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Back on September 16th, we introduced you to our <a href="https://scale.com/blog/introducing-our-new-api-docs" rel="noopener noreferrer" target="_blank">new API Docs</a>. Today, we’re pleased to follow up the original announcement with further details on how we implemented this with Next.JS, Tailwind CSS, and ReadMe. Furthermore, rather than simply describing how we used each platform and framework, we have packaged together an example for you to implement and build off of <a href="https://github.com/scaleapi/custom-documentation-example" rel="noopener noreferrer" target="_blank">in this repository</a>.<h2>Some Context:</h2> At Scale AI, we are building the critical tools that make it easier to build and scale real-world AI systems. To provide our customers with the best experience possible on the Scale AI platform, we believe that a clear, simple, and memorable front-end, user interface, and user experience is critical. Scale’s design team has been hard at work building out a design system and improving customer-facing interfaces to bring front-end consistency across our platform. Most important to this effort was to implement the design system across our different applications. We decided to use Tailwind CSS as a CSS framework and CSS Modules as a way to add custom elements when some customization was needed. To ensure our website and applications were highly performant and loaded quickly, we chose to use Next.js as our react framework.<h2>Why Tailwind?</h2> Scale’s design team have used Next.js and styled-jsx as a way of providing CSS styling since the initial launch of our website. While this was fine when there were fewer pages on the website, it proved to be hard to scale and maintain a design system without proper documentation. At this point the team faced two options: we could either spend a lot of time creating and improving our own CSS and writing documentation for it, or we could use a CSS framework that could adapt to our needs. Since the Scale team prefers not to re-invent the wheel when it isn’t needed, we decided that using a CSS framework would yield the best results in the shortest period of time. Tailwind has allowed us to expedite the development of new layouts and pages while maintaining consistency across our website. Tailwind also has the advantage of having strong documentation behind it to support future development. Using Tailwind has also freed the team up to spend more time focusing on special details for each page like custom animations and interactions.<h2>Why Next.js?</h2> Next.js is one of the best React Frameworks out today. Since the launch of <a href="https://nextjs.org/blog/next-9-3#next-gen-static-site-generation-ssg-support" rel="noopener noreferrer" target="_blank">SGG support</a> in March, it’s a no brainer for a fast and performant website and API docs. Quoting them from their own <a href="https://nextjs.org/blog/next-9-3#next-gen-static-site-generation-ssg-support" rel="noopener noreferrer" target="_blank">blog post</a>:<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/4a743efa-0436-43a8-18b6-9368be8e8a00/public" alt="Next.js Quote"> Next.js’s API routes also proved to be helpful as we used this to retrieve the search values made by the user. More on their API Routes <a href="https://nextjs.org/docs/api-routes/introduction" rel="noopener noreferrer" target="_blank">here</a>. <h2>Why Readme?</h2> As we highlighted in our previous <a href="https://scale.com/blog/introducing-our-new-api-docs" rel="noopener noreferrer" target="_blank">blog post</a>, we chose ReadMe as the platform for our API documentation because ReadMe allowed us to: <ol><li> </li><li> Unify the API reference and the Customer Dashboard documentation into a</li><li> unified website.</li><li> </li><li> </li><li> Improve the developer experience. Instead of only showing static code</li><li> examples, ReadMe enables developers to submit API requests directly with</li><li> their Scale API key. ReadMe also has built in search functionality to make</li><li> it easier for developers to find specific topics.</li><li> </li><li> </li><li> Democratize the content creation process. Instead of editing raw markdown,</li><li> making a pull request, and then deploying to a server, we just had to</li><li> update the text we needed in the ReadMe UI. Ultimately, the ReadMe</li><li> platform provided a richer and more dynamic user experience.</li><li> </li></ol><h2> </h2><h2> Sharing our example code with the developer community</h2><h2> </h2> We developed the new API documentation during Scale’s Hackathon. We are incredibly proud of what we accomplished in just one week and we wanted to share our solution with anyone who is looking to solve a similar problem. Rather than simply providing a step-by-step instruction for this solution, we have packaged together an example for you in our <a href="https://github.com/scaleapi/custom-documentation-example" rel="noopener noreferrer" target="_blank">Github repo</a>. We hope this sample code allows you to build your own beautiful and customized documentation.

How to customize your API Documentation’s front-end leveraging cutting-edge platforms and frameworks.

Customizing API Documentation with Next.JS, Tailwind CSS, and ReadMe.

