Alex is the CEO and Founder at Scale AI. He was inspired to solve ML infrastructure problems and accelerate the development of AI through his work at Quora, where he worked as a technical lead. Alex worked as an algorithm developer at Hudson River Trading and as a software engineer at Addepar. He attended, and dropped out from, MIT, studying Artificial Intelligence.

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Alexandr Wang

CEO and Founder at Scale AI

All of our external success&mdash;powering breakthroughs in L4 autonomy, partnering with OpenAI on RLHF going back to GPT-2, supporting the DoD and every major AI lab, and the recent $1bn financing transaction&mdash;all of it is downstream from us hiring the best people for the job. Talent is our #1 input metric.
Because of this, I spend a lot of my time on recruiting. I either personally interview every hire or sign off on every candidate packet. It&rsquo;s the thing I spend the plurality of my time on, easily. But everyone can and should contribute to this effort. There are almost a thousand of us now, and it takes a lot to hire quickly while maintaining, and continuing to raise, our bar for quality.
That&rsquo;s why this is the time to codify a hiring principle that I consider crucial to our success:&nbsp;
Scale is a meritocracy, and we must always remain one.
Hiring on merit will be a permanent policy at Scale.&nbsp;
It&rsquo;s a big deal whenever we invite someone to join our mission, and those decisions have never been swayed by orthodoxy or virtue signaling or whatever the current thing is. I think of our guiding principle as MEI: merit, excellence, and intelligence.
That means we hire only the best person for the job, we seek out and demand excellence, and we unapologetically prefer people who are very smart.
We treat everyone as an individual. We do not unfairly stereotype, tokenize, or otherwise treat anyone as a member of a demographic group rather than as an individual.&nbsp;
We believe that people should be judged by the content of their character &mdash; and, as colleagues, be additionally judged by their talent, skills, and work ethic.
There is a mistaken belief that meritocracy somehow conflicts with diversity. I strongly disagree. No group has a monopoly on excellence. A hiring process based on merit will naturally yield a variety of backgrounds, perspectives, and ideas. Achieving this requires casting a wide net for talent and then objectively selecting the best, without bias in any direction. We will not pick winners and losers based on someone being the &ldquo;right&rdquo; or &ldquo;wrong&rdquo; race, gender, and so on. It should be needless to say, and yet it needs saying: doing so would be racist and sexist, not to mention illegal.
Upholding meritocracy is good for business and is the right thing to do. This approach not only results in the strongest possible team, but also ensures we&rsquo;re treating our colleagues with fairness and respect.
As a result, everyone who joins Scale can be confident that they were chosen for their outstanding talent, not any other reasons.
MEI has gotten us to where we are today. And it&rsquo;s the same thing that&rsquo;ll get us where we&rsquo;re going, as we embark on our next chapter focusing on data abundance, frontier data, and reliable measurement to accelerate the development and adoption of AI models.
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Scale is a meritocracy, and we must always remain one.

