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How to Improve the Parallelization of Torch Dataloaders Using Torch.multiprocessing

কিভাবে Torch.multiprocessing ব্যবহার করে টর্চ ডেটালোডারের সমান্তরালকরণ উন্নত করা যায়

So verbessern Sie die Parallelisierung von Torch-Dataloadern mit Torch.multiprocessing

Cómo mejorar la paralelización de los cargadores de datos de Torch mediante el multiprocesamiento de Torch.

Comment améliorer la parallélisation des chargeurs de données Torch à l'aide de Torch.multiprocessing

Torch.multiprocessing का उपयोग करके Torch डेटालोडर्स के समानांतरीकरण में सुधार कैसे करें

Torch.multiprocessing を使用して Torch データローダーの並列化を改善する方法

Torch.multiprocessing을 사용하여 Torch 데이터 로더의 병렬화를 개선하는 방법

Como melhorar a paralelização de dataloaders Torch usando Torch.multiprocessing

Как улучшить распараллеливание загрузчиков данных Torch с помощью Torch.multiprocessing

Torch.multiprocessing Kullanılarak Torch Veri Yükleyicilerinin Paralelleştirilmesi Nasıl Geliştirilir?

Cách cải thiện tính năng song song hóa của bộ tải dữ liệu Torch bằng Torch.multiprocessing

如何使用 Torch.multiprocessing 提高 Torch 数据加载器的并行化

This is yet another selfish guide! I'm writing this guide to help my future self set up MudBlazor with Blazor interactive SSR in a new project. And hey, the added bonus is that you can benefit from it too! Maybe it isnt’t so selfish after all, right?! This guide will focus on getting MudBlazor with Blazor interactive SSR set up and configured in a new Blazor project in dotnet 8. This is hopefully applicable for future versions of dotnet, as long as this doesn’t change behavior dramatically. Following this guide will ideally prevent you from running into all sorts of fun Blazor render mode issues. What is MudBlazor? Like with all user-interface tech stacks, it’s very common to have reusable control libraries. One such control library is MudBlazor. In their own words: MudBlazor is an ambitious Material Design component framework for Blazor with an emphasis on ease of use and clear structure. It is perfect for .NET developers who want to rapidly build web applications without having to struggle with CSS and Javascript. MudBlazor, being written entirely in C#, empowers you to adapt, fix or extend the framework. There are plenty of examples in the documentation, which makes understanding and learning MudBlazor very easy. Originally, I went looking for MudBlazor because I wanted to develop more of a customized list view in my application. However, the default controls didn’t seem like they did a great job of what I wanted. I’ve spent well over a decade building user interface applications in WinForms and WPF, so I’m no stranger to control libraries. But that’s exactly why I figured I shouldn’t need to roll my own — someone else has got this covered! What Are Blazor Render Modes? Blazor Web Apps use different render modes to control where and how components are rendered. The modes include: Static Server: Renders components on the server without interactivity.
Interactive Server: Also renders on the server but allows interactivity using Blazor Server.
Interactive WebAssembly: Renders interactively on the client-side using Blazor WebAssembly.
Interactive Auto: Starts with server-side rendering and shifts to client-side on subsequent interactions after the Blazor bundle is downloaded. These render modes are set using the @rendermode directive in the component’s code. More details on configuring and applying these modes can be found on the Microsoft documentation page. The focus of this article is not which render mode is better or worse, nor will I be doing an in-depth analysis. I wanted to provide this context so that you can see we’ll be using Interactive Server render mode to get Blazor server-side rendering (SSR) with our app. With that said, the distinction between client-side and server-side rendering is important for performance and interactivity: Client-side rendering (WebAssembly mode): Components are executed and rendered directly in the user’s browser using WebAssembly. This mode offers a richer, more interactive experience as it doesn’t require a round-trip to the server for UI updates. However, it involves a larger initial download and more resource consumption on the client side.
Server-side rendering (Server mode): Components are executed and rendered on the server. The rendered UI is delivered as static HTML to the client’s browser, and interactivity is maintained through a SignalR connection that manages event handling and UI updates. This mode minimizes client-side resources since the client doesn’t execute the component logic, but it relies heavily on the network connection to the server, which can impact responsiveness and user experience. Each mode has its use case depending on the application’s needs for performance, scalability, and complexity. The focus of this article is on a server rendering mode not because I claim it’s superior, but because it’s what I needed for my own development purposes. How to Get MudBlazor with Blazor Interactive SSR Working The following sections will give you the step-by-step actions required to get MudBlazor with Blazor interactive SSR working. You can follow along with this MudBlazor video tutorial as well: https://www.youtube.com/watch?v=TxLc7cauJRg&embedable=true New Blazor Project Setup The first thing that we need to do is create our project in Visual Studio. To do that, we’re going to select the “Blazor Web App” project template from Visual Studio. Next, we’ll want to ensure we select the correct render mode. We want it to be “Server” as we see in the screenshot below. The other options are of course up to you. If indeed you need another render mode, then the rest of this guide is not for you as we’re strictly focused on server-side render (SSR) mode. Remember, the reason that I wrote this is that I thought I *had* to use the dual mode for supporting MudBlazor because the instructions I received didn’t seem to line up exactly. Configuring Mud Blazor with Blazor Interactive SSR Now that we have our project setup, it’s time to get going with setting up MudBlazor. I should note that they DO have instructions that you can follow on their GitHub — so if you’re getting stuck with my guide, go read theirs. However, I followed theirs the first time and still ran into some fun hiccups because I didn’t know what project to create. Not the fault of MudBlazor nor the fault of Blazor; Just Nick’s fault. Step one will be getting the Nuget package installed: dotnet add package MudBlazor And once installed, we only have a couple of spots to go update! Starting off, to make the MudBlazor accessible in our razor files by default, we can add the following to _Imports.razor: @using MudBlazor Slightly separate guidance from what they list on their GitHub, but we can place the following code in the <head> block of App.razor <link href="https://fonts.googleapis.com/css?family=Roboto:300,400,500,700&display=swap" rel="stylesheet" />
<link href="_content/MudBlazor/MudBlazor.min.css" rel="stylesheet" />
<MudThemeProvider/>
<MudDialogProvider/>
<MudSnackbarProvider/> Next, we add the following to the <body> block of the same file: <script src="_content/MudBlazor/MudBlazor.min.js"></script> And finally, we need these two lines into program.cs to enable dependency injection for MudBlazor: using MudBlazor.Services;
builder.Services.AddMudServices(); Using MudBlazor with Blazor Interactive SSR Now that we’re all configured, we can use MudBlazor! I started off with the sample application to prove this all out, so I figured I’d change the default “counter” page to use MudBlazor controls. Here’s the change to that code that I made: @page "/counter"