Quick Start Guide to Customized API Documentation

<h2>Introduction</h2> The ML Team at Scale hosts a weekly reading groups where members choose papers from the broad AI/ML community and discuss them ranging from topics in Computer Vision to NLP to Active Learning. Here, we describe a brief summary of some of the insights we gained from various papers and how we aim to use some of that knowledge in future research projects and applications to Scale AI’s business.<h3><a href="https://arxiv.org/abs/2008.09049" rel="noopener noreferrer" target="_blank">Discovering Useful Sentence Representations from Large Pretrained Language</a></h3><h3><a href="https://arxiv.org/abs/2008.09049" rel="noopener noreferrer" target="_blank"> Models</a></h3>Presenter:<a href="https://www.linkedin.com/in/nishantsubramani/" rel="noopener noreferrer" target="_blank">Nishant Subramani</a> This is Scale’s first research paper and we wrote a <a href="https://scale.com/blog/discovering-useful-sentence-representations" rel="noopener noreferrer" target="_blank">blog post summarizing the paper</a>. This paper focuses on whether we can adapt pre-trained language models off-the-shelf as universal decoders. To be considered “universal,” a decoder must have an implicit representation for any target sentence such that it can recover that sentence exactly when conditioned on its representation. We investigated whether such representations exist and whether they can be easily discovered. Experiments show that not only do these representations exist for sentences from a variety of different genres, but also that our methods can recover these sentences almost perfectly without fine-tuning the underlying language model at all.<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/733c8e61-0b1b-4a08-9092-43c494a9fc00/public" alt="Scale blog ml papers B_01"> <h3><a href="https://arxiv.org/abs/1910.04126" rel="noopener noreferrer" target="_blank">Scalable Nearest Neighbor Search for Optimal Transport</a></h3>Presenter:<a href="https://www.linkedin.com/in/anastasiia-alokhina-534b1014a/" rel="noopener noreferrer" target="_blank">Anastasiia Alokhina</a> The authors of this paper look at the problem of nearest neighbor search with respect to Wasserstein distance. As Wasserstein distance has become popular in high-resource data environments and domains (images, text, etc.), search becomes prohibitively slow. As a result, approximation methods are important. The authors introduce a Quadtree variant called a Flowtree that formally shows better asymptotic accuracy and empirically show that it beats existing methods on a variety of different real-world datasets on either running time or accuracy. Flowtree is a fast nearest neighbor search algorithm for Optimal Transport (Wasserstein-1 distance), which has a linear running time and speeds up SOTA performance by up to 7x. They do this by evaluating the optimal tree flow in the original metric space. <img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/2f1c10db-58ad-4966-b9aa-a69c64080500/public" alt="scale blog ml papers B_02"> At Scale AI, we work with lots of high dimensional data such as documents and images for a variety of tasks. For these, we often need to find good representation spaces for these data points such that we can do fast and highly accurate similarity search in order to find outliers in datasets in order to find errors in labeling and find examples to label to improve our models using active learning. We think the flowtree method here could be invaluable in giving us the ability to use a different distance for this end and is something we will explore going forward. <h3><a href="https://demuc.de/papers/schoenberger2016sfm.pdf" rel="noopener noreferrer" target="_blank">Structure-from-Motion Revisited</a></h3>Presenter:<a href="https://www.linkedin.com/in/ppyht2/" rel="noopener noreferrer" target="_blank">Hongbo Tian</a> Structure-from-Motion(SfM) is a field of computer vision techniques which aim to reconstruct full 3-dimensional scenes from only a series of images. Accurate SfM algorithm can reproduce colored point clouds similar to LiDAR. The authors of this paper along with <a href="https://colmap.github.io/bibliography.html" rel="noopener noreferrer" target="_blank">Schönberger’s thesis</a> introduced a robust computational pipeline for accurate dense 3D reconstruction. The authors also introduced <a href="https://colmap.github.io/index.html" rel="noopener noreferrer" target="_blank">COLMAP</a> - an open-sourced CUDA accelerated software to reproduce the results. <img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/62e7cdd5-e0b4-4fc0-7dee-619984c25800/public" alt="Scale blog ml papers B_03"> At Scale AI, our labelling pipeline is not subjected to the same online computational constraints compared to our customers. This allows us to pursue batch methods to enrich our 2D dataset, enabling our human labelers to capture 3D contexts from only images. <h3><a href="https://storage.googleapis.com/pub-tools-public-publication-data/pdf/59f3bb33216eae711b36f3d8b3ee3cc67058803f.pdf" rel="noopener noreferrer" target="_blank">Representation Learning for Information Extraction from Form-like</a></h3><h3><a href="https://storage.googleapis.com/pub-tools-public-publication-data/pdf/59f3bb33216eae711b36f3d8b3ee3cc67058803f.pdf" rel="noopener noreferrer" target="_blank"> Documents</a></h3><h3><a href="https://storage.googleapis.com/pub-tools-public-publication-data/pdf/59f3bb33216eae711b36f3d8b3ee3cc67058803f.pdf" rel="noopener noreferrer" target="_blank"> </a></h3>Presenter: <a href="https://www.linkedin.com/in/nishantsubramani/" rel="noopener noreferrer" target="_blank">Nishant Subramani</a> The authors aim to extract structured information from form-like documents. They observe that forms have fields that often correspond to well-understood types and are often associated with a specific key phrase that bears a visual relationship with it. They also observe that key phrases in documents are drawn from a small vocabulary. Due to these observations, the authors build a system with two parts: candidate generation and scoring &amp; assignment. In the candidate generation phase each field type is associated with a candidate generator based on a cloud-based entity extraction service. Spans are then detected. In an invoice there are multiple dates. Every date in an invoice becomes a candidate for every date field in the target schema. This process is repeated for every target field. In the scoring and assignment phase, the goal is to find the correct extraction candidate for each field. To do this, a score is computed for each candidate independently using a neural model. Next, the most likely scored candidate is assigned for each field. This process yields an independent learned representation based only on the candidates’ neighborhood (this is entirely independent of other candidates of fields). The neural scoring model is below. <img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/18c49c25-c170-4dcd-e6da-51144e415600/public" alt="Scale blog ml papers B_04"> Our major takeaways are that structured extraction is not super well-studied by the academic community, but is relatively simple. The extraction system presented has promising accuracy and generalizes well to different domains. The representations have some interpretability as well. Ultimately some of the learnings from this paper could influence some of our work on <a href="https://scale.com/document" rel="noopener noreferrer" target="_blank">Scale Document</a>. <h3><a href="https://arxiv.org/abs/1912.11370" rel="noopener noreferrer" target="_blank">Big Transfer (BiT): General Visual Representation Learning</a></h3>Presenter: <a href="https://www.linkedin.com/in/felix-lau-0386641a/" rel="noopener noreferrer" target="_blank">Felix Lau</a> This paper focuses on how to transfer pre-trained representations to other tasks in the visual space. They propose a recipe called Big Transfer (BiT) and achieve very strong performance on ILSVRC-2012 (87.5% top-1 accuracy), CIFAR-10 (99.4%), and the Visual Task Adaptation Benchmark (VTAB; 76.3%). <img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/e2a0b8c1-282d-4511-835f-a183b368ad00/public" alt="Scale blog ml papers B_05"> BiT has two phases: upstream and downstream. In upstream pre-training, the authors first investigate the scale of computational budget and how that affects performance. Secondly, they look at ground normalization and weight standardization, which helps significantly with small-batch and large-batch training alike. In downstream transfer, the authors propose a cheap fine-tuning methodology by using the BiT-HyperRule to select the most important hyperparameters for tuning as a function of the task’s intrinsic image resolution and number of datapoints. The hyperparameters they thought were important were training schedule length, resolution, and whether to use MixUp regularization. There are a few takeaways from this method we found interesting. There’s a lot of value in balancing the computational budget and simplifying the experimental pipeline when needed. The BiT-HyperRule focusing on only a few hyperparameters was illuminating. We were interested in the dynamics of how large batches, group normalization, and weight standardization interplayed and were surprised at how poorly batch normalization performed relative to group normalization and weight standardization for large batches. The empirical strength of group normalization and weight standardization surprised us as well and we will start exploring these techniques in some of our computer vision models with lots of data available. However, the most impressive part was the empirical few-shot learning performance relative to the baseline. We are trying to build models that quickly adapt to new customers and domains and often struggle with fine-tuning, so these ideas are very top of mind. <h3><a href="https://arxiv.org/abs/1905.03677" rel="noopener noreferrer" target="_blank">Learning Loss for Active Learning</a></h3>Presenter: <a href="https://www.linkedin.com/in/rishgoyell/" rel="noopener noreferrer" target="_blank">Rishab Goyal</a> The authors present an active learning method that is a task-agnostic by attaching a loss prediction module to a target network and train it to be able to predict target losses of unlabeled inputs. The module can then provide insight into data that the model could predict incorrectly and thus improve the overall model by choosing just those examples to go and label. <img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/105e736f-2bce-4ad5-6e99-7279ab964900/public" alt="Scale blog ml papers B_06"> Their general process is that for every labeled example you predict the target and predicted loss for your prediction. Next, you take the unlabeled pool and pass them through the model to get predicted losses. Lastly you annotate the top-k data points and add them to the labeled training set. The loss prediction module uses a ranking loss. As a data labeling company, we are very interested in active learning and how we can leverage our own strengths to better build ML models. We really liked the approach because it is widely applicable, but were concerned that there could be many degenerate cases. We weren’t convinced that the loss prediction module would be strong enough to offer significant improvements over uncertainty or coverage based methods such as entropy, query-by-committee, and coreset based methods. <h3><a href="https://arxiv.org/pdf/1912.13318v5.pdf" rel="noopener noreferrer" target="_blank">LayoutLM: Pre-training of Text and Layout for Document Image</a></h3><h3><a href="https://arxiv.org/pdf/1912.13318v5.pdf" rel="noopener noreferrer" target="_blank"> Understanding</a></h3>Presenter: <a href="https://www.linkedin.com/in/malcolm-greaves/" rel="noopener noreferrer" target="_blank">Malcolm Greaves</a> The authors try and solve the problem of automated understanding from scanned business documents. They use both computer vision and natural language processing techniques via textual and visual layout information for pre-training their system. Their system achieves SOTA on a set of layout analysis, receipt understanding, and document image classification tasks. They claim that their empirical performance is from their novel loss functions: a masked visual-language model (MVLM) loss and a multi-label document classification (MDC) loss. <img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/756680db-57a2-4448-2f6f-3f0976fa5500/public" alt="Scale blog ml papers B_07"> MVLM extends the masked language model loss from BERT by randomly choosing some words to mask. They then keep the masked token’s 2D positional embedding and forces the model to predict the word’s text. MVLM forces the model to understand language context and 2D relationships between words in the document. MDC assumes that documents have possibly several labels and is a standard cross-entropy loss. This is optional and seems to help on the document image classification tasks. We found this paper interesting, but computationally demanding: alongside their loss functions, a key contribution of this paper is their pre-training routine. Without significant pre-training, these models do not seem to work well. This work requires off-the-shelf OCR and treats that as ground truth, which simplifies the work, but makes it less practical for document analysis practitioners. For the business document tasks that our ML team takes on, we often like to have greater control on the OCR task. It's sometimes advantageous to be able to adjust OCR to provide better downstream task-dependent results. We do a lot of document analysis at Scale, but can’t obfuscate or explain away the OCR component, which do produce some errors (e.g. with handwriting). Although this approach needs word-level annotations, Scale is in a unique position to get large amounts of these labor-intensive labels using our labeling platform. <h3><a href="https://arxiv.org/abs/2006.16205v1" rel="noopener noreferrer" target="_blank">Simplifying Models with Unlabeled Output Data</a></h3>Presenter: <a href="https://www.linkedin.com/in/alexandre-matton-73042b170/" rel="noopener noreferrer" target="_blank">Alexandre Matton</a> This paper focuses on tasks whose outputs have to obey some constraints. This happens in pseudo-code to code transcription for example. What we ultimately want is a code that compiles and runs without error. The usual way to address these tasks is to build a single end-to-end model and hope that it will learn the input-output mapping and understand the output constraints. The authors provide a new way to tackle these tasks by replacing the original model by two sub-models: a base predictor and a denoiser. The goal of the base predictor is to learn the mapping between the input and the output. The goal of the denoiser is to learn the constraints of the output space, in order to fix the base predictor’s output. <img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/9578c3e1-2f67-408f-2bd1-a2972c25ee00/public" alt="Scale blog ml papers B_08"> There is a reason why this decomposition works well. As the denoiser simplifies the job of the base predictor, it is now possible to increase the regularization of the base predictor without affecting the overall model performance. This in turn leads to solutions that generalize better on unseen data. The big win of this architecture is that the denoiser can be trained on unlabelled output data, which is often abundant (e.g. there is a huge amount of compiling code pieces freely available on github). A big number of tasks actually impose constraints on the output (such as molecule generation, or translation from one language to another). At Scale, our ML team also tackles such problems. This paper provides a very general way to increase our downstream scores on them. <h3>Join our Team</h3> If you’re interested in joining our growing engineering team, take a look at our <a href="https://scale.com/careers" rel="noopener noreferrer" target="_blank">careers page</a> for open positions.