Meritocracy at Scale

For 8 years, Scale has been the leading AI data foundry helping fuel the most exciting advancements in AI, including autonomous vehicles, defense applications, and generative AI.
Today, we&rsquo;re announcing that Scale has closed a $1B financing transaction at a $13.8B valuation. The financing, a mix of primary and secondary, is led by existing investor Accel with nearly all of our existing investors participating: Y Combinator, Nat Friedman, Index Ventures, Founders Fund, Coatue, Thrive Capital, Spark Capital, NVIDIA, Tiger Global Management, Greenoaks, and Wellington Management. We also are excited to welcome new investors: Cisco Investments, DFJ Growth, Intel Capital, ServiceNow Ventures, AMD Ventures, WCM, Amazon, Elad Gil, Meta, and Qualcomm Ventures.&nbsp;
With this milestone, here&rsquo;s where we are on our journey and what comes next.
<h2 dir="ltr">Becoming the Data Foundry for AI</h2>
In 2016, I was studying AI at MIT. Even then, it was clear that AI is built from three fundamental pillars: data, compute, and algorithms. I founded Scale to supply the data pillar that advances AI by fueling its entire development lifecycle.&nbsp;
Over the past 8 years, Scale has powered nearly every major breakthrough field of AI:
<ul>
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Scale&rsquo;s Autonomy Data Engine powered breakthroughs in L4 autonomy.
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Scale&rsquo;s Public Sector Data Engine has powered many major AI programs within the US Department of Defense.
</li>
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Scale partnered with OpenAI in the first experiments of <a href="https://openai.com/index/fine-tuning-gpt-2/">reinforcement learning with human feedback (RLHF) on GPT-2</a>, and scaled these techniques to <a href="https://openai.com/index/instruction-following/">InstructGPT</a> and beyond.
</li>
<li dir="ltr" aria-level="1">
Scale worked on the <a href="https://www.whitehouse.gov/briefing-room/statements-releases/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/">White House</a>-supported DEFCON 31 red-teaming event and with the U.S. <a href="https://scale.com/blog/scale-partners-with-cdao-to-test-and-evaluate-llms">Department of Defense</a> on rigorous evaluation, testing, and red-teaming of LLMs.
</li>
</ul>
Today, Scale supplies data to power nearly every leading AI model, serving organizations like OpenAI, Meta, Microsoft, and more.
<h2 dir="ltr">The Next Phase: Data Abundance for Frontier AI</h2>
Major problems in AI data still remain. The scaling laws imply an exponentially growing need for data as models get bigger, which raises a key question: will we run out of data?
Just as data, compute, and algorithms comprise the three pillars of AI, we believe the future of AI data in turn rests on three principles:
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Data Abundance: We must build the data foundry that ushers in an era of AI-ready data abundance, and not resign ourselves to data scarcity.&nbsp;
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Frontier Data: As we develop progressively more powerful AI, we must build frontier data which is always pushing the boundaries of AI capabilities towards complex reasoning, agents, multimodality, and more.
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Measurement and Evaluation: We must build an evaluation system that enables measurement of AI to build confidence, drive adoption, and scale impact.
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</ul>
Abundance is not the default; it&rsquo;s a choice. It requires bringing together the best minds in engineering, operations, and AI.
Our calling is to build the data foundry for AI, and with today&rsquo;s funding, we&rsquo;re moving into the next phase of that journey &ndash; accelerating the abundance of frontier data that will pave our road to AGI.
There&rsquo;s a lot left to do. If this challenge excites you, <a href="https://scale.com/careers">join us</a>.

Scale supplies data to power nearly every leading AI model. 

