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 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 enjoying 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 “data problem” in AI?
I first began occupied with 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 quite a bit to be desired by way of a pleasing consumer 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 have been deployed into manufacturing. Just like my expertise in physics, I discovered that high-quality knowledge was vital in making correct fashions and that managing advanced, large-scale knowledge is tough. Ulrik had the same 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 knowledge growth course of, making it simpler for groups to get the most effective knowledge into fashions and construct reliable AI programs.
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 rework their knowledge into useful AI fashions. We’re the layer between an organization’s knowledge and their AI.
In some ways, AI mirrors earlier paradigm shifts like private computing and the Web in that it’ll 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 regulation of compounded computational development of 30x each 10 years, AI growth 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 are seeing compounded exponentials…We are compounding at a million times every ten years. Not a hundred times, not a thousand times, a million times.” With out hyperbole, we’re witnessing the fastest-moving expertise in human historical past.
The potential right here is huge: by automating and scaling the administration of high-quality knowledge 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 knowledge. How does it differentiate itself from different knowledge administration platforms at the moment obtainable?
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 knowledge and are restricted in consolidating all related knowledge in a corporation.
Index is versatile: Integrates straight with personal knowledge storage and cloud storage suppliers corresponding to AWS, GCP, and Azure. In contrast to different instruments which are restricted to a single cloud supplier or inner storage system, Index is agnostic to the place the info is situated. It enables you to handle knowledge from many sources with applicable governance and entry controls that permit them to develop safe and compliant AI functions.
Index is multimodal: Helps multimodal AI, managing knowledge within the type of pictures, movies, audio, textual content, paperwork and extra. Index shouldn’t be restricted to a single type of knowledge like many LLM instruments right this moment. Human cognition is multimodal, and we imagine multimodal AI can be on the coronary heart of the following wave of AI developments, which is able to supplant chatbots and LLMs.
In what methods does Encord Index improve the method of choosing the suitable knowledge for AI fashions, and what influence does this have on mannequin efficiency?
Encord Index enhances knowledge choice by automating the curation of enormous datasets, serving to groups determine and retain solely probably the most related knowledge whereas eradicating uninformative or biased knowledge. This course of not solely reduces the scale of datasets but additionally considerably improves the standard of the info 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 Phase 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 deal with offering a scalable, software-first method that simply incorporates developments like SAM, guaranteeing that our customers are all the time 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 advanced knowledge sorts corresponding to pictures, movies, and textual content, in order extra developments in multimodal AI come our approach, we’re prepared.
What are the commonest challenges corporations face when managing AI knowledge, and the way does Encord assist tackle these?
There are 3 fundamental challenges corporations face:
- Poor knowledge group and controls: As enterprises put together to implement AI options, they’re usually met with the fact of siloed and unorganized knowledge that isn’t AI-ready. This knowledge usually lacks robust governance round it, limiting a lot of it from being utilized in AI programs.
- Lack of human consultants: As AI fashions deal with more and more advanced issues, there’ll quickly be a scarcity of human area consultants to arrange and validate knowledge. 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 knowledge wanted for fine-tuning, validation, RAG, and different workflows. The earlier era of instruments shouldn’t be geared up to handle the quantity of information and sorts of knowledge required for right this moment’s production-grade fashions.
Encord fixes these issues by automating the method of curating knowledge at scale, making it straightforward to determine impactful knowledge from problematic knowledge and guaranteeing 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 knowledge 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 corresponding 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 knowledge, decreasing the necessity for handbook labor.
How does Encord deal with the difficulty of information bias and under-represented areas inside datasets to make sure honest and balanced AI fashions?
Tackling knowledge bias is a vital focus for us at Encord. Our platform robotically identifies and surfaces areas the place knowledge is perhaps biased, permitting AI groups to handle these points earlier than they influence mannequin efficiency. We additionally be certain that under-represented areas inside datasets are correctly included, which helps in growing fairer and extra balanced AI fashions. Through the use of our curation instruments, groups might be assured that their fashions are educated on various and consultant knowledge.
Encord just lately secured $30 million in Collection B funding. How will this funding speed up your product roadmap and growth plans?
The $30 million in Collection B funding can be used to drastically enhance the scale of our product, engineering, and AI analysis groups over the following 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 Collection B, what do you attribute to Encord’s speedy development and success?
One of many causes we now have been capable of develop rapidly is that we now 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 reasonably than hype, we’ve created a platform that resonates with high AI groups throughout varied industries. Our clients have been instrumental in getting us to the place we’re right this moment. Our means to scale rapidly and successfully handle the complexity of AI knowledge has made us a beautiful 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 knowledge platforms like Encord evolving within the subsequent 5 years?
As AI functions develop in complexity, the necessity for environment friendly and scalable knowledge administration options will solely enhance. I imagine that each enterprise will ultimately have an AI division, very similar to how IT departments exist right this moment. 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.
