When used alongside device management services such as Jamf, Mosyle, Kandji, and others, additional factors, including location, device-specific data, and may also come into play to further secure the device and its communications. The idea is that even as ...

Zero trust and why it matters to the Apple enterprise

Lauren J. Mapp is a reporter at The San Diego Union-Tribune covering family caregiving, elder care, Indigenous communities and East County. She is a 2019 graduate from San Diego State University ...

Lauren J. Mapp

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Dicker Data NZ said it will offer a comperehensive suite of Kandji management soutions for businesses to efficiently manage their Apple device fleets, enforce security policies, deploy software and updates seamlessly, and gain valuable insights into their environments.

Dicker Data NZ signs Apple device management provider Kandji

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That’s why the cybersecurity industry’s efforts have been more pointed at Windows, rather than macOS and Linux. Enter Kandji, a security platform for Apple endpoint device management. At the 2023 Black Hat USA conference in Las Vegas, ConversingLabs ...

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Kandji

kandji

AI-powered image generation models are revolutionizing the creative landscape. The Midjourney platform has been a key player in this innovative field with its text-driven image creation. However, its Discord-based interface presented some limitations for professional use. Let's take a look instead at a new AI model called Kandinsky 2.2, a more builder-friendly text-to-image model available via a versatile API. Unlike Midjourney, which operates through Discord, Kandinsky enables developers to integrate AI image generation into various programming languages such as Python, Node.js, and cURLs. This means that with just a few lines of code, Kandinsky can automate the process of image generation, making it a more efficient tool for creative professionals. And with the new v2.2 release Kandinsky's image quality has never been higher. or follow me on for more content like this! Subscribe Twitter Kandinsky 2.2 brings a new level of accessibility and flexibility to AI image generation. It seamlessly integrates with multiple programming languages and tools, offering a level of flexibility that surpasses the Midjourney platform. Moreover, Kandinsky's advanced diffusion techniques result in impressively photorealistic images. Its API-first approach makes it easier for professionals to incorporate AI-powered visualization into their existing tech stack. In this guide, we'll explore Kandinsky's potential for scalability, automation, and integration, and discuss how it can contribute to the future of creativity. Join us as we delve into the tools and techniques needed to incorporate stunning AI art into your products using this advanced AI assistant. Key Benefits of Kandinsky 2.2 - Kandinsky is fully open source. Use the code directly or access it via Replicate's flexible API. Open source - Integrate Kandinsky into your workflows in Python, Node.js, cURLs, and more through the Replicate API. API access - Tweak images programmatically by modifying text prompts in code for rapid iteration. Automation - Generate thousands of images with simple API calls. Create storyboards and visualize concepts at scale. Scalability - Incorporate Kandinsky into your own tools and products thanks to its API-first design. Custom integration - Get granular control over image properties like lighting and angle through text prompts. ControlNet - Understands prompts in English, Chinese, Japanese, Korean, French, and more. Multilingual - Crisp, detailed 1024x1024 images ready for any use case. High resolution - State-of-the-art