Kris Brown

(press `s`

for speaker notes)

4/24/24

Mission: to shape technology for public benefit by advancing sciences of connection and integration.

Three pillars of our work, from theory to practice to social impact:

**Collaborative modeling**in science and engineering**Collective intelligence**, including theories of systems and interaction**Research ethics**

- Physics simulations (PDEs) with Decapodes.jl
- Reaction networks with AlgebraicPetri.jl
- Epidemiological modeling with StockFlow.jl
- Agent-based modeling with AlgebraicRewriting.jl
- Interactive GUIs with Semagrams

- Vision: topos.institute
- Research: topos.site
- Blog: topos.site/blog

Topos Institute is a 501(c)(3) non-profit organization

- We can use verified, interpretable models (AI or otherwise) without losing too much economic value
- Reduce incentive to hand authority over to uninterpretable black boxes.

- We can use verified, interpretable models (AI or otherwise) without losing too much economic value
- Reduce incentive to hand authority over to uninterpretable black boxes.

- AI can target domains with a well-specified syntax and semantics allows for legibility and explanation
- Davidad’s ARIA proposal: world-models as autonomous AI output
- Anna Leshinaskaya’s talk on moral decisionmaking w/ combinatorial grammar
- Yoshua Bengio’s emphasis of separating model from implementation

- We can use verified, interpretable models (AI or otherwise) without losing too much economic value
- Reduce incentive to hand authority over to uninterpretable black boxes.

- AI can target domains with a well-specified syntax and semantics allows for legibility and explanation
- Davidad’s ARIA proposal: world-models as autonomous AI output
- Anna Leshinaskaya’s talk on moral decisionmaking w/ combinatorial grammar
- Yoshua Bengio’s emphasis of separating model from implementation

- We facilitate collaboration between diverse stakeholders, better alignment of values
- Opens opportunities for more equitable representation of values from all stakeholders (rather than just those who’ve mastered dependent type theory)

**I.** Future of software engineering

**II.** Mini examples of the future paradigm

**III.** Intuition for how structural mathematics works

- We solve problems one at a time, as they come.

**This starts feeling repetitive.**

- Abstractions act as a solution to an entire
*class*of problems.

**This feels repetitive insofar as we feel the problem classes are related.**

Common abstractions are things like functions / datatypes / scripts in our language.

- We
*migrate*abstractions to allow them to address changes in problem specification.

**Future paradigm: mathematically-principled + automatic abstraction reuse**

Understand *structure* of the abstraction + implementation + the relationship between them.

**New problem expressed in terms of old problem.**

```
"""Modify to allow it to overlap existing vertex IDs"""
def triangle(graph, v1=nothing, v2=nothing, v3=nothing):
vs = [add_vertex(graph, color) if isnothing(v) else v
for (v, color) in [v1=>"B", v2=>"R", v3=>"Y"]]
for i,j in [[1,2],[2,3],[3,1]]:
add_edge(graph, vs[i], vs[j])
return vs
"""Now expressible as an abstraction of `triangle!`"""
def big_tri(graph):
blue, red, _ = triangle(graph)
_, _, yellow = triangle(graph, blue)
triangle(graph, nothing, red, yellow)
```

```
"""Modify to allow it to overlap existing vertex IDs"""
def triangle(graph, v1=nothing, v2=nothing, v3=nothing):
vs = [add_vertex(graph, color) if isnothing(v) else v
for (v, color) in [v1=>"B", v2=>"R", v3=>"Y"]]
for i,j in [[1,2],[2,3],[3,1]]:
add_edge(graph, vs[i], vs[j])
return vs
"""Now expressible as an abstraction of `triangle!`"""
def big_tri(graph):
blue, red, _ = triangle(graph)
_, _, yellow = triangle(graph, blue)
triangle(graph, nothing, red, yellow)
```

**AlgebraicJulia**

```
"""Modify to allow it to overlap existing vertex IDs"""
def triangle(graph, v1=nothing, v2=nothing, v3=nothing):
vs = [add_vertex(graph, color) if isnothing(v) else v
for (v, color) in [v1=>"B", v2=>"R", v3=>"Y"]]
for i,j in [[1,2],[2,3],[3,1]]:
add_edge(graph, vs[i], vs[j])
return vs
"""Now expressible as an abstraction of `triangle!`"""
def big_tri(graph):
blue, red, _ = triangle(graph)
_, _, yellow = triangle(graph, blue)
triangle(graph, nothing, red, yellow)
```

