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Accelerating Supply Chain Success with AI in Supply Chains & Logistics
According to Gartner, supply chain organizations expect the level of machine automation in their supply chain processes to double in the next five years. At the same time, global spending on IIoT Platforms is predicted to grow from $1.67B in 2018 to $12.44B in 2024, attaining a 40% compound annual growth rate (CAGR) in seven years.Accelerating Supply Chain Success with AI in Supply Chains & Logistics
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Recent works are popping up combining optimization algorithms with LLMs both for model building and getting better solutions. These two works mentioned are not the only ones, naturally. But they are really good showcases.
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Architecture of LLM
A Large Language Model’s (LLM) architecture is determined by a number of factors, like the objective of the specific model design, the available computational resources, and the kind of language processing tasks that are to be carried out by the LLM.Architecture of LLM
Important Components, influencing a LLM
Model Size and Parameter Count
input representations
Self-Attention Mechanisms
Training Objectives
Computational Efficiency
Decoding and Output Generation
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Limitations of LLMs
Hallucinations:
A hallucination is when a LLM produces an output that is false, or that does not match the user's intent.Security:
Large language models present important security risks when not managed or surveilled properly. Bias:
The data used to train language models will affect the outputs a given model produces. As such, if the data represents a single demographic, or lacks diversity, the outputs produced by the large language model will also lack diversity.
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Hallucinations:
A hallucination is when a LLM produces an output that is false, or that does not match the user's intent.Security:
Large language models present important security risks when not managed or surveilled properly. Bias:
The data used to train language models will affect the outputs a given model produces. As such, if the data represents a single demographic, or lacks diversity, the outputs produced by the large language model will also lack diversity.
read more
Best Applications of LLMs:
1. Translation With Language Models 2. Malware Analysis
1. Translation With Language Models 2. Malware Analysis
3. Content Creation
3. Content Creation
4. Virtual Assistants and Customer Support
4. Virtual Assistants and Customer Support
- Optimizing Inference Time
- Optimizing LLM Performance Through Precision and Clarity
1. LLMs in Quality Control
1. LLMs in Quality Control
Automated Inspection
Customer Feedback Analysis
Automated Inspection
Customer Feedback Analysis
Predictive Quality
Predictive Quality
2. LLMs in Supply Chain
Management
2. LLMs in Supply Chain
Management
Natural Language Evaluations
Demand Forecasting
Supplier Evaluation
Inventory Optimization
Natural Language Evaluations
Demand Forecasting
Supplier Evaluation
Inventory Optimization
3. Predictive Maintainance:
3. Predictive Maintainance:
- Data Analysis
- Feature Extraction
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