Generative AI offers new possibilities for advanced analytics
The emergence of generative artificial intelligence (GenAI) brings exciting new prospects for industrial process analysis. This transformative technology generates content such as new text, code and images based on user prompts, offering process manufacturers the possibility to transform the way they analyze data, optimize operations and make critical decisions. This innovative capability enables companies to obtain the information they need more quickly and to use the generated content to guide decisions and improve industrial processes. The introduction of GenAI thus brings a powerful tool to industry that is expected to drive improvements in productivity and quality, leading to greater business success.
Interest in generative AI comes from the confusion process manufacturers feel when faced with a "data-rich and information-poor" situation, especially with the advent of the Industrial Internet of Things (IIoT), the volume, complexity and accessibility of operational and device data continues to increase. However, this excess data presents huge opportunities if it can be managed effectively.
The emergence of artificial intelligence and machine learning technologies offers the potential to uncover more meaningful insights, but for many organizations, the journey from raw data to meaningful insights remains a long one.
As a result, team members—including engineering, operations, and management—need software that can quickly derive valuable insights from data. Integrating generative AI into advanced analytics software will have an impact on the process industry as it makes it easier for domain experts to harness the power of the software, thereby increasing its effectiveness. With this software, team members can get data analysis results faster and take corresponding actions to improve production and business metrics. This will help improve the speed and accuracy of your team’s decision-making, thereby promoting business growth and development.
Using Generative AI to Enhance Advanced Analytics
Generative AI large language models excel at understanding human input and generating text and computer code. Advanced analytics solutions provide efficient access to cleansed and contextualized time series data, providing clear analytical results. Combining these two technologies can significantly improve a software solution's ability to identify patterns, gather insights, make predictions and provide action recommendations.
For generative AI-enhanced advanced analytics solutions to be successful, domain experts need to be provided with key elements so that they can perform effectively in alignment with business and technology strategies Analyze and make effective decisions.
To achieve maximum success, the key elements—reliable enterprise data, advanced analytics, and generative AI—need to be integrated with domain experts at the core, not just integrated in the backend (see Figure 1).
By enriching advanced analytics with generative AI, companies may gain many benefits, including:
- Enhanced decision-making: By providing summaries and detailed descriptions in natural language, domain experts can more effectively Easily understand the entire process and make data-driven decisions more accurately. The result is the ability to analyze massive data sets to identify trends, anomalies and opportunities and enable proactive decision-making.
- Higher analysis efficiency: Can quickly move from short, text-based task descriptions to functional computer code that performs those tasks, often with minimal adjustments and corrections. This enables domain experts such as engineers and data scientists to focus on high-value activities, reducing time to insights.
- Greater predictive capabilities: Generative AI improves the ability of algorithm-based analytics to detect anomalies, inform predictive maintenance, and predict production data. It also provides additional functionality for pattern detection, particularly in datasets representing sensor data combined with operating instructions or logs.
- Simplify onboarding and training: Generative AI can be used to support conversational and interactive user interfaces, making it easier for learners to master the craft of their manufacturing fields. Generative AI-based training also retains its relevance through continuous connections to the current knowledge base, thereby enhancing training retention.
By providing streamlined access to modern technologies that make domain experts’ jobs easier, companies can not only redefine business operations but also cultivate an inspired, engaged, and capable digital organization .
Limitations and Risks of Generative AI
While generative AI promises significant improvements in the future, organizations must acknowledge its limitations and associated risks. These challenges include data challenges, lack of transparency and data privacy issues.
Generative AI models are typically trained using public data sets that represent common human knowledge, which is available on the Internet but lacks private knowledge. This can lead to some inaccurate results due to the difficulty in removing the inherent bias present in the training data. Training models using domain-specific private data is cumbersome and technically difficult.
Complex generative AI models often look like a black box from the front end with no interpretability, which makes explaining the decision-making process challenging. Persons using models must exercise caution. When these models feed data to other software, it adds a layer of complexity when filtering generative AI results to reduce the spread of disinformation, creating the risk of harm.
When deploying generative AI in sensitive industries, data privacy and security issues must be addressed. Because generative AI platforms are open to the internet for model training, developers and implementers must be careful to separate confidential information from public-facing components to avoid leaking data.
As media hype about generative AI continues to grow, companies should also be wary of common misconceptions. Despite popular discourse, generative AI requires human supervision to operate effectively. It does not replace the need for domain experts, but rather complements their expertise.
Building effective generative AI models requires a lot of time and effort. It’s not a panacea for instant solutions. When deployed in process industries, these models need to be fine-tuned and customized to meet specific needs. Off-the-shelf solutions may not produce optimal or even reasonable results.
