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.

We at Scale AI felt inspired to use our computer vision training tools to put the two self driving methods to the test. 

Is Elon Musk wrong about LiDAR? We put the two self driving methods to the test.

Is Elon Wrong About LiDAR?

Our mission at Scale is to accelerate the development of AI applications. We’re proud of what we’ve built over the last three years, and today we’re announcing our Series C funding round to support our continued work against that mission. Scale has raised $100m at a valuation of over $1B. <a href="https://foundersfund.com/" rel="noopener noreferrer" target="_blank">Founders Fund</a> led the round, supported by <a href="https://accel.com/" rel="noopener noreferrer" target="_blank">Accel</a>, <a href="https://www.coatue.com/" rel="noopener noreferrer" target="_blank">Coatue Management</a>, <a href="https://www.indexventures.com/" rel="noopener noreferrer" target="_blank">Index Ventures</a>, <a href="http://www.sparkcapital.com/" rel="noopener noreferrer" target="_blank">Spark Capital</a>, <a href="https://www.thrivecap.com/" rel="noopener noreferrer" target="_blank">Thrive Capital</a>, <a href="https://en.wikipedia.org/wiki/Kevin_Systrom" rel="noopener noreferrer" target="_blank">Kevin Systrom</a>, <a href="https://en.wikipedia.org/wiki/Mike_Krieger" rel="noopener noreferrer" target="_blank">Mike Krieger</a> and <a href="https://en.wikipedia.org/wiki/Adam_D%27Angelo" rel="noopener noreferrer" target="_blank">Adam d’Angelo</a>. We are big believers in the transformational impact of AI over the coming decades. Machine learning is likely the most important technological shift of our time, and the overall benefits to the world will be comparable to those of the internet. What we noticed after working on AI at some of the most advanced organizations in the world was that building machine learning systems was challenging due to a lack of mature infrastructure. In particular, we noticed that the critical bottleneck to further progress today was data—in particular, labeled datasets. Many engineers will tell you that getting labeled data is the hardest part of building a machine learning model. Building AI represents a fundamentally different paradigm than building traditional software. The performance of an AI system depends more on the data than the algorithm—in most cases, without labeled data, there is no model. Similarly, for a model to improve and adapt, it requires more data rather than simply more code. This paradigm has been true since the very beginning of deep learning; the modern deep learning age was ignited by the launch of the ImageNet dataset by Fei-Fei Li’s lab at Stanford in 2009.<img src="https://cdn.builder.io/api/v1/image/assets%2Fe0438815ba51486bbb6a202747122d4b%2F8c80d06e32aa42c689836dbda1f7fd09" alt="AI DEV CYCLE"><img src="https://cdn.builder.io/api/v1/image/assets%2Fe0438815ba51486bbb6a202747122d4b%2Fb0518c76600f4b9fa58189d4bfe750ba" alt="TRADITIONAL DEV CICLE"> We set out to solve this problem by building a platform that enables organizations with large high-quality datasets needed to build AI systems. Especially now, as AI is being applied in more and more ways, this matters because:<ul><li> </li><li> Safe, accurate and unbiased AI systems depend on large volumes of high</li><li> quality training data.</li><li> </li><li> </li><li> The process to acquire, label, and verify training data is slow, manual and</li><li> expensive.</li><li> </li><li> </li><li> Today’s training data bottleneck limits AI's impact to a small group of</li><li> well-funded technology companies.</li><li> </li><li> </li></ul> Scale is a solution to these problems. Developers use our API to send their raw data to our platform. We then use a combination of machine learning and human insight to label and annotate this data. We pass this high-quality ground truth data back to our customers, who are then enabled to build world-class models to solve real-world problems. Scale is now used by world leading machine learning teams at companies like OpenAI, Airbnb, Nuro, and Lyft. Our platform is being used to provide high-quality training data to an ever growing number of machine learning teams and industries, including:<ul><li>Autonomous Vehicles: Advancements in AI for perception,</li><li> prediction, and planning have enabled the development of autonomy, which in</li><li> turn relies on data.</li><li> </li><li>Natural Language and Content Understanding: More than ever</li><li> before, systems can understand and drive decision-making on large corpuses</li><li> of speech, text, images, and other forms of content.</li><li> </li><li>E-commerce and Search: Powerful search systems that connect</li><li> consumers to specific content, images, or products increasingly rely on AI</li><li> systems to drive understanding of this content. Our platform helps engineers</li><li> train those systems.</li><li> </li><li>Mapping: Mapping using satellite, drone, and street-level</li><li> imagery for use cases from agriculture to insurance rely on high quality</li><li> training data to ensure accuracy.</li><li> </li><li>Robotics: Robots in factory or warehouse settings need to</li><li> be able to understand their environments, safely interact with humans and</li><li> recognize products or components.</li><li> </li><li>Augmented Reality and Virtual Reality: AR and VR systems</li><li> used in gaming, real estate or manufacturing rely on being able to perceive</li><li> the 3D environment around a camera, headset or user.</li><li> </li><li>Offline retail: Cashierless checkouts, inventory management</li><li> systems and footfall analysis all depend on perception systems, which in</li><li> turn depend on training data.</li><li> </li><li> </li></ul> We’re proud of the progress we’ve made building the Scale platform for our customers. It’s been an incredibly quick 3 years since I started the company, and the ability we’ve had to accelerate progress in machine learning is palpable. But, as we hope is always the case, we’re far more excited about what we will do in the future. There is a lack of mature infrastructure for building AI, forcing teams to build their own technology stack from scratch before they can have a meaningful impact. Per our mission, we know there are many problems beyond the training data bottleneck for us to solve to continue accelerating AI development. Despite our excitement for the potential of AI, we’re closer to the beginning of its history than we are to the end. We’re grateful to our customers for building world-leading AI products with Scale, and we’re looking forward to accelerating the development of machine learning in an even broader set of industries and technologies as we grow. If you are interested in being part of creating the infrastructure machine learning needs, take a look at our <a href="https://scale.com/careers?location=All&amp;team=All" rel="noopener noreferrer" target="_blank">open positions</a>. If you're interested in partnering with us, please <a href="https://scale.com/sales" rel="noopener noreferrer" target="_blank">reach out</a>.