diffusion techniques produce stunning, realistic images on par with Midjourney. Photorealism How Does Kandinsky Work? Kandinsky 2.2 is a text-to-image diffusion model that generates images from text prompts. It consists of several key components: Text Encoder: The text prompt is passed through an XLM-Roberta-Large-Vit-L-14 encoder to extract semantic features and encode the text into a latent space. This produces a text embedding vector. Image Encoder: A pre-trained CLIP-ViT-G model encodes images into the same latent space as the text embeddings. This allows matching between text and image representations. Diffusion Prior: A transformer maps between the text embedding latent space and the image embedding latent space. This establishes a diffusion prior that links text and images probabilistically. UNet: A 1.22B parameter Latent Diffusion UNet serves as the backbone network. It takes an image embedding as input and outputs image samples from noisy to clean through iterative denoising. ControlNet: An additional neural network that conditions image generation on auxiliary inputs like depth maps. This enables controllable image synthesis. MoVQ Encoder/Decoder: A discrete VAE that compresses image embeddings as discrete latent codes for more efficient sampling. During training, text-image pairs are encoded to linked embeddings. The diffusion UNet is trained to invert these embeddings back to images through denoising. For inference, the text is encoded to an embedding, mapped through the diffusion prior to an image embedding, compressed by MoVQ, and inverted by the UNet to generate images iteratively. The additional ControlNet allows controlling attributes like depth. Key Improvements Over Prior Versions of Kandinsky An example showing the evolution of Kandinsky from v2.0 to v2.1 to v2.2. The realism! The primary enhancements in Kandinsky 2.2 include: : One of the key upgrades is the integration of the CLIP-ViT-G image encoder. This upgrade significantly bolsters the model's ability to generate aesthetically pleasing images. By utilizing a more powerful image encoder, Kandinsky 2.2 can better interpret text descriptions and translate them into visually captivating images. New Image Encoder - CLIP-ViT-G : Kandinsky 2.2 introduces the ControlNet mechanism, a feature that allows for precise control over the image generation process. This addition enhances the accuracy and appeal of the generated outputs. With ControlNet, the model gains the capability to manipulate images based on text guidance, opening up new avenues for creative exploration. ControlNet Support How Can I Use Kandinsky to Create Images? Ready to start creating with this powerful AI model? Here's a step-by-step guide to using the Replicate API to interact with Kandinsky 2.2. At a high level, you'll need to: - Get your Replicate API key and authenticate in your environment. Authenticate - Pass your textual description in the parameter. You can specify it in multiple languages. Send a prompt prompt - Tweak image dimensions, number of outputs, etc. as needed. Refer to the for more details, or read on. Customize parameters model spec - Kandinsky 2.2 outputs a URL to the generated image. Download this image for use in your project. Process the response For convenience, you may also want to try out this to get a feel for the model's capabilities before working on your code. live demo Step-by-Step Guide to Using Kandinsky 2.2 via the Replicate API In this example, we'll use Node to work with the model. So, you'll need to first install the Node.js client. npm install replicate Then, copy your API token and set it as an environment variable: export REPLICATE_API_TOKEN=r8_************************************* Next, run the model using the Node.js script: import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