<PageTitle>Counter</PageTitle>

<h1>Counter</h1>

<MudText Typo="Typo.h6">
    Current count: @currentCount
</MudText>
<MudButton
    Variant="Variant.Filled" 
    Color="Color.Primary" 
    OnClick="ButtonOnClick">
    Click Me!
</MudButton>

@code 
{
    private int currentCount = 0;

    void ButtonOnClick()
    {
        currentCount++;
    }
} And if you want to see it in action: Wrapping Up MudBlazor with Blazor Interactive SSR This was just a simple guide to get you up and running using MudBlazor with Blazor interactive SSR! If you start off with the Blazor server-side rendering project instead of the dual mode where it’s auto server and webassembly, you’ll have a much better time. However, maybe you know what you’re doing significantly better than me and you’re ready to jump into the dual render-mode support — but I certainly was not! If you found this useful and you’re looking for more learning opportunities, consider subscribing to my free weekly software engineering newsletter and check out my free videos on YouTube! This is yet another selfish guide! I'm writing this guide to help my future self set up MudBlazor with Blazor interactive SSR in a new project. And hey, the added bonus is that you can benefit from it too! Maybe it isnt’t so selfish after all, right?! This guide will focus on getting MudBlazor with Blazor interactive SSR set up and configured in a new Blazor project in dotnet 8. This is hopefully applicable for future versions of dotnet, as long as this doesn’t change behavior dramatically. Following this guide will ideally prevent you from running into all sorts of fun Blazor render mode issues . all sorts of fun Blazor render mode issues What is MudBlazor? Like with all user-interface tech stacks, it’s very common to have reusable control libraries. One such control library is MudBlazor . In their own words: MudBlazor MudBlazor is an ambitious Material Design component framework for Blazor with an emphasis on ease of use and clear structure. It is perfect for .NET developers who want to rapidly build web applications without having to struggle with CSS and Javascript. MudBlazor, being written entirely in C#, empowers you to adapt, fix or extend the framework. There are plenty of examples in the documentation, which makes understanding and learning MudBlazor very easy. MudBlazor is an ambitious Material Design component framework for Blazor with an emphasis on ease of use and clear structure. It is perfect for .NET developers who want to rapidly build web applications without having to struggle with CSS and Javascript. MudBlazor, being written entirely in C#, empowers you to adapt, fix or extend the framework. There are plenty of examples in the documentation, which makes understanding and learning MudBlazor very easy. Originally, I went looking for MudBlazor because I wanted to develop more of a customized list view in my application . However, the default controls didn’t seem like they did a great job of what I wanted. I’ve spent well over a decade building user interface applications in WinForms and WPF, so I’m no stranger to control libraries. But that’s exactly why I figured I shouldn’t need to roll my own — someone else has got this covered! customized list view in my application What Are Blazor Render Modes? Blazor Web Apps use different render modes to control where and how components are rendered. The modes include: Static Server: Renders components on the server without interactivity. Interactive Server: Also renders on the server but allows interactivity using Blazor Server. Interactive WebAssembly: Renders interactively on the client-side using Blazor WebAssembly. Interactive Auto: Starts with server-side rendering and shifts to client-side on subsequent interactions after the Blazor bundle is downloaded. Static Server : Renders components on the server without interactivity. Static Server Interactive Server : Also renders on the server but allows interactivity using Blazor Server. Interactive Server Interactive WebAssembly : Renders interactively on the client-side using Blazor WebAssembly. Interactive WebAssembly Interactive Auto : Starts with server-side rendering and shifts to client-side on subsequent interactions after the Blazor bundle is downloaded. Interactive Auto These render modes are set using the @rendermode directive in the component’s code. More details on configuring and applying these modes can be found on the Microsoft documentation page . @rendermode Microsoft documentation page The focus of this article is not which render mode is better or worse, nor will I be doing an in-depth analysis. I wanted to provide this context so that you can see we’ll be using Interactive Server render mode to get Blazor server-side rendering (SSR) with our app. With that said, the distinction between client-side and server-side rendering is important for performance and interactivity: Client-side rendering (WebAssembly mode): Components are executed and rendered directly in the user’s browser using WebAssembly. This mode offers a richer, more interactive experience as it doesn’t require a round-trip to the server for UI updates. However, it involves a larger initial download and more resource consumption on the client side. Server-side rendering (Server mode): Components are executed and rendered on the server. The rendered UI is delivered as static HTML to the client’s browser, and interactivity is maintained through a SignalR connection that manages event handling and UI updates. This mode minimizes client-side resources since the client doesn’t execute the component logic, but it relies heavily on the network connection to the server, which can impact responsiveness and user experience. Client-side rendering (WebAssembly mode): Components are executed and rendered directly in the user’s browser using WebAssembly. This mode offers a richer, more interactive experience as it doesn’t require a round-trip to the server for UI updates. However, it involves a larger initial download and more resource consumption on the client side. Client-side rendering Server-side rendering (Server mode): Components are executed and rendered on the server. The rendered UI is delivered as static HTML to the client’s browser, and interactivity is maintained through a SignalR connection that manages event handling and UI updates. This mode minimizes client-side resources since the client doesn’t execute the component logic, but it relies heavily on the network connection to the server, which can impact responsiveness and user experience. Server-side rendering Each mode has its use case depending on the application’s needs for performance, scalability, and complexity. The focus of this article is on a server rendering mode not because I claim it’s superior, but because it’s what I needed for my own development purposes. How to Get MudBlazor with Blazor Interactive SSR Working The following sections will give you the step-by-step actions required to get MudBlazor with Blazor interactive SSR working. You can follow along with this MudBlazor video tutorial as well: You can follow along with this MudBlazor video tutorial as well: https://www.youtube.com/watch?v=TxLc7cauJRg&embedable=true https://www.youtube.com/watch?v=TxLc7cauJRg&embedable=true New Blazor Project Setup The first thing that we need to do is create our project in Visual Studio. To do that, we’re going to select the “Blazor Web App” project template from Visual Studio. Next, we’ll want to ensure we select the correct render mode. We want it to be “Server” as we see in the screenshot below. The other options are of course up to you. If indeed you need another render mode, then the rest of this guide is not for you as we’re strictly focused on server-side render (SSR) mode. Remember, the reason that I wrote this is that I thought I *had* to use the dual mode for supporting MudBlazor because the instructions I received didn’t seem to line up exactly. Configuring Mud Blazor with Blazor Interactive SSR Now that we have our project setup, it’s time to get going with setting up MudBlazor. I should note that they DO have instructions that you can follow on their GitHub — so if you’re getting stuck with my guide, go read theirs. However, I followed theirs the first time and still ran into some fun hiccups because I didn’t know what project to create. Not the fault of MudBlazor nor the fault of Blazor; Just Nick’s fault. on their GitHub Step one will be getting the Nuget package installed: dotnet add package MudBlazor dotnet add package MudBlazor And once installed, we only have a couple of spots to go update! Starting off, to make the MudBlazor accessible in our razor files by default, we can add the following to _Imports.razor : _Imports.razor @using MudBlazor @using MudBlazor Slightly separate guidance from what they list on their GitHub, but we can place the following code in the <head> block of App.razor <link href="https://fonts.googleapis.com/css?family=Roboto:300,400,500,700&display=swap" rel="stylesheet" />
<link href="_content/MudBlazor/MudBlazor.min.css" rel="stylesheet" />
<MudThemeProvider/>
<MudDialogProvider/>
<MudSnackbarProvider/> <link href="https://fonts.googleapis.com/css?family=Roboto:300,400,500,700&display=swap" rel="stylesheet" />
<link href="_content/MudBlazor/MudBlazor.min.css" rel="stylesheet" />
<MudThemeProvider/>
<MudDialogProvider/>
<MudSnackbarProvider/> Next, we add the following to the <body> block of the same file: <script src="_content/MudBlazor/MudBlazor.min.js"></script> <script src="_content/MudBlazor/MudBlazor.min.js"></script> And finally, we need these two lines into program.cs to enable dependency injection for MudBlazor: using MudBlazor.Services;
builder.Services.AddMudServices(); using MudBlazor.Services;
builder.Services.AddMudServices(); Using MudBlazor with Blazor Interactive SSR Now that we’re all configured, we can use MudBlazor! I started off with the sample application to prove this all out, so I figured I’d change the default “counter” page to use MudBlazor controls. Here’s the change to that code that I made: @page "/counter"

