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This presentation provides a comprehensive overview on ALMA, the EU-funded project aimed at leveraging AML properties to develop a new generation of interactive, human-centric machine learning systems. The presentation provides a deep overview of the whole project, covering from the basics on Algebraic Machine Learning (AML) technology to the specifics of the ALMA project.

- 1. May 19, 2022 This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 952091. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 952091. HUMAN CENTRIC ALGEBRAIC MACHINE LEARNING AML & ALMA Project overview Presenters: ● Fernando Martin-Maroto (ALG) ● Raúl Sánchez-Mateos Lizano (EPROS) June 1, 2023
- 2. 2 Agenda ● Algebraic Machine Learning (AML) ● General Overview ● ALMA Project ○ Consortium responsibilities ○ Overall Architecture Deﬁnition ○ Market positioning
- 3. 3 Agenda ● Algebraic Machine Learning (AML) ● General Overview ● ALMA Project ○ Consortium responsibilities ○ Overall Architecture Deﬁnition ○ Market positioning
- 4. | | 4 AML & ALMA Project Overview - Algebraic Machine Learning (AML) June 1, 2023 H2020-EIC-FETPROACT-2019 AML - The new technological direction Research in a new Machine Learning paradigm based on Algebra ALGEBRAIC MACHINE LEARNING Machine Learning from semantic embeddings of data and formal knowledge into discrete algebraic structures
- 5. | | 5 AML & ALMA Project Overview - Algebraic Machine Learning (AML) June 1, 2023 H2020-EIC-FETPROACT-2019 AML - What is it? AML vs Traditional approaches to machine learning: Symbolic AI: ⚙ Description of the world using formulas ⚙ Difﬁculty learning from data ⚙ High transparency Statistical Learning (including Neural Networks) ⚙ Learning from data ⚙ Difﬁculty using formal descriptions ⚙ Usually opaque Algebraic Machine Learning: ⚙ Description of the world using formulas ⚙ Learning from data ⚙ Can combine data and formal descriptions ⚙ High transparency
- 6. | | 6 AML & ALMA Project Overview - Algebraic Machine Learning (AML) June 1, 2023 H2020-EIC-FETPROACT-2019 AML - What is it? AML vs Traditional approaches to machine learning: Symbolic AI: ⚙ Uses symbols ⚙ Symbols represent real world objects ⚙ Mostly uses discrete mathematics ⚙ Symbols are permanent Statistical Learning (including Neural Networks) ⚙ Uses parameters ⚙ Parameters can map to the world or to intermediate internal descriptions ⚙ Mostly uses continuous mathematics ⚙ Parameters can change Algebraic Machine Learning: ⚙ Uses symbols ⚙ Symbols can map to the world (constants) or to intermediate internal descriptions (atoms) ⚙ Uses discrete mathematics ⚙ Can create new symbols ⚙ Symbols can change
- 7. | | 7 AML & ALMA Project Overview - Algebraic Machine Learning (AML) June 1, 2023 H2020-EIC-FETPROACT-2019 AML - What is it? AML vs Traditional approaches to machine learning: underlying principles Symbolic AI: ⚙ Satisﬁability, ⚙ Logic, deduction, inference Statistical Learning (including Neural Networks) ⚙ Error minimization ⚙ Fitting ⚙ Statistical inference Algebraic Machine Learning: ⚙ Indecomposability ⚙ Maximization of algebraic freedom ⚙ Small size ⚙ Stability of indecomposable components
- 8. | | 8 AML & ALMA Project Overview - Algebraic Machine Learning (AML) June 1, 2023 H2020-EIC-FETPROACT-2019 AML - What is it? Traditional approaches to machine learning DATA FORMAL KNOWLEDGE TRAINING DATA EMBEDDING IN AN ALGEBRAIC THEORY AML ENGINE = …. Φ Φ Φ Φ Φ symbols defined by the user symbols generated by the engine
- 9. | | 9 AML & ALMA Project Overview - Algebraic Machine Learning (AML) June 1, 2023 H2020-EIC-FETPROACT-2019 AML - Proven Cases Proven cases of Algebraic Machine Learning Learning a maze Queens completion problem Handwritten digit recognition - Supervised learning (MNIST) - Atoms: algebraic elements resulting from learning - 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
- 10. | | 10 AML & ALMA Project Overview - Algebraic Machine Learning (AML) June 1, 2023 H2020-EIC-FETPROACT-2019 AML - Proven Cases Proven cases of Algebraic Machine Learning Classiﬁcation of human motion Finding hamiltonian paths Resolving and creating sudokus - Learning from formal knowledge to solve sudoku games - Rules are encoded in the algebra - Inventing new sudoku games - Finding a hamiltonian path from a description of the tasks. - More efﬁcient than naive backtracking methods - Learning from both formal knowledge and data (OPPORTUNITY) - Activity recognition.
