Parashift is an Artificial Intelligence company. Our core business is Machine Learning.
What is Machine Learning?
To answer this big question, Mattia Rüfenacht, Parashift’s Head of Business Development, interviewed a specialist in this field: Andre Bieler. He is none other than our company’s Chief Research and Innovation Officer.
Before we start, let me explain in a few words the Parashift’ story; the AI company that is changing the game in Machine Learning…
A few years ago, while creating another startup, Alain Veuve (our CEO) needed a versatile API-based document data extraction solution. The problem? There were no solutions available on the market that could meet his requirements.
“Why not create our own solution?” He asked himself… This is how the fabulous story of Parashift started in 2018!
The teams then set to work for three years to build the best-performing model based on the basic principles and technology that Parashift operates on today.
Since 2021, the Parashift platform is available to companies that want an innovative game-changer tool!
Now that you know that, let’s get to the heart of the matter… I promise it’s understandable!
We have divided the exchange into four main parts. Feel free to click on each to get directly to the information you are interested in.
Part 1: Who is Andre Bieler
Find out who is the Chief Research and Innovation officer of Parashift, who pushes innovation to its peak!
Part 2: What is Machine Learning
Machine learning could be defined as “glorified functions approximation.” In other words, Machine Learning shows and demonstrates why and how a project can work by learning from data. This method is entirely scalable.
“You can have the same kind of model, but depending on what you are training, what the actual goal is, and how you formulate the personal goal, you can get completely different models.”
Andre Bieler
In this second part, Andre Bieler will simply explain what Machine Learning is and other related methods like “Convolution”.
Part 3: Machine Learning and Parashift
Now that we know what Machine Learning is, let’s understand how it works with Intelligent Document Processing (IDP).
Let’s take the example of receipt processing (this works with any document).
You take a photo of your receipt. The format is Jpeg. Thanks to Machine Learning, you can refine the document, cut it out, classify it, lay it out, extract data from it and structure it. And all this in an automated way!
Machine learning in IDP provides many application angles because of the different documents, formats, data, and qualities. This is what is exciting and challenging for Andre Bieler.
In this part, you will discover that Parashift’s Machine Learning, which delivers structured information from any document, has a unique feature that makes users’ work much more straightforward: the Swarm learning!
Part 4: The great future of Parashift
In terms of data extraction from documents, using an AI solution based on Machine Learning is almost indispensable nowadays, as Andre Bieler points out.
Indeed, for him, people overestimate humans in these repetitive tasks.
“We think a human will sit for hours extracting data from hundreds of documents without a single mistake. But this is far from reality! Machine Learning is now so powerful that if there is an error somewhere in the process, it is mostly the fault of the human.”
Andre Bieler
You, now, understand that it is necessary for a company to use an IDP solution such as Parashift, to extract data from documents.
Machine learning in the context of Intelligent Document Processing (IDP) is still a technological feat, as the types of documents from different industries, the types of writing, and the various data structures are thousands.
It is a real challenge that still leaves room for many improvements in the future!
To understand in more detail each theme discussed in this podcast, I invite you to watch the entire video (with the video is always better, but listening to a podcast while driving is not bad as well 😊)
Thanks to Andre Bieler and Mattia Rüfenacht for sharing their experiences and ideas on the subject.
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