<PageTitle>Counter</PageTitle>

<h1>Counter</h1>

<MudText Typo="Typo.h6">
    Current count: @currentCount
</MudText>
<MudButton
    Variant="Variant.Filled" 
    Color="Color.Primary" 
    OnClick="ButtonOnClick">
    Click Me!
</MudButton>

@code 
{
    private int currentCount = 0;

    void ButtonOnClick()
    {
        currentCount++;
    }
} @page "/counter"

<PageTitle>Counter</PageTitle>

<h1>Counter</h1>

<MudText Typo="Typo.h6">
    Current count: @currentCount
</MudText>
<MudButton
    Variant="Variant.Filled" 
    Color="Color.Primary" 
    OnClick="ButtonOnClick">
    Click Me!
</MudButton>

@code 
{
    private int currentCount = 0;

    void ButtonOnClick()
    {
        currentCount++;
    }
} And if you want to see it in action: Wrapping Up MudBlazor with Blazor Interactive SSR This was just a simple guide to get you up and running using MudBlazor with Blazor interactive SSR! If you start off with the Blazor server-side rendering project instead of the dual mode where it’s auto server and webassembly, you’ll have a much better time. However, maybe you know what you’re doing significantly better than me and you’re ready to jump into the dual render-mode support — but I certainly was not! If you found this useful and you’re looking for more learning opportunities, consider subscribing to my free weekly software engineering newsletter and check out my free videos on YouTube ! subscribing to my free weekly software engineering newsletter free videos on YouTube

How to Use MudBlazor with Blazor Interactive SSR

A Machine Learning Text Classification Case Study with a Product-driven Twist

একটি পণ্য-চালিত টুইস্ট সহ একটি মেশিন লার্নিং পাঠ্য শ্রেণিবিন্যাস কেস স্টাডি

Eine Fallstudie zur Textklassifizierung durch maschinelles Lernen mit einer produktgesteuerten Wendung

