Software To Help You Turn Your Data Into AI
Forget fragmented workflows, annotation tools, and Notebooks for building AI applications. Encord Data Engine accelerates every step of taking your model into production.
Last month, Encord was one of a number of global tech companies invited by Amazon Web Services (AWS) to attend the event dubbed Project Stormcloud.
In launching Project Stormcloud, The Royal Navy’s Office of the Chief Technology Officer challenged global technology giants Microsoft and AWS to demonstrate how companies could bring new, state-of-the-art cloud-based technology into the defence industry.
As part of the Stormcloud Community, we’ve been supporting the Royal Navy and British Defence by providing critical computer vision infrastructure for the project, enabling the defence industry to automate visual tasks, annotate data for internal intelligence analysis, and store data at a large scale. This support allows for the application of AI for an instant real-time, on-the-ground intelligence picture.
Being involved in Project Stormcloud has been a great experience for us as a company. It has been a privilege to be part of this consortium of innovators. We got to work and integrate with some of the leading tech companies in the government sector. The fact that the UK tech ecosystem could achieve so much in such a short period of time really speaks volumes about its quality.
We also gained a lot of insight into the importance of using the right data to achieve specific mission objectives. It was useful to learn how our applications can be used to achieve real-time situation awareness.
Stormcloud, with AWS, Microsoft, and their range of partners, will progress further over the next year to incorporate ideas from across Defence and to demonstrate how two of the leading global tech companies can revolutionize how to get technology into the hands of sailors and Royal Marines.
We look forward to continuing to be part of the journey.
Ready to automate and improve the quality of your data labeling?
Sign-up for an Encord Free Trial: The Active Learning Platform for Computer Vision, used by the world’s leading computer vision teams.
AI-assisted labeling, model training & diagnostics, find & fix dataset errors and biases, all in one collaborative active learning platform, to get to production AI faster. Try Encord for Free Today.
Want to stay updated?
"Say you want to watch a movie. To choose, you'll want to know what movies others liked and, based on what you thought of other movies you've seen if this is a movie you'd like. You'll be able to browse that information. Then you select and get video on demand. Afterward, you can even share what you thought of the movie. But thinking of it only in terms of movies on demand trivializes the ultimate impact. The way we find information and make decisions will be changed. Think about how you find people with common interests, pick a doctor, and decide what book to read. Right now, reaching out to a broad range of people is hard. You are tied into the physical community near you. But in the new environment, because of how information is stored and accessed, that community will expand. This tool will be empowering, the infrastructure will be built quickly and the impact will be broad." - The Bill Gates Interview, Playboy Magazine, July 1994 Sound familiar? Asked what else the personal computer was supposed to do other than process documents, Bill Gates prophesied the changes brought about by the coming of the information age that modern-day tech giants have since realized. From video-on-demand and movie recommendations (Netflix) to the way we find information (Google) to how you find people with common interests (Facebook) and deciding what book to read (Amazon 1.0), Gates' vision of the transformation that the information age would bring about turned out in more ways than one could conceivably imagine at the dawn of the Internet revolution. The Coming of the AI Revolution Fast forward 20 years, the AI revolution has begun. It will fundamentally transform our world, much just like the advent of the atomic bomb, microprocessor, personal computer, and the Internet. If the wealth generated from the emergence of each of these technologies offers any indication, we are poised to witness an unprecedented accumulation of wealth. As with any prophesied significant platform shift, there's a real risk that they fail to materialize in a big way at a particular moment in time (e.g. Web3, Blockchain, Crypto, Metaverse) or that they take much longer than anticipated to play out (admittedly, crypto can still find an actual use case). Until as recently as ten years ago, almost all AI systems failed to demonstrate significant value, and many still do not (e.g., purely logic-based AI systems and symbolic AI - the dominant paradigm from the 1950s to the mid-1990s - are still primarily research interests). We could be in for another AI hype cycle that may eventually fizzle. As an eternal optimist and founder of an AI company looking to raise a Series B in the not-too-distant future, I won't bother spelling out why AI is overrated. Instead, I'll argue why it will change the world. When I explain AI to my parents, I describe it as a new form of dynamic software built on answers, unlike traditional status software built on rules. Put simply, comparing AI to conventional software is like saying "show" instead of "tell." What's exciting about AI is that dynamic and answer-based software will enable us to create new products, applications, and systems that can solve unsolved problems that, until now, have been reserved for human cognition. Self-driving cars are the most obvious example - while traditional software can handle simple tasks like driving straight, building a fully autonomous vehicle would require an overwhelming number of static rules to cover even the basics of navigation. It is not a leap to believe that the total addressable market (TAM) of problems only solvable by human cognition is orders of magnitude higher than that of any of the problems for which we use traditional software. As AI can augment and - in some cases - replace humans, it can produce what I think of as "non-linear" productivity outcomes. Here are a few contrived examples across various vertical use cases to illustrate the potential non-linearity of AI systems: Building a faster car to reduce the amount of attention required to drive from A to B (linear) vs. self-driving vehicle (non-linear) More efficient organization of leads and tasks in a CRM system with a slightly better UI for salespeople (linear) vs. AI avatars that allow for infinite scaling of the salesperson (non-linear) Improved diagnostic equipment