BACK TO ALL BLOGS Expanding Our CSAM Detection API HiveNovember 21, 2024February 21, 2025 Contents Our Commitment to Child Internet SafetyHow the Classifier WorksBuilding a Safer Internet We are excited to announce that Hive is now offering Thorn’s predictive technology through our CSAM detection API! This API now enables customers to identify novel cases of child sexual abuse material (CSAM) in addition to detecting known CSAM using hash-based matching. Our Commitment to Child Internet Safety At Hive, making the internet safer is core to our mission. While our content moderation tools help reduce human exposure to harmful content across many categories, addressing CSAM requires specialized expertise and technology. That’s why we’re expanding our existing partnership with Thorn, an innovative nonprofit that builds technology to defend children from sexual abuse and exploitation in the digital age. Until now, our integration with Thorn focused on hash-matching technology to detect known CSAM. The new CSAM detection API builds on this foundation by adding advanced machine learning capabilities that can identify previously unidentified CSAM. By combining Thorn’s industry-leading CSAM detection technology with Hive’s comprehensive content moderation suite, we provide platforms with robust protection against both known and newly created CSAM. How the Classifier Works The classifier works by first generating embeddings of the uploaded media. An embedding is a list of computer-generated scores between 0 and 1. After generating the embeddings, Hive permanently deletes all of the original media. We then use the classifier to determine whether the content is CSAM based on the embeddings. This process ensures that we do not retain any CSAM on our servers. The classifier returns a score between 0 and 1 that predicts whether a video or image is CSAM. The response object will have the same general structure for both image and video inputs. Please note that Hive will return both results together: probability scores from the classifier and any match results from hash matching against the aggregated hash database. For a detailed guide on how to use Hive’s CSAM detection API, refer to the documentation. Building a Safer Internet Protecting platforms from CSAM demands scalable solutions. The problem is complex; but our integration with Thorn’s advanced technology provides an efficient way to detect and stop CSAM, helping to safeguard children and build a safer internet for all. If you have any further questions or would like to learn more, please reach out to sales@thehive.ai or contact us here.
BACK TO ALL BLOGS Announcing General Availability of Hive Models HiveOctober 4, 2024February 21, 2025 Contents Hive Proprietary ModelsAdditional Model OfferingsHow to Create a Project We are excited to announce that we are making select proprietary Hive models and popular open-source generative models directly accessible for customers to deploy and integrate into their workflows. Starting today, customers can now create projects by themselves with just a few clicks. Hive Proprietary Models We have made select proprietary Hive models accessible to customers across our Understand and Search model categories, ranging from our Celebrity Recognition API to our Speech-to-Text model. For a full list of generally available models, see our pricing page here. Additional Model Offerings We currently offer a variety of open-source image generation models and large language models (LLMs) that customers can directly access themselves. For image generation models, we have four different options available today, with additional models being served in the coming weeks: SDXL (Stable Diffusion XL), SDXL Enhanced, Flux Schnell, and Flux Schnell Enhanced. SDXL Enhanced and Flux Schnell Enhanced are Hive’s enhanced versions of the aforementioned base models, served exclusively to our customers. The differences are outlined in the table below. SDXL (Stable Diffusion XL)Latent diffusion text-to-image generation model produced by Stability AI. Trained on a larger dataset than the base model, with a larger UNet enabling better generation.SDXL EnhancedHive’s enhanced version of SDXL, served exclusively to our customers. Tailored toward a photorealistic and refined art style with extreme detail.Flux SchnellFlux’s fastest model in their suite of text-to-image models, capable of generating images in 4 or fewer steps. Best suited for local development and personal use.Flux Schnell EnhancedHive’s enhanced version of Flux Schnell that is trained on our proprietary data and retains the base model’s speed and efficiency, served exclusively to our customers. Generates images across a wide range of artistic styles with a specialization in photorealism, leading to high levels of customer satisfaction based on past user studies. For LLMs, we have a selection of Meta’s Llama models from their Llama 3.1 and 3.2 series available now. The differences are outlined in the table below. Llama 3.1 8B InstructLlama 3.1 8B Instruct is a multilingual, instruction-tuned text-only model. Compared to other available open source and closed chat models, Llama 3.1 instruction-tuned text-only models achieve higher scores across common industry benchmarks. We provide this model in one additional size (70B).Llama 3.1 70B InstructLlama 3.1 70B Instruct is a multilingual, instruction-tuned text-only model. Compared to other available open source and closed chat models, Llama 3.1 instruction-tuned text-only models achieve higher scores across common industry benchmarks. We provide this model in one additional size (8B).Llama 3.2 1B InstructLlama 3.2 1B Instruct is a lightweight, multilingual, instruction-tuned text-only model that fits onto both edge and mobile devices. Use cases where the model excels include summarizing or rewriting inputs, as well as instruction following. We provide this model in one additional size (3B).Llama 3.2 3B InstructLlama 3.2 3B Instruct is a lightweight, multilingual, instruction-tuned text-only model that fits onto both edge and mobile devices. Use cases where the model excels include summarizing or rewriting inputs, as well as instruction following. We provide this model in one additional size (1B). We plan to make more models available for direct use in the coming months. How to Create a Project Creating new projects has never been easier. To get started, go to thehive.ai and click on the “Go to Dashboard” button in the top-right corner. Home Page If you are not logged in, the “Go to Dashboard” button will redirect you to the sign in page. Then, either sign in to an existing account or click the blue “Sign up” hyperlink at the bottom of the page to sign up for a new account. Sign In Page You will receive an email to verify your account after signing up. After you’ve either logged into an existing account or verified your new account, you will be redirected to the main dashboard. For new accounts, a new organization named “(User Name)’s personal organization” will be automatically created. Your current organization will be