Eric Landau is the CEO & Co-Founding father of Encord, an lively studying platform for laptop imaginative and prescient. Eric was the lead quantitative researcher on a worldwide fairness delta-one desk, placing 1000’s 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 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 “data problem” in AI?
I first began excited about machine studying whereas working in particle physics and coping with very giant 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 lots to be desired when it comes to a nice person expertise. With simpler instruments, I might have been capable of run analyses a lot sooner.
Later, working in quantitative finance at DRW, I used to be chargeable for creating 1000’s 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 giant picture datasets for laptop imaginative and prescient.
Once 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 when it comes to 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 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 regulation of compounded computational progress 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 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 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 at present obtainable?
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 comparable to AWS, GCP, and Azure. In contrast to different instruments which can be restricted to a single cloud supplier or inner storage system, Index is agnostic to the place the info is situated. It allows you to handle information from many sources with acceptable governance and entry controls that enable them to develop safe and compliant AI functions.
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 at this time. Human cognition is multimodal, and we consider 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 fitting information for AI fashions, and what affect 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 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 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 a whole lot of 1000’s 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 shortly. We give attention to offering a scalable, software-first method that simply incorporates developments like SAM, making certain 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 complicated information sorts comparable to photos, 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 usually met with the fact of siloed and unorganized information that isn’t 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 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 will not be geared up to handle the quantity of knowledge and sorts 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 specialists to maximise workflow effectivity. This course of is especially essential in industries comparable to monetary providers and healthcare, the place AI trainers are pricey. We make it straightforward to handle and perceive all of a company’s unstructured information, decreasing the necessity for guide labor.
How does Encord sort out the problem of knowledge 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 routinely identifies and surfaces areas the place information could be biased, permitting AI groups to handle these points earlier than they affect 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 could be assured that their fashions are educated on various and consultant information.
Encord not too long ago 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 improve the dimensions 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 assist our rising buyer base.
Because the youngest AI firm from Y Combinator to boost a Collection B, what do you attribute to Encord’s fast progress and success?
One of many causes now we have been capable of develop shortly is that now we have 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 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 at this time. Our capability to scale shortly and successfully handle the complexity of AI information has made us a sexy answer 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 progress.
Given the rising 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 functions develop in complexity, the necessity for environment friendly and scalable information administration options will solely improve. I consider that each enterprise will ultimately 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 knowledge required for AI and get fashions to manufacturing shortly.
Thanks for the good interview, readers who want to be taught extra ought to go to Encord.