- 11. | | 11 AML & ALMA Project Overview - Algebraic Machine Learning (AML) June 1, 2023 H2020-EIC-FETPROACT-2019 AML - Ongoing case studies Ongoing Algebraic Machine Learning applications World models Robot control and path planning Interaction with gesture keyboard - Gesture keyboard - Conﬁdence feedback interface - Robot control and landscape navigation - Formal description of high level real-world concepts - Ethical aspects
- 12. | | 12 AML & ALMA Project Overview - Algebraic Machine Learning (AML) June 1, 2023 H2020-EIC-FETPROACT-2019 AML - Features Research in a new Machine Learning paradigm based on Algebra Less sensitive to statistical features of training data No tradeoff between memorization and learning (no overﬁtting) High mathematical transparency Large-scale distributed learning Interactive ML
- 13. | | 13 AML & ALMA Project Overview - Algebraic Machine Learning (AML) June 1, 2023 H2020-EIC-FETPROACT-2019 AML - Features Unique features of Algebraic Machine Learning Less sensitive to statistical features of training data High mathematical transparency Distributed ML ecosystem Interactive ML No tradeoff between memorization and learning (no overﬁtting)
- 14. | | 14 AML & ALMA Project Overview - Algebraic Machine Learning (AML) June 1, 2023 H2020-EIC-FETPROACT-2019 Symbolic AI capable of learning from: ⚙ Semantic embedding of data ⚙ Identify patterns in images (supervised learning) ⚙ Formal speciﬁcation of human knowledge ⚙ Solve the N-Queen completion problem from a formal description of the rules of the game (unsupervised learning) AML - Features Research in a new Machine Learning paradigm based on Algebra
- 15. | | 15 AML & ALMA Project Overview - Algebraic Machine Learning (AML) June 1, 2023 H2020-EIC-FETPROACT-2019 AML - Features Unique features of Algebraic Machine Learning Less sensitive to statistical features of training data High mathematical transparency Distributed ML ecosystem Interactive ML No tradeoff between memorization and learning (no overﬁtting)
- 16. | | 16 AML & ALMA Project Overview - Algebraic Machine Learning (AML) June 1, 2023 H2020-EIC-FETPROACT-2019 AML - Learning and memorization Unique features of Algebraic Machine Learning
- 17. | | 17 AML & ALMA Project Overview - Algebraic Machine Learning (AML) June 1, 2023 H2020-EIC-FETPROACT-2019 AML - Features Unique features of Algebraic Machine Learning Less sensitive to statistical features of training data High mathematical transparency Distributed ML ecosystem Interactive ML No tradeoff between memorization and learning (no overﬁtting)
- 18. | | 18 AML & ALMA Project Overview - Algebraic Machine Learning (AML) June 1, 2023 H2020-EIC-FETPROACT-2019 AML - Features Unique features of Algebraic Machine Learning Less sensitive to statistical features of training data High mathematical transparency Distributed ML ecosystem Interactive ML No tradeoff between memorization and learning (no overﬁtting)
- 19. | | 19 AML & ALMA Project Overview - Algebraic Machine Learning (AML) June 1, 2023 H2020-EIC-FETPROACT-2019 AML - Features Unique features of Algebraic Machine Learning
- 20. | | 20 AML & ALMA Project Overview - Algebraic Machine Learning (AML) June 1, 2023 H2020-EIC-FETPROACT-2019 AML - Features Unique features of Algebraic Machine Learning Less sensitive to statistical features of training data High mathematical transparency Distributed ML ecosystem Interactive ML No tradeoff between memorization and learning (no overﬁtting)
- 21. | | 21 AML & ALMA Project Overview - Algebraic Machine Learning (AML) June 1, 2023 H2020-EIC-FETPROACT-2019 AML - References ⚙ Method for large-scale distributed machine learning using formal knowledge and training data, (2018) PCT application, US patent application US20190385087A1 F. Martin-Maroto ⚙ Algebraic Machine Learning. F. Martin-Maroto, & G. de Polavieja (2018). Algebraic Machine Learning. arXiv:1803.05252. ⚙ Finite Atomized Semilattices. F. Martin-Maroto, F., & G. de Polavieja (2021). Finite Atomized Semilattices. arXiv:2102.08050. ⚙ In-memory Processing of Algebraic Machine Learning, (2021) PCT application, US patent application, F. Martin-Maroto, N. Abderrahaman-Elena, G. de Polavieja. ⚙ Semantic Embeddings in Semilattices. F. Martin-Maroto & G. de Polavieja. (2022). Publicly available documents