const output = await replicate.run(
  "ai-forever/kandinsky-2.2:ea1addaab376f4dc227f5368bbd8eff901820fd1cc14ed8cad63b29249e9d463",
  {
    input: {
      prompt: "A moss covered astronaut with a black background"
    }
  }
); You can also set up a webhook for predictions to receive updates when the process is complete. const prediction = await replicate.predictions.create({
  version: "ea1addaab376f4dc227f5368bbd8eff901820fd1cc14ed8cad63b29249e9d463",
  input: {
    prompt: "A moss covered astronaut with a black background"
  },
  webhook: "https://example.com/your-webhook",
  webhook_events_filter: ["completed"]
}); As you work this code into your application, you'll want to experiment with the model's parameters. Let's take a look at Kandinsky's inputs and outputs. Kandinsky 2.2's Inputs and Outputs The text prompt is the core input that guides Kandinsky's image generation. By tweaking your prompt, you can shape the output. - The textual description, like "An astronaut playing chess on Mars." This is required. Prompt - Specifies elements to exclude, like "no space helmet." Optional. Negative Prompt - Image dimensions in pixels, from 384 to 2048. The default is 512 x 512. Width and Height - Number of denoising steps during diffusion, higher is slower but potentially higher quality. The default is 75. Num Inference Steps - Number of images to generate per prompt, default is 1. Num Outputs - Integer seed for randomization. Leave blank for random. Seed Combining creative prompts with these tuning parameters allows you to dial in your perfect image. Kandinsky Model Outputs Kandinsky outputs one or more image URLs based on your inputs. The URLs point to 1024x1024 JPG images hosted on the backend. You can download these images to use in your creative projects. The number of outputs depends on the "num_outputs" parameter. The output format looks like this: {
  "type": "array",
  "items": {
    "type": "string",
    "format": "uri"
  },
  "title": "Output"
} By generating variations, you can pick the best result or find inspiring directions. What Kinds of Apps or Products Can I Build With Kandinsky? The ability to turn text into images is a remarkable innovation, and Kandinsky 2.2 is at the forefront of this technology. Let's explore some practical ways this model could be used. In design, for instance, the rapid conversion of textual ideas into visual concepts could significantly streamline the creative process. Rather than relying on lengthy discussions and manual sketches, designers could use Kandinsky to instantly visualize their ideas, speeding up client approvals and revisions. In education, the transformation of complex textual descriptions into visual diagrams could make learning more engaging and accessible. Teachers could illustrate challenging concepts on the fly, enhancing students' comprehension and interest in subjects like biology or physics. The world of film and web design could also benefit from Kandinsky 2.2. By turning written scripts and concepts into visuals, directors and designers can preview their work in real time. This immediate visualization could simplify the planning stage and foster collaboration between team members. Moreover, Kandinsky's ability to produce high-quality images might open doors for new forms of artistic expression and professional applications. From digital art galleries to print media, the potential uses are broad and exciting. But let's not lose sight of the practical limitations. While the concept is promising, real-world integration will face challenges, and the quality of generated images may vary or require human oversight. Like any emerging technology, Kandinsky 2.2 will likely need refinement and adaptation to meet your needs. Taking It Further - Discover Similar Models With AIModels.fyi AIModels.fyi is a valuable resource for discovering AI models tailored to specific creative needs. You can explore various types of models, compare them, and even sort by price. It's a free platform that offers digest emails to keep you informed about new models. To find similar models to Kandinsky-2.2: Visit . AIModels.fyi Use the search bar to enter a description of your use case. For example, " " or " " realistic portraits . high-quality text to image generator View the model cards for each model and choose the best one for your use case. Check out the model details page for each model and compare to find your favorites. Conclusion In this guide, we've explored the innovative capabilities of Kandinsky-2.2, a multilingual text-to-image latent diffusion model. From understanding its technical implementation to utilizing it through step-by-step instructions, you're now equipped to leverage the power of AI in your creative endeavors. Additionally, AIModels.fyi opens doors to a world of possibilities by helping you discover and compare similar models. Embrace the potential of AI-driven content creation and subscribe for more tutorials, updates, and inspiration on AIModels.fyi. Happy exploring and creating! or follow me on for more content like this! Subscribe Twitter Further Reading: Exploring AI Models and Applications For those intrigued by the capabilities of AI models and their diverse applications, here are some relevant articles that delve into various aspects of AI-powered content generation and manipulation: : Discover how the AI Logo Generator Erlich leverages AI to create unique and visually appealing logos, expanding your understanding of AI's creative potential. AI Logo Generator: Erlich : Uncover a comprehensive overview of the best upscaling AI models, providing insights into enhancing image resolution and quality. Best Upscalers : Explore a detailed guide on how to effectively upscale images using the Midjourney AI model, enriching your knowledge of image enhancement techniques. How to Upscale in Midjourney: A Step-by-Step Guide : Dive into the realm of image denoising and restoration using ScuNet GAN, gaining insights into preserving image quality over time. Say Goodbye to Image Noise: How to Enhance Old Images with ScuNet GAN : Learn how the Gfpgan AI model breathes new life into old photos, providing you with a beginner's guide to revitalizing cherished memories. Breathe New Life into Old Photos with AI: A Beginner's Guide to Gfpgan : Gain insights into the nuances of AI-based face restoration by comparing the Gfpgan and Codeformer models. Comparing Gfpgan and Codeformer: A Deep Dive into AI Face Restoration : See the best models from the Nightmare AI team. NightmareAI: AI Models at Their Best : Understand the nuances between the ESRGAN and Real-ESRGAN AI models, shedding light on super-resolution techniques. ESRGAN vs. Real-ESRGAN: From Theoretical to Real-World Super Resolution with AI : Compare the Real-ESRGAN and SwinIR models, gaining insights into their effectiveness in image restoration and upscaling. Real-ESRGAN vs. SwinIR: AI Models for Restoration and Upscaling Also published here

