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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

Scale AI is pleased to announce the launch of Scale Document - our endpoint for the secure processing of documents. Advancements in natural language processing (NLP) technologies have spurred initiatives across a wide range of industries to digitize document-based content. Led by the logistics, banking, financial services, and insurance industries, the intelligent document processing (IDP) market is expected to reach $1.1 billion by the end of 2020 according to <a href="https://www.everestgrp.com/2019-06-intelligent-document-processing-market-is-exploding-expected-to-grow-70-80-over-next-2-years-press-release-50323.html" rel="noopener noreferrer" target="_blank">research conducted by the Everest Group</a>. Scale Document builds on the previously released Scale Text product to better address customers with document processing and understanding use cases with tailored features. <img src="https://cdn.builder.io/o/assets%2Fe0438815ba51486bbb6a202747122d4b%2F90e89f53cb924b7fbf7860831a532383%2Fcompressed?apiKey=e0438815ba51486bbb6a202747122d4b&amp;token=90e89f53cb924b7fbf7860831a532383&amp;alt=media&amp;optimized=true">Multi-line transcription: one of many ML-powered tooling features to accelerate human review. Capable of supporting a range of inputs from PDFs, Microsoft Word Documents, JPEGs and more, Scale Document has a few key features: <ul><li>Machine learning to automatically classify and pre-label documents while also accelerating human review with active tooling. In addition to increasing the quality of documents delivered, this also enables the turnaround time for tasks to be as fast as a few hours to meet critical business requirements. (The workflow for how a document is processed is below.)</li></ul> <ul><li>Anonymization of personally identifiable information (PII) either with name and face blurring or placement of dummy content ensures compliance with data privacy principles. </li></ul> <ul><li>Flexible and varying levels of workforce security. Customers can select a global or U.S.-based workforce with options to add background checks or annotation in secure facilities depending on requirements. Our global work-from-home workforce also ensures continued delivery of data.</li></ul> <a href="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/000a7403-f16e-4886-03b2-7d9163505700/public" rel="noopener noreferrer" target="_blank" style="color: inherit;"><img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/98b87f7a-9829-4af5-6ff6-71d7bf000b00/public">The ML + human-in-the-loop workflow that processes documents at high quality and with fast turnaround.</a> Rigorously tested by a handful of customers during a private beta, Classification, Multilingual Transcription and Data Extraction annotation provide fine-grained understanding of document type, and allows for the ability to extract and understand document content and generate responses based off of document content, such as approving invoices or flagging applications for human review. Take a look at the <a href="https://scale.com/document" rel="noopener noreferrer" target="_blank">Scale Document product page</a> or <a href="https://scale.com/sales" rel="noopener noreferrer" target="_blank">contact us</a> to talk about your business needs.

Scale Document is the secure endpoint for document processing.