**AlgebraicJulia**

**New problem expressed in terms of old problem.**

```
def prepend(pre): return lambda s: pre + "_" + s
US, EU, E, W =
map(prepend, ["US", "EU", "east", "west"])
def mult_EU(petri:PetriNet):
res = PetriNet()
for state in petri.S:
us = add_state(res, US(state))
eu = add_state(res, EU(state))
add_transition(res, E(state), [us]=>[eu])
add_transition(res, W(state), [eu]=>[us])
for (T, (I, O)) in petri.T:
add_transition(res, US(T), US.(I)=>US.(O))
add_transition(res, EU(T), EU.(I)=>EU.(O))
return res
```

```
def multiply(r1:PetriNet, r2:PetriNet):
res = PetriNet()
for (s1, s2) in itertools.product(r1.S, r2.S):
add_state(res, f"{s1}_{s2}")
for rx1 in r1.T:
for s2 in r2.S:
add_transition(res, rename_rxn(rx1, s2))
for rx2 in r2.T:
for s1 in r1.S:
add_transition(res, rename_rxn(rx2, s1))
return RxnNet(rs)
def rename_rxn(rxn, species:str): # TODO
```

```
def prepend(pre): return lambda s: pre + "_" + s
US, EU, E, W =
map(prepend, ["US", "EU", "east", "west"])
def mult_EU(petri:PetriNet):
res = PetriNet()
for state in petri.S:
us = add_state(res, US(state))
eu = add_state(res, EU(state))
add_transition(res, E(state), [us]=>[eu])
add_transition(res, W(state), [eu]=>[us])
for (T, (I, O)) in petri.T:
add_transition(res, US(T), US.(I)=>US.(O))
add_transition(res, EU(T), EU.(I)=>EU.(O))
return res
```

```
def multiply(r1:PetriNet, r2:PetriNet):
res = PetriNet()
for (s1, s2) in itertools.product(r1.S, r2.S):
add_state(res, f"{s1}_{s2}")
for rx1 in r1.T:
for s2 in r2.S:
add_transition(res, rename_rxn(rx1, s2))
for rx2 in r2.T:
for s1 in r1.S:
add_transition(res, rename_rxn(rx2, s1))
return RxnNet(rs)
def rename_rxn(rxn, species:str): # TODO
```

```
def prepend(pre): return lambda s: pre + "_" + s
US, EU, E, W =
map(prepend, ["US", "EU", "east", "west"])
def mult_EU(petri:PetriNet):
res = PetriNet()
for state in petri.S:
us = add_state(res, US(state))
eu = add_state(res, EU(state))
add_transition(res, E(state), [us]=>[eu])
add_transition(res, W(state), [eu]=>[us])
for (T, (I, O)) in petri.T:
add_transition(res, US(T), US.(I)=>US.(O))
add_transition(res, EU(T), EU.(I)=>EU.(O))
return res
```

```
def multiply(r1:PetriNet, r2:PetriNet):
res = PetriNet()
for (s1, s2) in itertools.product(r1.S, r2.S):
add_state(res, f"{s1}_{s2}")
for rx1 in r1.T:
for s2 in r2.S:
add_transition(res, rename_rxn(rx1, s2))
for rx2 in r2.T:
for s1 in r1.S:
add_transition(res, rename_rxn(rx2, s1))
return RxnNet(rs)
def rename_rxn(rxn, species:str): # TODO
```

**New problem expressed in terms of old problem.**

\(\Rightarrow\)

\(\Rightarrow\)

**AlgebraicJulia**

**New problem expressed in terms of old problem.**

**New problem expressed in terms of old problem.**

```
"""
Species and transitions are vertices.
Inputs and outputs are edges.
"""
def petri_to_graph(p:PetriNet):
grph = Graph()
vs = {s: add_vertex(grph) for s in p.S}
for (t, (i, o)) in pairs(p.T):
t = add_vertex(grph)
for e in i:
add_edge(grph, vs[e], t)
for e in o:
add_edge(grph, t, vs[e])
return grph
```

**Common theme**: writing code vs *declaring* relationships between abstractions.

Problem | Python solution | AlgebraicJulia solution |
---|---|---|

Different pieces of a model need to be glued together. | Write a script which does the gluing or modifies how pieces are constructed. | Declare how overlap relates to the building blocks. (colimits) |

Different aspects of a model need to be combined / a distinction is needed. | Write a script which creates copies of one aspect for every part of the other aspect. | Declare how the different aspects interact with each other. (limits) |

We want to integrate systems at different levels of granularity. | Refactor the original code to incorporate the more detailed subsystem. | Separate syntax/semantics. Declare how the part relates to the whole at syntax level. (operads) |

We make a new assumption and want to migrate old knowledge into our new understanding. | Write a script to convert old data into updated data. | Declare how the new way of seeing the world (i.e. schema) is related to the old way. (data migration) |

- We can use verified, interpretable models (AI or otherwise) without losing too much economic value
- Reduce incentive to hand authority over to uninterpretable black boxes.

- We can use verified, interpretable models (AI or otherwise) without losing too much economic value
- Reduce incentive to hand authority over to uninterpretable black boxes.

- AI can target domains with a well-specified syntax and semantics allows for legibility and explanation
- Davidad’s ARIA proposal: world-models as autonomous AI output
- Anna Leshinaskaya’s talk on moral decisionmaking w/ combinatorial grammar
- Yoshua Bengio’s emphasis of separating model from implementation

- We can use verified, interpretable models (AI or otherwise) without losing too much economic value
- Reduce incentive to hand authority over to uninterpretable black boxes.

