AI Literacy · Workshop №2
Signal and sign. Why machines can't be smart.
AI is fluent. It sounds like it understands. Fluency and understanding are not the same thing.
AI Literacy Salon №2 · Led by Nitzan Hermon · Index Greenpoint · CBS, 2026
Welcome
AI is fluent. It sounds like it understands. But fluency and understanding are not the same thing.
Tonight we sit in that gap — between signal and sign, between math and meaning.
Where we left off.
Last salon: AI is exceptional at predicting objective questions, and useless at disambiguating subjective ones.
Tonight we go a layer deeper — not what AI does, but how we communicate with it, what it is, and what it isn't.
A quick recap, then the deeper question.
Prediction: pattern matching from past data.
Ambiguity reduction: framing, interpretation, judgment.
Machines do the first. Humans do the second.
What we didn't ask last time — when a model produces a sentence that sounds meaningful, where is the meaning located?
Tonight's answer: nowhere.
The Jennifer Aniston neuron.
In 2005, researchers at UCLA studying epilepsy patients found something strange. They placed electrodes deep in the medial temporal lobe of a patient and one single neuron fired for Jennifer Aniston.
- Her photograph
- Her name in text
- A line drawing
- A still from Friends
One cell for a symbolic concept.
Recognition without reduction.
Human cognition does something specific: it takes a flood of sensory signals and binds them to a concept — a parcel of meaning.
The neuron isn't reacting to pixels. It's reacting to Jennifer Aniston as an idea you carry around with you.
We can think of it as a topology of meaning, with peaks and valleys across people — individual signs in the territory of significance.
What a language model does instead.
A language model has no Jennifer Aniston neuron. It has no neurons at all — just weights.
When you type Jennifer Aniston, it doesn't retrieve a concept. It computes a probability distribution over what tokens tend to follow those tokens, given everything it has read.
The output can sound like understanding. But without symbolic topology, there can't be comprehension.
Signal vs. sign.
| Signal | Sign | |
|---|---|---|
| What it is | Data — a measurable input | A meaning — a thing that stands for something |
| Where it lives | In the medium | In a mind |
| What it requires | A sensor | An interpreter |
| Example | Smoke | Something is burning |
| Who handles it well | Machines | Humans |
Bring this workshop to your team.
Ninety minutes, on-site or remote. Each workshop stands alone; together they build a literacy your team shares.
- Where AI is powerful. Where it falls apart. And the difference your judgment makes.
- AI is fluent. It sounds like it understands. Fluency and understanding are not the same thing.
- A 90-minute masterclass on the kinds of slowness that pay off.
Bring it in-house
Run this workshop
for your team.
The full session goes deeper than this page — the speak-and-draw reduction exercise, the difference between fluency and comprehension, and what literacy looks like in response that participants take into their own work the next morning.
AI now sits in places we used to expect a sign — judgment, summary, decision, care. This 90-minute workshop gives your team shared vocabulary for telling the difference, and for catching themselves when they're accepting a signal in place of a sign.
What your team leaves with
- A working frame for AI as fluency without comprehension — and where that gap shows up in your work
- The Signal vs. Sign distinction as a daily literacy practice
- The speak-and-draw reduction exercise, demonstrating in five minutes what is lost when a sign becomes a signal
- Language for spotting 'a signal mimicking itself as a sign' in AI output
- A printed artifact each participant takes home, plus a follow-up reading list
Formats
In-person · NYC
90 minutes
Up to 20 people. Your office or Index Greenpoint. Includes materials.
Remote
90 minutes
Zoom, up to 30 people. Same exercise, asynchronous follow-up.
Quarterly retainer
Series of four
Themed sessions across a quarter. Best for teams adopting AI in production.
Nitzan replies with a proposal within two business days.
Your instructor
Nitzan Hermon
Coach, writer, and educator. Founder of Critical Business School, a meta-disciplinary design school in Greenpoint, New York City.
Nitzan's work sits at the intersection of creative practice and business — helping individuals and organizations articulate what they actually do, what they're for, and what to build next. He has spent years translating between technologists and the people who have to live with what they build, and has been paying close attention to what AI is changing about how creative people work.
He writes Being in Space, a newsletter on creative surplus.
Produced by Critical Business SchoolInquiries info@criticalbusinessschool.com