These days, artificial intelligence is capable of producing poetry, translating languages, and even simulating human emotions. However, it still has trouble with the straightforward and obvious—the kind of reasoning that a five-year-old uses naturally. A cat can be described by machines, but they are unable to explain why it should not be placed in a washing machine. Teaching AI common sense is the industry’s most ambitious and costly endeavor, and it is based on this paradox.
Soft-spoken Stanford visionary Douglas Lenat pursued this unattainable goal for forty years. His Cyc project sought to encode the common beliefs that people seldom express, such as the necessity for a key to unlock a closed door, the fact that people become hungry, and the fact that gravity pushes items downward. What seems simple turned out to be really intricate. Cyc has millions of facts but was still unable to understand the commonplace despite billions of dollars and innumerable research articles.
Despite being enormous, Lenat’s ambition was not misguided. His method, which envisioned an AI that thinks rather than mimics, was astonishingly forward for its time. He felt that lived logic, or the weave of everyday reality, was a prerequisite for true intellect. In addition to understanding that “fire burns,” machines also needed to understand why people might “avoid touching it.” Despite frequent criticism, his work served as a model for contemporary scholars who are currently resurrecting his ideas using different titles and approaches.
Data-driven behemoths like OpenAI, DeepMind, and Meta are driving the current version of Lenat’s vision. These companies are allowing AI to learn by association by giving it enormous databases of text, images, and interactions rather than manually coding knowledge. The idea is to allow machines to learn reasoning patterns via repetition and experience, just like children do. Even if this change is much quicker, there is a great deal of uncertainty.
Profile Overview: Douglas Lenat
| Category | Detail |
|---|---|
| Full Name | Douglas Lenat |
| Profession | AI Researcher; Creator of the CYC Project |
| Nationality | American |
| Known For | Long-term effort to encode common sense in machines |
| Major Project | CYC (enCYClopedic knowledge base, since 1984) |
| Key Roles | Principal scientist at MCC, later CEO of Cycorp |
| Contributions | Built a large knowledge base of everyday conceptual truths for AI |
| Influence | Groundwork for symbolic AI and common-sense reasoning efforts |
| Reference | https://www.cyc.com (official site of CYC / Cycorp) |
Common sense involves judgment in addition to reasoning. Humans have an innate ability to consider context; they know that it is wise to pour water on a blaze but not on an electrical outlet. The industry as a whole has become fixated on teaching algorithms that subtlety. As part of this endeavor, Meta has invested $65 billion in what it refers to as “world models”—AI systems that mimic settings and outcomes. With the help of these models, machines can “imagine” results before taking action, which is remarkably similar to human intuition.
“Teaching machines to think before they act” is how former Allen Institute for AI director Oren Etzioni characterized this endeavor. It’s a particularly creative method of lessening AI’s brittleness, or its propensity to excel in one situation while failing miserably in another. With funding from the late Paul Allen, a co-founder of Microsoft, Etzioni’s team started Project Alexandria to create a digital “commonsense database.” The goal of the project was to guarantee that AI is capable of reasoning in addition to knowing facts.
It’s a huge task. From science to empathy, common sense encompasses everything. It calls for machines to understand social and emotional dynamics in addition to mechanical ones. Think of an AI assistant setting up a meeting. It may have a faultless understanding of calendars and time zones, but it fails to recognize the subtleties that a “4 p.m. Friday” meeting could annoy a human coworker. The difference between competence and intelligence is this kind of contextual knowledge, and robots are still learning this distinction.
This is where large language models, such as ChatGPT or Gemini, have made significant progress. They respond to circumstances with increasingly natural thinking, displaying a sort of mimicked intuition. However, Quanta Magazine noted that these systems are still “brilliantly stupid”—remarkably intelligent but often absurd. Their common sense is deduced from probability rather than experiences; it is not actually learnt.
Therefore, meaning rather than memory is the problem. Petabytes of data can be stored by machines, yet they are still unable to decipher the meaning underlying basic human behavior. AI must establish causal relationships in order to achieve true knowledge; it cannot simply know that “ice melts”; it must also comprehend that heat causes it to do so. Although it may appear insignificant, this distinction is fundamental to ethical judgment, reasoning, and decision-making.
Researchers are fusing neural networks with symbolic logic, which is Lenat’s legacy, to close this gap. Because they combine the flexibility of learning with the strictness of rules, these hybrid systems are very advantageous. While machine learning offers flexibility, a symbolic engine offers structure. The most recent attempts to combine these formerly disparate paradigms are Google DeepMind’s neurosymbolic AI studies and Microsoft’s “Humanist Superintelligence” initiative.
There is nothing more at risk. If effective, AI could make decisions based on human-like comprehension while securely navigating courts, hospitals, and classrooms. Imagine a medical AI that can detect emotional distress in a patient’s tone in addition to identifying symptoms. Or an automated vehicle that can decipher a traffic cop’s nonverbal cues. These discoveries would signal a transformation as significant as the emergence of language itself, from mechanical proficiency to moral intelligence.
However, this race has a darker side. Synthetic reasoning overconfidence may result in misguided trust. Machines that have been educated on inadequate or biased data may misinterpret circumstances in ways that appear innocuous but end up being disastrous. An AI that manages finances, makes parole recommendations, or diagnoses illnesses must not only think, but also think fairly. The danger is when machines mimic knowledge without actually having it; this is known as “synthetic common sense,” according to researchers.
But there’s reason to be optimistic about this field. With every new development, AI gets impressively closer to thinking like a human. Systems that can forecast real events in virtual worlds, including whether a stack of blocks will topple, were recently demonstrated by researchers at Meta. Once believed to be unattainable by robots, this type of intuitive physics is finally becoming real. The models developed by OpenAI also exhibit early indications of causal thinking, learning to differentiate between correlation and consequence.
It is more important to cultivate common sense than to code intelligence while teaching it. It’s similar to parenting a digital child, who picks up knowledge through failure, observation, and trial. The work calls for humility, teamwork, and patience. “It’s not about creating machines that think like us; it’s about creating machines that can think with us,” as Etzioni famously stated.