Three Key Elements of Preparation and Implementation
To assess readiness to use generative AI to enhance process system data analysis, enterprises should examine three key attributes:
- Data Quality: Assess the completeness and accessibility of process data. High-quality data is critical to the effectiveness of generative AI and its relevance to the specific process problems being solved by the teams working on them.
- Skills Expertise: Assess proficiency in data science and AI relevant to process industries. Determine whether employees have the skills to develop and maintain generative AI solutions and understand the processes and business teams for which the solutions are intended.
- Infrastructure: Ensure the necessary computing infrastructure and data storage capabilities are in place to support resource-intensive generative AI deployments.
After considering these key factors above, enterprises should also follow the following guidelines to successfully apply and deploy generative AI:
- Invest in skills: in employees Conduct training in data science and AI while developing internal expertise to effectively drive generative AI initiatives.
- Define standards: Establish robust data governance practices to ensure data quality, privacy and compliance with industry regulations.
- Start Small: Start with pilot projects to test the applicability of generative AI to your organization’s specific use cases before scaling up.
- Promote continuous learning: Cultivate a culture that pursues knowledge and adapts as generative AI technologies evolve.
Unleashing the potential of generative AI
Generative AI has the potential to revolutionize industrial data analysis and decision-making methods. By combining generative AI with advanced analytics, process manufacturers can take efficiency, accuracy and innovation to new levels. Realizing the full potential of generative AI requires careful consideration of its limitations and risks, and a strategic approach to preparing your organization.
Process experts can leverage the power of generative AI to smartly integrate these solutions into workflows to drive favorable outcomes and stay ahead in an increasingly competitive environment.
The above is the detailed content of Generative AI offers new possibilities for advanced analytics. For more information, please follow other related articles on the PHP Chinese website!
Hot AI Tools
Undresser.AI Undress
AI-powered app for creating realistic nude photos
AI Clothes Remover
Online AI tool for removing clothes from photos.
Undress AI Tool
Undress images for free
Clothoff.io
AI clothes remover
Video Face Swap
Swap faces in any video effortlessly with our completely free AI face swap tool!
Hot Article
Hot Tools
Notepad++7.3.1
Easy-to-use and free code editor
SublimeText3 Chinese version
Chinese version, very easy to use
Zend Studio 13.0.1
Powerful PHP integrated development environment
Dreamweaver CS6
Visual web development tools
SublimeText3 Mac version
God-level code editing software (SublimeText3)
Hot Topics
1392
52
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
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
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
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
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
To provide a new scientific and complex question answering benchmark and evaluation system for large models, UNSW, Argonne, University of Chicago and other institutions jointly launched the SciQAG framework
Jul 25, 2024 am 06:42 AM
Editor |ScienceAI Question Answering (QA) data set plays a vital role in promoting natural language processing (NLP) research. High-quality QA data sets can not only be used to fine-tune models, but also effectively evaluate the capabilities of large language models (LLM), especially the ability to understand and reason about scientific knowledge. Although there are currently many scientific QA data sets covering medicine, chemistry, biology and other fields, these data sets still have some shortcomings. First, the data form is relatively simple, most of which are multiple-choice questions. They are easy to evaluate, but limit the model's answer selection range and cannot fully test the model's ability to answer scientific questions. In contrast, open-ended Q&A
SOTA performance, Xiamen multi-modal protein-ligand affinity prediction AI method, combines molecular surface information for the first time
Jul 17, 2024 pm 06:37 PM
Editor | KX In the field of drug research and development, accurately and effectively predicting the binding affinity of proteins and ligands is crucial for drug screening and optimization. However, current studies do not take into account the important role of molecular surface information in protein-ligand interactions. Based on this, researchers from Xiamen University proposed a novel multi-modal feature extraction (MFE) framework, which for the first time combines information on protein surface, 3D structure and sequence, and uses a cross-attention mechanism to compare different modalities. feature alignment. Experimental results demonstrate that this method achieves state-of-the-art performance in predicting protein-ligand binding affinities. Furthermore, ablation studies demonstrate the effectiveness and necessity of protein surface information and multimodal feature alignment within this framework. Related research begins with "S
SK Hynix will display new AI-related products on August 6: 12-layer HBM3E, 321-high NAND, etc.
Aug 01, 2024 pm 09:40 PM
According to news from this site on August 1, SK Hynix released a blog post today (August 1), announcing that it will attend the Global Semiconductor Memory Summit FMS2024 to be held in Santa Clara, California, USA from August 6 to 8, showcasing many new technologies. generation product. Introduction to the Future Memory and Storage Summit (FutureMemoryandStorage), formerly the Flash Memory Summit (FlashMemorySummit) mainly for NAND suppliers, in the context of increasing attention to artificial intelligence technology, this year was renamed the Future Memory and Storage Summit (FutureMemoryandStorage) to invite DRAM and storage vendors and many more players. New product SK hynix launched last year
