Dorilton Capital and Stephens (DCS) can interact with International Investment and Banking Relationships by partnering with IBM, Equitus.ai and Williams Race Engineering to developing a Advanced Racing Ai platform. Enhancing business intelligence and focusing efficient use of capital.
There are 2 main types of knowledge graphs: Property and Semantic
1. Property Graphs:
Property graphs generally comprise three elements:
- Nodes (representing entities or instances)
- Edges (representing relationships between nodes)
- Properties (representing attributes of nodes and edges)
Property graphs are commonly used in areas like social networks, recommendation systems, and fraud detection.
2. Semantic Knowledge Graphs (RDF Graphs):
Semantic knowledge graphs, also known as RDF graphs, are based on the Resource Description Framework (RDF) data model. They consist of:
- Entities (represented by URIs or blank nodes)
- Properties (representing relationships between entities)
- Literals (representing values like strings, numbers, dates)
Semantic knowledge graphs are designed to represent knowledge in a way that can be processed by machines, enabling reasoning and inference. They are often used in areas like linked open data, question answering, and scientific research.
The key differences are:
- Property graphs focus on representing instances and their relationships, while semantic knowledge graphs aim to represent conceptual knowledge and ontologies.
- Property graphs have a more flexible schema, while semantic knowledge graphs strictly follow the RDF data model and ontologies.
- Semantic knowledge graphs enable advanced reasoning and inference capabilities based on formal semantics and ontologies.
Both types of knowledge graphs are useful for representing and reasoning over interconnected data, but they serve different purposes and have different strengths based on the specific use case and requirements.
Ai Executive Concerns:
Why build/ shift to IBM Power10 system with Equitus.ai Knowledge Graph Neural Network (KGNN),
Job Displacement - Extend current uses and add Equitus Schema dev team
Data Security - Validate using Advanced Systems Integration
Decision Transparency - Augment root of trust and legacy platforms
Response Accuracy - TSRF - Time Space Relationship and Fact
Implementation Costs - Available and Energy Efficient
IBM Power10 servers with their Matrix Math Accelerators (MMA) and large memory capacity are well-suited for accelerating AI inferencing workloads like those from Equitus.ai's Knowledge Graph Neural Network (KGNN) system. The KGNN system incorporates Time, Space, Relationship and Facts to model complex real-world scenarios, which can help enterprises improve their AI capabilities in several ways:
## Improved Performance and Scalability
The Power10 MMA accelerators provide efficient parallelization for running AI models like KGNN, enabling high throughput and low latency for inferencing.[1] This allows enterprises to process large volumes of data and complex queries in real-time. The massive memory capacity of Power10 servers can accommodate the large knowledge graphs and models required by KGNN.[3]
## Secure and Trusted AI
Power10's built-in encryption capabilities like transparent memory encryption help secure the data flowing through AI models, preventing data leaks.[2] This allows enterprises to run sensitive AI workloads with confidence. The high reliability of IBM Power systems also ensures trusted insights from AI models.[3]
## Flexible Deployment
Power10 servers like the S1012 are designed for edge deployments, allowing enterprises to run AI inferencing at the point of data generation, reducing latency.[2] They can also leverage hybrid cloud flexibility to run KGNN workloads seamlessly across on-prem, cloud and edge environments using consistent infrastructure.[3]
## Broad Applicability
The KGNN's modeling of time, space, relationships and facts makes it applicable to a wide range of enterprise use cases like fraud detection, risk analysis, supply chain optimization, predictive maintenance and more.[1] Deploying it on scalable Power10 infrastructure enables enterprises to drive AI-powered improvements across their operations.
In summary, the combination of IBM Power10's accelerated performance, data security, deployment flexibility and Equitus.ai's comprehensive KGNN system can help enterprises efficiently scale trusted and broadly applicable AI capabilities to drive improvements across their business.[1][2][3]
Citations:
[1] https://community.ibm.com/community/user/powerdeveloper/blogs/marvin-gieing/2023/06/23/unleash-power10-for-accelerating-ai-model-inferenc
[2] https://newsroom.ibm.com/Blog-New-IBM-Power-server-extends-AI-workloads-from-core-to-cloud-to-edge-for-added-business-value-across-industries
[3] https://www.ibm.com/downloads/cas/36P74DJM
[4] https://www.nextplatform.com/2020/09/03/the-memory-area-network-at-the-heart-of-ibms-power10/
[5] https://www.theregister.com/2024/05/07/ibm_ai_edge/l
IBM Power10 servers with their Matrix Math Accelerators (MMA) and large memory capacity are well-suited for accelerating AI inferencing workloads like those from Equitus.ai's Knowledge Graph Neural Network (KGNN) system. The KGNN system incorporates Time, Space, Relationship and Facts to model complex real-world scenarios, which can help enterprises improve their AI capabilities in several ways:
## Improved Performance and Scalability
The Power10 MMA accelerators provide efficient parallelization for running AI models like KGNN, enabling high throughput and low latency for inferencing.[1] This allows enterprises to process large volumes of data and complex queries in real-time. The massive memory capacity of Power10 servers can accommodate the large knowledge graphs and models required by KGNN.[3]
## Secure and Trusted AI
Power10's built-in encryption capabilities like transparent memory encryption help secure the data flowing through AI models, preventing data leaks.[2] This allows enterprises to run sensitive AI workloads with confidence. The high reliability of IBM Power systems also ensures trusted insights from AI models.[3]
## Flexible Deployment
Power10 servers like the S1012 are designed for edge deployments, allowing enterprises to run AI inferencing at the point of data generation, reducing latency.[2] They can also leverage hybrid cloud flexibility to run KGNN workloads seamlessly across on-prem, cloud and edge environments using consistent infrastructure.[3]
## Broad Applicability
The KGNN's modeling of time, space, relationships and facts makes it applicable to a wide range of enterprise use cases like fraud detection, risk analysis, supply chain optimization, predictive maintenance and more.[1] Deploying it on scalable Power10 infrastructure enables enterprises to drive AI-powered improvements across their operations.
In summary, the combination of IBM Power10's accelerated performance, data security, deployment flexibility and Equitus.ai's comprehensive KGNN system can help enterprises efficiently scale trusted and broadly applicable AI capabilities to drive improvements across their business.[1][2][3]
Citations:
[1] https://community.ibm.com/community/user/powerdeveloper/blogs/marvin-gieing/2023/06/23/unleash-power10-for-accelerating-ai-model-inferenc
[2] https://newsroom.ibm.com/Blog-New-IBM-Power-server-extends-AI-workloads-from-core-to-cloud-to-edge-for-added-business-value-across-industries
[3] https://www.ibm.com/downloads/cas/36P74DJM
[4] https://www.nextplatform.com/2020/09/03/the-memory-area-network-at-the-heart-of-ibms-power10/
[5] https://www.theregister.com/2024/05/07/ibm_ai_edge/
No comments:
Post a Comment