Found a total of 2954 related content
How to manage generative AI
Article Introduction:Author丨Compiled by DomCouldwell丨Produced by Noah|51CTO Technology Stack (WeChat ID: blog51cto) According to McKinsey & Company estimates, generative artificial intelligence is expected to bring economic benefits of US$2.6 trillion to US$4.4 trillion to the global economy every year. This forecast is based on 63 new application scenarios that are expected to bring improvements, efficiency gains and new products to customers in multiple markets. This is undoubtedly a huge opportunity for developers and IT leaders. The core of generative AI lies in data. Data not only gives generative AI the ability to understand and analyze the world around it, but also powers its transformative potential. To succeed in the field of generative AI, companies need to effectively manage and prepare data.
2024-03-11
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Distributed ID generator in PHP8.0: GoSnowflake
Article Introduction:With the advent of the digital age, the demand for unique identifiers has become stronger and stronger. Especially in distributed systems, ensuring the generation of unique identifiers has become an important task. In PHP8.0, we can use the GoSnowflake distributed ID generator to meet this need. What is a distributed ID generator? A distributed ID generator is a tool for generating unique IDs. It is often used in distributed systems to ensure the generation of unique identifiers. In a distributed system, there are usually multiple nodes simultaneously
2023-05-14
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Five advantages of generative AI applications
Article Introduction:Generative AI refers to technology that uses AI and machine learning algorithms to enable machines to create new digital videos, images, text, audio, or code. Generative AI is driven by algorithms that have the potential to recognize underlying patterns in inputs, generate similar outputs, and deliver high-quality content. A more advanced form of generative AI goes beyond monitoring real-life environments to generate content; it can also leverage mathematical simulations and the unknown patterns of functionality they reveal. Typically, these types of institutions rely on the application of stress testing and sensitivity analysis. Let’s take a look at the five major applications of generative AI. Enhanced Identity Protection Generative AI helps create avatars that hide the real image of those who are unwilling to reveal their identity for any reason while going for interviews or working online. Even
2023-04-14
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How to automatically generate a directory. How to set the format of the automatically generated directory.
Article Introduction:Select the style of the catalog in Word, and it will be automatically generated after the operation is completed. Analysis 1. Go to Word on your computer and click to import. 2After entering, click on the file directory. 3 Then select the style of the directory. 4. After the operation is completed, you can see that the file directory is automatically generated. Supplement: The table of contents of the summary/notes article is automatically generated, including first-level headings, second-level headings and third-level headings, usually no more than third-level headings.
2024-02-22
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277
Basic principles and applications of generative AI
Article Introduction:Generative AI is a type of artificial intelligence model that is characterized by its ability to generate new data based on the distribution of training data, and these new data are different from the training data. The main goal of these models is to learn the distribution of data through statistical methods and use this learning to generate new data with similar characteristics. Generative AI has a wide range of applications, including but not limited to natural language processing, image generation and audio generation. Through generative AI, we can generate new data that is different from the training data but has similar characteristics, providing more possibilities for various applications. Generative AI models typically use neural networks. Neural network is a computing model that simulates the interaction between human neurons. It can learn from large amounts of data to extract commonalities in the data.
2024-01-24
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Distributed ID generation system based on go-zero
Article Introduction:With the continuous development of Internet business, the ID generation system has become one of the indispensable components. The distributed ID generation system can provide unique ID generation services for distributed systems to ensure the correct operation of the business system. This article will introduce the implementation of a distributed ID generation system based on go-zero. Why is a distributed ID generation system needed? In a distributed system, different parts of services need to work together. Generally, different services cannot communicate by referencing the object of another service. This requires the use of unique
2023-06-22
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IBM launches basic model for generative artificial intelligence
Article Introduction:The IBM Granite family of foundational models applies generative artificial intelligence to natural language and coding tasks. In addition, Watsonx generative artificial intelligence capabilities will also come to Watsonx data lake. IBM released a generative artificial intelligence basic model and an enhanced version of Watsonx.ai's generative artificial intelligence, improving the capabilities of the Watsonx artificial intelligence and data platform. On September 7, it released the IBM Granite series of multi-size basic models using the "decoder" architecture to Generative AI is applied to language and coding tasks. Supports enterprise NLP (natural language processing) tasks such as summarization, content generation and insight extraction. IBM plans to provide a comprehensive list of data sources and instructions for generating Grani
2023-09-16
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Prerequisites and precautions for using generative AI
Article Introduction:Generative AI is a powerful technology that can create data in many forms, including images, audio, and text. However, there are some prerequisites for using this technology, and there are some important things to note. 1. Prerequisites 1. The amount of data must be sufficient. Generative AI requires sufficient data to obtain rich knowledge and generate high-quality content. Therefore, you must ensure that the amount of data is sufficient before using generative AI. The amount of data depends on the application scenario, but generally speaking, the more data, the better. 2. The hardware must be powerful enough. Generative AI requires a large amount of computing resources for training and generation. Therefore, before using generative AI, you need to ensure that there is powerful enough hardware to support its operation. This usually means using a GPU or TPU, etc.