The machine learning research papers the Scale AI team read and discussed in Q3 2020.

Research Papers the Scale AI Team has Read in Q3 2020

Scale AI Machine Learning Digest - Q3 2020

At Scale AI, we envision a future where AI is as easy to build as any software. To help AI achieve its full potential, we are building the tools that make it easier to build and scale real-world AI systems. We began by focusing on the data - the foundation of all AI applications. With the Scale AI platform, customers can understand, visualize, and curate raw datasets, send raw data to be annotated, then debug model performance - all in one place. From the beginning, we knew the Scale AI platform had to be API-based to enable teams working on AI and ML to automate their data pipelines and seamlessly integrate Scale into ML development cycles. Since our start in 2016, our engineering team has built incredible products to deliver large volumes of training data at high-quality, whether the data input is 3D sensor data, video, image, document, or text. Despite the continued evolution of our products and the robustness of our APIs, until recently, the API documentation itself was static, with limited interactions and not a lot of details to fully understand our products, set up effective workflows, and optimize for quality.<h2>The Evolution of our Documentation:</h2> If you are a long-time Scale customer, you are probably familiar with this as our API documentation:<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/e4b49b7e-e94c-4e83-558b-ed0436a2c600/public" alt="Scale AI's Original API Documentation"> The early documentation simply served as an API reference guide. The parameters were well spaced out, included relevant examples, and showed some example code using our SDKs. As we expanded the documentation to include content on key concepts, the customer dashboard, and other elements of the Scale AI platform, the documentation became increasingly clunky and awkward to navigate. The early documentation was also very static, with limited interactions, no search capability, and overall proved to be a less than ideal user experience. <h3>Transition to ReadMe</h3> Earlier this year, our Customer Experience and Field Engineering team identified <a href="https://readme.com/" rel="noopener noreferrer" target="_blank">ReadMe</a> as an industry-leading documentation platform that could scale with our evolving needs. With ReadMe, we were able to realize three tangible benefits:<ol><li> </li><li> Unify the API reference and the Customer Dashboard documentation into a</li><li> unified website.</li><li> </li><li> </li><li> Improve the developer experience. Instead of only showing static code</li><li> examples, ReadMe enables developers to submit API requests directly with</li><li> their Scale API key. ReadMe also has built in search functionality to make</li><li> it easier for developers to find specific topics.</li><li> </li><li> </li><li> Democratize the content creation process. Instead of editing raw markdown,</li><li> making a pull request, and then deploying to a server, we just had to</li><li> update the text we needed in the ReadMe UI. Ultimately, the ReadMe</li><li> platform provided a richer and more dynamic user experience.</li><li> </li></ol> The only limitation of the ReadMe platform out-of-the-box was the lack of front-end customization to ensure visual consistency between the documentation and the rest of our website. <h3>Latest State:</h3> The latest update to our documentation marries the content and functionality of ReadMe with our front-end to provide a better and more consistent experience between our API documentation and the rest of our platform. <img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/e120309c-17d5-479b-4e47-fce17cdea400/public" alt="Latest State of API Documentation"> We accomplished this by effectively leveraging ReadMe as a content management system (CMS) on the back end. We then used <a href="https://tailwindcss.com/" rel="noopener noreferrer" target="_blank">Tailwind CSS</a>, a utility-first framework to build the UI for the front end, and <a href="https://nextjs.org/" rel="noopener noreferrer" target="_blank">Next.JS</a>’s static generation feature to pre-render the page into HTML on the server ahead of each request. The HTML can be globally cached by a CDN and served instantly. Technical details on how we implemented this will follow in another blog post. <h2>New Features:</h2><h3> </h3><h3> In-depth Customer Dashboard Documentation</h3><h3> </h3> As a Scale AI customer, the Dashboard is the central location to manage your account and your projects. The new in-depth Customer Dashboard documentation walks you through how to manage your account, your projects, get an overview of submitted and completed tasks, and audit a random sample of tasks for quality. <h3>Scale 101 Workflow Overviews</h3> Every institution, company, and even team has their own ways of describing the data labeling workflow. To ensure every customer is successful on the Scale AI platform, we introduce the terminology we’ve developed in this section. <img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/f3a4368d-6d0d-438c-0590-d59ddd39ad00/public" alt="Scale 101: Key Concepts"> <h3> </h3><h3> Make API calls using your Scale API key directly from the Documentation</h3><h3> </h3> Instead of static code examples, this feature enables developers to submit live API requests directly using their Scale API key. <img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/8c6eac81-5b29-4e0a-0d0b-1f9feebee400/public" alt="API Key"> <h3>Search</h3> As we continue to expand the documentation, we realize it can also become more challenging to navigate to and find details on a specific topic. To offer a faster and easier way to find keywords or key phrases, we have added a new search feature in our documentation powered by Algolia. <img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/f5cef952-a43c-429e-62a6-0eafe33f6e00/public" alt="Search Functionality"> <h3>Dark Mode</h3> Dark mode reduces the light emitted by device screens while maintaining the minimum color contrast ratios required for optimal readability. To view the API documentation in dark mode, select “Dark” in your computer’s display settings or “Auto” to switch from light mode to dark mode at sunset or at a specified time. <img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/3c6ca586-71f7-4928-de8e-d4ee154fff00/public" alt="Light Mode/Dark Mode"> <h2>What this Means:</h2> If you are an existing Scale customer, rest assured that the updates to the documentation do not impact any existing projects, batches, tasks, or workflows. If anything, we hope these updates will clarify the expected behavior, showcase our API’s flexibility, and enable easier task creation with more effective instructions to improve the overall quality of returned tasks. If you are just getting to know our products, we hope the updated documentation inspires you to come up with new project ideas.