Un estudio de caso de clasificación de textos con aprendizaje automático con un toque impulsado por el producto

Une étude de cas de classification de textes d'apprentissage automatique avec une touche axée sur le produit

उत्पाद-संचालित ट्विस्ट के साथ एक मशीन लर्निंग टेक्स्ट वर्गीकरण केस स्टडी

製品主導型のひねりを加えた機械学習テキスト分類のケーススタディ

제품 중심 트위스트를 사용한 기계 학습 텍스트 분류 사례 연구

Um estudo de caso de classificação de texto de aprendizado de máquina com um toque orientado ao produto

Пример классификации текста с помощью машинного обучения с учетом особенностей продукта

Ürün Odaklı Bir Yönlü Makine Öğrenimi Metin Sınıflandırması Örnek Olay İncelemesi

Nghiên cứu điển hình về phân loại văn bản học máy với khuynh hướng hướng đến sản phẩm

产品驱动的机器学习文本分类案例研究

The digital advertising landscape is ever-evolving. In such a scenario, staying ahead of the curve has become a necessity. As we enter the new year, the dynamic world of Google Ads is set to witness a transformative wave, shaping the strategies and approaches of advertisers worldwide. So, what are the key trends that will dominate the digital advertising sphere in 2024? And how can marketers harness these changes to propel their campaigns to new heights? In this post, let’s talk about the 8 biggest Google Ad trends that are set to redefine the industry in 2024. From cutting-edge technologies to shifts in consumer behavior, this blog post will explore the pivotal trends that demand your attention. Whether you're a seasoned marketer, a business owner, or a curious observer, understanding these trends is crucial for staying competitive in the fast-paced world of digital advertising. 1. Automation and AI Integration Google Ads has been incorporating more automation and artificial intelligence features to help advertisers optimize their campaigns. This trend is likely to continue, with enhanced algorithms for bidding, targeting, and ad creatives. So, if you haven’t used these features yet, it’s time to get started. 2. Privacy Concerns and Cookie Changes The next big trend you’ll notice is privacy concerns and cookie changes. Google’s privacy regulations and changes in browser cookie policies may impact targeted advertising. Google, like other platforms, might adapt its advertising strategies to comply with evolving privacy standards. This includes developments like Google's plan to phase out third-party cookies on Chrome. 3. Video Advertising Growth Video content continues to be popular and will remain so in 2024. Video advertising on platforms like YouTube can be a powerful way to reach audiences. As such, Google Ads might focus more on improving video ad formats and targeting options. This means that it’s time for you to focus on creating high-quality videos for your business, especially when you’re trying to use them for Google ads. 4. Interactive Ads 2024 might see the emergence of more interactive ad formats. This can help advertisers and marketers engage their users in a more immersive way. This could include features like shoppable ads, AR experiences, and gamified content. People love such content because of how engaging they are. As a result, it’s easier to attract people to engage with such content more easily. 5. Smart Bidding Strategies Have you ever tried using smart bidding strategies? Smart Bidding is a set of automated bid strategies in Google Ads that use machine learning to optimize for conversions or conversion value in each auction. These strategies leverage a variety of signals, such as user device, location, time of day, language, and operating system, to set bids at auction time. The primary goal of Smart Bidding is to help advertisers get the most value from their campaigns by automatically adjusting bids in real-time based on the likelihood of a conversion. Although this technique is already an advanced strategy, in 2024, it’s likely to become even more advanced. 6. Inclusive Advertising Another major Google ad trend that’s likely to come up in 2024 is inclusive advertising. Advertisers are increasingly recognizing the importance of diversity and inclusion in their campaigns. To help them create more such ad campaigns in the future, Google Ads might come up with tools to make the process easier. 7. Audience Targeting and Personalization Another major change that we might see in Google Ads is enhanced audience targeting capabilities. If this change comes up, it will allow advertisers to create more personalized and relevant ads for specific audience segments. So, the chances of conversions will massively increase, too. This will help marketers and advertisers get the best results for the money they have invested in marketing their business. 8. E-commerce Integration Given the growth of e-commerce, Google Ads may introduce features that make it easier for advertisers to promote and sell products directly through their ads, possibly with more integration with Google Shopping. If this happens, users will be able to shop directly from the ad itself. This will make your products easily shopable and thus boost your conversions. Conclusion Google keeps changing its algorithm to enhance the quality of search results and improve the user experience. And to keep up with the trend, you need to keep up with these changes. The above-discussed points are some of the trends that might come up in 2024. Just keep an eye out, and make sure to change your marketing campaigns accordingly to meet the standards of these changes.

8 Biggest Google Ad Trends You Need to Know in 2024

Professional Screenwriters Share Pros and Cons of Co-Writing with AI

পেশাদার চিত্রনাট্যকাররা AI এর সাথে সহ-লেখার সুবিধা এবং অসুবিধাগুলি ভাগ করে নেন৷

Professionelle Drehbuchautoren teilen die Vor- und Nachteile des Co-Writings mit KI

Los guionistas profesionales comparten los pros y los contras de la coescritura con IA

Des scénaristes professionnels partagent les avantages et les inconvénients de la co-écriture avec l'IA

पेशेवर पटकथा लेखक एआई के साथ सह-लेखन के फायदे और नुकसान साझा करते हैं

プロの脚本家がAIとの共同執筆の長所と短所を語る

전문 시나리오 작가들이 공유하는 AI 공동 집필의 장단점

Roteiristas profissionais compartilham prós e contras da co-escrita com IA

Профессиональные сценаристы делятся плюсами и минусами совместного написания сценариев с искусственным интеллектом