2024-01-22
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Generative AI explodes, how to solve security issues?
Article Introduction:Under the wave of generative AI, how to provide industry users with generative AI services that meet the needs of actual application scenarios is the next focus of the industry's digital transformation. The "Amazon Cloud Technology AIGC Guide to Accelerating Enterprise Innovation" white paper points out that AIGC has typical application scenarios in industries such as games, retail e-commerce, finance, media entertainment, and medical health. As a pathfinder for AI digital business, AIGC is expected to open a new chapter in the next digital business model. “Currently, generative AI has been applied to all aspects of corporate innovation, optimizing customer experience through intelligent customer service, improving technical team productivity through automatic code generation, accelerating creative content generation through text generation, and improving the efficiency of the entire organization through automated document processing. Operational efficiency, etc…
2023-09-08
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Using Redis to implement distributed global ID generation
Article Introduction:Using Redis to generate distributed global IDs With the development of the Internet, there are more and more application scenarios for distributed systems. How to generate globally unique IDs has become a very important issue. The traditional self-increasing ID cannot meet the needs of distributed systems due to the limitations of a single point of data source. This problem can be solved by using Redis as a global ID generator for distributed systems. Redis is a high-performance key-value storage system that supports persistence and memory data structure storage. Utilizing Redis’ atomic operations
2023-11-08
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1059
Seven challenges for learning generative AI
Article Introduction:Generative AI has become a transformative force, pushing the boundaries of what machines can achieve. From text and image generation to creating realistic simulations, generative AI has demonstrated its potential in a variety of fields. As the demand for professionals in the field of generative artificial intelligence continues to increase, the journey to mastering this technology becomes even more challenging. This requires a deep understanding of its complexity and challenges on multiple fronts, including complex model architectures, ethical considerations, and evolving technological competition. Learning generative AI requires overcoming difficulties, but it can also bring excitement and satisfaction. Learners need to constantly keep up with technological developments while responding to changing needs and challenges in order to achieve radical change in this field. 1. Technical complexity and understanding generative artificial intelligence
2024-02-19
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Can generative AI and data quality coexist?
Article Introduction:In this high-tech era, everyone must be familiar with generative artificial intelligence, or at least have heard of it. However, everyone always has concerns about the data generated by artificial intelligence, which has to involve data quality. In this modern era, everyone should be familiar with generative artificial intelligence, or at least have some understanding of it. However, there are still some concerns about the data generated by artificial intelligence, which has also led to discussions about data quality. What is generative artificial intelligence? Generative artificial intelligence is a type of artificial intelligence system whose main function is to generate new data, text, images, audio, etc., rather than just analyzing and processing existing data. Generative artificial intelligence systems learn from large amounts of data and patterns to generate new models with certain logic and semantics.
2024-02-20
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848
Generative AI in the cloud: Build or buy?
Article Introduction:Compiled by David Linsigao | Products produced by Yanzheng 51CTO Technology Stack (WeChat ID: blog51cto) There is an unwritten rule in the technology field: everyone likes to use other people’s technology. But for many businesses, generative AI doesn’t seem to fit that mold. Generative AI is rapidly driving some critical decisions. Every organization faces an important choice: whether to build a custom generative AI platform in-house or buy a prepackaged solution from an AI vendor (often offered as a cloud service). DIY favors volume and opportunity. It's weird, but the reason might surprise you. They might even lead you to rethink your enterprise genAI strategy 1. Complete customization and control Rewrite the content as follows: Build a
2023-12-19
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Three ways to generate url patterns in Laravel
Article Introduction:Below, the laravel tutorial column will introduce you to three methods of generating url patterns in Laravel. I hope it will be helpful to friends in need!
