Algebraic machine learning (AML) is a relatively new machine learning technique based on algebraic representations of data. Unlike statistical learning, AML algorithms are robust regarding the statistical properties of the data and are parameter-free. The aim of the EU-funded ALMA project is to leverage AML properties to develop a new generation of interactive, human-centric machine learning systems. These systems are expected to reduce bias and prevent discrimination, remember what they know when they are taught something new, facilitate trust and reliability and integrate complex ethical constraints into human–artificial intelligence systems. Furthermore, they are expected to promote distributed, collaborative learning. More info at https://alma-ai.eu.

1. HUMAN CENTRIC ALGEBRAIC MACHINE LEARNING
-LEVERAGING ABSTRACT ALGEBRA FOR A MORE TRANSPARENT AI-
ALMA

2. December 17, 2020 H2020-EIC-FETPROACT-2019
Problem
⚙ Current machine
learning algorithms
models
⚙ Difficult to understand
⚙ Opaque
⚙ Implicit biases in decision
making
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⚙ A new viable Artificial
Intelligence paradigm
⚙ Next AI frontier with
verifiable features of
explainability,
trustworthiness and
transparency.
⚙ New radical approach
Approach

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Human-Centric AI
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Training &
Decision Making
Process
Machine decisions can be challenged,
interpreted, refined and adjusted.
Mutual exchange, introspection and active
learning of both system and user.
User introspection
Explore models beyond the dominant off-line
and centralised data processing.
Pursue new avenues, such as incremental,
unsupervised, active, one-shot and ‘small data’
ML.
Machine learning

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⚙ ALMA project contributes to the debate on
⚙ the socio-technical,
⚙ organizational, and
⚙ ethical dimensions of AI.
⚙ Aligns with the Commission’s broader AI strategy.
Human-Centric AI

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The new technological direction:
ALGEBRAIC MACHINE LEARNING
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Machine learning from semantic embeddings of data and formal
knowledge into discrete algebraic structures

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Algebraic Machine Learning
Algebraic Machine Learning (AML) is a
form of symbolic AI capable of learning
from data or from formal descriptions
or both.
It can combine the bottom-up and
top-down approaches to learning as it
treats data and formalized knowledge
in the same way.
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True False
Atomized Model

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For example, the same AML algorithm can:
⚙ learn supervised to identify patterns in images (e.g. learn to identify digits from
the MNIST handwritten character dataset)
⚙ teach itself unsupervised to play Sudoku from a formal description of the rules
of the game
Algebraic Machine Learning

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AML: Supervised Classification

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AML: Unsupervised Learning

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AML: Proven Cases
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Handwritten
digit recognition
17 Queens
completion problem
Learning a maze
- Supervised learning (MNIST)
- Atoms: algebraic elements
resulting from learning
- Each atom is represented by is
components of B&W pixels
- AML can memorize mislabelled
examples
- Learning from formal knowledge
- Rules encoded in the algebra
- AML understands the game from
the beginning of learning process
- Learning from formal knowledge
- The path concept and geometry are
encoded in the algebra

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AML: Decentralized Learning
Independently learning AML agents can update each other
asynchronously and learn together without constraints on when and
how frequently they should communicate.
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AML conceptualizes the learning output

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AML: Generalization + Memorization
⚙ AML can generalize while
memorizing the training
dataset
No overfitting or reduced
overfitting compared to
statistical learning
techniques
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Memorized Edge Cases
Generalized Knowledge

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AML: Batch Training

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AML vs Statistical Learning
AML is more robust to the statistical composition of the training
dataset than statistical learning methods.
For example, the frequency of presentation of training examples
does not matter.
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AML vs Statistical Learning
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AML does not target learning error.
Error rate decreases as a side effect of finding an algebraic
representation of high algebraic freedom and indecomposability.
No gradient descent needed.
No local minima problems.

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⚙ Algebraic Machine Learning, F. Martin-Maroto and G. García de Polavieja,
arXiv:1803.05252
Method For Large-scale Distributed Machine Learning Using Formal
Knowledge And Training Data, International patent application
20190385087 and US patent app 16/480625.
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AML: References

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Proyectos y Sistemas de
Mantenimiento SL
Champalimaud
Foundation
German Research Center
for Artificial Intelligence
Inria
Universidad Carlos III de
Madrid
TU Kaiserslautern
FIWARE Foundation e.V
Technical Research Centre
of Finland
ALGEBRAIC AI
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Consortium

18. Thanks for listening
We'd be please to answer any question you may have
H2020-EIC-FETPROACT-2019December 17, 2020
alma@eprosima.com
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Contact us:
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