Replicating "What’s the Dollar of Mexico?" in HyperdimensionalComputing.jl

Kanerva showcases in this publication how we can create prototype concepts and mappings between them using Hyperdimensional Computing (HDC), a computing paradigm based on very big random vectors populated with binary values.

This very big random vectors, which we calls "words" or "codes", represent concepts, but when analyzing this as mathematical constructs they are nothing else than random signals. When combined using bundling and binding operations, one can construct concepts and reason based on the similarity of this words, always assuming that this retrieval is approximated.

Since the properties of the operations that enable composability of this "codes" are mathematically grounded, the characteristics of the operations permeate into this modelling mindset and enable for the creation of (i) prototype representations and (ii) mapping function, both based on the same underlying mathematical structure. Thanks to this, we can either model mapping one concept into another space or directly map a mapping function from one space into another, basically mimicking how the brain works:

The literal image created by the words is intended to be transferred to and interpreted in a

new context, exemplifying the mind's reliance on prototypes: the literal meaning provides the prototype. When the mind expands its scope in this way it creates an incredibly rich web of associations and meaning...

To showcase this, we will reimplement the example proposed by Kanerva on this paper.

using HyperdimensionalComputing

In HDC, concepts are represented as high-dimensional random vectors (hypervectors). Here, we create hypervectors for the abstract concepts of COUNTRY, CAPITAL, and MONEY.

COUNTRY = BipolarHV(:country)
CAPITAL = BipolarHV(:capital)
MONEY = BipolarHV(:money)

COUNTRY, CAPITAL, MONEY

(1 / -1 : 5011 / 4989, 1 / -1 : 5011 / 4989, 1 / -1 : 4957 / 5043)

Next, we create hypervectors for specific instances: countries, their capitals, and currencies.

USA = BipolarHV(:usa)
MEX = BipolarHV(:mexico)

WDC = BipolarHV(:wdc)      # Washington DC
MXC = BipolarHV(:mxc)      # Mexico City

DOL = BipolarHV(:dollar)
PES = BipolarHV(:peso)

USA, MEX, WDC, MXC, DOL, PES

(1 / -1 : 5043 / 4957, 1 / -1 : 4977 / 5023, 1 / -1 : 4894 / 5106, 1 / -1 : 4999 / 5001, 1 / -1 : 4883 / 5117, 1 / -1 : 4996 / 5004)

We now build holistic representations for the United States and Mexico by binding each concept (country, capital, money) to its specific instance and then adding them together.

USTATES = (COUNTRY * USA) + (CAPITAL * WDC) + (MONEY * DOL)
MEXICO = (COUNTRY * MEX) + (CAPITAL * MXC) + (MONEY * PES)

USTATES, MEXICO

(1 / -1 : 4972 / 5028, 1 / -1 : 5035 / 4965)

These composite hypervectors encode all the information about each country in a single vector. The Base.isapprox function or ≈ operator checks if two hypervectors are approximately equal (i.e., similar).

USTATES ≈ MEXICO

false

By binding (represented by the * operator) the holistic representations of USA and Mexico, we create a new hypervector that encodes the relationships between their respective elements: USA with Mexico, Washington DC with Mexico City, and dollar with peso, plus some noise due to the high-dimensional operations.

F_UM = USTATES * MEXICO

10000-element BipolarHV
1 / -1 : 4987 / 5013

F_UM ≈ (USA * MEX) + (WDC * MXC) + (DOL * PES)

true

To answer Kanerva's question "What in Mexico corresponds to United States' dollar?", we unbind (represented again by the * operator) the dollar hypervector with the USA-Mexico relationship vector. The result should be similar to the peso hypervector.

DOL * F_UM ≈ PES

true

Let's add another country, Sweden, with its capital and currency.

SWE = BipolarHV(:sweden)
STO = BipolarHV(:stockholm)
SEK = BipolarHV(:krona)

SWEDEN = (COUNTRY * SWE) + (CAPITAL * STO) + (MONEY * SEK)

10000-element BipolarHV
1 / -1 : 5063 / 4937

We can now explore more complex relationships by binding and combining these holistic representations. For example, F_SUencodes the relationship between Sweden and USA, and F_SM between Sweden and Mexico.

F_UM = USTATES * MEXICO
F_SU = SWEDEN * USTATES
F_SM = SWEDEN * MEXICO

10000-element BipolarHV
1 / -1 : 5028 / 4972

Combining these relationship vectors allows us to infer new relationships, such as how Sweden relates to Mexico via the USA.

F_SU * F_UM ≈ F_SM

true

We can also directly query for corresponding elements between countries:

For example, what is the currency of Mexico, given the currency of the USA?

USTATES * DOL ≈ MEXICO * PES

true

Or, what is the currency of Mexico, given the USA and its currency?

USTATES * DOL * MEXICO ≈ PES

true

Similarly, we can query for Sweden's currency using the same approach.

USTATES * DOL * MEXICO ≈ SEK

false

Or, recover the original currency of the USA.

USTATES * DOL * MEXICO ≈ DOL

false

This tutorial demonstrates how hyperdimensional computing enables analogical reasoning and flexible querying by representing and manipulating concepts as high-dimensional vectors.

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