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Industry automation with
Reinforcement Learning
In industry reinforcement, learning-based robots are used to perform various tasks. Apart from the fact that these robots are more efficient than human beings, they can also perform tasks that would be dangerous for people. A great example is the use of AI agents by Deepmind to cool Google Data Centers. This led to a 40% reduction in energy spending. The centers are now fully controlled with the AI system without the need for human intervention. There is obviously still supervision from data center experts.Industry automation with
Reinforcement Learning
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LLM Vs GenAI:
These two cutting-edge AI technologies sound like totally different, incomparable things. One’s ostensibly a type of AI focused on content creation, while the other is a class of models. If they sounded like they were comparable, it might have been more like generative-model AI vs. large-model AI, or maybe generative language models vs. large language models.Not exactly apples to apples. More like apples to eggplants. But despite the fact that these two sound divergent, their respective use cases have plenty of similarities. Plus, they’re not mutually exclusive; they can effectively complement each other as copilots, and when they do, they can fly high. With their respective markets worth billions, these two phenomena are charting a promising landscape in the future of healthcare, ecommerce (e.g., Amazon), real estate, and other industries. LLM Vs GenAI:
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- Automation of Quality Control and Other Business Processes
- Optimization of Supply Chain Processes
- Generation of Bill of Material (BOM)
- Futuristic Prediction of Maintenance Requirements
- Better Judgement of Quality of Suppliers
- Fine Tuning of Equipment Operating Parameters
- Generation of Technical Documentation
- Compliance with Regulatory Standards
- Analysis of Client Feedback and Recommendations
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Exposing LLMs to Users Via REST API
For prototyping and minimal viable AI products, you might consider using frontend-focussed prototyping systems like Streamlit or Gradio.But a more flexible enterprise approach would be to build a service that exposes a REST API as a bridge between external components and the underlying model. This enables communication with the model, asking users questions and receiving appropriate responses. The implementation of such a service can be achieved using popular web frameworks such as Flaskor FastAPI.
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For prototyping and minimal viable AI products, you might consider using frontend-focussed prototyping systems like Streamlit or Gradio.But a more flexible enterprise approach would be to build a service that exposes a REST API as a bridge between external components and the underlying model. This enables communication with the model, asking users questions and receiving appropriate responses. The implementation of such a service can be achieved using popular web frameworks such as Flaskor FastAPI.
read more
Revolutionize SEO with Multimodal AI:
Revolutionize SEO with Multimodal AI:
- Use GPT4's natural language processing capabilities to generate optimized content for websites that is both engaging and search engine friendly.
- Leveraging the neural network of GPT-4 to analyze keywords and provide insights that help businesses target their audience better.
- Utilizing GPT-4's advanced features to improve chatbot interactions on websites, thereby enhancing user experience.
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Supply-chain talent management has moved to front-of-mind in recent years, as borne out by surveys and recent events. The supply-chain dysfunctions seen in the last two years are largely a thing of the past, but they created a high-stress environment, marked by high turnover and a search for talent.
One consequence of this is that methods and practices developed over many years, both in management techniques and using software tools, were lost. Supply chain, in particular, relies on institutional knowledge that is difficult to capture in standards and guidelines. There is no common, agreed set of procedures or policies, as it is very much dependent on the context of each company and industry. Even if a company were disciplined enough to try to capture and train staff on in-house processes, there are gaps in knowledge atthe best of times..
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One consequence of this is that methods and practices developed over many years, both in management techniques and using software tools, were lost. Supply chain, in particular, relies on institutional knowledge that is difficult to capture in standards and guidelines. There is no common, agreed set of procedures or policies, as it is very much dependent on the context of each company and industry. Even if a company were disciplined enough to try to capture and train staff on in-house processes, there are gaps in knowledge atthe best of times..
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Impact on the Retail Industry -
A large language model can be used to analyze customer data, such as past purchases, browsing history, and social media activity, to identify patterns and trends. This information can then be used to generate personalized recommendations for products and services. For example, an LLM could be used to recommend products to customers based on their interests, needs, and budget.Impact on the Retail Industry -
- Answering Customer Queries
- Asisting with Purchases
- Fraud Detection
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