Scale AI’s Series C: Building the data platform for ML

Scale AI’s Series C: Building the data platform to accelerate machine learning

Scale AI is pleased to announce the launch of Sensor Fusion Segmentation - Scale’s endpoint for point cloud segmentation.<iframe class="ql-video" frameborder="0" allowfullscreen="true" src="https://lidar-now.scale.com/examples/semseg" height="100%" width="100%"></iframe> Sensor Fusion Viewer rendering a scene from nuScenes Released at CVPR 2019, Sensor Fusion Segmentation provides the highest precision for annotating complex objects that cannot be easily described with LiDAR cuboid labeling. Examples include vegetation, smoke/exhaust, splashes/puddles, rain and reflections.<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/34fe7aaa-a1a6-4737-823a-ffe19dbe8b00/public" alt="fog seen through LiDAR">Fog<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/bbbb9168-544a-4866-b096-9bb07cf87c00/public" alt="fog seen through LiDAR">Smoke/Exhaust<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/6cd0ad4c-0d0a-48c6-65f3-e18ac64a1700/public" alt="vegetation seen through LiDAR">Vegetation<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/ae620a78-79bd-4c4c-2ade-0e7de9ddf500/public" alt="Trucks with open backs seen through LiDAR">Trucks with open backs<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/9cb13a70-42a2-4559-1c44-a5b34bebbc00/public" alt="reflections seen through LiDAR">Reflections<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/dff8770c-e1cc-4e77-8d73-d35007f3f200/public" alt="Splashes &amp; Puddles seen through LiDAR">Splashes &amp; Puddles Semantic scene understanding is important for a variety of applications, particularly autonomous driving. Rigorously tested by a handful of Scale’s customers during a private beta, Sensor Fusion Segmentation annotation provides fine-grained understanding of surfaces and objects in a 3D point cloud. Take a look below, for how we helped the Toyota Research Institute (TRI) accelerate their research projects by giving them greater flexibility and the ability to amend workflows.<iframe class="ql-video" frameborder="0" allowfullscreen="true" src="https://www.youtube.com/embed/2XURB1nE11w"></iframe> Since starting its work with Scale, TRI has been able to support four large annotation pipelines without significantly increasing the size of their team.<a href="https://scale.ai/sensor-fusion-segmentation" rel="noopener noreferrer" target="_blank">Learn More</a>

Introducing Point Cloud Segmentation

We believe that creating jobs can be very impactful. We asked some labelers for times when working for Scale made a positive difference in their lives.

Positive Externalities

Positive Externalities: Life of a Labeler

Deep Learning models have amazing capacity, <a href="https://ai.googleblog.com/2017/07/revisiting-unreasonable-effectiveness.html" rel="noopener noreferrer" target="_blank">getting better with more data</a> seemingly without limit. To get a well-functioning model, it is not enough to have large amounts of data, you also need accurate annotations. Although large amounts of data will help the model resolve data inconsistencies between different annotations, humans can still make repeated mistakes that become ingrained in the model. For example, when asked to draw a box around an object, it is natural for humans to always ensure that the box contains the object - i.e., to err on the side of making the box too big. Using such a model for collision avoidance will result in false positives, causing the autonomous vehicle to halt unnecessarily. The oversizing of bounding boxes is an example of a systematic error, as opposed to a random error. Random and systematic errors impact the trained model differently. <h2>Random vs Systematic Errors</h2> Random errors cause the model to require more data to converge to the same result. A model trained with data with random errors will ultimately arrive at the same parameters, just more slowly and after having been fed more data. Systematic errors cause the model to converge to a different result - this does not get better with data quantity. A model trained with oversized boxes will output oversized boxes regardless of how much data it is trained with. <h2>Mitigation Strategies</h2> One simple, if expensive, way to deal with random errors is to use more training data. There are also other more sophisticated and cost-efficient approaches - for example, if your training data has multiple sources, and if each source has a different amount of random errors, you can mitigate the effects of the errors by weighing the low-error sources more heavily while training. Systematic errors, on the other hand, can only be resolved by fixing the data, or understanding the nature of error well enough to turn it into a random error. Many interesting techniques exist in this area, including <a href="https://arxiv.org/abs/1802.05300" rel="noopener noreferrer" target="_blank">Golden Label Correction</a>, where you carefully annotate a subset of the data in order to characterize the non-randomness of the rest of the data. At the end of the day, the key to overcoming annotation errors effectively and efficiently is understanding how the errors are generated. Let’s take the previous example of oversized bounding boxes. Taking into account the tendency of inexperienced workers to oversize boxes, Scale is able to use simple models to check for oversizing. Over time, with feedback from those models, workers learn to make fewer systematic mistakes, consistently drawing tight bounding boxes. By matching complex annotation specifications with annotator training and an understanding of the systematic errors, Scale is able to efficiently produce quality annotations in quantity, facilitating cost-effective training of accurate models.

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