Introducing a new look and new features for our API Documentation

Introducing Scale AI's updated API Documentation

Introducing: Our New API Docs

At Scale AI, customers come first. This is a core credo that drives many of our daily decisions. We take the trust of our customers and the security of their data seriously, which is why we are pleased to announce the availability of our System and Organizational Controls (SOC) 2 Type 1 report. As a cloud-based data annotation provider, our customers need to be confident that we securely handle their data. SOC reports are prepared by independent Certified Public Accountant (CPA) auditors based on the internationally recognized <a href="https://www.aicpa.org/content/dam/aicpa/interestareas/frc/assuranceadvisoryservices/downloadabledocuments/trust-services-criteria.pdf" rel="noopener noreferrer" target="_blank">Trust Services Criteria</a> framework developed by the American Institute of Certified Public Accountants (<a href="https://www.aicpa.org/" rel="noopener noreferrer" target="_blank">AICPA</a>). Achieving compliance with SOC 2 requires Scale demonstrating the achievement of their service commitments and system requirements based on the Security Trust Services Category. We take our commitments to security and the responsibilities they entail seriously, which is why we have partnered with CPA firm Schellman &amp; Company, LLC to provide ourselves and, most importantly, our customers with independent, third-party validation of our processes and controls to keep our systems and customer data safe. Our commitment to enterprise-grade security is one reason leading ML teams such as NVIDIA, Toyota Research Institute, Open AI, and more rely on Scale AI to accelerate the development of their AI applications. Scale continues to invest in security best practices, and to that end, we are pursuing a Type 2 report moving forward. If you’d like further details on our SOC 2 report, please contact us at security@scale.com or sales@scale.com.

At Scale AI, we take the trust of our customers and the security of their data seriously. We are pleased to announce the availability of our SOC 2 report. 

Reaffirming Our Commitment to Building Customer Trust: Scale+ SOC 2

Reaffirming Our Commitment to Building Customer Trust: Scale + SOC 2

<h2>AI/ML Research at Scale AI:</h2> Scale AI is dedicated to accelerating the development of AI applications. As part of this mission, Scale AI’s growing AI/ML Team aims to meaningfully contribute to the AI ecosystem with research and resources that can bring significant and practical benefits to our customers and the broader research community. Research and experimentation happens throughout Scale. In some cases, our contribution is in areas where the bar has already been set high. Some examples include the open-source datasets we have released with various partners such as <a href="https://pandaset.org/" rel="noopener noreferrer" target="_blank">PandaSet</a>, <a href="https://www.nuscenes.org/" rel="noopener noreferrer" target="_blank">nuScenes</a>, and <a href="https://scale.com/resources/download/nlp-dataset-balanced-conll" rel="noopener noreferrer" target="_blank">CoNLL Balanced</a>. As our AI/ML team continues to grow, we aim to contribute in entirely new spaces by publishing cutting edge research papers. In this blog post, we spotlight a research paper we recently submitted. The research paper was a collaboration with <a href="https://www.linkedin.com/in/nivedita-suresh/" rel="noopener noreferrer" target="_blank">Nivedita Suresh</a> of Arrive. <a href="https://arriveorigin.com/" rel="noopener noreferrer" target="_blank">Arrive</a> links multi-omics and AI cell characterization to discover hidden patterns in data and improve outcomes in precision medicine.<h2> </h2><h2> Conditioning Unconditional Language Models to Recover Sentences without</h2><h2> Fine-Tuning:</h2> Pretrained language models are used as encoders with widespread success for a wide variety of NLP tasks. These models learn useful representations that can be directly applied to certain tasks. Despite the success of these models on the encoder, there isn't much research out today on whether these large pretrained language models can be used as universal decoders. To be considered "universal," a decoder must have an implicit representation for any sentence, such that it can recover that sentence exactly when conditioned on its representation. Task-specific decoders are typically trained from scratch for each task. In this research, we propose a method to try and learn useful representations of sentences using large, pretrained, transformer-based decoders. Our work investigates whether a universal decoder is even possible. If we had access to such a decoder, we could use it for a wide variety of sequence generation tasks without having to retrain or rebuild the decoder on new data. This could open up a new paradigm for low-resource machine translation, summarization, image captioning, and dialog generation.<h2> </h2><h2> The Sentence Space: How Language Models Represent Sentences</h2> Transformer-based language models like ELMo, BERT, and GPT-2 have replaced word vectors as off-the-shelf general-purpose encoders for natural language understanding. These models are trained on large amounts of text data and represent a sentence as a sequence of hidden states that come from a final layer of the transformer model. Representations in this sentence space are sequence length dependent, making comparisons between sentences with differing lengths inequitable and measuring the efficacy of using an unconditional language model as a universal decoder impossible. To resolve these issues and to make analysis easier, we propose to reparametrize the original sentence space into a lower-dimensional and sentence length agnostic vector space. We do this by adding a bias term to the fixed language model and finding the representation that minimizes the cross entropy loss of the sentence. This reparameterization now gives us the ability to project sentence tokens to representations (sentence encoding) and recover sentences from the representation (sentence recovery) via the fixed language model. We develop three representation injection mechanisms and inject biases at three different locations.<img src="https://cdn.builder.io/api/v1/image/assets%2Fe0438815ba51486bbb6a202747122d4b%2F7b93f3caad104979b5aa2042a6c7a7ff"> We add a bias Z’ to the embedding, transformer layers, and before the language modeling head in GPT-2. ’SA’ refers to selfattention, ’LN’ to layer normalization, ’FFN’ to a fully-connected layer, and ’LM Head’ to the last fully-connected layer. <h2>Experiments:</h2> Since we are testing the efficacy of a large pretrained language model as a universal decoder, we measure how well a representation can recover a sentence when injected into a language model. We measure performance across sentences from 4 different genres: books, news articles, Wikipedia, and movie dialogs. We conduct controlled experiments, varying the representation injection mechanism, representation injection location, initialization, and dimensionality of the representation. <h2>Results:</h2> We find that our representations recover sentences nearly perfectly even across genres -- achieving a BLEU score of over 98. <img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/696b5ccc-0d02-4fea-4baf-16390e159600/public" alt="BLEU Scores"> BLEU score performance stratified by genre for different dimensionalities of Z. In addition, we observe that the intrinsic dimension of the representation space is the model’s latent dimension, which indicates that the language model uses its entire capacity to represent sentences. <img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/0e7fa636-8519-4ff8-c7ef-019293850b00/public" alt="Sentence length v. BLEU score"> Plot of sentence length vs. BLEU score on the dataset. Additionally, our interpolation experiments reveal that the representation space has some human-understandable meaning. Our learned representations seem to have some synonym awareness. In the first sentence pair example below, the word “tale” transforms to the word “story” and the word “long” transforms to “long-running” when referring to a war. In the second example, we observe some syntactic awareness at the 0.7 mixture level. The syntax of the first sentence is retained with mostly words from the second sentence. <img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/6de41532-7363-4b27-5524-0bb6e8e94800/public" alt="Linear interpolations between perfectly recovered pairs."> Two linear interpolations between perfectly recovered pairs of representations. Pink indicates token overlap to the first sentence, while blue indicates token overlap to the second sentence. <h2>Broader Impact:</h2> This work informs us that we can discover meaningful, sentence-length agnostic sentence representations, hinting at the possibility of a “universal” decoder. Such a decoder would improve low-resource sequence generation task performance and allow for considerable parameter sharing in memory and data-limited environments. <h2>Conclusion:</h2> For further details on how we set up the experiment, results, and analysis, you can find the full paper on <a href="https://arxiv.org/abs/2008.09049" rel="noopener noreferrer" target="_blank">arXiv</a>. If you’re interested in joining our growing machine learning team, you can find all of the open positions on our <a href="https://scale.com/careers?location=All&amp;team=Engineering" rel="noopener noreferrer" target="_blank">careers page</a>.

Learning useful sentence representations from large transformer-based language models.

