apologizing?”
- Google DeepMind’s AlphaGeometry combines symbolic logic with neural networks to solve Olympiad-level geometry problems, showcasing AI’s growing ability to reason about spatial and mathematical structures.
A Look at the Numbers: How Far Have We Come?
A 2024 survey on AI commonsense benchmarks revealed that leading models still fall short on real-world scenarios. The best-performing systems could only answer 70% of common-sense questions accurately across datasets like SocialIQA, PIQA, and HellaSwag.
Meanwhile, Meta AI’s BEHAVIOR benchmark attempts to evaluate embodied AI agents on tasks involving physical and social reasoning in simulated households. Early results show that while agents excel in structured tasks, they still fail at improvising or adapting to unexpected changes.
The Next Wave: From Data to Embodiment
The field is moving beyond large language models into embodied AI. The idea is simple: just like toddlers learn by interacting with the world, machines might gain common sense through sensory input and interaction.
Projects like Google’s PaLM-E aim to merge language understanding with physical robotics. By combining NLP with environmental sensors, robots can learn how actions affect the real world and adjust accordingly.
Boston Dynamics and Toyota Research Institute are investing in robot learning labs where machines are trained through real-world trial and error, not just data ingestion.
This trend points to a future where AI is not just informed, but experienced. Common sense may not emerge from data alone, but from doing, failing, and adapting.
Final Thoughts from The Futurism Today
We’ve taught machines to beat grandmasters, paint like Van Gogh, and write like Shakespeare. But unless they understand that a spilled drink can ruin a laptop, they remain outsiders to our world.
The path to artificial general intelligence doesn’t just go through bigger models or better data. It goes through building AI that can reason like a child, adapt like a dog, and respond like a human. That path is longer, harder, and messier. But it is also the only path to building machines we can truly collaborate with.
At The Futurism Today, we believe the next frontier of AI isn’t intelligence alone, it’s understanding. And understanding starts with common sense.
Keep following us as we break down breakthroughs like these in simple terms and sharp insights. Because the future won’t just be smart, it will need to be sensible too.