国产av日韩一区二区三区精品,成人性爱视频在线观看,国产,欧美,日韩,一区,www.成色av久久成人,2222eeee成人天堂

Table of Contents
Understand LLM
Information Synthesis
Decision Support System
Language Translation and Communication
Risk Assessment
Human Factor
Summary
Home Technology peripherals AI How LLM enhances the decision-making process

How LLM enhances the decision-making process

Apr 17, 2024 pm 01:10 PM
AI llm Large language model

The digital age is changing the decision-making process as technological capabilities become increasingly important. Large Language Models (LLM) are a noteworthy technology praised for their ability to enable better decision-making in various domains. But to what extent can LLM enhance the decision-making process? If so, how?

How LLM enhances the decision-making process

Understand LLM

Recent Natural Language Processing Systems , such as OpenAI's GPT series and Google's BERT, are very complex artificial intelligence programs that are trained on large amounts of text databases. These models can understand and output human-like text, which is a big advantage for use in natural language processing.

Information Synthesis

One of the main advantages of LLM is that such machines can process large amounts of information quickly and flawlessly. LLM obtains a comprehensive, multifaceted view of a specific topic by analyzing text data from different sources, enabling decision makers to make informed decisions. Whether it's market trends, scientific research or customer feedback, LLM is best suited for information processing roles, creating easy-to-understand and useful indicators from complex data.

Decision Support System

LLM participation in the decision support system is an improvement in the decision-making cycle as it can provide instant suggestions and recommendations based on analyzed data. These systems can operate on data from multiple sources, consider multiple factors and constraints, and make individual recommendations for a specific decision-making environment.

Language Translation and Communication

Bilingual LLM can perform translation purposes and can be used to streamline communication and collaboration across language boundaries, allowing decision-makers to access data and wisdom from the wider world. LLM can play a vital role in real-time translation of documents, emails, etc., thereby breaking language barriers and promoting informed decision-making.

Risk Assessment

The data and trends provided by LLM enable risk assessment to be conducted by reviewing past data and trends, and predicting possible outcomes. When LLM provides information about the feasibility and severity of various scenarios, decision makers can make informed investment decisions, identify project risks, and predict potential hazards.

Human Factor

Although artificial intelligence is very beneficial and capable, this does not mean that humans should use their intelligence and experience to change. Decision makers are empowered by providing data-based insights and reasoning based on LLM capabilities that both inspire and inform and advise. On the other hand, the fundamental point of this approach is that decisions are still based on human judgment, values, or context. Human supervision involves not only the correct understanding of LLM results, but also the validation of recommendations and the consideration of factors that cannot be textualized in decision-making outcomes.

Summary

In short, LLM has the potential to significantly improve the efficiency of the decision-making process in aggregating, evaluating, recommending, and facilitating such actions. Appropriate incorporation of LLM into decision support systems requires a thorough review of ethical, technical, and human factors.

The above is the detailed content of How LLM enhances the decision-making process. For more information, please follow other related articles on the PHP Chinese website!

Statement of this Website
The content of this article is voluntarily contributed by netizens, and the copyright belongs to the original author. This site does not assume corresponding legal responsibility. If you find any content suspected of plagiarism or infringement, please contact admin@php.cn

Hot AI Tools

Undress AI Tool

Undress AI Tool

Undress images for free

Undresser.AI Undress

Undresser.AI Undress

AI-powered app for creating realistic nude photos

AI Clothes Remover

AI Clothes Remover

Online AI tool for removing clothes from photos.

Clothoff.io

Clothoff.io

AI clothes remover

Video Face Swap

Video Face Swap

Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Tools

Notepad++7.3.1

Notepad++7.3.1

Easy-to-use and free code editor

SublimeText3 Chinese version

SublimeText3 Chinese version

Chinese version, very easy to use

Zend Studio 13.0.1

Zend Studio 13.0.1

Powerful PHP integrated development environment

Dreamweaver CS6

Dreamweaver CS6

Visual web development tools

SublimeText3 Mac version

SublimeText3 Mac version

God-level code editing software (SublimeText3)

Hot Topics

PHP Tutorial
1502
276
Bytedance Cutting launches SVIP super membership: 499 yuan for continuous annual subscription, providing a variety of AI functions Bytedance Cutting launches SVIP super membership: 499 yuan for continuous annual subscription, providing a variety of AI functions Jun 28, 2024 am 03:51 AM

This site reported on June 27 that Jianying is a video editing software developed by FaceMeng Technology, a subsidiary of ByteDance. It relies on the Douyin platform and basically produces short video content for users of the platform. It is compatible with iOS, Android, and Windows. , MacOS and other operating systems. Jianying officially announced the upgrade of its membership system and launched a new SVIP, which includes a variety of AI black technologies, such as intelligent translation, intelligent highlighting, intelligent packaging, digital human synthesis, etc. In terms of price, the monthly fee for clipping SVIP is 79 yuan, the annual fee is 599 yuan (note on this site: equivalent to 49.9 yuan per month), the continuous monthly subscription is 59 yuan per month, and the continuous annual subscription is 499 yuan per year (equivalent to 41.6 yuan per month) . In addition, the cut official also stated that in order to improve the user experience, those who have subscribed to the original VIP