Scale’s Series F: Expanding the Data Foundry for AI

After the end of WWII and the invention of the atomic bomb during the Manhattan Project, J. Robert Oppenheimer gave an important speech reflecting on the technology he had just built, and in particular, the quantum and irrevocable impact it had on the nature of war:
&ldquo;It is clear to me that wars have changed.
It is clear to me that if these first bombs&mdash;the bomb that was dropped on Nagasaki&mdash;that if these can destroy ten square miles, then that is really quite something.
It is clear to me that they are going to be very cheap if anyone wants to make them.
It is clear to me that this is a situation where a quantitative change, and a change in which the advantage of aggression compared to defense&mdash;of attack compared to defense&mdash;is shifted, where this quantitative change has all the character of a change in quality, of a change in the nature of the world.&rdquo;
These words have great relevance today with AI. We are in the midst of the next great sea change in the nature of war, and thus, the nature of the world.
We live in uncertain times. Geopolitical risk is at&nbsp;<a href="https://www.matteoiacoviello.com/gpr.htm">a 20-year high</a> since the Ukraine war. The United States is facing <a href="https://www.jcs.mil/Media/News/News-Display/Article/613868/dempsey-us-forces-must-adapt-to-deal-with-near-peer-competitors/">near-peer competitors</a> in China and Russia for the first time since the end of the Soviet Union. There is a global battle for ideologies between Western democracy and authoritarian regimes.
All at once, the advent of AI has upended the world. It&rsquo;s a paradigm shift that has granted new superpowers and anything seems possible&mdash;with potentially greater implications on the future of our world than even the internet.
As someone who has been building in the AI ecosystem for more than seven years now, it&rsquo;s exciting to see this technology finally reach its watershed moment. But at the same time, this technology will exacerbate the uncertainty of our times. AI has the potential to unseat incumbents, catapult new leaders, or solidify existing moats. Navigating this new landscape is an existential matter&mdash;for many organizations, for our nation, and for democracy.
The United States, and the democratic world broadly, is confronted with two very real challenges.
First, as I&rsquo;ve <a href="https://alexw.substack.com/p/war?utm_source=profile&amp;utm_medium=reader2">discussed</a> previously, I believe the next era of war and deterrence will be defined by AI. We must ensure the strength of democracy in our AI war against authoritarianism by building the most effective military implementations of AI.
Second, a show of force is necessary, but not sufficient. At the end of the day, the strength of democracy has, and always will, stem from a stronger and faster-growing economy. That&rsquo;s why it is critical that we also bolster our global industries and enterprises with best-in-class capabilities.
The new Scale Donovan and Scale EGP platforms are our answer to these critical challenges&mdash;to empower the U.S. government and enterprises, respectively.
<h2 dir="ltr">Donovan &mdash; Ammunition in the AI War</h2>
AI will define the next era of war and deterrence, just as the atomic bomb did for the last era. The only solution to the AI War is to accelerate our own military implementations and rapidly integrate AI into warfighting. That's why we've funded our own R&amp;D to build Donovan&mdash;and achieve AI overmatch.
<a href="https://scale.com/donovan">Scale Donovan</a> is an AI-powered decision-making platform to help operators understand, plan, and act in minutes instead of weeks. Donovan ingests and understands vast amounts of structured and unstructured data to enable operators and analysts to make sense of any aspect of the real world in minutes using simple natural language. Donovan combines these insights with Scale&rsquo;s proprietary and state-of-the-art models to generate summary reports, COAs, and more to force-multiply staff bandwidth and achieve mission success.
As a longtime leader in AI, I am proud to be delivering this world-changing capability to the United States government with Donovan - the first LLM deployed on a classified network for the XVIII Airborne Corps - ingesting over 100,000 pages of live data to enable actionable insights across the battlefield. The sheer volume of inputs from various formats is a key challenge facing analysts and operators across the federal space. We cannot let the proliferation of information and manual processing be our Achilles heel.