Introducing Scale AI's latest product: Scale Document

Now Available: Scale Document

<h2>Notice any changes to our website?</h2>
&nbsp;
&nbsp;
The Scale AI team is excited to introduce a new look and new structure for our
product line-up. Scale AI was founded almost four years ago because we
believed that high quality training data is the key bottleneck to the
development of AI applications. Thanks to feedback from incredible customers,
we have significantly expanded our data annotation products over the years to
support an increasing range of data inputs and annotation types for computer
vision and natural language (NLP) applications.
&nbsp;
As we expanded our data annotation capabilities, it became clear that we
needed a better way to group and name our products to reflect both the current
state and future vision of our product offering.
<h2>What exactly are the changes?</h2>
&nbsp;
&nbsp;
Sensor Fusion Cuboids and Sensor Fusion Segmentation is now one product called
<a href="https://scale.com/3d-sensor-fusion" target="_blank" rel="noopener noreferrer">Scale 3D Sensor Fusion</a>.
<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/8ef57c88-a258-41a3-a359-5c9fed59b400/public">
&nbsp;
&nbsp;
3D Cuboids, Bounding Boxes, Image Categorization, Lines &amp; Splines,
Polygons and Semantic Segmentation are also now one product called
Scale Image with an
addition of Point Annotation.
<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/132eb95b-d69d-47a9-f657-bb3ec16e2e00/public">
&nbsp;
&nbsp;
Video Annotation is now
Scale Video, and has been expanded to include classification, cuboid and polygon
annotation in addition to the existing bounding box annotation.
<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/28098202-ca0e-436e-606d-f39ff82eea00/public">
&nbsp;
&nbsp;
Our Natural Language products have been updated to
<a href="https://scale.com/text" target="_blank" rel="noopener noreferrer">Scale Text</a> with
Scale Audio currently in private beta.
<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/41c67cce-2a87-4057-2353-667527172500/public">
&nbsp;
<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/8b1ab848-ccda-4691-3f23-77daa83f0100/public">
&nbsp;
&nbsp;
You may also wonder, &ldquo;how does this product restructure affect me?&rdquo; If you are
one of our amazing customers, from a practical standpoint, impact to you is
minimal. While the product names are different, the products themselves are
still the same, and we will continue to deliver data at the quality you
expect. If you&rsquo;re learning about Scale AI for the first time, we hope this
rebrand allows you to better understand our full product suite and its
capabilities.
<h2>What&rsquo;s Next?</h2>
&nbsp;
&nbsp;
The Scale AI team remains committed to delivering products customers love and
rely on. We are grateful for all of our incredible and innovative customers
for their support. In the coming weeks and months, we&rsquo;ll be announcing and
rolling out new products and additional annotation types to existing products.
&nbsp;
In the meantime, if you have any questions about todays's announcement, reach
out to us at support@scale.com. Otherwise, we invite you to explore our
updated product line up.

Explore the new product line-up: Scale 3D Sensor Fusion, Scale Image, Scale Video and Scale Text

