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Accelerating Supply Chain Success with the application of AI in 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 the application of AI in 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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Solution Architecture of GPT 3.5
GPT 3.5 is based on GPT-3 but works within specific policies of hu previous versioman values and on by 100X. sometnly 1.3 billion imes called Instparameters fewerructGPT trained than theon the same datasets of GPT-3 but with additional fine-tuning process that adds a concept called ‘reinforcement learning with human feedback’ or RLHF to the GPT-3 model.Solution Architecture of GPT 3.5
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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 demographics, 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 demographics, or lacks diversity, the outputs produced by the large language model will also lack diversity.
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GPT 3 vs GPT 3.5GPT-3.5 builds on the foundation of GPT-3 with several key enhancements. It utilizes a larger and more diverse dataset for training, leading to improved language understanding and generation capabilities. The fine-tuning process in GPT-3.5 is more sophisticated, allowing the model to produce more accurate and contextually appropriate responses. Additionally, architectural improvements and optimizations in GPT-3.5 result in better handling of complex queries, fewer errors, and a more reliable performance overall, making it more effective in a wide range of applications compared to its predecessor.
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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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