An attention mechanism where every token in a sequence attends to every other token in the same sequence, computing query-key compatibility scores to produce context-aware weighted value representations that capture intra-sequence dependencies without recurrence.
Semantic Classification
Content
- An attention mechanism where every token in a sequence attends to every other token in the same sequence, allowing the model to capture intra-sequence dependencies.
7️⃣ 🟢 Transformers
- Description: Circa 2017, introduced self-attention mechanism to capture dependencies between different words in a sequence.
- Explain: Examines the interdependencies across a wider view of words / tokens
- Paper: Attention Is All You Need (arxiv.org) (underpinned recent advances)
- Not the only game in town State Space Sequence Models and others
- Next presentation slide Proprietary Large Language Models
Bridging the Gap: SSMs, Attention, and Structured Matrices
- Before diving into the specifics of Mamba2, the paper establishes a crucial theoretical framework that connects three seemingly disparate concepts: structured state space models (SSMs), attention mechanisms, and structured matrices. This framework, termed “structured state space duality” (SSD), lays the groundwork for understanding the core innovations of Mamba2 and its relationship to existing sequence modelling paradigms.
- The paper reveals a fundamental truth about SSMs: they can be viewed as matrix transformations operating on input sequences. This insight reframes the computation of SSMs as a matrix multiplication problem, where the matrix itself exhibits a specific structure known as semiseparability. Semiseparable matrices are characterized by low-rank submatrices, a property that Mamba2 cleverly exploits for efficiency gains.
- A particularly important class of semiseparable matrices, the 1-semiseparable matrices, take center stage in understanding the efficiency of autoregressive sequence modelling. These matrices correspond to simple scalar recurrences, the building blocks of many efficient SSM algorithms. The paper demonstrates that the efficient computation of autoregressive attention, a crucial capability for language modelling, is intrinsically linked to the properties of semiseparable matrices.
7️⃣ 🟢 Transformers
- Description: Circa 2017, introduced self-attention mechanism to capture dependencies between different words in a sequence.
- Explain: Examines the interdependencies across a wider view of words / tokens
- Paper: Attention Is All You Need (arxiv.org) (underpinned recent advances)
- Not the only game in town State Space Sequence Models and others
- Next presentation slide Proprietary Large Language Models
Bridging the Gap: SSMs, Attention, and Structured Matrices
- Before diving into the specifics of Mamba2, the paper establishes a crucial theoretical framework that connects three seemingly disparate concepts: structured state space models (SSMs), attention mechanisms, and structured matrices. This framework, termed “structured state space duality” (SSD), lays the groundwork for understanding the core innovations of Mamba2 and its relationship to existing sequence modelling paradigms.
- The paper reveals a fundamental truth about SSMs: they can be viewed as matrix transformations operating on input sequences. This insight reframes the computation of SSMs as a matrix multiplication problem, where the matrix itself exhibits a specific structure known as semiseparability. Semiseparable matrices are characterized by low-rank submatrices, a property that Mamba2 cleverly exploits for efficiency gains.
- A particularly important class of semiseparable matrices, the 1-semiseparable matrices, take center stage in understanding the efficiency of autoregressive sequence modelling. These matrices correspond to simple scalar recurrences, the building blocks of many efficient SSM algorithms. The paper demonstrates that the efficient computation of autoregressive attention, a crucial capability for language modelling, is intrinsically linked to the properties of semiseparable matrices.
Ecosystem Interventions
- The proposed infrastructure would be particularly valuable in areas spans, polarization, fake news, and breakdown of democracy”. These were not the intended consequence of engineers who aimed to maximize engagement. The underlying arms race for attention led to what they call ‘an engagement monster’ that rewrote the rules of society.
- These lessons should be learnt and the problems should be pro-actively mitigated. This proposal is bfnot a social metaverse, and deliberately limits both numbers of participants and avatar optionality.
Attention is too concentrated
- We have become dependant on algorithmically generated personal feeds, we have lost the capacity to explore our digital society
- These platforms are very vulnerable and exposed to manipulation, especially as the previous guardrails are removed for profit margins.
- These technologies are reductionist, which feels like a shortcut, but is actually a simplification across the manifold of American English, and is likely making people intellectually lazy.
- Increasingly there will be no other choice, trending toward Human-AI Capability Complementarity.
Pitch section
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Personalised emergent narratives for our visitors. What problem does the user, business or industry have that you want to solve?