- 22. 22 Agenda ● Algebraic Machine Learning (AML) ● General Overview ● ALMA Project ○ Consortium responsibilities ○ Overall Architecture Deﬁnition ○ Market positioning
- 23. | | 23 AML & ALMA Project Overview - General Overview June 1, 2023 H2020-EIC-FETPROACT-2019 General Overview AML - a new generation of interactive human-centric learning systems 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 state.of-the-art ofﬂine and centralised data processing. Pursue new avenues, such as incremental, unsupervised, active, one-shot and ‘small data’ ML. Machine learning
- 24. | | 24 AML & ALMA Project Overview - General Overview June 1, 2023 H2020-EIC-FETPROACT-2019 General Overview Objectives of ALMA project Models, ethics and culture with AML Dissemination of AML Use cases 4 5 6 1 2 3 Foundations of AML Methodologies to work with AML Computing and networking tools Principles of generalization in AML and combination with other ML techniques AML Description Language to enhance Human-Computer interaction Decentralised platform to integrate AML-based nodes and connect them with other SWs Represent complex human concepts with AML Promote the adoption of AML Verify AML ideas and requirements of project developments
- 25. | | 25 AML & ALMA Project Overview - General Overview June 1, 2023 H2020-EIC-FETPROACT-2019 General Overview AML - The new technological direction Problem ⚙ Traditional ML ⚙ High sensitivity to statistical properties of training data ⚙ Major difﬁculties combining heterogeneous knowledge ⚙ Current ML algorithms models ⚙ Difﬁcult to understand ⚙ Statistical learning “black-boxes” ⚙ Implicit biases in decision making Approach ⚙ AML - a new viable Artiﬁcial Intelligence paradigm ⚙ New radical approach based on algebraic embeddings ⚙ Next AI frontier with veriﬁable features of ⚙ Explainability ⚙ Trustworthiness ⚙ Transparency
- 26. 26 Agenda ● Algebraic Machine Learning (AML) ● General Overview ● ALMA Project ○ Consortium responsibilities ○ Overall Architecture Deﬁnition ○ Market positioning
- 27. | | 27 AML & ALMA Project Overview - ALMA Project June 1, 2023 H2020-EIC-FETPROACT-2019 ALMA Project Overview 1. Consortium responsibilities 2. Overall architecture deﬁnition 3. Market positioning Table of contents
- 28. | | 28 AML & ALMA Project Overview - ALMA Project June 1, 2023 H2020-EIC-FETPROACT-2019 ALMA Project Overview 1. Consortium responsibilities 2. Overall architecture deﬁnition 3. Market positioning Table of contents
- 29. | | 29 AML & ALMA Project Overview - ALMA Project June 1, 2023 H2020-EIC-FETPROACT-2019 ALMA Consortium Proyectos y Sistemas de Mantenimiento SL (eProsima) German Research Center for Artiﬁcial Intelligence Technical Research Centre of Finland
- 30. | | 30 AML & ALMA Project Overview - ALMA Project June 1, 2023 H2020-EIC-FETPROACT-2019 WP1 - PM, Architecture & Tech. coordination Project management ● Project organisation and communication ● Reporting, ﬁnancial management ● Progress monitoring and risk mitigation Overall Architecture deﬁnition ● Coordinate scientiﬁc and technical inputs ● AML-DL and AML-IP speciﬁcations ● Complete software, interfaces, dependencies, and interactions design Innovation management plan Consortium responsibilities regarding WP1 1 2 3
- 31. | | 31 AML & ALMA Project Overview - ALMA Project June 1, 2023 H2020-EIC-FETPROACT-2019 WP2 - Fundamentals of Interactive AML Generalization of AML ● How well AML generalizes outside training dataset Work with traditional ML systems ● Compare AML result with statistical learning systems ● Couple other ML techniques (deep learning) with AML Human-AML interaction ● Test the ability of AML to learn from formal knowledge ● How AML teach the human the results (human in the training loop) Collective learning ● Learn from many algebras running in parallel Consortium responsibilities regarding WP2 1 2 3 4
- 32. | | 32 AML & ALMA Project Overview - ALMA Project June 1, 2023 H2020-EIC-FETPROACT-2019 WP3 - AML Description language AML-DL speciﬁcation ● Write the AML-DL speciﬁcation and AML-DL interpreter Consistency checker ● Validate algebraic instruction blocks Debugging tools ● Tools to assist AML-DL developers AML accelerator ● Research on SW/HW AML acceleration Consortium responsibilities regarding WP3 1 2 3 4