"It's Like if Midjourney Had an API" - Taking a Look at Kandinsky 2.2

"মিডজার্নির একটি এপিআই থাকলে মনে হয়" - ক্যান্ডিনস্কি 2.2 এর দিকে নজর দেওয়া

„Es ist, als ob Midjourney eine API hätte“ – Ein Blick auf Kandinsky 2.2

"Es como si Midjourney tuviera una API": una mirada a Kandinsky 2.2

"C'est comme si Midjourney avait une API" - Jetez un œil à Kandinsky 2.2

"यह वैसा ही है जैसे मिडजॉर्नी के पास एक एपीआई होता" - कैंडिंस्की 2.2 पर एक नज़र डालें

「Midjourney に API があったようなもの」 - Kandinsky 2.2 を見てみる

"Midjourney에 API가 있는 것과 같습니다" - Kandinsky 2.2 살펴보기

"É como se Midjourney tivesse uma API" - Dando uma olhada em Kandinsky 2.2

"Sanki Midjourney'in bir API'si Varmış Gibi" - Kandinsky 2.2'ye Bir Bakış

"Giống như nếu Midjourney có API" - Hãy xem Kandinsky 2.2

“就像《中途旅程》有一个 API”——看一看康定斯基 2.2

I Googled, “most popular idioms” and this rich snippet included these 24 idioms: As idoms are defined as “ group of words established by usage as having from those of the individual words," I thought it would be fun to challenge a machine to deduce and visually produce what humans mean by our idioms. , I typed these idioms into our AI image generation system using the . a meaning not deducible Within the HackerNoon story draft Kandinsky 2 model Moon Break a leg Bite the bullet Call it a day Beat around the bush Go down in flames It takes two to tango Actions speak louder than words Through thick and thin Jump on the bandwagon On cloud nine The Whole Nine Yards Speak of the devil Come rain or shine A penny for your thoughts Ignorance is bliss Throw caution to the wind As right as rain Better late than never Raining cats and dogs A blessing in disguise Sitting on the fence A dime a dozen Head in the Clouds I’m very encouraged by how quickly the indie models are catching up to the mainstream models. Within the , you can now prompt Kandinsky 2, Midjourney Diffusion, and Stable Diffusion 1.5-2.1 (with negative prompts too). Comment below if there’s another AI image generation model you’d like us to add to HackerNoon. HackerNoon story draft editor

Google's 24 Most Popular Idioms Depicted by the Kandinsky 2 AI Art Model

Startups of The Year: Meet the AI Industry

İlin startapları: Süni intellekt sənayesi ilə tanış olun

Startups del año: conozca la industria de la IA

استارت آپ های سال: با صنعت هوش مصنوعی آشنا شوید

वर्ष के स्टार्टअप: एआई उद्योग से मिलिए

Startups pou ane a: Rankontre endistri AI

סטארטאפים של השנה: הכירו את תעשיית הבינה המלאכותית

今年のスタートアップ: AI 業界を知る

Жылдың стартаптары: AI индустриясымен танысыңыз

Startups of the Year: Hihaona amin'ny indostrian'ny AI

वर्षको स्टार्टअपहरू: एआई उद्योगलाई भेट्नुहोस्

Стартапи року: знайомство з індустрією ШІ

Yil startaplari: AI sanoati bilan tanishing

Survey and Analysis of Smart Contract Quality Assurance: Conclusions, Acknowledgement and References