Introducing a new look & new structure 4 our product line-up

Introducing a New Look for Scale AI Products

Machine learning is a field of study with tremendous strides being made through active academic research. The machine learning team at Scale AI regularly hosts reading groups to discuss papers they find interesting to give us context in a rapidly evolving, research-heavy field. In this blog post, we go over the papers the team has read throughout the quarter and provide insights on how a paper influenced our own work here at Scale AI when relevant.<h3><a href="https://arxiv.org/abs/1803.03635" rel="noopener noreferrer" target="_blank">The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks</a></h3> Authors: Jonathan Frankle, Michael Carbin Presenter:<a href="https://www.linkedin.com/in/ppyht2/" rel="noopener noreferrer" target="_blank">Hongbo Tian</a> Pruned neural network architectures created by removing unnecessary weights from trained dense neural networks can attain various benefits without sacrificing accuracy. For example, smaller architectures have lower storage requirements, and certain hardware architectures can take advantage of sparsity, leading to massive computational performance gains at inference time. However, training the pruned network from scratch (as opposed to training the full network and pruning) has historically achieved poor accuracy. This paper presents the hypothesis that dense neural networks will contain some subnetwork that can reach the same performance of the original network when trained in isolation, and the authors cover some techniques to uncover the “winning subnetwork.” Larger network architectures have typically been considered superior, but this paper demonstrates that such a belief is not necessarily true. The lottery ticket conjecture matters when developing machine learning models here at Scale AI, since we know that we can build networks that tackle ambitious challenges without being blocked on unreasonable monetary or compute requirements.<h3><a href="https://arxiv.org/abs/1911.08265" rel="noopener noreferrer" target="_blank">Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model</a></h3> Authors: Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert, Karen Simonyan, Laurent Sifre, Simon Schmitt, Arthur Guez, Edward Lockhart, Demis Hassabis, Thore Graepel, Timothy Lillicrap, David Silver Presenter:<a href="https://www.linkedin.com/in/ppyht2/" rel="noopener noreferrer" target="_blank">Hongbo Tian</a><img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/d923bed9-5bf7-471d-b24b-5171bb888a00/public"> MuZero’s performance, compared to the state of the art. After sufficient training, MuZero’s Elo (denoted by the blue lines) surpasses that of AlphaZero for boardgames and R2D2 in Atari. Games like chess and Go tend to have deterministic and well-defined rules. Such games aren’t representative of real world problems because most realistic problems occur in an environment with complex or unknown rules. This paper presents MuZero, a successor to AlphaZero that matches AlphaZero’s performance in Go but can learn to play Atari games, all without any prior knowledge to the game rules. MuZero builds off of AlphaZero’s search algorithms, but it also uses a learned environment model when training to capture aspects of the future that are relevant for planning. Although MuZero don’t know the game rule or environmental dynamics of the challenges it faces, it matches AlphaZero in logically complex games and beats the state-of-the-art model-free reinforcement learning algorithms in visually complex games (e.g. Atari games). Such a feat makes MuZero a pioneer in developing AI to solve realistic problems where there’s no perfect simulator and demonstrates the feasibility of applying learning methods to more ambiguous problems.<h3><a href="https://arxiv.org/abs/2001.07685" rel="noopener noreferrer" target="_blank">FixMatch: Simplifying Semi-Supervised Learning with Consistency and</a></h3><h3><a href="https://arxiv.org/abs/2001.07685" rel="noopener noreferrer" target="_blank"> Confidence</a></h3>Authors: Kihyuk Sohn, David Berthelot, Chun-Liang Li, Zizhao Zhang, Nicholas Carlini, Ekin D. Cubuk, Alex Kurakin, Han Zhang, Colin Raffel Presenter:<a href="https://www.linkedin.com/in/felix-lau-0386641a/" rel="noopener noreferrer" target="_blank">Felix Lau</a><img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/dfa605ca-6f5c-4108-6219-ff05ea4ba300/public"> The general architecture of FixMatch. The model is trained to make predictions on a strongly-augmented version of an unlabeled image, based off the prediction results of a weakly-augmented version of the same image. The authors combine two common semi-supervised learning methods, consistency regularization and pseudo-labeling, to create a new algorithm that achieves state-of-the-art performance on standard, semi-supervised learning benchmarks. For unlabeled data, the algorithm applies both strong augmentation and weak augmentation. If the confidence for unlabeled data is higher than a certain threshold, the algorithms apply a cross-entropy loss between the prediction from weakly and strongly augmented images.<h3><a href="https://arxiv.org/abs/2001.04451" rel="noopener noreferrer" target="_blank">Reformer: The Efficient Transformer</a></h3> Authors: Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya Presenter: <a href="https://www.linkedin.com/in/ppyht2/" rel="noopener noreferrer" target="_blank">Hongbo Tian</a><img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/1b7fdb75-94c4-4d06-f940-35f3660be800/public"> A visual depiction of attention with locality sensitive hashing. By hashing and then bucketing keys, attention doesn’t need to handle all pair-wise keys. Training good transformers can be prohibitively expensive: the largest version of GPT-2 contains 1.5 billion parameters, while NVIDIA’s Megatron-LM has up to 8.3 billion parameters. The authors of Reformer present a model that performs on par with preceding transformers while being more memory-efficient and faster to train. Due to the decreased resource requirements, Reformer makes Transformers more widely available to use in practice while also being able to handle longer sequences in general. Rather than using the standard dot-product attention, Reformer uses a less expressive attention mechanism combined with locality sensitive hashing, which reduces the complexity of attention. The classical self-attention mechanism attends to all pair-wise elements in the input sequence, giving it quadratic algorithmic complexity. By reducing the expressiveness of self-attention, Reformer can leverage locality-sensitive hashing via bucketing similar elements together, thus simplifying self-attention to linearithmic complexity. This mechanism ultimately enables Reformer to process the entirety of Crime and Punishment in a single training example.