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For today’s digital experience venue managers navigating the complexities of providing unique experiences, our AI solution, KnoWhere, offers a unique approach which will result in the capability to enhance visitor experiences. By utilising images from on-premise cameras, we enable to leverage data on visitors attention. Our solution’s unique value propositions include spatial and attention tracking through AI, because of our ability to understand the needs of experience designers.
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It works like this: Combine personal data, with visitor gaze Provide location and attention data stream Venue provides this to experience designers Designers build incredible emergent journeys
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We believe this solution will impact our business/industry by: Elevating interactions through personalisation Making attention in physical spaces quantifiable Providing feedback data to experience designers
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We will measure our impact by: Performing A/B testing on visitors engagement This can be a KPI that changes, ex: a productivity score
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or it can be an amount saved because of the soluion
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Describe what data is behind this AI model? Alphapose (2) Insightface (5)
Rate the quality/quantity of each point of data from 1-5 (1 being little data / low quality – 5 being lots of data / high quality)
What will be the biggest challenge in implementing the AI model? Real time pose engine is noncommercial Occlusion can be tricky with space constraints The rich dataset is a privacy concern
Here are some areas to think about in terms of challenges:
Data: How much data exists? How representative is it of what we’re trying to model? Are there issues in how it is collected which could impact the model? Is it likely to contain any missing values? Adoptance from users/customers Will it be easy to get people to use the AI in their business?
Governance Is the data accessible and are you allowed to use it? Who is responsible once the AI model is in use? How will make the final decisions?
Impact of solution What do we know about the need for this type of solution
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is it nice to have or need to have? Can we find out if we don’t know? Feasibility What will be the biggest challenge in implementing an AI solution to solve this problem? Can the issue / problem we’re solving actually be measured / forecasted?
Ethics
Regulations
Cybersecurity
“Our goal is to empower venue owners to provide an advanced platform that allows world class exhibition creators to tailor unique experiences for each visitor. This enables the crafting of rich, interconnected stories for groups of people, all while ensuring unforgettable, safe experiences for individuals and families.
4.12.1 Perception of AI and Society
The examination of AI’s implications on societal structures should undoubtedly receive the necessary attention. Soros’s language and perception of reality seem particularly interesting, especially in the era of AI. He emphasizes his belief in reality and its importance in providing moral guidance, a concept that seems increasingly challenged in the age of AI.
Attention is too concentrated
- We have become dependant on algorithmically generated personal feeds, we have lost the capacity to explore our digital society
- These platforms are very vulnerable and exposed to manipulation, especially as the previous guardrails are removed for profit margins.
- These technologies are reductionist, which feels like a shortcut, but is actually a simplification across the manifold of American English, and is likely making people intellectually lazy.
- Increasingly there will be no other choice, trending toward Human-AI Capability Complementarity.
Pitch section
-
Personalised emergent narratives for our visitors. What problem does the user, business or industry have that you want to solve?
-
For today’s digital experience venue managers navigating the complexities of providing unique experiences, our AI solution, KnoWhere, offers a unique approach which will result in the capability to enhance visitor experiences. By utilising images from on-premise cameras, we enable to leverage data on visitors attention. Our solution’s unique value propositions include spatial and attention tracking through AI, because of our ability to understand the needs of experience designers.
-
It works like this: Combine personal data, with visitor gaze Provide location and attention data stream Venue provides this to experience designers Designers build incredible emergent journeys
-
We believe this solution will impact our business/industry by: Elevating interactions through personalisation Making attention in physical spaces quantifiable Providing feedback data to experience designers
-
We will measure our impact by: Performing A/B testing on visitors engagement This can be a KPI that changes, ex: a productivity score
-
or it can be an amount saved because of the soluion
-
Describe what data is behind this AI model? Alphapose (2) Insightface (5)
Rate the quality/quantity of each point of data from 1-5 (1 being little data / low quality – 5 being lots of data / high quality)
What will be the biggest challenge in implementing the AI model? Real time pose engine is noncommercial Occlusion can be tricky with space constraints The rich dataset is a privacy concern
Here are some areas to think about in terms of challenges:
Data: How much data exists? How representative is it of what we’re trying to model? Are there issues in how it is collected which could impact the model? Is it likely to contain any missing values? Adoptance from users/customers Will it be easy to get people to use the AI in their business?
Governance Is the data accessible and are you allowed to use it? Who is responsible once the AI model is in use? How will make the final decisions?
Impact of solution What do we know about the need for this type of solution
-
is it nice to have or need to have? Can we find out if we don’t know? Feasibility What will be the biggest challenge in implementing an AI solution to solve this problem? Can the issue / problem we’re solving actually be measured / forecasted?
