ElectrifAi | FAQ
FAQ
What is Artificial Intelligence?

Artificial intelligence is the ability for computer systems to perform tasks that traditionally require human intelligence. Examples include visual perception, speech recognition, decision-making and language translation.

What is the difference between artificial intelligence and machine learning?
  • Machine learning (ML) is an application of artificial intelligence (Ai) that allows a computer to learn and improve based on its own experiences without being programmed to do so. ElectrifAi uses ML to ensure its model are always improving.
  • For example, our Revenue Cycle solution predicts missed charges in healthcare billing using Ai while simultaneously keeping track of its own successes and errors. Learning from these experiences ensures the next prediction is always more accurate than the last. This is Ai/ML in action.
What is an Intelligence Layer?

At ElectrifAi, we call ourselves the “Intelligence Layer” because we are the filter from which disparate, complex data becomes usable and trackable with data analysis tools. In practice, this Intelligence Layer works for our clients like a funnel: the input (our client’s old data system) is messy and chaotic while the output (what we provide our clients with) is the same data presented in a clean, practical and intelligent form.

What kind of data is the best fit for models?
  • As long as the data is representative of what the machine would see while at work, all kinds of data can be used to fit machine learning (ML) models. Including data in numeric, text, or image form of varying sizes (from hundreds to millions of records), all data needs to be transformed and standardized in specific forms before a model can start learning. The Intelligence Layer plays a critical part in connecting, cleaning, and transforming data as appropriate for models to learn from.
  • For example, our ContractAi solution extracts intelligence from contracts by first converting the text data into machine readable form, cleaning and standardizing the data to remove noise and errors introduced during the scanning process. Finally, the text is transformed into intelligence to capture various aspects, grammatical components and opinions from each sentence. This is then leveraged by the models to learn and identify key insights for human consumption.
What does it mean to cleanse data?

The data from different data systems can be full of noise, errors and inconsistencies introduced at various steps of data entry – either due to system designs or manually entered data or system errors, etc. Developing machine learning (ML) models to avoid “Garbage in, garbage out” requires the data to be cleaned prior to removing problems. Applying multiple techniques with varying levels of complexity achieves: removal of duplicates and irrelevant data, type conversion, outlier and missing value treatment, fixing spelling mistakes, language translation, etc.

Why does it take so long to cleanse data?

A significant portion of time developing machine learning algorithms is spent on cleansing data, requiring a detailed understanding of client data and domain knowledge. Once the data is cleaned, the real art of data science begins with feature engineering and selection of the best machine learning algorithm(s). Yes, multiple models can be fit to learn different aspects of the data and combined to form the smartest machines – these are called ensemble models.

How does a model learn?
  • Machine learning (ML) models learn by extracting repeating patterns in data and correlating them with outcomes.
  • For example, if a machine is learning to identify a cat and dog, pictures of cats and dogs are presented and told which one is which. Initially, the machine will make identification mistakes, similarly to how a baby would when first learning information. Re-enforcement of the knowledge helps improve accuracy.
  • The machine identifies different features of each picture (such as legs, face, ears, tail) and tries to correlate them with outcomes (cats and dogs), allowing it to make more accurate observations over time. Other types of models can also learn trends in patterns over time and systems of ranking.
How does ElectrifAi build Ai models?

ElectrifAi leverages its extensive domain expertise and skilled team of machine learning (ML) scientists to develop state-of-the-art artificial intelligence (Ai) models in the least amount of time. ElectrifAi’s analytics platform combines domain expertise, standardized data integration, and clean-up approaches to boost the speed of model development lifycycle. This allows our scientists to focus more on the art aspect of machine learning. Our approach focuses on alleviating the intelligence extracted from the data to drive Ai in our solutions through advanced ML models.

What technology/language/platform does ElectrifAi use?

Our technology is built on an open source Spark unified-computation engine. Large scale distributed data processing can be ingested, extracted, and transformed to apply machine learning with embedded zeppelin notebook experience. Our own data scientists and our customers can write code and access data in any programming language of their choice. Our core IP, built over a 16+ year timeline, is part of a microservices framework embedded within docker containers and Kubernetes to build and deploy enterprise class solutions at scale. Tangible and measurable business value is seen in weeks rather than months.

Where is ElectrifAi located?

We are headquartered right on the Hudson River in Jersey City, New Jersey. We also have U.S. offices in New Delhi and Shanghai!

Where can I learn more about ElectrifAi’s business solutions?

Read all about the different solutions ElectrifAi can offer on our products page.

How can my company contact ElectrifAi?

Visit our contact page or reach out to us now at info@electrifai.net

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