<h3><a href="https://arxiv.org/abs/1712.01208" rel="noopener noreferrer" target="_blank">The Case for Learned Index Structures</a></h3> Authors: Tim Kraska, Alex Beutel, Ed H. Chi, Jeffrey Dean, Neoklis Polyzotis Presenter:<a href="https://www.linkedin.com/in/ppyht2/" rel="noopener noreferrer" target="_blank">Hongbo Tian</a><img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/64d24dc4-d8a4-43ba-59f5-d437cf189200/public"> A comparison of a traditional and learned hash map. Learned data structures replace the hashing function with a model that achieves better performance. Many classic data structures (such as B-Trees, Hash maps, and Bloom filters) rely on some index structure to map a key to a position of a record. However, index structures can be generalized as a model that takes in a key and infers information about a data record. The authors explore replacing traditional index structures with deep-learning models, and their results demonstrate that machine learning can greatly increase speed of classic data structures that have already been optimized over decades. This paper is novel in the sense that it exposes opportunities for large performance improvements in data structures that have already been optimized for decades. It also serves as a reminder to our team at Scale AI, that excellence can exceed that of our initial expectations and that machine learning can have very non-obvious use cases.<h3><a href="https://arxiv.org/abs/1912.08193" rel="noopener noreferrer" target="_blank">PointRend: Image Segmentation as Rendering</a></h3> Authors: Alexander Kirillov, Yuxin Wu, Kaiming He, Ross Girshick Presenter:<a href="https://www.linkedin.com/in/felix-lau-0386641a/" rel="noopener noreferrer" target="_blank">Felix Lau</a><img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/3d238643-0e8b-4136-427f-65b72c477f00/public"> Inference results on a scene with and without PointRend. PointRend is able to handle the edges, such that it properly segments the side mirrors of a car. Image segmentation can suffer in accuracy around the borders of various classes and difficult regions. As a result, the authors of this paper create a neural network module call PointRend that adaptively runs point-based segmentation in higher resolutions at selected locations, leading to crisp boundaries compared to previous segmentation techniques. PointRend can be applied on top of existing instance and semantic segmentation tasks. At Scale AI, we’ve found that getting the edges between classes right is the most critical when it comes to producing high quality labels for 2D segmentation.<h3><a href="http://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/milioto2019iros.pdf" rel="noopener noreferrer" target="_blank">RangeNet++: Fast and Accurate LiDAR Semantic Segmentation</a></h3> Authors: Andres Milioto, Ignacio Vizzo, Jens Behley, Cyrill Stachniss Presenter:<a href="https://www.linkedin.com/in/danielhavir/" rel="noopener noreferrer" target="_blank">Daniel Havir</a> <img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/098bb5c7-5edc-42af-8003-dbc16ba10b00/public"> Diagram of the various states of data representation during LiDAR segmentation. Each letter corresponds to a step in the approach that RangeNet++ takes. High-level semantic perception tasks in autonomous vehicles are primarily solved through high resolution cameras, even though there are a wider variety of sensor modalities available for autonomous vehicles. At Scale AI, we are familiar with the problems defined in this paper. The paper details an approach to improving LiDAR-only semantic segmentation. The authors use a 4-step approach:<ol><li> </li><li> Reduce the LiDAR point cloud to two-dimensional range images that encode the</li><li> Euclidean distance along with the original coordinates and LiDAR remission.</li><li> </li><li> </li><li> Run the range image representation through a 2D semantic segmentation model.</li><li> </li><li>Project the model output back to the original point cloud format.</li><li> </li><li> Post-process the output using a novel KNN approach to account for occlusion</li><li> errors introduced with the 2D projection.</li><li> </li><li> </li></ol><h3><a href="https://arxiv.org/abs/2002.08871" rel="noopener noreferrer" target="_blank">Fast Differentiable Sorting and Ranking</a></h3> Authors: Mathieu Blondel, Olivier Teboul, Quentin Berthet, Josip Djolonga Presenter:<a href="https://www.linkedin.com/in/chiao-lun-cheng-87a4a34/" rel="noopener noreferrer" target="_blank">Chiao-Lun Cheng</a><img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/75bc368c-8496-47be-c579-dde544e2c300/public"> Illustration of the soft sorting and ranking operators. Sorting and ranking are basic and common operations in computer science and machine learning. However, due to their natural properties, they are not differentiable. The authors propose the first differentiable sorting and ranking operations, while maintaining their time and space complexity. To reduce sorting and ranking to a differentiable form, the authors construct the operators as projections onto a permutohedron, take the convex hull of the ordering permutations, and convert the results to an isotonic optimization problem. 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 Q1 2020.