that provides more detailed images for radiologists to analyze (linear) vs. AI-driven systems that scan medical images and highlight potential anomalies for doctors or even predict possible illnesses before symptoms manifest based on health data (non-linear) Better tractors and machinery to help farmers plant and harvest crops (linear) vs. drones and robots that monitor the health of individual plants, apply precise amounts of fertilizer or pesticide, and harvest crops with minimal human intervention (non-linear) A digital learning and education platform with improved video lectures and homework targeted specifically at programming (linear) vs. an adaptive chatbot that can be prompted to "explain this concept like I'm 12 years old" (non-linear) The digital learning example is interesting as it is playing out in real-time: Chegg, the education technology company, saw its stock price tumble 47% (down ~63% year-to-date) after admitting that ChatGPT was pressuring its subscriber growth, leading them to suspend their full-year outlook. You get the idea. Just as the Internet's value skyrocketed with evolving applications, tools, and increased user participation, so too will the AI sector's worth. Despite the Internet's basic components remaining similar to those of the early 1990s, its value has grown exponentially over 20 years due to expanded applications and user engagement. As more individuals and businesses embrace AI and develop applications, the supporting tools and infrastructure will improve. Increased data availability will also enhance product quality. This cyclical improvement will fuel exponential growth in AI. Undoubtedly, AI is poised to be the next major technological platform shift within the next 20 years. While previous technological revolutions, like the Internet or the Industrial Revolution, were monumental in reshaping societies and economies, AI encapsulates something far more profound: the essence of human cognition. The wealth generation from novel solutions to previously unsolvable problems, combined with the heightened productivity and efficiency across all sectors, implies that the economic impact of AI could dwarf that of all prior technological shifts. The emergence of AI will give birth to an unfathomable number of unicorns. Who Wins: Titans, Challengers, or Innovators? Ok, so AI will be huge, but who wins the biggest slice of the pie, and where will the most value be generated? While the Twitter VC community may have its own predictions, here are my thoughts on a potential outcome. I could of course be entirely wrong. Early winners like NVIDIA have already experienced a surge in their stock price, and investors believe that generative AI and LLM developers will be the next big thing, as evidenced by the high valuations and significant investment flowing into those companies. The landscape is already fiercely competitive, especially among "neo" foundation model/LLM providers (e.g., Cohere, Anthropic, Mistral). Given the high valuations and evolving competitive landscape, I question the viability of venture-scale returns for most of these new entrants. There is a limit to how many chatbots the market can absorb, after all. Additionally, OpenAI is also so far ahead (8 years of R&D and billions of queries via ChatGPT generating valuable RLHF data) that it will be difficult for any of these companies to catch up. Perhaps one or two will succeed, but for emerging LLM companies to truly thrive, they will likely need to uncover unique niches or pivot towards refining larger models using specialized, proprietary datasets for distinct needs and/or partnering with downstream application developers. Some corporate/venture combinations could also happen, where partnerships like OpenAI/MSFT, Anthropic/Google, and Cohere/Meta-type will combine distribution and data advantage with R&D expertise. Elad Gil made some interesting observations on this here. Separately, foundation model providers will likely realize lower margins in the first few years of operation, as they more closely resemble 'hardware-type companies' with significant upfront training expenses. However, this does not mean that developers who create applications using these large models won't achieve considerable margins even if the TAM of those markets is much smaller, for example, by offering specialized expertise, products, and other value-added services related to these models. Jasper is an example of a company that has done this perfectly - they've built a product that serves marketers, and just marketers, well. All things considered, the AI market will probably resemble that of the current software market in 20 years. There will be a few huge "Big AI" companies with over $100 billion in revenue (this could very well end up being the foundation model developers, but it could also be companies that we haven't even conceived of yet) and a diaspora of large companies focusing on specific applications (e.g., Stripe for payments, Uber for transportation, Figma for design - this could be Jasper for marketers, Cruise for autonomous vehicles, Viz AI for medical imagery, and so on). For context, Apple, Microsoft, Amazon, Meta, and Alphabet constitute ~$9 trillion of the value of the NASDAQ's ~$22 trillion market cap. This is a substantial chunk, no doubt, but the total size of the pie is undoubtedly only going to get bigger as the AI market gets underway and secular trends in technology continue to reverberate. Why This Time is Different Technological revolutions often occur due to a convergence of pivotal factors, and the AI sector is currently experiencing such a juncture. Similar to the Internet's ascendance, which was enabled by ubiquitous personal computers and faster connectivity, the current AI boom is a product of simultaneous advances in computing power, vast data availability, and increasingly advanced models. Eric and I founded Encord at a pivotal moment when object detection models transitioned from often being erroneous and requiring highly controlled "sandbox"-type environments to delivering tangible ROI. Similarly, the release of ChatGPT marked a paradigm shift in how we approached natural language processing and understanding. Looking into the near future, I anticipate AI delving deeper into multi-modal applications, offering higher ROI and increasingly viable solutions to more complex problems, and even stepping into realms of human reasoning. In short, the market is just getting started. The value, revenue, TAM, etc., will naturally accrue as the complexity of the problems that we solve with AI increases. After all, it was impossible to stream a movie over your Internet connection 20 years ago, but now its table stakes.