Ethics
Regulations
Cybersecurity
“Our goal is to empower venue owners to provide an advanced platform that allows world class exhibition creators to tailor unique experiences for each visitor. This enables the crafting of rich, interconnected stories for groups of people, all while ensuring unforgettable, safe experiences for individuals and families.
4.12.1 Perception of AI and Society
The examination of AI’s implications on societal structures should undoubtedly receive the necessary attention. Soros’s language and perception of reality seem particularly interesting, especially in the era of AI. He emphasizes his belief in reality and its importance in providing moral guidance, a concept that seems increasingly challenged in the age of AI.
Attention is too concentrated
-
We have become dependant on algorithmically generated personal feeds, we have lost the capacity to explore our digital society
-
These platforms are very vulnerable and exposed to manipulation, especially as the previous guardrails are removed for profit margins.
-
These technologies are reductionist, which feels like a shortcut, but is actually a simplification across the manifold of American English, and is likely making people intellectually lazy.
-
Increasingly there will be no other choice, trending toward Human-AI Capability Complementarity.
Characteristics
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Intra-Sequence Dependencies: Each token computes attention over all other tokens in the same sequence
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Query-Key-Value Framework: Uses query, key, and value representations for attention computation
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Permutation Invariance: Requires positional encoding as self-attention is inherently permutation-invariant
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Computational Complexity: O(n²) complexity with sequence length, challenging for very long sequences
Academic Foundations
Primary Source: Vaswani et al., “Attention Is All You Need”, arXiv:1706.03762 (2017)
Key Innovation: Demonstrated that self-attention alone, without recurrence, could model long-range dependencies effectively.
Technical Context
Self-attention models compute representations by allowing each token to attend to all other tokens using query-key compatibility scores. This mechanism enables capturing complex dependencies without the sequential constraints of recurrent architectures.
Ontological Relationships
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Broader Term: Attention Mechanism
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Related Terms: Multi-Head Attention, Scaled Dot-Product Attention, Cross-Attention
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Component Of: Transformer Architecture
Usage Context
“Self-attention models compute representations by allowing each token to attend to all other tokens using query-key compatibility scores.”
OWL Functional Syntax
Characteristics
-
Intra-Sequence Dependencies: Each token computes attention over all other tokens in the same sequence
-
Query-Key-Value Framework: Uses query, key, and value representations for attention computation
-
Permutation Invariance: Requires positional encoding as self-attention is inherently permutation-invariant
-
Computational Complexity: O(n²) complexity with sequence length, challenging for very long sequences
Academic Foundations
Primary Source: Vaswani et al., “Attention Is All You Need”, arXiv:1706.03762 (2017)
Key Innovation: Demonstrated that self-attention alone, without recurrence, could model long-range dependencies effectively.
Technical Context
Self-attention models compute representations by allowing each token to attend to all other tokens using query-key compatibility scores. This mechanism enables capturing complex dependencies without the sequential constraints of recurrent architectures.
Ontological Relationships
-
Broader Term: Attention Mechanism
-
Related Terms: Multi-Head Attention, Scaled Dot-Product Attention, Cross-Attention
-
Component Of: Transformer Architecture
Usage Context
“Self-attention models compute representations by allowing each token to attend to all other tokens using query-key compatibility scores.”
OWL Functional Syntax
References
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Vaswani, A., et al. (2017). “Attention Is All You Need”. arXiv:1706.03762
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Bahdanau, D., et al. (2014). “Neural Machine Translation by Jointly Learning to Align and Translate”. arXiv:1409.0473
Ontology Term managed by AI-Grounded Ontology Working Group UK English Spelling Standards Applied
Academic Context
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Self-attention is an attention mechanism where every token in a sequence attends to every other token within the same sequence, enabling models to capture intra-sequence dependencies effectively.
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It forms the core of the Transformer architecture introduced by Vaswani et al. (2017), which revolutionised natural language processing by replacing recurrent structures with parallelisable attention layers.
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The mechanism uses learned weight matrices to project input embeddings into queries, keys, and values, which interact via scaled dot-product attention to compute context-aware representations.
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This approach allows models to weigh the relevance of different tokens dynamically, improving understanding of long-range dependencies without the sequential bottlenecks of RNNs or LSTMs.
Current Landscape (2025)
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Self-attention is widely adopted across industry, powering large language models (LLMs), vision transformers, and graph attention networks.