2021-06-03
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2321
Three major challenges of generative AI to cloud operation and maintenance
Article Introduction:Author | Planning by David Linthicum | Words Today, no one doubts the power of AI, but enterprises must be aware that it can also lead to the deployment of too many applications, scaling issues and cost overruns. I understand the benefits of generative AI; my background is in AI development and integration with enterprise and cloud architectures. However, I also know that while there are many benefits, there are also drawbacks that must be considered simultaneously. Generative AI is no exception, and the pace at which it is developing makes it critical to decide how to manage it effectively and reduce any negative impacts. I propose three major drawbacks of generative AI that cloud computing professionals need to understand and manage. 1. Too many cloud application deployments This is the biggest problem I see. Now we can make generative AI-driven
2023-06-27
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944
How generative AI is reshaping retail
Article Introduction:Today, there is a strong global interest in generative artificial intelligence. And for good reason. Generative AI is already creating countless new opportunities for retailers and consumer brands. It represents a leap in capabilities because it is both extremely powerful and extremely flexible. It’s this ability to adapt that will help leaders reinvent the way they run their businesses, serve their customers and get their jobs done. Until recently, outside of cutting-edge AI, almost no one had heard of large-scale language models or generative AI. At the end of 2022, OpenAI released ChatCPG. A few months later, companies like Google, Microsoft, and Meta created their own large-scale language models. For retailers, generative AI opens up endless opportunities. For retailers and brands, ask
2023-06-07
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945
How to deal with the 'double-edged sword' of generative large models? Zhejiang Lab releases 'White Paper on Security and Privacy of Generative Large Models'
Article Introduction:Currently, generative large models have brought profound changes to academic research and even social life. Represented by ChatGPT, the capabilities of generative large models have shown the possibility of moving towards general artificial intelligence. But at the same time, researchers have also begun to realize that large generative models such as ChatGPT face security risks in data and models. In early May this year, the U.S. White House held a collective meeting with CEOs of AI companies such as Google, Microsoft, OpenAI, and Anthropic to discuss the explosion of AI-generated technology, the risks hidden behind the technology, how to develop artificial intelligence systems responsibly, and how to formulate effective regulatory measures. Domestic generative large model technology is also under development, but at the same time, security issues need to be addressed first.
2023-06-07
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949
Explore the mutual benefits of generative AI and the cloud
Article Introduction:It’s no coincidence that interest in generative AI and cloud convergence has continued to grow in recent years. Generative artificial intelligence (AI) and cloud computing have revolutionized the IT industry, redefining industries and bringing unprecedented functionality to new technology tools. Let’s take a deeper look at the profound impact of generative AI on cloud computing, and how cloud computing empowers and enhances the capabilities of generative AI. The emergence of generative artificial intelligence has brought new opportunities and challenges to cloud computing. By combining generative artificial intelligence with cloud computing, enterprises can better utilize data resources, improve work efficiency, and accelerate innovation and development. Cloud computing provides efficient computing and storage resources for generative artificial intelligence, allowing it to handle complex tasks and large-scale tasks faster.
2024-02-28
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324
How to parse and generate JSON format in PHP
Article Introduction:How to parse and generate JSON format in PHP In modern network development, JSON (JavaScriptObjectNotation) has become a commonly used data exchange format. It is lightweight, easy to read and write, and is widely used in various programming languages. PHP, as a popular server-side scripting language, also provides powerful support for parsing and generating JSON format data. This article will introduce how to parse and generate JSON format in PHP, including using
2023-07-28
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Generative AI: Types, skills, opportunities and challenges
Article Introduction:Generative AI refers to a class of machine learning techniques designed to generate new data that is similar, but not identical, to training data. In other words, generative AI models learn to create new data samples that have similar statistical properties to the training data, allowing them to create new content that has never been seen before, such as images, videos, audio, or text. There are several types of generative AI models, including: Variational Autoencoders (VAEs): VAEs are a generative model that learns to encode input data into a low-dimensional latent space and then decode the latent space back to an output space, to generate new data similar to the original input data, often used for image and video generation. Generative Adversarial Network (GAN): GAN is a generative model that works by making two neural networks (generator and
2023-04-11
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