- AI can target domains with a well-specified syntax and semantics allows for legibility and explanation
- Davidad’s ARIA proposal: world-models as autonomous AI output
- Anna Leshinaskaya’s talk on moral decisionmaking w/ combinatorial grammar
- Yoshua Bengio’s emphasis of separating model from implementation

- We facilitate collaboration between diverse stakeholders, better alignment of values
- Opens opportunities for more equitable representation of values from all stakeholders (rather than just those who’ve mastered dependent type theory)

Informal definition: a category is a bunch of things that are related to each other

Key intuition: category theory is concerned with *universal properties*.

- These change something that we once thought of as a property of an object into a kind of relationship that object has towards related objects.

Consider mathematical sets which are related to each other via functions.

**Definition in terms of internal properties**

The empty set is the unique set which has no elements in it.

But if we we look at how the empty set relates to all the other sets, we’ll eventually notice something about these relations.

**Definition in terms of external relationships (universal properties)**

The empty set is the unique set which has *exactly* one function into every other set.

Consider colored graphs related to each other via vertex mappings which preserve color and edges.

**Definition in terms of internal properties**

The empty graph uniquely has no vertices nor edges in it.

But if we we look at how it relates to all the other graphs, we’ll eventually notice something characteristic.

**Definition in terms of external relationships (universal properties)**

The empty graph is the unique graph which has *exactly* one graph mapping into every other graph.

Category theory enforces good conceptual hygeine - one isn’t allowed to depend on “implementation details” of the things which feature in its definitions.

This underlies the ability of models built in AlgebraicJulia to be extended and generalized without requiring messy code refactor.

CT is useful for the same reason interfaces are generally useful.

In particular, CT provides *generalized*^{1} notions of:

- multiplication / multidimensionality
- adding things side-by-side
- gluing things along a common boundary
- looking for a pattern
- find-and-replace a pattern
- parallel vs sequential processes

- Mad Libs style filling in of wildcards
- Zero and One
- “Open” systems
- Subsystems
- Enforcing equations
- Symmetry

These abstractions all fit very nicely with each other:

- conceptually built out of basic ideas of
**limits**,**colimits**, and**morphisms**.

We can use them to replace a large amount of our *code* with high level, conceptual *data*.

```
"""Define the multiphysics"""
Diffusion = @decapode DiffusionQuantities begin
C::Form0{X}
ϕ::Form1{X}
ϕ == k(d₀{X}(C)) # Fick's first law
end
Advection = @decapode DiffusionQuantities begin
C::Form0{X}
(V, ϕ)::Form1{X}
ϕ == ∧₀₁{X}(C,V)
end
Superposition = @decapode DiffusionQuantities begin
(C, Ċ)::Form0{X}
(ϕ, ϕ₁, ϕ₂)::Form1{X}
ϕ == ϕ₁ + ϕ₂
Ċ == ⋆₀⁻¹{X}(dual_d₁{X}(⋆₁{X}(ϕ)))
∂ₜ{Form0{X}}(C) == Ċ
end
compose_diff_adv = @relation (C, V) begin
diffusion(C, ϕ₁)
advection(C, ϕ₂, V)
superposition(ϕ₁, ϕ₂, ϕ, C)
end
```

```
"""Assign semantics to operators"""
funcs = sym2func(mesh)
funcs[:k] = Dict(:operator => 0.05 * I(ne(mesh)),
:type => MatrixFunc())
funcs[:⋆₁] = Dict(:operator => ⋆(Val{1}, mesh,
hodge=DiagonalHodge()), :type => MatrixFunc());
funcs[:∧₀₁] = Dict(:operator => (r, c, v) -> r .=
∧(Tuple{0,1}, mesh, c, v), :type => InPlaceFunc())
```

- Papers and talks: algebraicjulia.org
- Blog posts: algebraicjulia.org/blog and topos.site/blog
- Code: github.com/AlgebraicJulia

Focuses on *relationships between things* without talking about the things themselves.

Invented in the 1940’s to connect different branches of math.

**A category consists of objects and morphisms (arrows).**

- We don’t need to know anything about the objects.
- Compose \(A \rightarrow B\) and \(B \rightarrow C\) to get \(A \rightarrow C\).
- Like a graph, but we care about
*paths*, not edges.

CT studies certain shapes of combinations of arrows.

- These can be
*local*shapes, e.g. a*span*: \(\huge \cdot \leftarrow \cdot \rightarrow \cdot\)

- These can be
*global*, e.g. an*initial*object: \(\huge \boxed{\cdot \rightarrow \cdot\rightarrow \cdot \rightarrow \dots}\)

Compare to *interfaces* in computer science:

- declare that some collection of things are related in a particular way
*without*saying what they are.

In some sense a category is just a particular interface.

- Category of sets and functions
- Category of sets and subsets
- Category of \(\mathbb{Z}\) and \(\leq\)
- Category of categories and functors

- Category of chemical reaction networks
- Category of chemical structures
- Category of datasets
- Category of datatypes and programs

**CT is also the study of interfaces in general. It knows which are good ones.**