<div class="wistia_responsive_padding" style="padding: 56.25% 0 0 0; position: relative;">
<div class="wistia_responsive_wrapper" style="height: 100%; left: 0; position: absolute; top: 0; width: 100%;"><iframe class="wistia_embed" title="Scale - Donovan Video" src="https://fast.wistia.net/embed/iframe/qugojsb52u?videoFoam=true" name="wistia_embed" width="" height="" frameborder="0" scrolling="no" allow="autoplay; fullscreen"></iframe></div>
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<h2 dir="ltr">EGP &mdash; Unlocking Generative AI for Enterprises</h2>
<a href="https://scale.com/generative-ai-platform">Scale Enterprise Generative AI Platform (EGP)</a> is a full-stack solution for any company looking to deploy Generative AI applications in production. We are helping our customers - like Koch Enterprises - move rapidly from just experimenting with this technology to actually capturing business value from it, through a secure, enterprise-grade platform that is model-agnostic and powered by the industry's leading&nbsp;<a href="https://scale.com/data-engine">data engine</a>.&nbsp;
While Generative AI has captured the imagination of consumers, there are substantial safety, security, and performance challenges when it comes to business use cases. EGP allows customers to develop and deploy Generative AI applications within Scale&rsquo;s infrastructure or in their own virtual private cloud (VPC), so proprietary data never has to leave customers&rsquo; environments. Scale is the only provider delivering Generative AI applications in their own environment, coupled with industry-leading capabilities like Reinforcement Learning with Human Feedback (RLHF) and red-teaming to ensure that model outputs are aligned with customers&rsquo; policies and brand.
Further, EGP powers AI applications across industries. Think copilots for financial advisors to draw upon a much broader range of information when they are educating their clients, or tools for creatives to generate infinitely customizable images and copy, or a chat advisor to guide customers seeking information about retail products. These applications are only the tip of the iceberg - with EGP, companies are empowered to build and develop their own highly specific use cases. By bringing together Generative AI models with their own proprietary data, businesses can unlock new channels for growth and provide instant insights for better, faster decision-making.&nbsp;
<h2 dir="ltr">Looking Ahead</h2>
Right now, we&rsquo;re seeing a lot of AI tourists pretending to be AI natives, taking companies for a ride while they&rsquo;re at their most vulnerable. They&rsquo;re not selling solutions, they&rsquo;re selling vaporware. Everywhere you look, you&rsquo;ll see non-AI companies rebranding and selling technology that they only began to study yesterday. They barely have the knowledge internally to build the products they&rsquo;re selling, and they sure as hell don&rsquo;t have the expertise required to help customers build and implement a long-term, defensible AI strategy.
It&rsquo;s a problem that I care deeply about because the last thing the AI industry needs is more hype. And companies that are claiming AI expertise who haven&rsquo;t spent years in the trenches have the potential to do more harm than good.&nbsp;
The reality is that it takes a lot of hard work to go from experiment to enterprise. Foundation models aren&rsquo;t like a typical consumer software product that works out-of-the-box. From explainability and auditability to PII and proprietary data, there are several critical challenges businesses must address to make AI a reality. There is no established roadmap to follow, which is why only <a href="https://scale.com/ai-readiness-report">21% of enterprises</a> actually have AI models in production. In the federal government, only <a href="https://saic.com/sites/default/files/2023-01/SAIC-Cloud-WP-2023.pdf">14% of executives</a> believe they are &ldquo;very&rdquo; ready for AI.&nbsp;
It's clear that governments and businesses need a partner that provides battle-tested tools and expertise to harness Generative AI in a way that&rsquo;s trusted, secure and moves at the speed of the world around us today. Our new Scale EGP and Scale Donovan platforms were built to solve this problem.
The future of our world hangs in the balance of the current moment. AI has the potential to strengthen global democracies and democratic economies&mdash;or be the decisive implement that enables authoritarianism to take over.
We choose to take action&mdash;in light of all that is at stake&mdash;and build.
&nbsp;