Discovering Useful Sentence Representations from Large Pre-trained Language Models

Discovering Useful Sentence Representations from Large Pre-Trained Language Models

<h2>Introduction</h2>
Machine learning is a once-in-a-generation technological shift that is creating huge value by unlocking insights from data. But algorithmic bias remains a major concern as ML becomes more widespread. Unless ML models are trained on representative data, they can develop serious biases, significantly harming underrepresented groups and leading to ineffective products. We investigated the CoNLL-2003 dataset&mdash;a standard for building algorithms that recognize named entities in text&mdash;and found that the data is highly skewed toward male names. Using Scale&rsquo;s technology we were able to systematically mitigate this bias by:
<ol>
<li>Enriching the data to illuminate the hidden biases</li>
<li>Enhancing the dataset with underrepresented examples to mitigate gender bias</li>
</ol>
A model trained on our enhanced CoNLL-2003 dataset has both less bias and better performance&mdash;demonstrating how bias can be removed without any changes to the model. We&rsquo;ve open-sourced the Named Entity Recognition annotations for the original CoNLL-2003 dataset and our augmentation of it <a href="https://scale.com/resources/download/nlp-dataset-balanced-conll" target="_blank" rel="noopener noreferrer">here</a>.
<h2>Algorithmic bias: AI's weakness</h2>
Today, thousands of engineers and researchers are building systems that teach themselves how to achieve significant breakthroughs&mdash;improving road safety with self-driving cars, curing diseases with AI-optimized treatments, and combating climate change by managing energy usage.
But the power of self-learning systems is also their weakness. Because data is the foundation of all machine learning applications, learning from imperfect data can lead to biased outputs.
The power of AI systems means their potential to cause harm if they are biased is significant. Recent protests against the police brutality that has led to the tragic deaths of George Floyd, Breonna Taylor, Philando Castile, Sandra Bland and many others are an essential reminder of the systemic inequalities in our society that AI systems must not perpetuate. But, as we know from countless examples&mdash;whether it&rsquo;s image search results <a href="http://www.mjskay.com/papers/chi_2015_gender-bias-in-image-search.pdf" target="_blank" rel="noopener noreferrer">perpetuating gender stereotypes</a>, offender management systems <a href="https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing" target="_blank" rel="noopener noreferrer">discriminating against black defendants</a>, or facial recognition systems <a href="https://www.nist.gov/news-events/news/2019/12/nist-study-evaluates-effects-race-age-sex-face-recognition-software" target="_blank" rel="noopener noreferrer">misidentifying people of color</a> &ndash; we have a long way to go before we can say the problem of AI bias has been solved.
Bias is prevalent because it is so easily introduced. It creeps up, for example, in gold-standard open-sourced models and datasets that are the foundation of huge volumes of work in ML. The word2vec sentiment dataset for building other language models is <a href="https://blog.conceptnet.io/posts/2017/how-to-make-a-racist-ai-without-really-trying/" target="_blank" rel="noopener noreferrer">skewed by ethnicity</a>, and word embeddings&mdash;the way an ML algorithm represents words and their meanings&mdash;have been found to contain <a href="https://arxiv.org/pdf/1608.07187.pdf" target="_blank" rel="noopener noreferrer">strongly biased assumptions</a> about what occupations women are associated with.
The problem&mdash;and at least part of the solution&mdash;lie in data. To illustrate this, we&rsquo;ve conducted an experiment into one of the most popular datasets for building systems that can recognize named entities in text: CoNLL-2003.
<h2>What is Named Entity Recognition?</h2>
Named-Entity Recognition (NER) is one of the fundamental building blocks of natural language models&mdash;without it, online searches, information extraction, and sentiment analysis would be impossible.
At Scale AI, our mission is to accelerate the development of AI. Natural language is one of our main areas of focus. Our Scale Text offering includes NER&mdash;which involves annotating text according to a predefined list of labels. In practice, this might help major retailers analyze how their products are being discussed online, among other applications.
Many NER systems are trained and <a href="https://paperswithcode.com/sota/named-entity-recognition-ner-on-conll-2003" target="_blank" rel="noopener noreferrer">benchmarked</a> on CoNLL-2003&mdash;a dataset of roughly 20,000 sentences from Reuters news articles that are annotated with attributes such as &lsquo;PERSON&rsquo;, &lsquo;LOCATION&rsquo;, and &lsquo;ORGANIZATION&rsquo;.
We wanted to explore if the data was biased. To do this, we used our Scale AI labeling pipeline to categorize all the names in the dataset, asking whether they could be male, female, or either, inferring gender based on the traditional use of the name.
We found a dramatic difference. Male names are mentioned almost five times more than female names, and less than 2% of names were gender-neutral:
<img src="https://cdn.builder.io/api/v1/image/assets%2Fe0438815ba51486bbb6a202747122d4b%2F4a4527dd52b94b5ba47317dccb308c59">
This is because news articles, for societal reasons, tend to contain mostly male names. But an NER model trained on this data will be better at picking out male names than female. For example, search engines use NER models to help classify names in search queries to give more accurate results. But deploy a biased NER model, and the search engine will be worse at identifying names of women than of men&mdash;exactly the kind of subtle, pervasive bias that can creep into many real-world systems.
<h2>A novel experiment to reduce bias</h2>
To illustrate this, we trained an NER model to investigate how this gender bias would affect its performance. We built a name extraction algorithm to pick out PERSON labels using <a href="https://spacy.io/" target="_blank" rel="noopener noreferrer">spaCy</a>, a popular NLP library, and trained the model on a subset of the CoNLL data. When we tested the model on novel names in the test data that weren&rsquo;t present in the training data, we found that the model was 5% more likely to miss a new female name than a new male name&mdash;a significant discrepancy in performance:
<img src="https://cdn.builder.io/api/v1/image/assets%2Fe0438815ba51486bbb6a202747122d4b%2F728080fd7e5f47fc9ef84471bd1905d5">
We saw similar results when we used our model on the template &ldquo;NAME is a person&rdquo;, substituting the 100 most popular male and female names for each year of the US census&mdash;the model performs significantly worse on female names for all years of the census:
<img src="https://cdn.builder.io/api/v1/image/assets%2Fe0438815ba51486bbb6a202747122d4b%2Fc85d17115813441ab877a2796c4c0a6e">
Crucially, biased training data means errors are skewed toward underrepresented categories. This census experiment illustrates this a second way, too: the model&rsquo;s performance degrades significantly after 1997&mdash;the cut-off point of the Reuters articles in the CoNLL dataset&mdash;because the dataset is not representative of the popularity of names in the years since.
Models learn to fit the trends of the data they are trained on. They can&rsquo;t be expected to perform well on cases for which they have seen few examples.
<h2>If you're de-biasing the model, it's too late</h2>
So how do we fix this?
One way is to try and de-bias the model&mdash;perhaps by post-processing the model, or adding an objective function to mitigate bias, leaving it to our model to figure out the details.
But there are several reasons why this is not the best approach:
<ol>
<li>Fairness is a highly complex issue that we can&rsquo;t expect an algorithm to define on its own. Research has shown that training an algorithm to perform equally on all subsets of the population will not ensure fairness and will <a href="http://blog.mrtz.org/2016/09/06/approaching-fairness.html" target="_blank" rel="noopener noreferrer">cripple the model's learning</a>.</li>
<li>Adding extra objective functions can hurt model accuracy, causing a tradeoff. Better instead to keep the algorithm simpler and ensure the data is balanced&mdash;improving model performance and avoiding the tradeoff.</li>
<li>It&rsquo;s unreasonable to expect a model to perform well on cases for which it&rsquo;s seen few examples. The best way to ensure good results is to improve the diversity of the data.</li>
<li>Attempting to de-bias a model with engineering techniques is expensive and time-consuming. It&rsquo;s much cheaper and easier to train your models on unbiased data in the first place, freeing up your engineers to focus on applications.</li>
</ol>
Data is only one part of the bias problem. But it is foundational, affecting everything that comes after it. That&rsquo;s why we think it holds the key to some of the solution, providing potential systematic fixes at the source. Unless you explicitly label for protected classes, like gender or ethnicity, it is impossible to properly mitigate these classes as a source of bias.
This is counterintuitive. Surely, if you want to build a model that doesn&rsquo;t depend on sensitive characteristics, like gender, age or ethnicity, it&rsquo;s best to omit those properties from the training data so the model can&rsquo;t take them into account?
&ldquo;Fairness by ignorance&rdquo; actually makes the problem worse. ML models excel at drawing inferences across features&mdash;they don&rsquo;t stop doing this just because we haven&rsquo;t explicitly labeled those features. The biases simply remain undetected, making them harder to remove.
The only robust way to deal with the problem is to label more data to balance out the distribution of names. We used a separate ML model to identify sentences in the <a href="http://kdd.ics.uci.edu/databases/reuters21578/reuters21578.html" target="_blank" rel="noopener noreferrer">Reuters</a> and <a href="https://archive.org/details/BrownCorpus" target="_blank" rel="noopener noreferrer">Brown</a> corpora likely to contain female names, and then labeled those sentences via our NER pipeline to augment CoNLL.
The resulting dataset, which we&rsquo;ve called CoNLL-Balanced, has over 400 more female names. When we retrained our NER model on it, we found that our algorithm is no longer biased to perform worse on female names:
<img src="https://cdn.builder.io/api/v1/image/assets%2Fe0438815ba51486bbb6a202747122d4b%2Fef676477e28e42c68c40edba41ee4091">
Not only that, but the model also performed better at identifying male names, as well.
This is an impressive demonstration of the power of data. Mitigating bias at the source meant we did not have to make any adjustments to our ML model&mdash;saving engineering time. And we achieved this without any tradeoff in our model&rsquo;s performance&mdash;in fact, its performance slightly improved.
<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/5c767c25-9e21-47a5-717d-725505342600/public">
To allow the community to build on our work and mitigate gender bias in models built on CoNLL-2003, we&rsquo;ve open-sourced Scale AI&rsquo;s augmented dataset, which also includes gender information on <a href="https://scale.com/resources/download/nlp-dataset-balanced-conll" target="_blank" rel="noopener noreferrer">our website</a>.
The AI/ML community has its own diversity problems, but we are cautiously excited by these results. It suggests how we might be able to offer a technical solution to a pressing social problem&mdash;provided we tackle the issue head on, illuminating the hidden biases and improving the model&rsquo;s performance for everyone.
We&rsquo;re now exploring how we might apply this approach to another highly sensitive attribute&mdash;ethnicity&mdash;to explore whether we can create a robust framework for eliminating dataset bias that scales to other protected classes.
It also illustrates why we at Scale AI pay so much attention to data quality. Unless data is provably accurate, balanced, and unbiased, there&rsquo;s no guarantee that models built on it will be safe and accurate. And without that, we won&rsquo;t be able to build the transformational AI technologies that benefit everyone. If you are developing AI technologies and would like help in balancing your own datasets to ensure your models work for everyone, <a href="https://scale.com/sales" target="_blank" rel="noopener noreferrer">reach out to us</a>.
Acknowledgements:
The CoNLL 2003 Dataset referenced in this blog post is the Reuters-21578, Distribution 1.0 test collection, available on the project page for the original 2003 experiment: <a href="https://www.clips.uantwerpen.be/conll2003/ner" target="_blank" rel="noopener noreferrer">https://www.clips.uantwerpen.be/conll2003/ner/</a>.