Step-by-step guide to using Groq Llama 3 70B locally Step-by-step guide to using Groq Llama 3 70B locally Jun 10, 2024 am 09:16 AM

Translator | Bugatti Review | Chonglou This article describes how to use the GroqLPU inference engine to generate ultra-fast responses in JanAI and VSCode. Everyone is working on building better large language models (LLMs), such as Groq focusing on the infrastructure side of AI. Rapid response from these large models is key to ensuring that these large models respond more quickly. This tutorial will introduce the GroqLPU parsing engine and how to access it locally on your laptop using the API and JanAI. This article will also integrate it into VSCode to help us generate code, refactor code, enter documentation and generate test units. This article will create our own artificial intelligence programming assistant for free. Introduction to GroqLPU inference engine Groq

Context-augmented AI coding assistant using Rag and Sem-Rag Context-augmented AI coding assistant using Rag and Sem-Rag Jun 10, 2024 am 11:08 AM

Improve developer productivity, efficiency, and accuracy by incorporating retrieval-enhanced generation and semantic memory into AI coding assistants. Translated from EnhancingAICodingAssistantswithContextUsingRAGandSEM-RAG, author JanakiramMSV. While basic AI programming assistants are naturally helpful, they often fail to provide the most relevant and correct code suggestions because they rely on a general understanding of the software language and the most common patterns of writing software. The code generated by these coding assistants is suitable for solving the problems they are responsible for solving, but often does not conform to the coding standards, conventions and styles of the individual teams. This often results in suggestions that need to be modified or refined in order for the code to be accepted into the application

Seven Cool GenAI & LLM Technical Interview Questions Seven Cool GenAI & LLM Technical Interview Questions Jun 07, 2024 am 10:06 AM

To learn more about AIGC, please visit: 51CTOAI.x Community https://www.51cto.com/aigc/Translator|Jingyan Reviewer|Chonglou is different from the traditional question bank that can be seen everywhere on the Internet. These questions It requires thinking outside the box. Large Language Models (LLMs) are increasingly important in the fields of data science, generative artificial intelligence (GenAI), and artificial intelligence. These complex algorithms enhance human skills and drive efficiency and innovation in many industries, becoming the key for companies to remain competitive. LLM has a wide range of applications. It can be used in fields such as natural language processing, text generation, speech recognition and recommendation systems. By learning from large amounts of data, LLM is able to generate text

Can fine-tuning really allow LLM to learn new things: introducing new knowledge may make the model produce more hallucinations Can fine-tuning really allow LLM to learn new things: introducing new knowledge may make the model produce more hallucinations Jun 11, 2024 pm 03:57 PM

Large Language Models (LLMs) are trained on huge text databases, where they acquire large amounts of real-world knowledge. This knowledge is embedded into their parameters and can then be used when needed. The knowledge of these models is "reified" at the end of training. At the end of pre-training, the model actually stops learning. Align or fine-tune the model to learn how to leverage this knowledge and respond more naturally to user questions. But sometimes model knowledge is not enough, and although the model can access external content through RAG, it is considered beneficial to adapt the model to new domains through fine-tuning. This fine-tuning is performed using input from human annotators or other LLM creations, where the model encounters additional real-world knowledge and integrates it

GraphRAG enhanced for knowledge graph retrieval (implemented based on Neo4j code) GraphRAG enhanced for knowledge graph retrieval (implemented based on Neo4j code) Jun 12, 2024 am 10:32 AM

Graph Retrieval Enhanced Generation (GraphRAG) is gradually becoming popular and has become a powerful complement to traditional vector search methods. This method takes advantage of the structural characteristics of graph databases to organize data in the form of nodes and relationships, thereby enhancing the depth and contextual relevance of retrieved information. Graphs have natural advantages in representing and storing diverse and interrelated information, and can easily capture complex relationships and properties between different data types. Vector databases are unable to handle this type of structured information, and they focus more on processing unstructured data represented by high-dimensional vectors. In RAG applications, combining structured graph data and unstructured text vector search allows us to enjoy the advantages of both at the same time, which is what this article will discuss. structure

Plaud launches NotePin AI wearable recorder for $169 Plaud launches NotePin AI wearable recorder for $169 Aug 29, 2024 pm 02:37 PM

Plaud, the company behind the Plaud Note AI Voice Recorder (available on Amazon for $159), has announced a new product. Dubbed the NotePin, the device is described as an AI memory capsule, and like the Humane AI Pin, this is wearable. The NotePin is

Five schools of machine learning you don't know about Five schools of machine learning you don't know about Jun 05, 2024 pm 08:51 PM

Machine learning is an important branch of artificial intelligence that gives computers the ability to learn from data and improve their capabilities without being explicitly programmed. Machine learning has a wide range of applications in various fields, from image recognition and natural language processing to recommendation systems and fraud detection, and it is changing the way we live. There are many different methods and theories in the field of machine learning, among which the five most influential methods are called the "Five Schools of Machine Learning". The five major schools are the symbolic school, the connectionist school, the evolutionary school, the Bayesian school and the analogy school. 1. Symbolism, also known as symbolism, emphasizes the use of symbols for logical reasoning and expression of knowledge. This school of thought believes that learning is a process of reverse deduction, through existing

See all articles