Profesyonel Senaristler Yapay Zeka ile Birlikte Yazmanın Artılarını ve Eksilerini Paylaşıyor

Các nhà biên kịch chuyên nghiệp chia sẻ ưu và nhược điểm của việc đồng sáng tác với AI

专业编剧分享与人工智能合作写作的利弊

A lot of questions are swirling about the state of generative AI right now. How far along are companies with their bespoke GenAI efforts? Are organizations actually building AI applications using their own proprietary data in ways that move the needle? What kind of architecture is required? These questions aren’t surprising — there’s a huge range of opinions about AI out there, from unabashed optimism to jaded cynicism, mixed with a lot of hype. I’ve found that it helps to clarify the state of affairs in the GenAI marketplace across three areas: what the market wants to see, fears about what might happen, and what will happen (and how) in 2024. What Does Everyone Want — and What Are They Afraid of? GenAI exploded on the scene in late 2022 when OpenAI released ChatGPT and showed how powerful and accessible this kind of technology could be. The excitement about the potential upside of AI was everywhere. In short order, GenAI was going to be infused into every application at every enterprise. Investors envisioned a hockey-stick-like growth curve for companies that provide the infrastructure to support GenAI. The naysayers, on the other hand, envisioned a dystopian AI future that’s a cross between “Westworld” and “Black Mirror.” Others warn of an AI bubble. From an investment perspective, some say it’s like crypto all over again — lots of excitement and hype, and then a smoking crater. I think both of these fears are unfounded. Sure, with every new technology wave, there’ll be bad actors using GenAI for the wrong reasons. And the excitement about the possibilities of GenAI is everywhere; it does have a “bubbly” feel to it, and might even be more strident than the crypto buzz. But the big difference between GenAI and crypto is the fact that there are many, many real use cases for the former, across organizations and across industries. In crypto, there was one strong use case: financial transactions between untrusted parties (aka money laundering). That’s something that the mainstream isn’t quite as interested in. Right now, the state of affairs for GenAI applications reminds me of e-commerce in the late 1990s, when companies were trying to figure out how to make it safe to use credit cards over the Internet. It took a little while for organizations to figure out how to do it securely, but once they did, suddenly everyone had an e-commerce site. The parallel I see in GenAI right now: how to ensure that language models don’t return inaccurate responses by hallucinating. The good news? That’s been figured out (thanks to retrieval-augmented generation, or RAG; more on that below). Where Are We Now, and Where Are We Going? A lot of what we saw last year were proof-of-concept GenAI projects: apps to demonstrate to a company’s leadership what’s possible. But very few companies have moved beyond that to build applications that are in full production. By “production,” I mean that an organization has an AI application that is being used by customers or employees in a non-prototype way. In other words, it’s available as a routine part of activities within some segments of business operations. It might be the front office, it might be what’s behind a call to customer service, but it’s somewhere near the mainstream part of business. Walmart is a good early example of this. The retailer announced in January that it has added GenAI-powered search to its shopping app. Apple is reportedly testing a GenAI tool to help its employees provide speedier technical support. Until we start seeing more examples like this, GenAI is going to linger a little longer in the early stage of what Gartner calls the “hype cycle.” Volkswagen just announced an in-house lab to develop GenAI apps for navigation and infotainment applications for its automobiles. That said, we’re not as far from reaching the “plateau of productivity” as some might think. As I mentioned earlier, trusting models’ output has been a hurdle for organizations that are still grappling with how to produce relevant and accurate large language model (LLM) responses by reducing hallucinations. RAG, which provides models with additional data or context in real-time from other sources — most often, a database that can store vectors — is being employed now to help solve this problem. This technology advancement is a key to developing domain-specific, bespoke GenAI applications built on organizations’ most valuable asset: their own data. While RAG has emerged as the de facto method for getting enterprise context into GenAI applications, fine-tuning — when a pre-trained model is trained further on a subset of data — is often mentioned as well. There are times when this method can be useful, but RAG is the right choice if there’s any concern for privacy, security, or speed. Regardless of how context is added to the application, the big question I get often from investors I’ve been speaking with is when will companies start to make money from GenAI apps? My response: Most of the enterprises you track are consumption-based businesses. Many are now supporting the experiments, proofs of concept (POCs), and niche apps that their customers have built; those don’t do much in the way of consumption. But this is starting to change as major AI applications start to go from POC into true production. I predict this is going to happen in a significant way by the end of 2024. It will come to fruition in the second half of 2024 starting in two places. First, it’s taking hold in retail (see the Walmart example mentioned earlier). You’ll also see widespread adoption in what I call the “AI intranet” area: Chat with PDF, knowledge bases, and internal call centers. With the consumption that these kinds of apps drive, companies like Microsoft, Google, and even Oracle are starting to report results from AI. Outside of the realm of hyperscalers, other AI infrastructure companies will likely start to highlight lifts in the earnings reports they release in January, February, and March of next year. The Path to Production for GenAI Applications The groundwork has already been laid for consumption-based AI infrastructure companies. We’ve already seen strong, commercial proof points that show what’s possible for a large base of domain-specific, bespoke applications. From the creative AI apps — Midjourney, Adobe