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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

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> 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. 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> Sensor Fusion Cuboids and Sensor Fusion Segmentation is now one product called <a href="https://scale.com/3d-sensor-fusion" rel="noopener noreferrer" target="_blank">Scale 3D Sensor Fusion</a>.<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/8ef57c88-a258-41a3-a359-5c9fed59b400/public"> 3D Cuboids, Bounding Boxes, Image Categorization, Lines &amp; Splines, Polygons and Semantic Segmentation are also now one product called <a href="https://scale.com/image" rel="noopener noreferrer" target="_blank">Scale Image</a> with an addition of Point Annotation.<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/132eb95b-d69d-47a9-f657-bb3ec16e2e00/public"> Video Annotation is now <a href="https://scale.com/video" rel="noopener noreferrer" target="_blank">Scale Video</a>, 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"> Our Natural Language products have been updated to <a href="https://scale.com/text" rel="noopener noreferrer" target="_blank">Scale Text</a> with Scale Audio currently in private beta.<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/41c67cce-2a87-4057-2353-667527172500/public"> <img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/8b1ab848-ccda-4691-3f23-77daa83f0100/public"> You may also wonder, “how does this product restructure affect me?” 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’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’s Next?</h2> 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’ll be announcing and rolling out new products and additional annotation types to existing products. 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.<img src="https://images.ctfassets.net/1qgw036lpkfx/4WVGAESruBGqfVBitOG0FX/3be113efdad428155b3cce7b86e373f4/06.jpg">

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

Introducing a new look and new structure for our core 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

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