We sat down with Denis Gavrielov, Full Stack Engineer at Encord, to learn more about his day-to-day. Denis is an essential player on the Engineering team, he has been a part of Encord's exciting journey from a small 8-person team to the dynamic startup it is today. He walks us through the highs and lows of his experiences, the camaraderie he shares with his team, and the thrill of working on some truly fascinating projects. If you've ever wondered what it's like to work at a startup like Encord, strap in and join us for this conversation! Denis, first question. What inspired you to join Encord? I applied about two years ago when the team was still getting started (we were eight people strong — all engineers!). At that time, I wasn't sure if I wanted to join a startup as I had been working in a larger company up until then (Bloomberg). I met most of the team throughout the interview process, and the more I spoke to everyone, the more I felt I'd really enjoy working with them. I also had a great first impression of the founders (Eric & Ulrik) and the more I spoke with them the more I got a sense for how strong their skillset was and how unique and unparalleled of an opportunity it'd be. The combination of team, founders, and opportunity is what ultimately led me to join Encord. I still remember my first week sitting in our 10m2 office space in the middle of Soho without any meeting rooms or kitchen, quite different from today! Very different from now indeed! What does a typical day as a full-stack engineer at Encord look like? Every day is a bit different. Some people like to focus on back-end engineering and some like front-end work — I like to work on both! Typically, I start by catching up with everyone's messages on Slack. I might have one or two meetings in the morning before starting to code. Throughout the day there's a lot of collaboration via impromptu short catch-ups in person or on Slack. During these meetings, I often discuss architectural designs and problem-solving strategies with more senior team members. A significant component of my day is spent coding and solving problems. And I guess you fit in lunch in between that as well, right? That's true, a good lunch break is important for recharging! I usually grab lunch with a few colleagues — if the weather is nice, like during the summer, we enjoy our lunch outside. On Fridays, the entire team gets together in the office for a company lunch, that's always fun and one of my favorite Encord traditions. You mentioned teamwork and customer centricity — how would you describe the company culture at Encord more broadly? The company culture is very collaborative. The founders and senior managers are encouraging and open to suggestions, so it's easy to take (good) decisions independently. We try to maintain a relaxed office culture focused on team success, where people are free to work in a manner that works best for them and for us as a team more broadly. People are always trying to help each other out and break down any silos there might arise — although we've been very intentional though about building the right environment, so luckily there are very little silos in the first place. For example, our engineering and commercial team all work in one big room and getting context or feedback is incredibly quick. Another key part of our culture is being able to adapt quickly. Previously, I was working at Bloomberg in an infrastructure team, where we had to plan everything very precisely. Everything had to be correct from the start, and every decision would need to go through many layers of approval and planning sessions. Speed of progress was naturally quite different. Here at Encord, we are a nimble, rapid team — we move quickly and have been able to achieve things that teams many times our size have struggled with. Customers tell us every week how much better our product is compared to other alternatives they're looking at. It is always very rewarding when we hear this feedback. What we aim for as a team is to be great — ship high-performing industry-leading products quickly, get feedback from customers and prospects early, and consistently focus on building what customers and users really want. I think these are the two principles that stand out to me as an engineer the most. Can you tell us a bit about a project that you're currently working on? One of the projects I'm working on is overhauling of our task management system. The core problem we're trying to solve is that our customers need a detailed overview and a flexible interface to control the annotation and review process for their data operations. The process can be complicated, involving multiple review stages and different levels of scrutiny. So we're developing a system to allow our clients to implement more complex workflows and be able to review the process even faster! In terms of how I spend my time between collaborating and individual development, it varies heavily by what point of the project we're in. At the beginning, a large part of my time is spent brainstorming, talking through the objectives with other engineers and product managers, and listening in on client calls. After gathering ideas and refining our approach, I spend most of my time coding and focusing on the project — while still staying in sync with the rest of the team so that we keep moving quickly in the same direction. Lastly, what advice would you give to someone considering working at Encord? Many people reach out to me each week asking this and there's a few things I think many people