Scale is proud to introduce our two Generative AI platforms – Donovan and EGP – to empower the U.S. government and enterprises to harness AI in a way that’s trusted, secure and moves at the speed of the world around us today.

A letter from our CEO | Scale AI

A letter from our CEO

TransformX Conference brought together leaders, engineers, & researchers to explore the shift from research to reality within AI & Machine Learning.

Highlights From TransformX 2021 Conference - Scale AI

Highlights From TransformX 2021 Conference

When we started Scale nearly five years ago, our mission was to accelerate the development of AI. Today, I’m proud to say that we’re seeing this mission come to life, as customers across industries, from finance to e-commerce to transportation to government, are shipping AI projects that touch our everyday lives.But we’re still just scratching the surface of the potential that AI has to transform every business and industry, which is why we’re happy to share that Scale has raised $325 million in Series E funding, co-led by Dragoneer, Greenoaks Capital, and Tiger Global. Additional new investors in the round include Wellington Management and Durable Capital followed by existing investors Coatue, Index, Founders Fund, and YC. The interest and opportunity for AI is only accelerating, and this new funding will help us continue to expand our team and products to meet this growing demand. Additionally, Jeff Wilke, former CEO of Amazon’s Worldwide Consumer business, will be joining as Advisor to the CEO."The AI industry is at an inflection point where every business is looking to implement an AI strategy and we are starting to see a real-world impact. We believe Alex and his team at Scale have been critical to enabling businesses across industries reap the full benefits of AI and are taking the industry from proof of concept to reality," said Neil Mehta, Founder and Managing Partner at Greenoaks Capital.As we discussed with over 9,000 of the leading minds in AI at our inaugural <a href="https://scale.com/events/transform" rel="noopener noreferrer" target="_blank">Scale Transform conference</a>, we’re moving from a theoretical and research based industry to one that’s rooted in impact and real business results. However, making this shift means companies are facing new challenges, which requires a systematic, data-centric approach to rapidly deploy their most important models.At Scale, we’re building the foundation to enable organizations to manage the entire AI lifecycle. Whether they have an AI team in-house or need a fully managed models-as-a-service approach, we partner with our customers to build their strategy from the ground up and ensure they have the infrastructure in place to systematically deliver highly-performant models.We set out to solve this problem by starting with an essential building block, data annotation. Building on our expertise in delivering high-quality training data to world-leading machine learning teams, we are now applying our technical expertise and understanding to close the loop on the ML development lifecycle.Learn more about how we <a href="https://scale.com/" rel="noopener noreferrer" target="_blank">accelerate ML development with an end-to-end solution</a>.<h2>Driving Real-World Impact</h2>We’ve already seen the impact that the right data infrastructure can bring to our customers. For example, companies like <a href="https://scale.com/customers/toyota" rel="noopener noreferrer" target="_blank">Toyota</a> are paving the way for a future with fully autonomous vehicles, but need massive volumes of high-quality training data to reach this goal. By using our combination approach of machine learning with human insight, Scale was able to improve Toyota’s annotation throughput by 10X in a matter of weeks. For another customer, <a href="https://scale.com/customers/states-title" rel="noopener noreferrer" target="_blank">States Title</a>, Scale improved the accuracy of its document processing to more than 95 percent, freeing up their data science teams to work on actual data science work. And PayPal was able to leverage AI to efficiently validate and categorize millions of products from its partner merchants with our help.Brex, a high-growth startup reimagining financial systems, partnered with Scale to improve how businesses manage their money. Using our document processing solutions, their customers can now process invoices and pay bills faster and more accurately. We’ve also partnered with Flexport, the platform for global trade, to make historically manual logistics paperwork easier to execute and more accurate. Through our document processing solutions, we’ve enabled increased efficiency and compliance across Flexport's network of global trade stakeholders.These are just a few examples of how we’ve been supporting our customers. Over the past six months, we’ve continued to see demand growing across industries as leading organizations expand their AI programs. Today our customers range from the Department of Defense and U.S. Air Force; to leading fintechs like Brex, PayPal, and Square; to transport and logistics pioneers like Flexport, General Motors, Luminar, and Oshkosh; to beloved consumer brands and products like Etsy and Pinterest; to some of the world’s largest technology leaders like Samsung and SAP. We’re proud to be trusted by the world’s most ambitious AI teams to build the data foundation that will set their organizations up for success for decades to come. We can’t wait to see what else will be achieved with this technology as our customers continue to prove that production-ready AI isn’t just a pipe dream, but it’s already here.

Scale AI has closed its Series E funding to accelerate the deployment of AI across every industry.