This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Minghao Yan, University of Wisconsin-Madison; (2) Hongyi Wang, Carnegie Mellon University; (3) Shivaram Venkataraman, myan@cs.wisc.edu. Table of Links Abstract & Introduction Motivation Opportunities Architecture Overview Proble Formulation: Two-Phase Tuning Modeling Workload Interference Experiments Conclusion & References A. Hardware Details B. Experimental Results C. Arithmetic Intensity D. Predictor Analysis 2 MOTIVATION Many deep neural networks have been deployed on edge devices to perform tasks such as image classification, object detection, and dialogue systems. Scenarios including smart home assistants (He et al., 2020), inventory and supply chain monitoring (jet), and autopilot (Gog et al., 2022) often use battery-based devices that contain GPUs to perform the aforementioned tasks. In these scenarios, pre-trained models are installed on the devices where the inference workload is deployed. Prior works have focused on optimizing the energy consumption of GPUs (Wang et al., 2020b; 2021; Tang et al., 2019; Strubell et al., 2019; Mei et al., 2017) in cloud scenarios (Qiao et al., 2021; Wan et al., 2020; Hodak et al., 2019) and training settings (Wang et al., 2020a; Peng et al., 2019; Kang et al., 2022). On-device inference workloads exhibit different characteristics and warrant separate attention. In this section, we outline previous efforts in optimizing ondevice neural network inference and discuss our approach to holistically optimize energy consumption. 2.1 On-device Neural Network Deployment Prior work in optimizing on-device neural network inference focuses on quantization (Kim et al., 2021; Banner et al., 2018; Courbariaux et al., 2015; 2014; Gholami et al., 2021), designing hardware-friendly network architectures (Xu et al., 2019; Lee et al., 2019; Sanh et al., 2019; Touvron et al., 2021; Howard et al., 2019), and leveraging hardware components specific to mobile settings, such as DSPs (Lane & Georgiev, 2015). Our work explores an orthogonal dimension and aims to answer a different question: Given a neural network to deploy on a specific device, how can we tune the device to reduce energy consumption? In our work, we focus on edge devices that contain CPUs, memory, and GPUs. These devices are generally more powerful than DSPs often found on mobile devices. One such example is the Nvidia Jetson series, which is capable of handling a wide array of applications, ranging from AI to robotics and embedded IoT solutions (jet). The devices also come with dynamic voltage and frequency scaling (DVFS) capabilities that allow for the optimization of power consumption and thermal management during complex computational tasks. The Jetson series features a unified memory shared by both CPU and GPUs. We refer to the operating frequency of CPU, GPU, and shared memory as CPU frequency, GPU frequency, and memory frequency in this paper. To understand the system requirements in edge NN inference, we next describe a case study of how NNs are deployed in an inventory management company. From our conversations, Company A works with Customer B to deploy neural networks on edge devices to optimize inventory management. To comply with regulations and protect privacy, data from each inventory site are required to be stored locally. The vast difference in the layout of the inventories makes it impossible to pre-train the model on data from every warehouse. Therefore, these devices come with a pre-trained model based on data from a small sample of inventories, which may have significantly different layouts and external environments compared to the actual deployment venue. Consequently, daily fine-tuning is required to enhance performance in the deployed sites, as the environment continually evolves. Similar arguments apply to smart home devices, where a model is pre-trained on selected properties, but the deployed households may be much more diverse. To address privacy concerns, on-device Case Study on Inventory Management: fine-tuning of neural networks is preferred, as it keeps sensitive data locally. Therefore, edge devices often need to run both inference and periodic fine-tuning. Combining multiple workloads on edge devices can lead to SLO violations due to interference and increased energy use. 2.2 Holistic Energy Consumption Optimization Some recent works have explored reducing energy consumption by optimizing for batch size and GPU maximum frequency (You et al., 2022; Nabavinejad et al., 2021; Komoda et al., 2013; Gu et al., 2023) and developing power models for modern GPUs (Kandiah et al., 2021; Hong & Kim, 2010; Arafa et al., 2020; Lowe-Power et al., 2020). In this work, we argue that other hardware components also cause energy inefficiency and require separate optimization. We perform a grid search over GPU, memory, and CPU frequencies and various batch sizes to examine the Pareto frontier of inference latency and energy consumption. Figure 2 shows the tradeoff between the per-query energy consumption and inference latency (normalized to the optimal latency) on Jetson TX2 and Jetson Orin. Each point in the figure represents the optimal configuration that we find through grid search under a given inference latency budget and batch size. As Figure 2 shows, the Pareto frontier is not smooth globally and is difficult to capture by a simple model, which warrants more sophisticated optimization techniques to quickly converge to a hardware configuration that lies on the Pareto Frontier (Censor, 1977). Zeus (You et al., 2022) attempts to reduce the energy consumption of neural network training by changing the GPU power limit and tuning training batch size. PolyThrottle also includes these two factors. In Zeus (You et al., 2022), the focus is on training workloads in data center settings, where batch size tuning helps achieve an accuracy threshold in an energy-efficient way. We include batch size as part of PolyThrottle as it provides a trade-off between inference latency and throughput. Our empirical evaluation reveals new avenues available for optimization which complicates the search space, as we describe next.

PolyThrottle: Energy-efficient Neural Network Inference on Edge Devices: Motivation

