ElectrifAi | FAQ
FAQ
What is Artificial Intelligence?

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

What is the difference between Artificial Intelligence and Machine Learning?

Machine Learning is an application of Artificial Intelligence that allows a computer to learn and improve based on its own experiences without being programmed to do so. ElectrifAi uses Machine Learning to ensure that its models are always improving. For example, our Revenue Cycle solution predicts missed charges in health care bills using AI while simultaneously keeping track of its own successes and errors. Through learning from these experiences, it ensures that its next prediction is always more accurate than the last. This is AI/ML in action.

What is an Intelligence Layer?

We at ElectrifAi 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 clients’ old data systems) 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 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 ML models. This could include data in numeric, text, or image form that is of varying sizes (from hundreds to millions of records). However, all data needs to be transformed and standardized in specific forms before a model can start learning from it. This is where the Intelligence Layer is critical in connecting, cleaning and transforming data as appropriate for models to learn from. For example, our Contract Ai solution extracts intelligence from contracts by first converting the text data in to machine readable form, cleaning and standardizing the data to remove noise and errors introduced during the scanning process and finally transforming the text 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. An important step as pre-requisite to developing effective Machine Learning models (and avoid “Garbage in, garbage out”) is to clean the data to remove such issues. This is achieved by applying multiple techniques with varying levels of complexity: removal of duplicates, type conversion, removal of irrelevant data, outlier and missing value treatment, fixing spelling mistakes, language translation, etc.

Why does it take so long?

A significant portion of time in developing machine learning algorithms is spent on cleansing data, which requires 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). And 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?

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 a dog, it will be presented with pictures of cats and dogs while being told which one is which. Initially, the machine will make mistakes in identifying them correctly, similarly to how a baby would when first learning this information, but re-enforcement of the “knowledge” will help improve accuracy. The machine identifies different features of each picture (like 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 its AI Models?

ElectrifAi leverages its extensive domain expertise and skilled team of machine learning scientists to develop state-of-the-art AI models in the least amount of time. The combination of domain expertise and standardized data integration and clean-up approaches in ElectrifAi’s analytics platform boosts the speed of model development lifecycle and 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 machine learning models.

What technology/language/platforms does ElectrifAi use?

Our technology is built on an open source Spark unified-computation engine that allows large scale distributed data processing to ingest, extract, transform and apply advanced machine learning with embedded zeppelin notebook experience. This allows our own data scientists and our customers to write code and access data in any programming language of their choice. We enable our core IP built over 15+ year timelines as part of micro services framework; embedded within docker containers and Kubernetes to build and deployment of enterprise class solutions at scale, seeing tangible and measurable business value in weeks rather than months.

Why haven’t I heard of ElectrifAi before?

Our company has undergone a top-to-bottom transformation from Opera Solutions to ElectrifAi in 2019. The identity shift came with a change in management. Rebranding as ElectrifAi signals a shift in our company’s culture, but the technology experts and industry-leading thinkers that grew Opera Solutions since 2004 are the same ones leading ElectrifAi to new heights.

Our new management team has transformed the way we sell our products. What had been a company reliant on existing relationships and word-of-mouth referrals became one with revamped energy to engage in the marketplace. ElectrifAi has recently hired over a dozen sales, business development, and marketing experts who are dedicated to building our pipeline, researching our targeted industries and appearing at conferences and events across the country to get the word out on our products.

Where is ElectrifAi Located?

We are headquartered right on the Hudson River in Jersey City, New Jersey. We also have U.S. offices in Boston and San Diego, and international 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

If you have data, we have a solution.

We have an array of ways we implement AI and ML to benefit your business. Oh and really smart folks to assist you along the way. Let us know what your interest is and we can talk.