Scale AI's platform can help mitigate bias before training a model.

The importance of detecting and mitigating bias from training data before even starting to build an algorithm.

If You’re De-Biasing The Model, It’s Too Late

We founded Scale to create the infrastructure needed to build AI in any industry, by anyone. We started tackling this complex problem at the root — turning raw data into high quality training data for models. In this pursuit, we spent the last four years building ML-augmented annotation products for all data types, expanding our solutions for major industries, and making significant technological strides in <a href="https://scale.com/blog/how-we-scale-machine-learning" rel="noopener noreferrer" target="_blank">scaling our use of ML</a> in doing so. But the problem of building effective, accurate, unbiased ML models still remains. To do this, aggregate metrics in ML are not good enough. Better ML starts with understanding your data in depth. To improve production ML, you need to understand their qualitative failure modes, fix them by gathering the right data, and curate diverse scenarios.<ol><li>Before training a model, ML engineers must curate and sample their</li><li> data, ensuring that they have the right data to solve a specific problem. This</li><li> process is too often very manual. For example, to teach a self-driving car</li><li> how to handle left turns, an ML team has to manually crawl through their</li><li> driving sequences to isolate examples of left turns to create a training</li><li> dataset. The data also needs to be representative of the ground</li><li> truth of the problem you’re trying to solve. If you’re building a model to</li><li> assign a gender to faces, you have to ensure representative data for all</li><li> genders to have unbiased outputs. Again, that’s too often a manual and</li><li> highly inefficient process.</li><li> </li><li>After training, ML teams test and benchmark model performance, ensuring that the test dataset is also sufficiently representative of the</li><li> problem they are trying to assess its performance against. For example, a</li><li> model learning how to tell cars apart from pedestrians needs enough of both</li><li> examples for accurate benchmarking. This requires ML engineers to spend</li><li> significant amounts of time building one-off UIs to chart and share</li><li> performance data.</li><li> </li><li>After deployment, debugging the model, identifying failure</li><li> modes and fixing them. Too often issues in data only arise after a model has</li><li> entered deployment – requiring time-consuming debugging. One Scale customer,</li><li> for example, found that their vehicle recognition algorithm didn’t perform</li><li> well in certain environments – it turned out that the model was trained on a</li><li> dataset where vehicles were mostly in the bottom of the image, so the model</li><li> associated “bottom of the image” with “likelihood of being a car.”</li><li> </li><li> </li></ol> The Scale team has been working to productize the concept that Andrej Karpathy calls "<a href="https://www.youtube.com/watch?v=oBklltKXtDE&amp;feature=youtu.be&amp;t=475s" rel="noopener noreferrer" target="_blank">Operation Vacation</a>." Nucleus is a new way—the right way—to develop ML models, helping us move away from the concept of one dataset and towards a paradigm of collections of scenarios and giving ML engineers the ability to automate time-consuming manual steps in the ML development process. <img src="https://cdn.builder.io/api/v1/image/assets%2Fe0438815ba51486bbb6a202747122d4b%2F004680ecf1424e3193faf21d626662b8" alt="ML Development Cycle"><a href="https://scale.com/nucleus" rel="noopener noreferrer" target="_blank">Scale Nucleus</a> provides advanced tooling for understanding, visualizing, curating, and collaborating on your data – allowing teams to build better ML models via a powerful interface and APIs. With Scale Nucleus, you can: <ul><li>Visualize your dataset, ground truth, and model</li><li> predictions to improve model performance</li><li> </li><li>Curate interesting slices within your dataset for active</li><li> learning and identifying key edge cases</li><li> </li><li>Upload and choose data to be annotated for rare event</li><li> mining and dataset balancing</li><li> </li><li>Search your data based on metadata or ML-produced</li><li> attributes</li><li> </li><li>Identify edge cases through visual search</li><li>Measure key metrics like dataset balance, class</li><li> correlation, and confusion, via a powerful insights tab that shows the</li><li> overall health of the data</li><li> </li><li>Debug model performance</li><li>Share your dataset seamlessly to provide a single source</li><li> of truth for data within your team.</li><li> </li></ul> <img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/a41b6e07-5da7-41e2-5d94-542a8a933900/public" alt="Scale Nucleus Query"> Scale Nucleus’s query function, allowing users to return images matching automatically-generated meta-tags. Scale Nucleus is directly integrated with Scale AI’s data labeling pipeline – allowing teams to fix any issues in their data at the source. Nucleus currently supports Image data, with support for 3D Sensor Fusion, Video, Text and Document data coming soon. We built Scale Nucleus to allow data scientists and ML engineers to manage data more efficiently and increase the marginal value of their data. Deeper insight into a dataset’s features can have several transformational effects on the development of AI: <ul><li> </li><li> For engineers, the easier visualization and curation of datasets lowers</li><li> the barriers to entry to building ML systems.</li><li> </li><li> </li><li> Spotting hidden modes of failure in datasets before deployment (such as a</li><li> set of driving sequences that doesn’t include any sequences at night)</li><li> makes it much easier to train high-quality models, and provides a robust</li><li> way of eliminating issues like bias at source.</li><li> </li><li> </li><li> The ease of debugging could significantly improve iteration speed on model</li><li> fine-tuning post-deployment.</li><li> </li></ul> We are excited to take the next step in building the infrastructure to enable efficient, accurate, and unbiased ML development. If you’d like to join us in this journey and try Scale Nucleus, contact us at nucleus@scale.com or sign up on our <a href="https://scale.com/nucleus#sales" rel="noopener noreferrer" target="_blank">website</a>.

