Startup Ensemble gets $3.3M in funding to repair data quality issues with Dark Matter

Ensemble AI Inc. is trying to tackle headaches around data quality and help firms construct more powerful artificial intelligence models after closing on a $3.3 million seed funding round.

Today’s round was led by Salesforce Ventures, with Amplo, M13 and Motivate also participating. They’re backing Ensemble since the startup has created a pioneering approach to data representation with a view to enhance the performance of AI models, without pumping them with vast amounts of additional data or creating more complicated model architectures.

What the startup is doing is using machine learning techniques to boost AI models, by helping them uncover hidden relationships between their datasets. The corporate explains that if AI goes to find a way to unravel real-world problems, it needs access to more and better-quality data. Many firms struggle with limited and sparse or one-dimensional datasets, and that stops their AI models from generating meaningful or useful results.

Data scientists spend hours attempting to fix their data to beat this, and a few progress has been made with more sophisticated AI model architectures, but such endeavors require vast resources and technical expertise that not every company has.

To resolve these issues, Ensemble has created a novel embedding model it calls Dark Matter, which uses an “objective function” to create richer representations of knowledge for predictive tasks. Dark Matter, the corporate says, can understand the complex, nonlinear relationships inside datasets through a light-weight data transformation. It distills the complexity of those relationships into an easy “data representation,” so engineers can construct higher quality AI models that may tackle much harder problems.

Ensemble co-founder and Chief Executive Alex Reneau explained that Dark Matter slots in between the feature engineering and model training and inference processes inside data pipelines.

“We’re in a position to enable customers to maximise their very own data that they’re working with, even when it’s limited, sparse or highly complex, allowing them to coach effective models with less comprehensive information,” he said. “This foundational technology frees up data scientists to deal with experimentation and likewise makes ML viable for problems previously unable to be modeled, unlocking latest capabilities for our customers.”

The startup believes Dark Matter is a superior solution to synthetic data, which is commonly utilized by AI developers to compensate for low-quality or sparse datasets. It explains that though Dark Matter does create latest variables, the mechanics are fundamentally different.

Because synthetic data recreates existing distributions from Gaussian noise, it signifies that no latest information is definitely created. The synthetic data merely mirrors the statistical properties of the prevailing data, so there’s no meaningful impact on predictive accuracy, the corporate explained.

However, Dark Matter learns how you can create latest embeddings with fundamentally different statistical properties and distributions that end in measurable improved predictive accuracy.

Salesforce Ventures’ Caroline Fiegel told VentureBeat that Ensemble offers a promising solution that may potentially speed up the adoption of AI. She explained that many organizations are struggling to deploy AI models in production given issues with poor data quality and the potential use of personally identifiable information.

“If you peel that back and really start to know why, it’s because the info is disparate. It’s form of low-quality,” she said. “It’s riddled with PII.”

Ensemble says Dark Matter has already been put to make use of by numerous early adopters in areas similar to biotechnology, healthcare, personalization and promoting technology, with promising results. As an illustration, one biotech customer has used its tech to create a model that’s higher in a position to predict virus-host interactions throughout the gut microbiome, it said.

Looking forward, Ensemble said it should use the funds from today’s round to expand its team and speed up its product development and go-to-market plan.

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