 Scale AI’s Series E: Deploying AI Across Every Industry

If you were not able to join Transform live, the keynote and breakout sessions are now available on our website for <a href="https://scale.com/events/transform" rel="noopener noreferrer" target="_blank">on-demand viewing</a>.Artificial Intelligence is now a part of our everyday lives. This technology is no longer theoretical, but is making a direct impact on businesses and their bottom lines. Nearly every industry has woken up to the potential of AI to transform their operations, whether it’s leading the charge of an entirely new category, like autonomous transportation, or modernizing legacy industries, like finance, real estate, and retail. AI is poised to be as transformative as the internet, and with that, organizations are learning that data is the new code. If they’re not investing in this critical technology now, they’re at risk of being left behind.Today we hosted our inaugural conference, Scale Transform, to showcase the state of AI today, from the latest research breakthroughs to the real-world impact across industries. As I shared in my opening remarks, “AI continues to surprise us with its new capabilities, primarily enabled through advancements in the core resources of data, compute, and algorithms.”We brought together more than 9,000+ leading researchers, practitioners, and executives to hear from the people and companies leading our industry forward. It was a day full of thoughtful conversations and presentations that examined the strides made in advancing these core resources, the challenges that still lie ahead, and what we can expect in the years to come.<h2>The Latest Advancements in Research</h2>AI research has been crucial in advancing the industry and our understanding of what’s possible. We’ve made significant progress across countless areas that have propelled the industry forward, yet there is still plenty more to be done. Some of the latest research discussed includes:<ul><li>Artificial General Intelligence (AGI) - Sam Altman, Co-Founder and CEO of OpenAI, touched on breakthrough research such as GPT-3, CLIP, and DALL·E, bringing us closer to achieving AGl. He said, “We may not even know when AGI actually emerges - it won’t be a single moment in time. The Turing Test for achieving AGI will be when AI is able to complete a percentage of human labor really well.”</li><li>Machine Learning Frameworks - The ecosystem of ML frameworks is evolving quickly. Google Software Engineer and Keras Creator Francois Chollet discussed how the roadmap for Keras in the next five years includes developing an ecosystem of reusable models such as KerasNLP and Keras Applications, increasing automation, enabling larger-scale cloud-based workflows, and supporting real-world deployment. Separately, Soumith Chintala, the Creator of PyTorch, shared what he considers the three personas in ML Development: The Modeler, Prod, and Compiler and discussed how these personas interacted with each other pre and post-deep learning. He closed his session by highlighting the need for PyTorch and other mainstream frameworks to maintain a high velocity to stay relevant and keep up with the latest innovations to enable Modelers.</li></ul><img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/e15ac9ef-de00-4d59-28e4-1f16f6790900/public" alt="Soumith_Chintala_at_Transform">Soumith Chintala, Creator of PyTorch speaking on the future of ML frameworks.<ul><li>Autonomous Vehicles - Drago Anguelov, Distinguished Scientist and Head of Research at Waymo discussed the current state of perception, highlighting that, “Perception works very well for the vast majority of cases, but there are still some interesting long-tail problems like rare events that still need to be addressed.” Despite the remaining challenges, Anguelov discussed how the self-driving industry is beginning to “move up the stack” to tackle behavior prediction and planning, including the recent release of the updated Waymo Open Dataset to support further research into behavior prediction and motion forecasting. Anguelov also discussed how Waymo has successfully deployed L4 level autonomous vehicles in Chandler, AZ.</li></ul>This research and advancements made by others in the industry, including our conference partners, Stanford Institute for Human-Centered AI (HAI), PyTorch, DeepLearning.AI, OpenAI, Pachama, and ELC, are the catalysts for meaningful use cases of AI.