visible in the top-right corner. Before you can submit tasks, you will need to accept the Terms of Use and add credits to your account. To accept the Terms of Use, click the “View Terms and Conditions” button at the bottom of the page. You will need to do this for every additional organization you create. Main Dashboard To add funds to your credit balance, locate the “Billing” section in the bottom-left corner of the dashboard and click the blue “Add Credit” button, which will redirect you to another page where you can add a payment method. Billing Add Payment Method Now you’re ready to create your own projects. On any page, click on the “Products” tab on the left side of the header. From the dropdown menu that appears, select “Models.” It will redirect you to the following page, where you can view all of your current projects. To create a new project, click on the plus (+) sign next to “Projects” on the top-left side of the screen. You will be redirected to the following page, where you can choose your project type. Select “Hive Models.” Project Types Then, you will be redirected to another page containing our available models. Click to select the desired model for your project. Project Format After selecting your desired model, you will need to configure your project. Change your project’s name using the text box below. Once you hit the blue “Create” button, your project will be live. Project Configure Upon project creation, you will be redirected to the following interface. Here, you can view your API key by clicking the “API Keys” Button on the top right. Project Interface Using this API key, you can call the API by making a cURL request in your terminal. To interpret the results, please refer to our documentation and look up the relevant model and its class definitions. Sample cURL Request and Result For pricing details, please reference our model pricing table here. If you run into any issues building your projects, please feel free to reach out to us at support@thehive.ai and we will be happy to help. If you have any further questions or would like to learn more, please reach out to sales@thehive.ai or contact us here.
BACK TO ALL BLOGS Announcing Hive’s Integration with NVIDIA NIM Hive to Accelerate AI Adoption in Private Clouds and On-Prem Environments Using NVIDIA NIM HiveSeptember 23, 2024March 3, 2025 Contents Secure and Accelerated Deployments with NIMHow Customers Use Our Leading AI Detection ModelsManaging the Risks of Generative AI Hive is excited to announce the groundbreaking integration of our proprietary AI models with NVIDIA NIM. Our collaboration will allow, for the first time, Hive customers to deploy our industry-leading AI models in private clouds and on-premises data centers. We are also announcing that for the remainder of 2024, internet social platforms can receive up to 90 days of free trial access to our models. To learn more, check out the press release here. The first Hive models to be made available with NVIDIA NIM are our AI-generated content detection models, which allow customers to identify AI-generated images, video, and audio. However, we plan to make additional models available through NVIDIA NIM in the coming months, including content moderation, logo detection, optical character recognition, speech transcription, custom models through Hive’s AutoML platform, and more. Secure and Accelerated Deployments with NIM Short for NVIDIA Inference Microservices, NIM provides models as optimized containers to prospective customers. This enables organizations to run AI models on NVIDIA GPUs on private clouds, workstations, and on-premises. NVIDIA NIM is part of the NVIDIA AI Enterprise software platform and connects the power of the Hive’s proprietary AI models, securely deployed on NVIDIA’s accelerated infrastructure, with enterprise customers everywhere. While Hive’s cloud-based APIs process billions of customer requests every month, among prospective customers’ top requests has been the ability to deploy Hive models in private clouds or on-premises. These are often enterprises whose strict data governance standards challenge the use of our cloud-based APIs. Our integration with NIM solves this challenge. How Customers Use Our Leading AI Detection Models Our AI-detection tools—the first Hive models to be made available with NVIDIA NIM—have been widely recognized as best-in-class, including by an independent research study from the University of Chicago. The researchers found that Hive’s model was the “clear winner” against both its automated competitors and highly-trained human experts in classifying images as either AI-generated or human-created. With generative AI on the rise, Hive’s AI detection models have become crucial in combating the technology’s misuse. Here are select ways that customers use our models to protect themselves from the potential misuse of AI-generated and synthetic content. Internet social platforms leverage our AI detection models to proactively screen content for the presence of AI-enabled misinformation in real time. Digital platforms can leverage our detections to provide transparency to their users by tagging content as AI-generated, or moderate potential misinformation by implementing sitewide bans. Insurance companies use our models to automate the process of identifying AI-enabled fraud in evidence submitted with insurance claims. By scanning claims evidence for AI-generated augmentations, insurers can quickly, confidently and securely weed out fraud, saving significant cost from paying out fraudulent claims. Banks, brokers, and other financial institutions use our AI-generated content detection models to secure their user identification verification and KYC processes, leveraging Hive’s industry-leading AI-generated audio detection model to verify voice recognition workflows and prevent sophisticated financial fraud. Digital marketplaces use our models to automate the detection and moderation of fraudulent listings. Moreover, marketplaces protect their customers’ experience by verifying that both users and their product reviews are authentic. Video conferencing and live streaming platforms integrate our AI detection models to authenticate video and audio in real time, preventing both impersonation and the misuse of likenesses. While not all-encompassing, these are select ways that customers use our models today. Managing the Risks of Generative AI The increasing accessibility of Generative AI tools poses a newfound set of risks to companies and organizations. It can be difficult to moderate the proliferation of AI-generated content in a scalable, automated and secure way. We are proud to provide a solution that supports our customers in managing these risks, now made more accessible for enterprises to deploy on-premises or in private clouds with NVIDIA NIM. If you’re interested in accessing Hive’s AI models through NVIDIA NIM, you can learn more on our website here or on NVIDIA’s website here. If you have any questions or would like to learn more, please reach out to sales@thehive.ai or contact us here.