Scale Nucleus: The mission control for your data

Introducing Scale AI's latest product: Scale Nucleus

Introducing: Scale Nucleus

Fostering connection when everyone is remote can be tricky. Wind back to four months ago-- it was easy to high five a coworker, give surprise birthday cakes outside of meeting rooms, and hop down to the juice shop downstairs for a walk and talk. Shelter-in-place restrictions pose new challenges that inspired our people team to think of creative ways to keep the team connected.Our initiatives centered around the following dimensions: <ul><li>Social Connection - How do we facilitate interaction</li><li> between people across different teams and departments?</li><li> </li><li>Wellness - How do we encourage people to take care of</li><li> themselves and remain mentally well during uncertain times?</li><li> </li><li>Alignment - How can we keep teams aligned, engaged, and</li><li> focused on company-wide goals and objectives?</li><li> </li><li> </li></ul><img src="https://cdn.builder.io/api/v1/image/assets%2Fe0438815ba51486bbb6a202747122d4b%2F6949f75d7b644a00a6f53c671030cbcf"> For the purpose of this blog post, we chose to highlight and share our social connection initiatives. We are happy to share these ideas with other companies looking to provide employee social support. <h2>Wellness Programs</h2> The pandemic threw a curveball of new work environments, screen-focused social interactions, along with uncertainty around the current economy. To help our employees prioritize their health and well-being, we launched a wellness program. We created a space for our employees to participate together, foster camaraderie, and not feel alone in their personal wellness endeavors. <h3> </h3><h3> April Wellness Program: Building Healthy Habits</h3><h3> </h3> <img src="https://cdn.builder.io/api/v1/image/assets%2Fe0438815ba51486bbb6a202747122d4b%2F6337777c19fd458499b69ea586548a0b"> Details of the Program The program reminded employees to track key health criterias to build healthy habits. In a two-week span, participants recorded if they met the following criterias: <ul><li>7+ hours of sleep</li><li>Drink 64 oz + of water</li><li>20+ minutes of exercise</li><li>10+ minutes of mindfulness</li></ul>Social Interaction We encouraged social interaction through a leaderboard, seeing how we stack rank against each other. We also had a slack channel dedicated to this challenge to share knowledge with each other and asking, “Does tea count towards our water intake?” We could also commiserate on the channel about how drinking a lot of water was difficult. We ended the challenge with a virtual celebration highlighting winners for: <ol><li>Highest consecutive points</li><li>Category leaders</li><li> </li><li> Week 2 leaders (to motivate late joiners) while celebrating everyone who</li><li> participated</li><li> </li></ol>Overall, the participation rate was 20 percent of the whole company.<a href="https://www.linkedin.com/in/gabbyzetino/" rel="noopener noreferrer" target="_blank">Gabby Zetino</a>, our sales development representative, shares her experience with the challenge: <img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/c816a981-01f8-4994-f396-93de79a5fd00/public" alt="Gabby Zetino Quote"> <h3> </h3><h3> May Wellness Program: Bingo Board and Scale Sponsored Events</h3><h3> </h3> <img src="https://cdn.builder.io/api/v1/image/assets%2Fe0438815ba51486bbb6a202747122d4b%2F4671d96bd17a4035b0dd85b1b8b5ee0e"> Details of the Program This month, in honor of Mental Health Awareness Month, we assembled a mental health bingo board which highlighted a list of activities employees can do at their own pace. If an employee completed ten of the activities, they were entered into a raffle. <a href="https://www.linkedin.com/in/taisiakaraseva/" rel="noopener noreferrer" target="_blank">Taisia Karaseva</a>, our Pilots &amp; POC lead, shares how the bingo board pushed her to reflect on wellness habits along with the Scale-sponsored event: <img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/857d2ef2-2c09-428d-fabd-9b1006f73500/public" alt="Taisia Karaseva Quote"> Social Interaction We had a column dedicated to social interactions: “Stay Connected.” Also, Scale sponsored events on the bingo board’s first row: <ul><li> </li><li> Stress Relief Workshop + Guided Meditation - we brought in a self-care</li><li> coach and meditation instructor for a 15-minute workshop on how to relieve</li><li> stress and 15-minute meditation to regain balance.</li><li> </li><li> </li><li> Yoga with Scale - we brought in a yoga instructor for a quick 30-minute</li><li> session in the morning</li><li> </li><li> </li><li> Scale’s Creative Time- we set aside time to hang out and work on something</li><li> creative together (paint, write, dance, bake etc)</li><li> </li></ul><h2>Scale Themed Trivia Nights</h2> Scale Trivia Nights were born out of shelter-in-place and is one of our highly attended events hosted by our workplace operations team, <a href="https://www.linkedin.com/in/monicabreton1/" rel="noopener noreferrer" target="_blank">Monica Breton</a> and <a href="https://www.linkedin.com/in/eileen-h-66956b121/" rel="noopener noreferrer" target="_blank">Eileen Ho</a>. <img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/7920720c-a084-489c-e382-4cae0ff8db00/public">Details of the Program The program administrator prepared questions in advance, selected employees into groups randomly and sent them off to their breakout virtual rooms. Similar to regular trivia, each team had a lead and a team name. The trivia administrator gave category questions to the leaders via slack and the teams in their <a href="https://support.zoom.us/hc/en-us/articles/206476313-Managing-breakout-rooms" rel="noopener noreferrer" target="_blank">breakout rooms</a>. Then within the breakout rooms, teams discussed and laughed about the answers they’ll give and relayed the answers back to the program administrator. Social Connection<a href="https://www.linkedin.com/in/hannahmoreno1/" rel="noopener noreferrer" target="_blank">Hannah Moreno</a>, our marketing operations specialist, shares a few words on her experience with Trivia Night: <img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/2c2eb5b0-a905-4639-1f03-2f52f35c4a00/public" alt="Hannah Moreno Quote"> <h2>Speed Scaling</h2> To play on the words of “speed dating”, at Scale, <a href="https://www.linkedin.com/in/margowheeler/" rel="noopener noreferrer" target="_blank">Margo Wheeler</a>, our people operations manager, hosted a networking event, “Speed Scaling” for us to connect with employees across different teams with fun icebreaker questions. <img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/d69b131d-b35d-4b3e-1f1d-833263924700/public">Details of the Program To do this remotely, Margo broke us into different breakout groups and posted the questions to us in real time. We popcorned around in a circle answering questions. Our answers organically led us to unprompted conversations. When we had all spoken and experienced just the right amount of awkward silence, we moved onto the next question to continue learning about each other. For quarantine edition, we also included these questions:<ul><li> </li><li> What are you most looking forward to doing once Shelter-In-Place is</li><li> lifted? </li><li> </li><li> What’s the best meal you’ve cooked (or someone’s cooked for you) during</li><li> Shelter-In-Place? … or baked if you’re a baker</li><li>What is your Go-To Quarantine Snack?</li></ul>Social Connection<a href="https://www.linkedin.com/in/gmaliandi/" rel="noopener noreferrer" target="_blank">Guido Maliandi</a>, our engineering manager, had attended both the live and the remote Speed Scaling sessions. He shares his experience with both: <img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/3d4bbc17-2ded-4bcb-4e58-129f7ff31800/public" alt="Guido Maliandi Quote"> <h2>Small Groups Scale Socializing</h2> Socializing remotely could be difficult for a lot of people, especially in larger groups. To foster a genuine connection, smaller groups of one-on-one or groups with no more than four were scheduled by Eileen Ho, our program manager for Scale Socializing. <img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/0a9db851-d6e4-4450-e68c-f2c3cd46e200/public" alt="Coffee Chat">Details of the Program We sent a survey out to the company so that anyone who chooses to can opt in to socialize. There are logistical nuances we consider such as:<ul><li> </li><li> Who they want to meet (new hires, cross functional team members)</li><li>How often they want to socialize</li><li> </li><li> If they prefer one-on-one settings or groups of three. </li><li> </li><li> In addition to <a href="https://www.donut.com/" rel="noopener noreferrer" target="_blank">donut</a> integration</li><li> we’re already using, this initiative facilitates further connections for</li><li> employees.</li><li> </li></ul>Social Connection Our field engineering manager, <a href="https://www.linkedin.com/in/svanweelden/" rel="noopener noreferrer" target="_blank">Shaun VanWeelden</a> has participated in several of these virtual coffee chats. Shaun shares his experience with Scale Socializing below: <img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/b27dce3a-4e5a-4e49-5ccf-1183bba1c200/public" alt="Shaun VanWeelden Quote"> <h2> </h2><h2> Slack Channels and Prompts: Scalien Share</h2><h2> </h2> Slack is one of the main sources we use to keep in touch, so it only made sense to take advantage of it when we started “Scalien Share.” Similar to “Show and Tell” it is a fast way to take a step away from work and open up about ourselves. In addition to “Scalien Share”, we keep track of our interest-group slack channels for new hires to join. Most of these groups are grassroots-led such as #thebookclub, #cats, and #scaleai-women. <img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/5074ebb3-a9f0-4723-e533-ae7c12ae2000/public" alt="Scaliens Share: Recent Foods">Details of the Program For Scalien Share, the Workplace Operations team wanted to kick off a series of questions sent to our #social channel on Slack. Some prompts include “What has been your favorite indoor weekend activity?” and “Share a photo of your go-to mug!” <img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/b85a7771-58e7-43e6-02f5-9a6b6666f300/public" alt="Scaliens Share: Favorite TV Show">Social Connection While our design manager <a href="https://www.linkedin.com/in/jhilnbrand/" rel="noopener noreferrer" target="_blank">Jeff Hilnbrand</a> is taking an intentional break from design work and managing his team, he also has been a facilitator of culture at Scale. We keep our interest group channels engaging with prompts like “What did you cook last week?” in #cooking or “What are you bumping?” in #music. Jeff shares his reflection on social connection through Slack: <img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/9ea96e6a-a8ba-49bd-ffab-2473e0820c00/public" alt="Jeff Hilnbrand Quote"> <h2>Learnings</h2> While transitioning events to a remote setting, we couldn’t always smoothen out every detail before launch of the program. We take some of the areas of improvements as opportunities to learn and improve our future initiatives. Balancing Competition with Participation Our first wellness challenge emphasized competition over participation. Looking back, in addition to the competitive aspect, we would ideally reward those who were just participating. Focusing on competition only rewards the top individuals. Wellness programs shouldn’t always be about winning or performance. Encouraging grassroots participation As the people team rallies to foster connection, we could’ve also encouraged employees or given ownership to them to drive some of these initiatives. For example, asking an employee to lead a Scalien share prompt would potentially ensure that these prompts are not always coming from the people team but also inspired cross-functionally from other teams. Finding balance between being structured and unstructured Lastly, finding the balance between structured connections and organic connections is challenging. For Speed Scaling, we messaged out the questions in timed intervals to keep the group moving to the next question. In retrospect, we realized that some groups want to stay on one question longer than another question. Giving all questions in advance would allow the breakout groups to talk more organically, feeling free to stay on a particular discussion point that they were interested in lingering on. Remote events and programs posed new types of challenges, but we’re grateful for Scale giving us the space to experiment ways to keep our employees close during these uncertain times.