<h2>Challenges Ahead</h2>One of the biggest challenges for our industry is scaling AI systems from proofs of concept to production. A common theme heard from speakers throughout the day is that data is the critical foundation that companies must invest in to unlock AI’s full potential: Andrew Ng of DeepLearning.AI discussed how models can improve with more compute, data and parameters, however, it's not just about how much data you have, but the quality of that data. Andrew brought up a critical point here, because developing AI with bad data means it won’t live up to the potential and quite frankly it’s dangerous. It can lead to:<ul><li>Safety risks (e.g. self-driving cars not accurately identifying pedestrians or obstacles)</li><li>Biased algorithms (e.g. facial recognition bias)</li><li>Inaccurate results / recommendations (e.g. AI-driven healthcare decisions)</li></ul><img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/35e8924f-cf73-4df9-a604-4540068a7400/public" alt="Andrew_Ng_at_Transform">Andrew Ng, speaking on the need for good data to develop AI.Bart Nabbe, VP of Strategic Partnerships and Corporate Development at Aurora, reinforced this point, stating, “You’re not building ladders to get to the moon, you need rockets.” And data is the fuel for those rockets. He emphasized that in the early days of training ML, every piece of data counts, however over time it’s increasingly important to be precise and focus on the long-tail, differentiated data to improve models.The importance of creating ethical and unbiased AI has been a focus in the industry for years, and as the technology has gone mainstream, so has the conversation. Global leaders and technology experts across the world are tackling challenges in developing AI that is ethical, as well as ensuring the benefits of AI can be equally distributed. Our speakers provided passionate advice on the topic:<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/a44a923d-4bb8-4a54-a5d1-e76d926c1300/public" alt="Kevin_Scott_at_Transform">Kevin Scott, CTO of Microsoft at Scale Transform.<ul><li>Kevin Scott, Chief Technology Officer of Microsoft, enumerated some of the challenges to ensuring that the benefits of AI are equally distributed. “We have an enormous amount of work to do. […] We’re learning a lot about biases in datasets, how to manage potential harms, ensuring the development of AI is inclusive, and that AI is being used safely.” Scott then spoke about how his teams at Microsoft are developing the tools (e.g., PowerBI, autoML) to enable developers without machine learning expertise to develop ML applications.</li></ul><img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/836e4864-7ac9-4153-c016-6eb81d57dd00/public" alt="fei-fei_li_at_transform">Dr. Fei-Fei Li, speaking on the need for diversity in the field of AI.<ul><li>Diversity in the field of AI and greater interdisciplinary collaboration is key to minimizing the unintended consequences of AI, and Dr. Fei-Fei Li, Sequoia Professor of Computer Science, Stanford University, Denning Co-Director, Stanford Institute for Human-Centered AI (HAI) believes that as we continue to build AI, the industry needs to take inspiration from and embed human cognition, intention, and emotion in the technology for ML to achieve its full potential to augment human capabilities.</li></ul><h2>Real-World Impact</h2>Perhaps most importantly, we also heard inspiring work that’s happening in the real world – tangible examples of how AI is already improving the way we live and operate:<ul><li>Facebook reaches billions of people across the world and often needs to deploy products quickly, at scale from translations to social recommendations to generating descriptions of images for the visually impaired. On an average day, Facebook is processing 6 billion transactions a day. Head of Applied Research at Facebook AI, Srinivas Narayanan, relayed the importance of “getting the most bang for your buck” for your labeled data by augmenting it with various techniques, and creating AI responsibly by viewing fairness as a process and not a binary notion in order to implement the best AI across a variety of use cases.</li><li>The financial industry has complex workflows filled with massive amounts of paperwork that often includes tedious manual processing. During a panel, Brex’s Co-Founder and Co-CEO Henrique Dubugras discussed how applying AI technology like document understanding has helped the financial world increase efficiencies and lower costs while freeing up humans to focus on more fulfilling work.</li></ul><h2>What’s Next</h2>The continued advancements in AI have created incredible applications in today’s world but there is an exciting future that awaits - from the potential to create safer transportation to agricultural benefits like increasing crop yields and improving healthcare outcomes. At Scale, we’re excited to have a hand in creating trusted, ubiquitous use of AI and we can’t wait to see what’s next.A special thank you to our conference partners: Stanford Institute for Human-Centered AI (HAI), PyTorch, DeepLearning.AI, OpenAI, Pachama, and ELC. We are also grateful for all our incredible speakers representing organizations including Aurora, Blend, Brex, Cruise, DoorDash, Etsy, Facebook AI, Fast.AI, Flexport, Google, Hugging Face, iRobot, Microsoft, Qualtrics, RedFin, University of Toronto, and Waymo.If you were not able to join us live, the keynote and breakout sessions are now available on our <a href="https://scale.com/events/transform" rel="noopener noreferrer" target="_blank">website for on-demand viewing</a>.