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Notable platforms include OpenAI’s GPT series, Google’s BERT and T5, and various open-source frameworks utilising Transformer-based architectures.
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Recent innovations focus on efficiency improvements, such as “shared weight self-attention” which reduces parameters by over 60% while maintaining or improving performance on benchmarks like GLUE and SQuAD.
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In the UK, especially in North England, AI research hubs in Manchester and Leeds are integrating self-attention mechanisms into NLP and bioinformatics applications.
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For example, Manchester’s AI research groups have contributed to optimising Transformer models for domain-specific tasks, including healthcare data analysis.
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Technical capabilities:
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Self-attention excels at modelling complex dependencies but remains computationally intensive, especially for very long sequences.
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Current research addresses scaling challenges through sparse attention, low-rank approximations, and parameter sharing.
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Standards and frameworks:
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Transformer-based models with self-attention are standardised in many machine learning libraries (e.g., Hugging Face Transformers, TensorFlow, PyTorch).
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Benchmarks like GLUE, SuperGLUE, and SQuAD remain key for evaluating self-attention model performance.
Research & Literature
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Key academic papers:
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Vaswani, A., Shazeer, N., Parmar, N., et al. (2017). Attention Is All You Need. Advances in Neural Information Processing Systems, 30, 5998–6008. [DOI: 10.5555/3295222.3295349]
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Kowsher, M., Prottasha, N. J., Yu, C.-N., Garibay, O. O., & Yousefi, N. (2025). Shared Weight Self-Attention: Parameter-Efficient Transformer Models. arXiv preprint arXiv:2412.00359. [URL: https://arxiv.org/abs/2412.00359]
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Bahdanau, D., Cho, K., & Bengio, Y. (2014). Neural Machine Translation by Jointly Learning to Align and Translate. arXiv preprint arXiv:1409.0473.
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Ongoing research directions:
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Efficiency improvements via parameter sharing and sparse attention.
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Extending self-attention to non-sequential data such as graphs.
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Robustness to noisy and out-of-domain data.
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Interpretability and explainability of attention weights.
UK Context
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The UK has a strong presence in Transformer and self-attention research, with institutions like the University of Manchester and University of Leeds leading projects on NLP and healthcare AI.
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Manchester’s AI research centres focus on adapting self-attention for clinical text analysis and patient data modelling.
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Leeds is notable for applying attention mechanisms in environmental and chemical data modelling.
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North England innovation hubs:
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Sheffield and Newcastle are emerging centres for AI startups leveraging self-attention in natural language understanding and computer vision.
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Collaborative initiatives between academia and industry in these cities foster practical applications of self-attention models.
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Regional case studies:
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A Manchester-based project successfully applied self-attention models to improve diagnostic accuracy in radiology reports, demonstrating the mechanism’s utility beyond pure language tasks.
Future Directions
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Emerging trends:
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Development of more parameter- and compute-efficient self-attention variants to enable deployment on edge devices.
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Integration of self-attention with other modalities (e.g., audio, video, sensor data) for multimodal AI systems.
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Advances in unsupervised and few-shot learning leveraging self-attention’s contextual capabilities.
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Anticipated challenges:
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Balancing model complexity with interpretability.
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Managing the environmental impact of large-scale self-attention models.
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Addressing biases encoded in attention patterns.
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Research priorities:
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Enhancing robustness and generalisation in diverse real-world settings.
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Exploring biologically inspired attention mechanisms to inform artificial models.
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Expanding UK and North England’s role in global self-attention research through collaborative funding and infrastructure.
References
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Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention Is All You Need. Advances in Neural Information Processing Systems, 30, 5998–6008. https://doi.org/10.5555/3295222.3295349
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Kowsher, M., Prottasha, N. J., Yu, C.-N., Garibay, O. O., & Yousefi, N. (2025). Shared Weight Self-Attention: Parameter-Efficient Transformer Models. arXiv preprint arXiv:2412.00359. https://arxiv.org/abs/2412.00359
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Bahdanau, D., Cho, K., & Bengio, Y. (2014). Neural Machine Translation by Jointly Learning to Align and Translate. arXiv preprint arXiv:1409.0473. https://arxiv.org/abs/1409.0473
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Additional sources include technical blogs and IBM’s overview on attention mechanisms, which provide accessible explanations without compromising technical accuracy.
Metadata
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Last Updated: 2025-11-11
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Review Status: Comprehensive editorial review
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Verification: Academic sources verified
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Regional Context: UK/North England where applicable