- 33. | | 33 AML & ALMA Project Overview - ALMA Project June 1, 2023 H2020-EIC-FETPROACT-2019 WP4 - Human AML Interaction Cognitive foundations for Human-AML interaction ● How human learn from and control interaction with AML Interaction paradigm methodology ● Enable AI researchers to create more effective human-computer partnerships ● Requirements for AML based interactive machine learning Working prototype ● Demonstrate the design methodology and interaction paradigm Evaluation methods ● Efﬁciency of the interaction from human perspective Consortium responsibilities regarding WP4 1 2 3 4
- 34. | | 34 AML & ALMA Project Overview - ALMA Project June 1, 2023 H2020-EIC-FETPROACT-2019 WP5 - Models, ethics and culture with AML AML based world models ● Embed complex models into AML Represent complex human concepts with AML ● Human centric AI Human-AML co-creation of complex models ● AML ability to recognize complex real world situations AML ethical and cultural concepts model ● Reﬁnement of Human-AML co-creation of complex domain models Consortium responsibilities regarding WP5 1 2 3 4
- 35. | | 35 AML & ALMA Project Overview - ALMA Project June 1, 2023 H2020-EIC-FETPROACT-2019 WP6 - System tools Consortium responsibilities regarding WP6 AML Integrating Platform (AML-IP) ● Interconnect AML components ● Cloud and edge computing environments Robotics and Constrained Devices ● Extend AML-IP for ROS 2 compatibility Open source tools for AML experimentation ● Libraries with reusable AML algorithms for AI/ML problems 1 2 3
- 36. | | 36 AML & ALMA Project Overview - ALMA Project June 1, 2023 H2020-EIC-FETPROACT-2019 WP7 - Use cases Image Classiﬁcation using interactive AML ● Study image classiﬁcation improvements with AML Intelligent Tools for supporting creative professionals ● Provide support for cultural, gender and related issues Higher-level cognition for domestic assistance robots ● Encode domestic tasks using AML-DL Consortium responsibilities regarding WP7 1 2 3
- 37. | | 37 AML & ALMA Project Overview - ALMA Project June 1, 2023 H2020-EIC-FETPROACT-2019 WP8 - Dissemination, Exploitation and Collaboration Communication and dissemination strategy ● Publish results ● Participation in events and workshop organization Collaboration with other projects and initiatives ● Collaborate with ROS and FIWARE to build open source tools ● Collaborate with AI/ML european communities Exploitation and Outlook Plan ● Short and long term plans for AML dissemination and exploitation ● Alignment with the European Research Agenda for AI Consortium responsibilities regarding WP8 1 2 3
- 38. | | 38 AML & ALMA Project Overview - ALMA Project June 1, 2023 H2020-EIC-FETPROACT-2019 ALMA Project Overview 1. Consortium responsibilities 2. Overall architecture deﬁnition 3. Market positioning Table of contents
- 39. | | 39 AML & ALMA Project Overview - ALMA Project June 1, 2023 H2020-EIC-FETPROACT-2019 Overall architecture deﬁnition ALMA Architecture and WP dependencies
- 40. | | 40 AML & ALMA Project Overview - ALMA Project June 1, 2023 H2020-EIC-FETPROACT-2019 ALMA Project Overview 1. Consortium responsibilities 2. Overall architecture deﬁnition 3. Market positioning Table of contents
- 41. | | 41 AML & ALMA Project Overview - ALMA Project June 1, 2023 H2020-EIC-FETPROACT-2019 ALMA Mission To provide a new ML paradigm, known as AML ⚙ Easily understandable by (no black-box) ⚙ Ease of interaction (Human-AML interaction) ⚙ Seamless integration with AML-IP ⚙ Ensure long-term maintenance of AML environments Spain Portugal Germany France Finland
- 42. | | 42 AML & ALMA Project Overview - ALMA Project June 1, 2023 H2020-EIC-FETPROACT-2019 USP - Value Proposition Core message and brand promise of ALMA project Controllable transparent, distributed and non-centralized machine learning. An algebraic approach to ML that can complement statistical methods. Brand promise AML: The novel Machine Learning paradigm leveraging abstract algebra for better control and more transparent AI. Core message
- 43. | | 43 AML & ALMA Project Overview - ALMA Project June 1, 2023 H2020-EIC-FETPROACT-2019 Market positioning Community impact and engagement ● alma-ai.eu ● eprosima.com/products-all/r-d-projects/eu-project-alma ● github.com/eProsima/AML-IP
- 44. | | alma-ai.eu 44 AML & ALMA Project Overview June 1, 2023 alma@eprosima.com alma-ai.eu H2020-EIC-FETPROACT-2019