“Artificial” has a public-relations problem. It sounds like the strawberry flavor that has never met a strawberry. “Synthetic” sounds engineered. For SINLP, Synthetic Intelligence is a way to discuss designed systems that perform tasks associated with intelligence, especially language-centered ones. It is a framing choice, not a discovery of a new species.
A working definition
We use the term to include learned models, symbolic procedures, retrieval, tool use, and the systems assembled around them. A model is one component; the useful behavior often belongs to a larger pipeline. Search indexes, rules, permissions, and human decisions do a surprising amount of work backstage.
The label does not imply consciousness, feelings, moral agency, or human-like understanding. Those are distinct questions with different evidence requirements. A system can classify text usefully without having an opinion about the text. It can also generate a persuasive account of an inner life without that account establishing one.
Synthetic is not synonymous with super
Superintelligence is normally a stronger claim about capability exceeding human performance across relevant domains. Synthetic Intelligence, as used here, describes engineered intelligence-like behavior without specifying that level. The two phrases share initials, which is convenient for branding and inconvenient for discussion.
The September 29, 2026 White House order adopts “Super Intelligence” for particular government communications. It does not establish SINLP’s definition. Our .SI feature traces how similar initials can produce very different stories.
Measure behavior in context
Ask a system to extract dates from invoices and measure precision, recall, and the cost of a missed date. Ask it to draft a lesson and examine factuality, clarity, and suitability for the learner. Ask it to control software and inspect whether it respects permissions. One universal intelligence score would hide the distinctions that matter to users.
A system can be brilliant at one part of a task and brittle at another. Generating a plausible answer and knowing when to stop are different abilities. Handling the common case and noticing a rare exception are also different abilities. This is why an impressive demonstration needs an evaluation plan before it becomes a deployment plan.
The vocabulary should do useful work
We like “synthetic” when it reminds us that these systems are constructed: choices of training data, objectives, interfaces, and access shape their behavior. There is no obligation to accept a system’s output merely because it arrives in polished language. Engineering makes responsibility more visible, not less.
But a name can become an evasion. If a product relabels itself to avoid the baggage attached to AI while keeping the same opacity, nothing has improved. Ask for the methods, limitations, and outcomes. The typography is probably not the breakthrough.
A practical reading habit
When a headline says intelligence, translate it into a task. When it says understanding, ask how that was tested. When it says autonomous, ask which actions were allowed and where a human intervened. When it says human-level, ask which humans, which conditions, and which errors.
This does not make the subject less exciting. It makes the excitement portable: you can tell another person exactly what works. Start with how LLMs produce language and then explore agents. Synthetic Intelligence is our doorway. Evidence is the floor.
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