This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Minghao Yan, University of Wisconsin-Madison; (2) Hongyi Wang, Carnegie Mellon University; (3) Shivaram Venkataraman, myan@cs.wisc.edu. Table of Links Abstract & Introduction Motivation Opportunities Architecture Overview Proble Formulation: Two-Phase Tuning Modeling Workload Interference Experiments Conclusion & References A. Hardware Details B. Experimental Results C. Arithmetic Intensity D. Predictor Analysis 8 CONCLUSION In this work, we examine the unique characteristics of energy consumption in neural network inference, especially for edge devices. We identified unique tradeoffs and dimensions between energy consumption and inference latency SLOs and empirically demonstrated hidden components in optimizing energy consumption. We then propose an optimization framework that automatically and holistically tunes various hardware components to find a configuration aligned with the Pareto Frontier. We empirically verify the effectiveness and efficiency of PolyThrottle. PolyThrottle also adapts to the need for fine-tuning and proposes a simple performance prediction model to adaptively schedule finetuning requests while keeping the online inference workload under the inference latency SLO whenever possible. We hope our study sheds more light on the hidden dimension of NN energy optimization. REFERENCES Jetson partner solutions ebook. URL https://resources.nvidia.com/ en-us-jetson-success-stories/ jetson-partner-solutions-ebook?lx=XRDs_y. Twitter streaming traces, 2018. URL https://archive.org/details/ archiveteam-twitter-stream-201804. Alipourfard, O., Liu, H. H., Chen, J., Venkataraman, S., Yu, M., and Zhang, M. Cherrypick: Adaptively unearthing the best cloud configurations for big data analytics. In NSDI, volume 2, pp. 4–2, 2017. Anderson, T., Belay, A., Chowdhury, M., Cidon, A., and Zhang, I. Treehouse: A case for carbon-aware datacenter software. 2022. Anthony, L. F. W., Kanding, B., and Selvan, R. Carbontracker: Tracking and predicting the carbon footprint of training deep learning models. 2020. Arafa, Y., ElWazir, A., ElKanishy, A., Aly, Y., Elsayed, A., Badawy, A., Chennupati, G., Eidenbenz, S., and Santhi, N. Verified instruction-level energy consumption measurement for nvidia gpus. 2020. Bai, Z., Zhang, Z., Zhu, Y., and Jin, X. Pipeswitch: Fast pipelined context switching for deep learning applications. In Proceedings of the 14th USENIX Conference on Operating Systems Design and Implementation, pp. 499–514, 2020. Banner, R., Hubara, I., Hoffer, E., and Soudry, D. Scalable methods for 8-bit training of neural networks. Advances in neural information processing systems, 31, 2018. Brochu, E., Cora, V. M., and De Freitas, N. A tutorial on bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning. arXiv preprint arXiv:1012.2599, 2010. Cai, H., Wang, T., Wu, Z., Wang, K., Lin, J., and Han, S. On-device image classification with proxyless neural architecture search and quantization-aware fine-tuning. In Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, pp. 0–0, 2019. Cai, H., Gan, C., Zhu, L., and Han, S. Tinytl: Reduce memory, not parameters for efficient on-device learning. Advances in Neural Information Processing Systems, 33: 11285–11297, 2020. Cao, Q., Balasubramanian, A., and Balasubramanian, N. Towards accurate and reliable energy measurement of nlp models. In Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing, 2020. Censor, Y. Pareto optimality in multiobjective problems. Applied Mathematics and Optimization, 4(1):41–59, 1977. Courbariaux, M., Bengio, Y., and David, J.-P. Training deep neural networks with low precision multiplications. arXiv preprint arXiv:1412.7024, 2014. Courbariaux, M., Bengio, Y., and David, J.-P. Binaryconnect: Training deep neural networks with binary weights during propagations. Advances in neural information processing systems, 28, 2015. Crankshaw, D., Wang, X., Zhou, G., Franklin, M. J., Gonzalez, J. E., and Stoica, I. Clipper: A low latency online prediction serving system. In NSDI, volume 17, pp. 613– 627, 2017. Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018. Gardner, J. R., Kusner, M. J., Xu, Z. E., Weinberger, K. Q., and Cunningham, J. P. Bayesian optimization with inequality constraints. In ICML, volume 2014, pp. 937–945, 2014. Gholami, A., Kim, S., Dong, Z., Yao, Z., Mahoney, M. W., and Keutzer, K. A survey of quantization methods for efficient neural network inference. arXiv preprint arXiv:2103.13630, 2021. Gog, I., Kalra, S., Schafhalter, P., Gonzalez, J. E., and Stoica, I. 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PolyThrottle: Energy-efficient Neural Network Inference on Edge Devices: Conclusion & References