Research Papers the Scale AI Team has Read in Q1 2020

Scale AI Machine Learning Digest - Q1 2020

As the COVID-19 pandemic continues to spread, it affects all of our communities in different ways. The health and safety of Scale AI’s employees, as well as our global community of labelers, is a critical matter and a responsibility we do not take lightly. That’s why we’ve taken action over the past several weeks to protect both our full-time and contract employees, and ensure we can continue to offer the high-quality services our customers expect. We also want Scale AI to make a contribution to combating this disease if we can. Research to track and contain the virus is in its early days, but we know that better data could make a difference. To help these efforts, Scale AI will provide free data annotation services for any researcher working on diagnosing, containing, tracking or finding a vaccine or long-term cure for the virus. If you’re working on a project, you can reach out to us <a href="https://events.scale.com/accelerating-covid-19-research" rel="noopener noreferrer" target="_blank">here</a>. <h2>Scale AI's Employees, Labeling Community and Customers</h2> The safety of our team, both full-time and contractors, is a top priority. As a technology company, we are fortunate to be able to leverage existing communications tools to maintain collaboration. Scale AI’s ML-enabled platform also gives a global, work-from-home workforce the ability to deliver data annotation at the level of quality and volume our customers expect. Anyone working with a basic laptop and internet connection can utilize our platform. We are proud to provide a safe option for workers to continue to earn wages to support themselves and their families throughout this period of uncertainty. We have also reached out to our customers to let them know how we're working to deliver products and services. We do not expect any disruption to our work at the moment, however, because the situation remains fluid, we will continue to closely monitor our operations. In the event of a disruption, we will make sure to coordinate with your teams to minimize any impact. <h2>Accelerating COVID-19 Research</h2> Adjusting to the reality of a new disease has been difficult and unsettling, but it has been heartening to see a range of companies and organizations come together to help combat this pandemic. We want to make our own contribution to these efforts where we can. Given the scale of the pandemic, researchers should have every tool at their disposal as they try to track and counter this virus. Researchers have <a href="https://deepmind.com/research/open-source/computational-predictions-of-protein-structures-associated-with-COVID-19" rel="noopener noreferrer" target="_blank">already shown</a> how new machine learning techniques can help shed new light on this virus. But as with all new diseases, this work is much harder when there is <a href="https://www.wsj.com/articles/coronavirus-reveals-limits-of-ai-health-tools-11582981201" rel="noopener noreferrer" target="_blank">so little existing data to go on</a>. In those situations, the role of well-annotated data to train models or diagnostic tools is even more critical. Our mission at Scale AI is to accelerate the development of AI applications. To help in those efforts, Scale AI will provide free data annotation services for any researcher working on diagnosing, containing, tracking or finding a vaccine or long-term cure for the virus. Advancing and accelerating health research and science is critical to reducing the spread of viral infections. If you have data labeling needs related to this research, please let us know <a href="https://events.scale.com/accelerating-covid-19-research" rel="noopener noreferrer" target="_blank">here</a>.