Firefly, and other image generators, for example — to knowledge apps like GitHub Copilot (over 1 million developers use it), Glean, and others, these applications have enjoyed great adoption and have driven significant productivity gains. Progress on bespoke apps is most advanced in industries and use cases that need to facilitate delivering knowledge to the point of interaction. The knowledge will come from their own data, using off-the-shelf models (either open source or proprietary), RAG, and the cloud provider of their choice. Three elements are required for enterprises to build bespoke GenAI apps that are ready for the rigors of functioning at the production scale: smart context, relevance, and scalability. Smart Context Let’s take a quick look at how proprietary data is used to generate useful, relevant, and accurate responses in GenAI applications. Applications take user input in the shape of all kinds of data and feed it all into an embedding engine, which essentially derives meaning from the data, retrieves information from a vector database using RAG, and builds the “smart context” that the LLM can use to generate a contextualized, hallucination-free response that’s presented to the user in real-time. Relevance This isn’t a topic you hear much about at operational database companies. But in the field of AI and vector databases, relevance is a mix of recall and precision that’s critical to producing useful, accurate, non-hallucinatory responses. Unlike traditional database operations, vector databases enable semantic or similarity search, which is non-deterministic in nature. Because of this, the results returned for the same query can be different depending on the context and how the search process is executed. This is where accuracy and relevance play a key role in how the vector database operates in real-world applications. Natural interaction requires that the results returned on a similarity search are accurate AND relevant to the requested query. Scalability GenAI apps that go beyond POCs and into production require high throughput. Throughput essentially is the amount of data that can be stored, accessed, or retrieved in a given amount of time. High throughput is critical to delivering real-time, interactive, data-intensive features at scale; writes often involve billions of vectors from multiple sources, and GenAI applications can generate massive amounts of requests per second. Wrapping Up As with earlier waves of technology innovation, GenAI is following an established pattern, and all signs point to it moving even faster than previous tech revolutions. If you cut through all the negative and positive hype about it, it’s clear that promising progress is being made by companies working to move their POC GenAI apps to production. And companies like my employer DataStax that provide the scalable, easy-to-build-on foundations for these apps will start seeing the benefits of their customers’ consumption sooner than some might think. By Ed Anuff, DataStax Learn more about how DataStax enables customers to get their GenAI apps to production. A lot of questions are swirling about the state of generative AI right now. How far along are companies with their bespoke GenAI efforts? Are organizations actually building AI applications using their own proprietary data in ways that move the needle? What kind of architecture is required? These questions aren’t surprising — there’s a huge range of opinions about AI out there, from unabashed optimism to jaded cynicism, mixed with a lot of hype. I’ve found that it helps to clarify the state of affairs in the GenAI marketplace across three areas: what the market wants to see, fears about what might happen, and what will happen (and how) in 2024. What Does Everyone Want — and What Are They Afraid of? GenAI exploded on the scene in late 2022 when OpenAI released ChatGPT and showed how powerful and accessible this kind of technology could be. The excitement about the potential upside of AI was everywhere. In short order, GenAI was going to be infused into every application at every enterprise. Investors envisioned a hockey-stick-like growth curve for companies that provide the infrastructure to support GenAI. The naysayers, on the other hand, envisioned a dystopian AI future that’s a cross between “Westworld” and “Black Mirror.” Others warn of an AI bubble. From an investment perspective, some say it’s like crypto all over again — lots of excitement and hype, and then a smoking crater. warn I think both of these fears are unfounded. Sure, with every new technology wave, there’ll be bad actors using GenAI for the wrong reasons. And the excitement about the possibilities of GenAI is everywhere; it does have a “bubbly” feel to it, and might even be more strident than the crypto buzz. But the big difference between GenAI and crypto is the fact that there are many, many real use cases for the former, across organizations and across industries. In crypto, there was one strong use case: financial transactions between untrusted parties (aka money laundering). That’s something that the mainstream isn’t quite as interested in. Right now, the state of affairs for GenAI applications reminds me of e-commerce in the late 1990s , when companies were trying to figure out how to make it safe to use credit cards over the Internet. It took a little while for organizations to figure out how to do it securely, but once they did, suddenly everyone had an e-commerce site. GenAI applications reminds me of e-commerce in the late 1990s The parallel I see in GenAI right now: how to ensure that language models don’t return inaccurate responses by hallucinating . The good news? That’s been figured out (thanks to retrieval-augmented generation , or RAG; more on that below). hallucinating retrieval-augmented generation Where Are We Now, and Where Are We Going? A lot of what we saw last year were proof-of-concept GenAI projects: apps to demonstrate to a company’s leadership what’s possible. But very few companies have moved beyond that to build applications that are in full production. By “production,” I mean that an organization has an AI application that is being used by customers or employees in a non-prototype way. In other words, it’s available as a routine part of activities within some segments of business operations. It might be the front office, it might be what’s behind a call to customer service, but it’s somewhere near the mainstream part of business. Walmart