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Description
A-SCEND, developed by A-LIGN, is an innovative compliance management platform created by industry specialists, drawing inspiration from client feedback, and tailored to address both current and future demands throughout the audit process. This platform revolutionizes the audit and compliance experience, enabling organizations to shift their focus towards business transformation. By simplifying the audit process, A-SCEND establishes a strategic compliance framework that significantly reduces the costs associated with conducting multiple audits, while also decreasing the operational burdens caused by lost productivity. It transforms audits from mere tactical tasks into a more strategic compliance initiative by centralizing the collection of evidence and standardizing requests, facilitating the consolidation of audits into a single comprehensive annual review. Moreover, A-SCEND lowers the barriers to compliance, empowering users to perform audits from any location at any time, even if they lack prior audit experience, which enhances the overall accessibility and efficiency of compliance management. Ultimately, A-SCEND not only improves the audit lifecycle but also fosters a culture of continuous compliance within organizations.
Description
Recent breakthroughs in natural language processing, comprehension, and generation have been greatly influenced by the development of large language models. This research presents a system that employs Ascend 910 AI processors and the MindSpore framework to train a language model exceeding one trillion parameters, specifically 1.085 trillion, referred to as PanGu-{\Sigma}. This model enhances the groundwork established by PanGu-{\alpha} by converting the conventional dense Transformer model into a sparse format through a method known as Random Routed Experts (RRE). Utilizing a substantial dataset of 329 billion tokens, the model was effectively trained using a strategy called Expert Computation and Storage Separation (ECSS), which resulted in a remarkable 6.3-fold improvement in training throughput through the use of heterogeneous computing. Through various experiments, it was found that PanGu-{\Sigma} achieves a new benchmark in zero-shot learning across multiple downstream tasks in Chinese NLP, showcasing its potential in advancing the field. This advancement signifies a major leap forward in the capabilities of language models, illustrating the impact of innovative training techniques and architectural modifications.
API Access
Has API
API Access
Has API
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Pricing Details
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Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
A-Lign
Founded
2009
Country
United States
Website
a-lign.com/a-scend/
Vendor Details
Company Name
Huawei
Founded
1987
Country
China
Website
huawei.com
Product Features
Compliance
Archiving & Retention
Artificial Intelligence (AI)
Audit Management
Compliance Tracking
Controls Testing
Environmental Compliance
FDA Compliance
HIPAA Compliance
ISO Compliance
Incident Management
OSHA Compliance
Risk Management
Sarbanes-Oxley Compliance
Surveys & Feedback
Version Control
Workflow / Process Automation
GRC
Auditing
Disaster Recovery
Environmental Compliance
IT Risk Management
Incident Management
Internal Controls Management
Operational Risk Management
Policy Management
Risk Management
Alerts/Notifications
Auditing
Business Process Control
Compliance Management
Corrective Actions (CAPA)
Dashboard
Exceptions Management
IT Risk Management
Internal Controls Management
Legal Risk Management
Mobile Access
Operational Risk Management
Predictive Analytics
Reputation Risk Management
Response Management
Risk Assessment