Scale AI will offer free labeling to researchers working to contain the spread of the virus.

Scale AI is committed to containing the spread and being a resource to researchers working on COVID-19.

Supporting researchers and our community during COVID-19

Self-driving cars promise to improve mobility, build a more efficient transportation system and even save lives. But while the industry has been making great progress, to date the field has struggled to build cars and artificial intelligence models that can handle all weather conditions. Snow is hard to drive in—as many drivers are well aware. But wintry conditions are especially hard for self-driving cars because of the way snow affects the critical hardware and AI algorithms that power them. Take a car trained to drive on the wide, sunny boulevards of Arizona or California and place it on a snowy street like this, and things will go wrong:<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/8403db34-aa39-4091-ee14-85abee753600/public"> Snow covering the road markings would make it hard for the car to stay in its lane. Accumulated snow significantly reduces the color contrast, making it harder for the car’s system to recognize objects, like trees or parked cars covered in snow—or even pedestrians. Most problematically, snowfall makes it hard for a vehicle’s cameras and LiDAR sensors to see, which significantly reduces safety:<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/b8835be9-1a7e-4ece-2e1f-eba02a3ce900/public"> A skilled human driver can handle the same road in all weathers—but today’s AV models can’t generalise their experience in the same way. To do so, they need much more data. Until now, there has not yet been an open dataset of annotated LiDAR data and images of driving in snowy weather that researchers can use to develop more robust systems.<h2> </h2><h2> CADC: A new dataset for wintry conditions</h2> In partnership with the University of Waterloo and the University of Toronto, we’ve been applying our technology to this challenge. Today, we’re open sourcing the world’s first AV dataset in wintry conditions—the Canadian Adverse Driving Conditions dataset (CADC, or cad-see.) Drawn from 20 km of driving in harsh, snowy conditions in southwestern Ontario over the past two winters, CADC contains 7,000 frames of LiDAR and camera data spread over 70 driving sequences—featuring a wide range of perception challenges for AV models that can handle wintry conditions. Scale provided the annotations that makes the data usable by researchers. Researchers at the Universities of Waterloo and Toronto have also released a detailed academic paper outlining the technical features of the data collected and object detection labels provided, which you can view on arXiv <a href="https://arxiv.org/abs/2001.10117" rel="noopener noreferrer" target="_blank">here</a>. Ultimately, this is about safety. Without robust data, self-driving models can’t learn how to handle snowy conditions—preventing us from being able to make AV systems a reality for the many millions of people who live in places that receive wintry weather. As Professor Krzyzstof Czarnecki at the University of Waterloo says, “We want to engage the research community to generate new ideas and enable innovation. This is how you can solve really hard problems, the problems that are just too big for anyone to solve on their own.” We hope this dataset will make it easier for researchers to achieve the next-generation breakthroughs in AV systems that will take us one step closer to self-driving vehicles that work safely in a wide range of different environments. As Professor Steven Waslander at the University of Toronto says, “We’re hoping that both industry and academia go nuts with it. We want the world to be working on driving everywhere.“Bad weather is a condition that is going to happen.” You can find the dataset on the <a href="http://cadcd.uwaterloo.ca/" rel="noopener noreferrer" target="_blank">Waterloo website</a>, with dataset support tools available on <a href="https://github.com/mpitropov/cadcd_devkit" rel="noopener noreferrer" target="_blank">GitHub</a>.

Open source autonomous vehicle dataset for wintry conditions.