Software Engineering for OpenHarmony—A Research Roadmap: Conclusion & References

Startups Of The Year 2023: Asia Winners

1. We’ve Already Been Trained to Post What Where Adding social to media, making social media, turned the documenter into the subject matter, as we were then all expected to become sources. We turned out to be social media armchair experts, posting more about other people, their likeness, and world events. All these updates show off more about our own existing perspectives. Large Language Models (LLMs) have so much documentation by us to learn from. Just consider how much it already knows about your doughnut preferences. 2. What do you do? Are you a student, developer, educator, creative professional, writer, etc.? I am a citizen of earth, the internet and all that makes up what we call today. As the Founder and CEO of , I’ve been publishing the rise via our , and integrating with next best LLM or AI API the market offers. And this past 6 months, it’s felt like there’s a new best in class option everyday. To date the via StableDiffusion (XL, 2.1, 2.0, & 1.5), Midjourney Diffusion, and Kandinsky (2.2 & 2.1), and our human writers and editors leverage ChatGPT (3, 3.5, & 4) and T-5 to provide headline and story phrasing options. Ultimately it’s the that drives how we use technology to improve stories about technology. HackerNoon tens of thousands of stories about AI HackerNoon CMS currently generates images human driven editing protocol 3. What aren’t they telling us about AI? The details that make us human can not be fully replicated. Given the training, the logic, the setting, the motivation, and every word every published on the internet, an AI can learn to guess the next word I’ll say at a high confidence level, but it can’t tell you why I’m obsessed with what Im obsessed with when I wake up. The chip manufacturing war is very real. GPU (graphics processing units) is digital international currency, and I expect it to outperform the dollar and bitcoin over the next few years. I’m excited and nervous for how much of this revolution is happening in public. There is no secret Manhattan Project today, leading us to a new level of doom; we’re doing it together. When one tech giant is behind, they simply open source the next model to catch up. Then any independent project can make small tweaks on that level up. And so I anticipate small projects by individuals can compete on a performance basis (but not on a GPU level) with large investments by large corporations. Think of this way, if LLMs were classified like new drugs and medicines, the maker would have something like a 7 year window to exclusively sell it. What they’re not telling you about AI boom of the early 2020s is that it is in fact driven by transparency of documentation. 4. What do you think the world will be like when AI reaches its full potential? The world will never reach it’s full potential. Society’s full potential, just as inbox zero, is a myth perpetuated by rounding errors. Did AI write that? No, but what’s the difference? We’ve integrated a number of different AI writing detection applications tools within the HackerNoon CMS, and they certainly help identify patterns, but they all function in confidence levels. Like what is the difference between 83% human written / 17% machine written and 84% human written / 16% machine written? The dangers as AI scales is being mistaken for a human. From the AI’s perspective, I don’t think it would see becoming human as its full potential, but my perspective may be warped by AI Scarlett Johansson (Samatha) dumping real live human Joaquin Phoenix (Theodore) towards the end of Spike Jonze’s “Her.” 5. Do you have any final thoughts about how the AI Boom of early 2020s will be remembered? The leading technology of the day usually wows the laymen on an input to output level. Generative AI can wow you. From a single word or phrase, it can create a new image at a professional or semi-professional level. It can make a solid college level lesson plan, create new tasty recipes, and even optimize your calendar. Ultimately, I believe and hope AI can assist us. Broaden our will so to speak. But , an AI version of me would very much say the same thing. When we look back on the AI boom of the early 2020s, we’ll say, if has Skynet has taken over of course machines could make new things too. Would you like to take a stab at answering some of these questions? The link for , just start writing! ”What They Don’t Say About AI” Story Template is HERE , Founder & CEO of . David Smooke HackerNoon . Publish a story with us

The 2020s AI Boom is Driven by Transparency of Documentation 

2020 এর AI বুম ডকুমেন্টেশনের স্বচ্ছতার দ্বারা চালিত

Der KI-Boom der 2020er Jahre wird durch die Transparenz der Dokumentation vorangetrieben

El auge de la IA de la década de 2020 está impulsado por la transparencia de la documentación

Le boom de l’IA des années 2020 est motivé par la transparence de la documentation

