Eric Landau is the CEO & Co-Founding father of Encord, an lively studying platform for pc imaginative and prescient. Eric was the lead quantitative researcher on a worldwide fairness delta-one desk, placing hundreds of fashions into manufacturing. Earlier than Encord, he spent almost a decade in high-frequency buying and selling at DRW. He holds an S.M. in Utilized Physics from Harvard College, M.S. in Electrical Engineering, and B.S. in Physics from Stanford College.
In his spare time, Eric enjoys enjoying with ChatGPT and huge language fashions and craft cocktail making.
What impressed you to co-found Encord, and the way did your expertise in particle physics and quantitative finance form your method to fixing the “information downside” in AI?
I first began serious about machine studying whereas working in particle physics and coping with very massive datasets throughout my time on the Stanford Linear Accelerator Middle (SLAC). I used to be utilizing software program designed for physicists by physicists, which is to say there was loads to be desired by way of a pleasing consumer expertise. With simpler instruments, I’d have been capable of run analyses a lot sooner.
Later, working in quantitative finance at DRW, I used to be chargeable for creating hundreds of fashions that have been deployed into manufacturing. Just like my expertise in physics, I discovered that high-quality information was vital in making correct fashions and that managing complicated, large-scale information is troublesome. Ulrik had the same expertise visualizing massive picture datasets for pc imaginative and prescient.
After I heard about his preliminary thought for Encord, I used to be instantly on board and understood the significance. Collectively, Ulrik and I noticed an enormous alternative to construct a platform to automate and streamline the AI information improvement course of, making it simpler for groups to get the most effective information into fashions and construct reliable AI methods.
Are you able to elaborate on the imaginative and prescient behind Encord and the way it compares to the early days of computing or the web by way of potential and challenges?
Encord’s imaginative and prescient is to be the foundational platform that enterprises depend on to remodel their information into practical AI fashions. We’re the layer between an organization’s information and their AI.
In some ways, AI mirrors earlier paradigm shifts like private computing and the Web in that it’s going to grow to be integral to workflows for each particular person, enterprise, nation, and business. In contrast to earlier technological revolutions, which have been largely bottlenecked by Moore’s legislation of compounded computational development of 30x each 10 years, AI improvement has benefited from simultaneous improvements. It’s thus shifting at a a lot sooner tempo. Within the phrases of NVIDIA’s Jensen Huang: “For the very first time, we’re seeing compounded exponentials…We’re compounding at one million occasions each ten years. Not 100 occasions, not a thousand occasions, one million occasions.” With out hyperbole, we’re witnessing the fastest-moving know-how in human historical past.
The potential right here is huge: by automating and scaling the administration of high-quality information for AI, we’re addressing a bottleneck stopping broader AI adoption. The challenges are harking back to early-day hurdles in earlier technological eras: silos, lack of greatest practices, limitations for non-technical customers, and a scarcity of well-defined abstractions.
Encord Index is positioned as a key instrument for managing and curating AI information. How does it differentiate itself from different information administration platforms at present out there?
There are a couple of ways in which Encord Index stands out:
Index is scalable: Permits customers to handle billions, not hundreds of thousands, of knowledge factors. Different instruments face scalability points for unstructured information and are restricted in consolidating all related information in a company.
Index is versatile: Integrates straight with non-public information storage and cloud storage suppliers equivalent to AWS, GCP, and Azure. In contrast to different instruments which are restricted to a single cloud supplier or inside storage system, Index is agnostic to the place the information is situated. It enables you to handle information from many sources with acceptable governance and entry controls that enable them to develop safe and compliant AI purposes.
Index is multimodal: Helps multimodal AI, managing information within the type of photos, movies, audio, textual content, paperwork and extra. Index will not be restricted to a single type of information like many LLM instruments immediately. Human cognition is multimodal, and we consider multimodal AI will probably be on the coronary heart of the subsequent wave of AI developments, which can supplant chatbots and LLMs.
In what methods does Encord Index improve the method of choosing the suitable information for AI fashions, and what influence does this have on mannequin efficiency?
Encord Index enhances information choice by automating the curation of enormous datasets, serving to groups determine and retain solely probably the most related information whereas eradicating uninformative or biased information. This course of not solely reduces the dimensions of datasets but in addition considerably improves the standard of the information used for coaching AI fashions. Our clients have seen as much as a 20% enchancment of their fashions whereas reaching a 35% discount in dataset dimension and saving tons of of hundreds of {dollars} in compute and human annotation prices.