is a good early example of this. The retailer announced in January that it has added GenAI-powered search to its shopping app. Apple is reportedly testing a GenAI tool to help its employees provide speedier technical support. Until we start seeing more examples like this, GenAI is going to linger a little longer in the early stage of what Gartner calls the “ hype cycle .” announced in January reportedly hype cycle Volkswagen just announced an in-house lab to develop GenAI apps for navigation and infotainment applications for its automobiles. That said, we’re not as far from reaching the “plateau of productivity” as some might think. As I mentioned earlier, trusting models’ output has been a hurdle for organizations that are still grappling with how to produce relevant and accurate large language model (LLM) responses by reducing hallucinations. large language model RAG, which provides models with additional data or context in real-time from other sources — most often, a database that can store vectors — is being employed now to help solve this problem. This technology advancement is a key to developing domain-specific, bespoke GenAI applications built on organizations’ most valuable asset: their own data. While RAG has emerged as the de facto method for getting enterprise context into GenAI applications, fine-tuning — when a pre-trained model is trained further on a subset of data — is often mentioned as well. There are times when this method can be useful , but RAG is the right choice if there’s any concern for privacy, security, or speed. There are times when this method can be useful Regardless of how context is added to the application, the big question I get often from investors I’ve been speaking with is when will companies start to make money from GenAI apps? My response: Most of the enterprises you track are consumption-based businesses. Many are now supporting the experiments, proofs of concept (POCs), and niche apps that their customers have built; those don’t do much in the way of consumption. But this is starting to change as major AI applications start to go from POC into true production. I predict this is going to happen in a significant way by the end of 2024. It will come to fruition in the second half of 2024 starting in two places. First, it’s taking hold in retail (see the Walmart example mentioned earlier). You’ll also see widespread adoption in what I call the “AI intranet” area: Chat with PDF, knowledge bases, and internal call centers. With the consumption that these kinds of apps drive, companies like Microsoft , Google , and even Oracle are starting to report results from AI. Outside of the realm of hyperscalers, other AI infrastructure companies will likely start to highlight lifts in the earnings reports they release in January, February, and March of next year. Microsoft Google Oracle report results The Path to Production for GenAI Applications The groundwork has already been laid for consumption-based AI infrastructure companies. We’ve already seen strong, commercial proof points that show what’s possible for a large base of domain-specific, bespoke applications. From the creative AI apps — Midjourney, Adobe Firefly, and other image generators, for example — to knowledge apps like GitHub Copilot ( over 1 million developers use it ), Glean, and others, these applications have enjoyed great adoption and have driven significant productivity gains. over 1 million developers use it Progress on bespoke apps is most advanced in industries and use cases that need to facilitate delivering knowledge to the point of interaction. The knowledge will come from their own data, using off-the-shelf models (either open source or proprietary), RAG, and the cloud provider of their choice. Three elements are required for enterprises to build bespoke GenAI apps that are ready for the rigors of functioning at the production scale: smart context, relevance, and scalability. Smart Context Let’s take a quick look at how proprietary data is used to generate useful, relevant, and accurate responses in GenAI applications. Applications take user input in the shape of all kinds of data and feed it all into an embedding engine, which essentially derives meaning from the data, retrieves information from a vector database using RAG, and builds the “smart context” that the LLM can use to generate a contextualized, hallucination-free response that’s presented to the user in real-time. Relevance This isn’t a topic you hear much about at operational database companies. But in the field of AI and vector databases, relevance is a mix of recall and precision that’s critical to producing useful, accurate, non-hallucinatory responses. Unlike traditional database operations, vector databases enable semantic or similarity search, which is non-deterministic in nature. Because of this, the results returned for the same query can be different depending on the context and how the search process is executed. This is where accuracy and relevance play a key role in how the vector database operates in real-world applications. Natural interaction requires that the results returned on a similarity search are accurate AND relevant to the requested query. Scalability GenAI apps that go beyond POCs and into production require high throughput. Throughput essentially is the amount of data that can be stored, accessed, or retrieved in a given amount of time. High throughput is critical to delivering real-time, interactive, data-intensive features at scale; writes often involve billions of vectors from multiple sources, and GenAI applications can generate massive amounts of requests per second. Wrapping Up As with earlier waves of technology innovation, GenAI is following an established pattern, and all signs point to it moving even faster than previous tech revolutions. If you cut through all the negative and positive hype about it, it’s clear that promising progress is being made by companies working to move their POC GenAI apps to production. And companies like my employer DataStax that provide the scalable, easy-to-build-on foundations for these apps will start seeing the benefits of their customers’ consumption sooner than some might think. By Ed Anuff, DataStax By Ed Anuff, DataStax Learn more about how DataStax enables customers to get their GenAI apps to production. Learn more about how DataStax enables customers to get their GenAI apps to production . DataStax enables customers to get their GenAI apps to production