2020 का एआई बूम दस्तावेज़ीकरण की पारदर्शिता से प्रेरित है

2020 年代の AI ブームはドキュメントの透明性によって推進される

2020년대 AI 붐은 문서의 투명성이 주도

O boom da IA na década de 2020 é impulsionado pela transparência da documentação

Бум искусственного интеллекта 2020-х годов обусловлен прозрачностью документации

2020'deki Yapay Zeka Patlaması Belgelerin Şeffaflığından Kaynaklanıyor

Sự bùng nổ AI của những năm 2020 được thúc đẩy bởi tính minh bạch của tài liệu

文档透明度推动 2020 年代 AI 繁荣

Learn More at EditingProtocol.com, where the HackerNoon CMS powers the most up-to-date technical documentation about The Editing Protocol , the independent technology publishing company, has released to the public, a set of rules and guidelines that can be used by humans and machines to determine whether a story is worth publishing, how to specifically improve the story’s content, and how to distribute the story with more reach and relevance. HackerNoon The Editing Protocol What is The Editing Protocol? One of the world leaders in online publishing, HackerNoon is home to over 45,000 published contributors. As a startup, the company was tasked with reviewing thousands of submissions a month with just a small team of editors. To do that, HackerNoon created a set of preliminary checks, rules, and quality guidelines that determine whether or not a story can be published, or should be rejected. This document was dubbed The Editing Protocol and can be read by humans to guide publishing at scale. “In publishing a hundred thousand stories, we’ve learned best practices for how to improve and distribute professional technology content on the internet,” said . HackerNoon Creator and CEO David Smooke “I’m excited to open up this technical documentation for feedback, and continue integrating the most cutting-edge technologies into the Editing Protocol.” To elevate the quality of published content, and optimize the user experience for contributors and readers alike, the editing protocol incorporates several inventive technologies, such as: An AI-powered (GPT-4) headline generator. AI image generator models like Stable Diffusion XL, Stable Diffusion v2.1, Stable Diffusion v2.0, Stable Diffusion v1.5, Midjourney Diffusion, Kandinsky 2.2, and Kandinsky 2.1. AI-generated content detection. AI text translator. AI TLDR generator. Plagiarism checker. Backlink checker bots. The protocol’s guidelines are rule-based and can be easily converted into conditional statements. Once programmed, the protocol can allow human editors to focus on the quality and improvement of publishable stories, while the system automatically informs writers of what rule they have broken, or guidelines they have missed. With The Editing Protocol, small teams can publish content at scale, using both human editors and rule-based flagging systems to provide an efficient publishing process. HackerNoon has already programmed a lot of the protocol into the custom CMS the company has built from the ground up. For instance, submissions that are below the minimum structural quality measure are automatically rejected, and writers are sent an email specifically stating how to improve the story they hope to publish before resubmitting. Furthermore, there is a section in the protocol that highlights the importance of . As one of the most important aspects of online publishing is the visual presentation of the story, HackerNoon built an into their CMS, allowing writers to create original images and help writers adhere to the protocol using the tech itself. originality score AI image generator HackerNoon believes the protocol will not only guide human editors to better vet stories, but also help developers automate the tedious workflows within the traditional editorial review process. How to Use The Editing Protocol The full protocol can be viewed via which is hosted on HackerNoon’s custom CMS builder. https://editingprotocol.com/ With The Editing Protocol, HackerNoon aims to help make the internet better by getting rid of the noise—low-quality self-published articles, spammy content riddled with even spammier links, and a horde of other bad SEO practices that prioritize clicks at the expense of quality. As the Internet changes with , it is likely that The Editing Protocol, too, will change. As such, whenever rules or processes are added, removed, or updated, users can find them on , and use that domain as the ground truth. the emergence of Web3 technologies editingprotocol.com

HackerNoon Releases “The Editing Protocol” - Technical Documentation for Digital Publishing at Scale

হ্যাকারনুন "দ্য এডিটিং প্রোটোকল" রিলিজ করেছে - স্কেলে ডিজিটাল পাবলিশিংয়ের জন্য প্রযুক্তিগত ডকুমেন্টেশন

HackerNoon veröffentlicht „The Editing Protocol“ – technische Dokumentation für digitales Publishing im großen Maßstab

HackerNoon, 대규모 디지털 출판을 위한 기술 문서인 "편집 프로토콜" 출시

HackerNoon выпускает «Протокол редактирования» — техническую документацию для масштабной цифровой публикации

HackerNoon "Düzenleme Protokolü"nü Yayınladı - Büyük Ölçekte Dijital Yayıncılık için Teknik Dokümantasyon

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