Overcoming a critical challenge to the widespread use of autonomous vehicles

Open Sourcing the World's First AV Dataset for Wintry Environments

Building on the news of our <a href="https://scale.com/blog/series-c" rel="noopener noreferrer" target="_blank">Series C</a> round of funding valuing Scale AI over $1B, and our selection as one of <a href="https://scale.com/blog/linkedin-top-startup-2019" rel="noopener noreferrer" target="_blank">LinkedIn’s Top Startups in the US</a>, we are pleased to be selected as one of <a href="https://www.forbes.com/sites/jilliandonfro/2019/09/17/ai-50-americas-most-promising-artificial-intelligence-companies/#53699d8a565c" rel="noopener noreferrer" target="_blank">Forbes AI 50: America’s Most Promising Artificial Intelligence Companies</a>. Three years ago, we started Scale to tackle the most critical bottleneck in modern AI development: generating robust training data. It was a problem born out of our own frustrations building AI models—the majority of our time was spent on producing high quality data rather than building the model itself. This first-hand experience inspired us to solve the data problem for everybody. All companies recognized on the Forbes list leverage artificial intelligence as a core part of their business model and future success. A panel of experts selected the list based on the technology, business model, customers and financials. We are big believers in the transformational impact of AI over the coming decades, and we are proud of our work in enabling our customers to build incredible AI products more quickly. There has never been a better time to join Scale. If you’re interested in working with us, take a look at all of our <a href="https://scale.com/careers?location=All&amp;team=All#jobs" rel="noopener noreferrer" target="_blank">open positions</a>!

Scale AI selected as one of Forbes' Most Promising Artificial Intelligence Companies

Scale AI Selected as One of America's Most Promising AI Companies

Scale AI is pleased to be selected as one of <a href="http://lnkd.in/USTopStartups2019" rel="noopener noreferrer" target="_blank">LinkedIn’s 2019 Top Startups</a> in the U.S. Our mission is to accelerate the development of AI applications. Labeled data is the key bottleneck to the growth of the machine learning industry, and by building the infrastructure for machine learning, we are enabling a new age of software. There are three broad reasons why someone should join Scale AI: <ul><li> </li><li>Our Work:</li><li> </li><li> </li><li>Machine learning (ML) is likely the most significant technological shift happening today. Powered by a dynamic research community, computers can now recognize images and audio, translate languages, generate realistic text, and even beat humans at games. But aside from a few innovative products from large tech companies, ML has not yet made a tremendous impact. </li><li> </li><li> </li><li>Labeled data is the key bottleneck to the growth of the machine learning industry. Scale AI not only provides high quality labeled data for a wide range of AI applications, but our team is working to build the infrastructure needed to realize ML’s potential impact. </li><li> </li><li>Timing:</li><li> </li><li> </li><li>In the last year, Scale AI has grown from 20 employees to just north of 100. We have experienced phenomenal revenue growth and have also tripled the number of enterprise customers year on year. Customers also quickly realize the quality, scalability and value of leveraging Scale AI, and increase their usage on the platform rapidly. Scale AI is positioned to continue growing at an incredibly fast pace. </li><li> </li><li> </li><li>Now is a great time to join Scale AI, because as we continue to expand into new use cases and new verticals, employees (we like to call ourselves Scaliens) have the ability to make an incredible impact with their work. By joining now, you have the ability to move and execute on projects quickly, have the steep upside of joining early, but with significantly less risk. </li><li> </li><li>Culture:</li><li> </li><li> </li><li>Scale AI succeeds or fails as a team, and we make an effort to constantly trust and invest in each other to build a robust, scalable company. Scaliens include people who helped scale Cruise, Dropbox, Facebook, Lyft and Uber as well as new graduates just starting their careers. We believe that having diverse perspectives is crucial to build products to accelerate the development of AI, and we strive to create an environment where employees can give their all at work, while still being able to give their all at home. </li><li> </li></ul>There has never been a better time to join Scale. If you’re interested in working with us, take a look at all of our <a href="https://scale.com/careers?location=All&amp;team=All#jobs" rel="noopener noreferrer" target="_blank">open positions</a>!

Scale AI selected as one of LinkedIn’s 2019 Top Startups in the U.S..

Scale AI Selected as One of LinkedIn’s 2019 Top Startups in the U.S.

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