don't consider. Encord is obviously in a very exciting position — we're a strong team, in the right market (AI infrastructure), and have a clear vision of what the future will be like and what we need to build to get there. Yet early-stage startups, especially ones moving very quickly, require working-modes and dispositions that the vast majority of people are not looking for nor comfortable with — and that's okay! If you don’t particularly enjoy collaborating on tasks, or you prefer being told very precisely/prescriptively what to do, or you're not at the stage in your career where you want to own processes, then I'd say Encord (and similar-type companies) are probably not the best fit. It really depends on what excites you. Here, you need to embrace pace, ownership, collaboration, and autonomy, often to degrees you may not have considered possible. These are key traits that, I think, have all made us successful in our roles. If you want to own problem areas and find solutions, if you like collaborating and prototyping MVPs quickly to get client feedback early, and if you pride yourself in making the right decisions with limited amounts of information — then Encord may be a good fit! Thank you, Denis! We have big plans for 2023 & are hiring across all teams. Find here the roles we are hiring for!
Learn more about life at Encord from our Product Designer, Mavis Lok! Mavis Lok, or ‘Figma Queen’ as we’d like to call her, thrives in using innovation and creativity to enhance the user experience (UX) and user interface (UI) of our products. She listens closely to our customers’ needs, conducts user discovery, and translates insights into tangible and elegant solutions. You will find Mavis collaborating with various teams at Encord (from the Sales and Customer Success teams, to the Product and Engineering teams) to ensure that the product aligns with our business goals and user needs. Hi, Mavis, first question is what inspired you to join Encord? When I was planning the next steps in my career, I knew that I wanted to join an emerging and innovative tech startup. In the process, I stumbled upon Encord - with a pretty big vision of helping companies build better AI models with quality data. A problem that seemed ambitious and compelling. I had my first chat with Justin [Encord's Head of Product Engineering], and he gave me great insights into the role, the company, and the domain space, which tied nicely with my design experience and what I was looking for in my next role. I was evaluating many companies, and I made sure (and I'd recommend to anyone reading!) to speak to as many employees from the company I could meet. The more people I met from Encord, the more and more eager I became to join the team. Could you tell me a little about what inspired you to pursue a career in product design? Hah, great question! I was previously in creative advertising and was trained as a Creative/Art Director. During my free time, I would participate in advertising competitions where I would pitch ideas for brands, and I’d always maximize my design potential through digital-led ideas. That brought me to work as a Digital Designer and then as a Design Manager, where I got my first glimpse of what it was like to work closely with co-founders, engineers, and designers. The company I was working at, was going through a transition from an agency to a SaaS type business model, and I found many of the skills I'd developed were actually an edge for what product design requires. Having an impact in balancing business needs, and product development challenges whilst creating products that are user-centric and delightful to use - is why I love what I do every day. How would you describe the company culture? I think the people at Encord are what sets us apart. With a team of over 20 nationalities, it’s an incredible feeling to work in an environment where diversity of thought is encouraged. The grit, ambition, vision, and thoughtfulness of the team are why I enjoy being part of Encord. What have been some of the highlights of working at Encord? Encord has given me the space to throw light on the impact that design can bring to the company and build more meaningful relationships with the team and, of course, our customers. Another big highlight for me is practicing the notion of coming up with ideas rapidly whilst being able to identify the consequences of every design decision. Brainstorming creativity whilst critically is something I hold dearly in my creative/design life, so it’s definitely a highlight of my day-to-day at Encord. On a side note, Encord is also a fun place to work. Whether it is Friday lunches, monthly social activities, or company off-sites, there are plenty of opportunities to have a good time with the team. Lastly, what advice would you give someone considering joining Encord? The first thing I would say is you have to be authentic during the interview, and you should also genuinely care about the mission of the company because there is a lot of buzz around the AI space right now - genuine interest lasts longer than hype. I would recommend reading our blogs on the website; it's a great place to start, as you can gain a lot of insight from it. From learning more about our customers, to exploring where our space is headed. We have big plans for 2023 & are hiring across all teams. Find here the roles we are hiring for.
Forget fragmented workflows, annotation tools, and Notebooks for building AI applications. Encord Data Engine accelerates every step of taking your model into production.