Eric Landau is the CEO & Co-Founding father of Encord, an energetic studying platform for pc imaginative and prescient. Eric was the lead quantitative researcher on a world fairness delta-one desk, placing hundreds of fashions into manufacturing. Earlier than Encord, he spent practically 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 taking part in with ChatGPT and enormous 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 drawback” in AI?
I first began fascinated by machine studying whereas working in particle physics and coping with very massive datasets throughout my time on the Stanford Linear Accelerator Heart (SLAC). I used to be utilizing software program designed for physicists by physicists, which is to say there was loads to be desired when it comes to a nice person expertise. With simpler instruments, I’d have been capable of run analyses a lot quicker.
Later, working in quantitative finance at DRW, I used to be accountable for creating hundreds of fashions that had been deployed into manufacturing. Much like my expertise in physics, I discovered that high-quality information was important in making correct fashions and that managing complicated, large-scale information is troublesome. Ulrik had an analogous expertise visualizing massive picture datasets for pc imaginative and prescient.
Once I heard about his preliminary concept 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 perfect 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 when it comes to potential and challenges?
Encord’s imaginative and prescient is to be the foundational platform that enterprises depend on to rework their information into useful 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’ll turn into 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 quicker tempo. Within the phrases of NVIDIA’s Jensen Huang: “For the very first time, we’re seeing compounded exponentials…We’re compounding at 1,000,000 occasions each ten years. Not 100 occasions, not a thousand occasions, 1,000,000 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 paying homage 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 software for managing and curating AI information. How does it differentiate itself from different information administration platforms presently out there?
There are just a few ways in which Encord Index stands out:
Index is scalable: Permits customers to handle billions, not hundreds of thousands, of information factors. Different instruments face scalability points for unstructured information and are restricted in consolidating all related information in a corporation.
Index is versatile: Integrates straight with personal information storage and cloud storage suppliers comparable 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 helps you to handle information from many sources with acceptable governance and entry controls that permit them to develop safe and compliant AI purposes.
Index is multimodal: Helps multimodal AI, managing information within the type of photographs, movies, audio, textual content, paperwork and extra. Index isn’t restricted to a single type of information like many LLM instruments at this time. Human cognition is multimodal, and we consider multimodal AI might 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 proper information for AI fashions, and what influence does this have on mannequin efficiency?
Encord Index enhances information choice by automating the curation of huge datasets, serving to groups establish and retain solely essentially the most related information whereas eradicating uninformative or biased information. This course of not solely reduces the scale of datasets but additionally 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 lots of of hundreds of {dollars} in compute and human annotation prices.
With the fast 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 rapidly. We concentrate on offering a scalable, software-first method that simply incorporates developments like SAM, making certain that our customers are at all times outfitted 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 comparable to photographs, movies, and textual content, in order extra developments in multimodal AI come our method, 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 fundamental challenges corporations face:
- Poor information group and controls: As enterprises put together to implement AI options, they’re typically met with the fact of siloed and unorganized information that isn’t AI-ready. This information typically lacks robust governance round it, limiting a lot of it from being utilized in AI methods.
- Lack of human consultants: As AI fashions sort out more and more complicated issues, there’ll quickly be a scarcity of human area consultants to arrange and validate information. As an organization’s AI calls for improve, scaling that human workforce is difficult and expensive.
- Unscalable tooling: Performant AI fashions are very data-hungry when it comes to information wanted for fine-tuning, validation, RAG, and different workflows. The earlier era of instruments isn’t outfitted to handle the quantity of information and forms of information required for at this time’s production-grade fashions.
Encord fixes these issues by automating the method of curating information at scale, making it straightforward to establish 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 straightforward to scale up or down as information administration wants change. Our AI-assisted annotation instruments empower human-in-the-loop area consultants to maximise workflow effectivity. This course of is especially essential in industries comparable to monetary companies and healthcare, the place AI trainers are expensive. We make it straightforward to handle and perceive all of a corporation’s unstructured information, decreasing the necessity for handbook labor.
How does Encord sort out the difficulty of information bias and under-represented areas inside datasets to make sure truthful and balanced AI fashions?
Tackling information bias is a important focus for us at Encord. Our platform mechanically identifies and surfaces areas the place information is likely to be biased, permitting AI groups to handle 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 could be assured that their fashions are educated on numerous and consultant information.
Encord just lately secured $30 million in Sequence B funding. How will this funding speed up your product roadmap and enlargement plans?
The $30 million in Sequence B funding might be used to drastically improve the scale 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 lift a Sequence B, what do you attribute to Encord’s fast development and success?
One of many causes now we have been capable of develop rapidly is that now we have adopted an especially customer-centric focus in all areas of the corporate. We’re always speaking with clients, listening carefully to their issues, and “bear hugging” them to get to options. By hyper-focusing on buyer wants somewhat than hype, we’ve created a platform that resonates with prime AI groups throughout numerous industries. Our clients have been instrumental in getting us to the place we’re at this time. Our means to scale rapidly and successfully handle the complexity of AI information has made us a lovely answer for enterprises.
We additionally owe a lot of our success to our teammates, companions, and buyers, 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 information 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 improve. I consider that each enterprise will finally have an AI division, very similar to how IT departments exist at this time. Encord would be the solely platform they should handle the huge quantities of information required for AI and get fashions to manufacturing rapidly.
Thanks for the nice interview, readers who want to study extra ought to go to Encord.