With the speedy integration of cutting-edge applied sciences like Meta’s Section Something Mannequin, how does Encord keep forward within the fast-evolving AI panorama?
We deliberately constructed the platform to have the ability to adapt to new applied sciences shortly. We deal with offering a scalable, software-first method that simply incorporates developments like SAM, making certain that our customers are at all times geared up with the most recent instruments to remain aggressive.
We plan to remain forward by specializing in multimodal AI. The Encord platform can already handle complicated information varieties equivalent to photos, movies, and textual content, in order extra developments in multimodal AI come our approach, we’re prepared.
What are the most typical challenges corporations face when managing AI information, and the way does Encord assist deal with these?
There are 3 major challenges corporations face:
- Poor information group and controls: As enterprises put together to implement AI options, they’re usually met with the fact of siloed and unorganized information that’s not AI-ready. This information usually lacks sturdy governance round it, limiting a lot of it from being utilized in AI methods.
- Lack of human specialists: As AI fashions sort out more and more complicated issues, there’ll quickly be a scarcity of human area specialists to organize and validate information. As an organization’s AI calls for enhance, scaling that human workforce is difficult and dear.
- Unscalable tooling: Performant AI fashions are very data-hungry by way of information wanted for fine-tuning, validation, RAG, and different workflows. The earlier era of instruments will not be geared up to handle the quantity of knowledge and forms of information required for immediately’s production-grade fashions.
Encord fixes these issues by automating the method of curating information at scale, making it simple to determine impactful information from problematic information and making certain the creation of efficient coaching and validation datasets. It makes use of a software-first method that’s simple to scale up or down as information administration wants change. Our AI-assisted annotation instruments empower human-in-the-loop area specialists to maximise workflow effectivity. This course of is especially essential in industries equivalent to monetary companies and healthcare, the place AI trainers are expensive. We make it simple to handle and perceive all of a company’s unstructured information, lowering the necessity for guide labor.
How does Encord sort out the difficulty of knowledge bias and under-represented areas inside datasets to make sure truthful and balanced AI fashions?
Tackling information bias is a vital focus for us at Encord. Our platform mechanically identifies and surfaces areas the place information is perhaps biased, permitting AI groups to deal with these points earlier than they influence mannequin efficiency. We additionally make sure that under-represented areas inside datasets are correctly included, which helps in creating fairer and extra balanced AI fashions. Through the use of our curation instruments, groups may be assured that their fashions are educated on various and consultant information.
Encord not too long ago secured $30 million in Sequence B funding. How will this funding speed up your product roadmap and growth plans?
The $30 million in Sequence B funding will probably be used to drastically enhance the dimensions of our product, engineering, and AI analysis groups over the subsequent six months and speed up the event of Encord Index and different new options. We’re additionally increasing our presence in San Francisco with a brand new workplace, and this funding will assist us scale our operations to help our rising buyer base.
Because the youngest AI firm from Y Combinator to boost a Sequence B, what do you attribute to Encord’s speedy development and success?
One of many causes we’ve got been capable of develop shortly is that we’ve got adopted an especially customer-centric focus in all areas of the corporate. We’re always speaking with clients, listening intently to their issues, and “bear hugging” them to get to options. By hyper-focusing on buyer wants quite than hype, we’ve created a platform that resonates with high AI groups throughout numerous industries. Our clients have been instrumental in getting us to the place we’re immediately. Our means to scale shortly and successfully handle the complexity of AI information has made us a lovely resolution for enterprises.
We additionally owe a lot of our success to our teammates, companions, and traders, who’ve all labored tirelessly to champion Encord. Working with world-class product, engineering, and go-to-market groups has been enormously impactful in our development.
Given the growing significance of knowledge in AI, how do you see the position of AI information platforms like Encord evolving within the subsequent 5 years?
As AI purposes develop in complexity, the necessity for environment friendly and scalable information administration options will solely enhance. I consider that each enterprise will finally have an AI division, very similar to how IT departments exist immediately. Encord would be the solely platform they should handle the huge quantities of knowledge required for AI and get fashions to manufacturing shortly.
Thanks for the good interview, readers who want to study extra ought to go to Encord.