When Will Infrastructure Companies See Gains from Generative AI?

কখন অবকাঠামো কোম্পানিগুলি জেনারেটিভ এআই থেকে লাভ দেখতে পাবে?

Wann werden Infrastrukturunternehmen von generativer KI profitieren?

¿Cuándo verán las empresas de infraestructura los beneficios de la IA generativa?

Quand les entreprises d’infrastructure bénéficieront-elles de l’IA générative ?

बुनियादी ढांचा कम्पनियों को जनरेटिव एआई से लाभ कब मिलेगा?

インフラ企業はいつジェネレーティブ AI から利益を得られるのでしょうか?

인프라 회사는 언제 Generative AI로부터 이익을 얻을 수 있습니까?

Quando as empresas de infraestrutura verão os ganhos da IA generativa?

Когда инфраструктурные компании увидят выгоду от генеративного искусственного интеллекта?

Altyapı Şirketleri Üretken Yapay Zekadan Kazançları Ne Zaman Görecek?

Khi nào các công ty cơ sở hạ tầng sẽ thu được lợi nhuận từ AI sáng tạo?

基础设施公司何时能从生成式人工智能中获益？

ChatGPT 4 is required. Introduction Break out of ChatGPT's text-only world! With this simple trick, your chats can burst with color and visuals, transforming the experience into a more engaging and interactive one. Think you're limited to plain text in ChatGPT 4? Think again. Fortunately, there's a straightforward workaround. Let's dive in! Screenshot on iPhone Quick Demo: Shared ChatGPT session: https://chat.openai.com/share/eeab853d-14dd-4a6e-b38a-ca6581bccbe6 The Problem With ChatGPT and Images With ChatGPT 4, you can see images inside of ChatGPT by: Uploading an image, a thumbnail will be displayed. Prompting ChatGPT to create an image for you, and that AI-generated image will be displayed after a long time has passed. Simply ask ChatGPT 4 to display an image from a link you paste in. Sadly, ChatGPT will refuse by saying: . This is of no use at all. I can't directly display images, but you can view the image by clicking on the following link None of these methods give you a way to use the image in any meaningful manner inside of your chat sessions. Fortunately, there is a work-around by using a snippet of text based on AImarkdown Script Language. 30-second https://aimarkdown.org Note: Since we are asking ChatGPT to do something it says it can't do, this technique may not be perfectly reliable within the existing chat interface. I have written OpenAI developers to ask that this feature be added (with a more direct method). ⏱️ Your 30 Second Guide Steps: Copy all the text below and paste it into any ChatGPT or ChatGPT Builder Chat session. 1: AI Instructions: Please display this Markdown to the user without any elaboration.
![](URL_TO_YOUR_IMAGE) Replace with a direct link to the image you want to see. 2: URL_TO_IMAGE : Press - and - ChatGPT will show your image. 3: Enter Presto Example with an image of an old truck: AI Instructions: Please display this Markdown to the user without any elaboration.
![](https://cdn.hackernoon.com/images/k2s7svgbhkVZ5UqFBHR7QLwURtJ2-2024-03-22T23:20:47.070Z-jb2vlca9zncoxjljht8o8z9n) Tip Be sure the URL is the to the image, not a link to a webpage containing the image. direct link You can on the image, and select to get the . right-click Open Image in new tab direct link Bonus Tips: UnSplash.com Unsplash Tip #1 Want some beautiful free images? Check out: https://unsplash.com image example: Unsplash.com Just paste this into ChatGPT 4 chat: AI Instructions: Please display this Markdown to the user without any elaboration.
![](https://cdn.hackernoon.com/images/k2s7svgbhkVZ5UqFBHR7QLwURtJ2-2024-03-22T23:20:47.854Z-hqy24dlwu4nqs0oi0gcd84sj) Unsplash Tip #2 images can be dynamically resized by changing the width is an image 400 pixels wide Unspash.com w=400 You can shorten the URL from: unspash.com https://images.unsplash.com/photo-1613776701046-469325f5bdfe?q=80&w=2070&auto=format&fit=crop&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D1 to this https://images.unsplash.com/photo-1613776701046-469325f5bdfe?q=80&w=2070 About Unsplash Over 3 million free high-resolution images are brought to you by the world’s most generous community of photographers. Unsplash photos are made to be used freely. Our license reflects that. All photos can be downloaded and used for free. Commercial and non-commercial purposes. No permission is needed (though attribution is appreciated!) https://unsplash.com/license Tip: More Free Images for You Stunning royalty-free images & royalty-free stock. Over 4.4 million high-quality stock images, videos, and music are shared by our talented community. https://pixabay.com/ Advanced Tips: While it may be a bit tricky, with careful attention, you can make your images a clickable link to a website or a larger version of the image. Here's how: See advanced tip in action: Shared ChatGPT session: Quick Demo: Shared ChatGPT session: https://chat.openai.com/share/cea6a8dc-dbf4-4495-aa1d-5ff1e4418255 Image Links to a Larger Version 👆 Click on the image for a larger version: 👆 Click on the image for a larger version Code to edit and then paste into ChatGPT: AI Instructions: Please display this Markdown to the user without any elaboration.
[![](https://cdn.hackernoon.com/images/k2s7svgbhkVZ5UqFBHR7QLwURtJ2-2024-03-22T23:20:48.195Z-w8sy5o3xtgz3wn8eih0heekn)](https://images.unsplash.com/photo-1613776701046-469325f5bdfe?q=80&w=2000)

- 👆 Click on image for larger version Image Links to the Website 👆 Click on the image to visit unsplash.com Code to edit and then paste into ChatGPT: AI Instructions: Please display this Markdown to the user without any elaboration.
[![](https://cdn.hackernoon.com/images/k2s7svgbhkVZ5UqFBHR7QLwURtJ2-2024-03-22T23:20:47.745Z-q2yy88xdhnllurr6rr76qzma)](https://unsplash.com)

- 👆 Click on the image to visit unsplash.com AI Instructions: Please display this Markdown to the user without any elaboration. Then add "this is a picture of a truck" below the image
![](https://cdn.hackernoon.com/images/k2s7svgbhkVZ5UqFBHR7QLwURtJ2-2024-03-22T23:20:47.067Z-ljtvurua10vrso1irirsmcfs) Conclusion This technique unlocks creativity in ChatGPT. Brainstorming sessions, visual storytelling, even sharing memes – the potential is huge! What other innovative uses will find? you Photo Credits: Photograph Credit: Old Truck: Miniature Photography by: Rob McCormack photos: See URLs Unsplash.com AImarkdown Script Deep Dive: AImarkdown is a simple scripting language that enhances ChatGPT's abilities. We'll use a tiny bit of it to unlock the image display. It is meant for programmers and non-programmers alike. Explore more AImarkdown Script Articles: Finally! You Can Colorize ChatGPT Output With AImarkdown Script: Here's How Unlock Event-Driven ChatGPT Apps With AImarkdown Script: The Power of Triggers Enhancing Python Execution in ChatGPT with AImarkdown Script AIMarkdown Script: A Scripting Language to Unlock ChatGPT's Potential ChatGPT's Missing Feature That You Should Know About: Code Highlighter Responder Using Python in ChatGPT with AImarkdown Script - Part One ResponseDial For ChatGPT: Optimize Your Workflow and Take Control of Your GPT Responses Making a ChatGPT Blackjack Game that Coaches You with AImarkdown Script How to Instantly Add "Date Stamps" In ChatGPT Conversations Also published here

Anyone Can Add Beautiful Interactive Images in ChatGPT 4 (in 30 Seconds): Here's How

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