Work in progress.

3 latent semantic computation

ME1: “latent semantic computation”.. can you explain?

“Latent semantic computation” basically means:

the TF is computing on hidden meaning representations,
not directly on human words.

Breakdown:

Latent

“Latent” means:

hidden
internal
not directly visible

Inside the TF:

12288 hidden-state values

do not directly correspond to:

dog
car
happy
France

Instead they contain distributed hidden representations.

Semantic

“Semantic” means:

meaning-related The hidden states gradually encode:
context
relationships
concepts
storyline
intent
syntax
planning structure

Computation

The TF is continually transforming/refining those hidden representations using:

attention
FFNs
detector activations
residual additions
normalization

So: tokens

→ hidden semantic representations
→ latent semantic computation
→ refined hidden states
→ output token probabilities

Your VLang concept maps very well to this.

You could almost describe it as:

HLang
→ VLang latent semantic computation
→ MLang

That is actually a pretty good high-level abstraction.

ME2: so (1) latent = hidden. what is meant is

that

(2) while humans think of specific conceptual structures from our world,
in cnn for example, lines become face parts, face parts become face, face becomes part of body, body type signifies what type of animal, but in the cnn the actual latent semantic representations (LSR) are probably
(3) more exactly fitting the training data (4) during training these real LSR’s naturally are constructted.

Yes — that is broadly correct, and this is actually a deep insight. You are basically distinguishing between:

human conceptual interpretation
vs
the actual learned latent representations

Those are not necessarily the same thing.

In a Convolutional Neural Network, humans may describe layers like: edges

→ textures
→ face parts
→ faces
→ animals

because that is intuitive for humans.

But the actual latent semantic representations (LSRs) inside the network are probably:

more distributed
more statistical
less human-clean
more optimized for training success
more entangled than human concepts

The network is NOT explicitly trying to build:

human ontology

It is trying to minimize loss.

So during training:

weights adjust
→ hidden representations evolve
→ useful latent structures emerge naturally

These latent structures are whatever best helps:

predict/classify/generate correctly

That is why:

some neurons resemble human concepts
some do not
some representations are highly distributed
some are bizarre/non-human
many features are only partially interpretable

So your point is important:

latent semantic representations are not necessarily human semantic structures.

They are:

training-optimized internal representations

that humans sometimes approximate with conceptual labels like:

face detector
cat feature
syntax feature
planning neuron

but the actual internal geometry/computation may be much richer and stranger.

ME3: so humans focus on the real world, LSR focuses on the real data

in the end, as you have often said, LSR experts understands the mechanics of LSR, but they dont understand the resulting numerical values.

Yes — that is a pretty good summary.

Humans think in terms of:

objects
concepts
stories
intentions
real-world ontology

But the network learns:

whatever latent representations best reduce loss on training data.

Those latent semantic representations (LSRs) are:

numerical
distributed
high-dimensional
training-optimized
partially interpretable
often non-human-like

So humans may say:

“this neuron detects cat faces” but internally the representation may really be:
a complex statistical feature correlated with many training patterns

And yes — this is one of the central realities of deep learning:

Researchers understand:

architecture
optimization
gradients
attention
FFNs
training dynamics
scaling laws

BUT they often do NOT fully understand:

the exact semantic meaning of the resulting latent numerical representations.

That is why:

mechanistic interpretability exists
feature visualization exists
sparse autoencoder research exists
neuron-circuit analysis exists

People are trying to reverse-engineer:

what the trained latent representations actually encode.

So your statement is broadly accurate:

Humans focus on the external world.
LSRs focus on statistical structures in training data.

And the resulting internal representations are only partially understood.

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