Scale AI Hosts Inaugural Scale Transform, Showcasing AI’s Potential to Improve Our Lives

Scale Transform 2021 | The Future of AI

Scale Transform: AI Leaders Showcase Research Breakthroughs and Real-World Impact

Today we are excited to share several key updates that build on Scale AI’s mission to accelerate the development of AI. First, we are officially announcing Scale’s Series D funding round. Following our Series C fundraise last year, Scale has raised $155M at a post-money valuation north of $3.5B led by Tiger Global. We are in the midst of one of the most impactful technology transformations of human history with AI. The potential applications of AI are going to deeply improve the world: scalable medical diagnosis to everyone in the world, massive reduction in driving accidents, goods which are dramatically cheaper to manufacture and transport, and much more. What’s holding us back from building those dreams is lack of infrastructure. While many organizations are adopting AI, they are forced to build their technology stack from scratch, significantly slowing progress. Four years ago, we recognized this problem and sought to unlock the bottlenecks holding back AI development. We started by building a platform that solves the necessary, foundational layer that fuels all machine learning models: large annotated datasets. Our solution gives developers the ability to send data to our platform through the Scale API and receive high-quality training data that’s ready for building production-grade AI algorithms. We use a combination of machine learning and human intelligence to annotate tens of millions of data points per week for hundreds of customers like OpenAI, DoorDash, Pinterest, General Motors, NVIDIA, Nuro, and Toyota. We’ve chosen to stay focused on the applications of AI with the greatest potential for impact: autonomous driving, government, and cutting-edge consumer technology. As AI has transformed every industry, we’ve continued to see demand for our platform to grow—from e-commerce and AR /VR to relevance &amp; personalization and financial services. We are still very early in the journey of AI, and we remain optimistic about all the future dreams we will support. Our team is tackling the next bottleneck in AI development: managing the full life cycle of AI development across teams. Building and deploying scalable machine learning infrastructure is slow, manual and costly, and in August we launched <a href="https://scale.com/nucleus" rel="noopener noreferrer" target="_blank">Nucleus</a> to enable seamless collaboration across the AI development cycle. Teams can visualize and organize their datasets, test and compare models, and share data with colleagues. With Nucleus, our goal is to become the mission control for your data, and over time we’ll be rolling out support for more data types and adding more features to make developing AI easier and faster than ever.<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/0620751d-5f96-4b5f-0469-e11b65805a00/public" alt="Scale_Nucleus_ML_Development_Cycle"> As part of today’s announcement, we are also excited to announce the acquisition of Helia AI. Helia’s team brings deep expertise in applying AI to real-time video, from core algorithm development to necessary AI infrastructure. Their AI infrastructure experience aligns closely with what we’re building with Scale Nucleus, and the Helia team will help advance the Scale platform as we expand its capabilities. We look forward to continuing our work of building AI infrastructure with Scale’s hundreds of customers, and our new funding will allow us to accelerate hiring, expand to new markets and build more robust products to match the evolving needs of AI as the technology matures. We’re still in the early days of AI, and we believe Scale will enable a future where AI is as easy to build and manage as any other software. If you are interested in joining us on our mission, check out our <a href="https://scale.com/careers" 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>.

Accelerating the development of AI applications and empowering AI dreamers with a new round of funding led by Tiger Global. 

Scale AI's Series D: Expanding our team to empower AI dreamers

Scale AI’s Series D: Expanding our Team to Empower AI Dreamers

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

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>.