Shelter-in-place restrictions posed new challenges that inspired our people team to think of creative ways to keep the team connected.

Fostering connection when everyone is remote

Keeping The Team Close While Distancing

In these unprecedented times, COVID-19 has brought out a renewed and inspiring sense of collaboration in AI and research communities as we work toward solving pressing issues. But the pandemic has also exacerbated some of the difficulties of developing new technologies at scale. For example, as we shelter in place around the world, the promise of autonomous vehicles (AVs) to improve access to critical goods and services has never felt more <a href="https://mashable.com/article/autonomous-vehicle-perception-coronavirus/" rel="noopener noreferrer" target="_blank">relevant</a>. But as we realize more ways these technologies could improve our lives, the essential data collection and testing that power them have rightly been suspended to ensure the safety of those involved. That’s why today we’re launching <a href="https://pandaset.org/" rel="noopener noreferrer" target="_blank">PandaSet</a>: a new open-source dataset for training machine learning (ML) models for autonomous driving released in partnership with the LiDAR manufacturer Hesai. While many AV companies are turning to complementary techniques and simulated data to continue their work, there is often no substitute for high-quality data that captures the complex and often messy reality of driving in the real world.<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/a9340c61-2c49-46f1-b0c2-3b904bf7e500/public" alt="LiDAR Comparison"> High-quality data is crucial to building safe and effective AV systems. PandaSet is the world’s first publicly available dataset to include both mechanical spinning and forward-facing LiDARs (Hesai’s Pandar64 and PandarGT)—allowing ML teams to take advantage of the latest technologies. It is also the first to be released without any major restrictions on its commercial use. There are three reasons why we hope AV teams will find PandaSet to be a valuable resource: its content, its quality, and its no-cost commercial license. <h3>Content</h3> Covering some of the most challenging driving conditions for full level 5 autonomy, PandaSet includes complex urban environments, their dense traffic and pedestrians, steep hills, construction, and a variety of lighting conditions in the day, dusk and evening. There are more than 48,000 camera images and over 16,000 LiDAR sweeps—more than 100 scenes of 8s each. Capturing sequences in busy urban areas also means there is a high density of useful information, with many more objects in each frame than in other datasets. <h3>Quality</h3> By combining the strengths of both mechanical spinning and forward-facing LiDARs, PandaSet captures the complex variables of urban driving in rich detail. It also includes 28 different annotation classes for each scene as well as 37 semantic segmentation labels for the majority of scenes. With LiDAR data far beyond the capabilities of traditional cuboid labeling, it features Scale’s Point Cloud Segmentation that enables the highest precision and quality annotation of complex objects, such as smoke or rain. PandaSet also features Scale’s market-leading <a href="https://scale.com/3d-sensor-fusion" rel="noopener noreferrer" target="_blank">Sensor Fusion</a> technology, allowing ML teams to blend multiple LiDAR, RADAR and camera inputs into a single point cloud that allows for the semantic segmentation of different objects in LiDAR data. By allowing ML teams to exploit their LiDAR data much more systematically, this makes PandaSet ideal for building highly-performant autonomous systems. <img src="https://cdn.builder.io/o/assets%2Fe0438815ba51486bbb6a202747122d4b%2F00e35e3fc46c40309cf37c6a0b93d8ae%2Fcompressed?apiKey=e0438815ba51486bbb6a202747122d4b&amp;token=00e35e3fc46c40309cf37c6a0b93d8ae&amp;alt=media&amp;optimized=true"> <h3>No-cost commercial license</h3> Many existing open-source datasets have restrictive licensing terms that allow only research or limited commercial uses. While that can be important for helping ensure data is used appropriately, we wanted to make PandaSet available to the entire community, democratizing access to the latest LiDAR technologies for ML teams around the world at a time when the barriers to data collection are higher. We all want to accelerate the safe deployment of AVs—and the need for the right data has never been more pressing. By filling the gap for AI and ML developers who might otherwise be unable to build and test new technologies, we hope PandaSet will provide a useful resource for teams building a future for mobility that is safer and more accessible for everyone. You can find more information about PandaSet <a href="https://scale.com/open-datasets/pandaset" rel="noopener noreferrer" target="_blank">here</a>, with dataset support tools available on <a href="https://github.com/scaleapi/pandaset-devkit" rel="noopener noreferrer" target="_blank">GitHub</a>.

Introducing PandaSet, the world's most advanced LiDAR dataset for commercial use.

Launching the world’s most advanced LiDAR dataset for commercial use.

Introducing: PandaSet. The world's most advanced LiDAR dataset for commercial use

How Scale AI uses ML to supercharge data annotation pipelines.

Developing scalable ML solutions

How we Scale Machine Learning

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