A Deep Guide to Text-Guided Open-Vocabulary Segmentation
Discover the power of text-guided open-vocabulary segmentation using large language models like GPT-4 & ChatGPT for automating image and video processing tasks.
With the world becoming more digital, companies are looking for ways to cut down on bottlenecks and speed up some of their slowest processes. For banks and commercial loan lawyers one of the slowest processes is the extraction of key information and information exchange during the loan process. Any loan reviewer or processor will tell you it takes a ton of time and communication. Different things like running credit checks or signing the paperwork can cause a simple loan to take forever, and understanding key information is required at all stages of the process. For example, ICE Mortgage Technology (a leading cloud-based provider for lenders) reported that, on average, it took about 50 days to close a loan. (Source).
As automation continues to grow in industries requiring large amounts of documents and document review the timeline from start to finish is getting shorter and shorter. Loan review and processing automation has evolved tremendously in the past 2 years and is at the forefront of the ROI document processing automation.
Automated loan processing is a streamlining technique to decrease the amount of paperwork, communication, time, and data re-entry during the loan process by utilizing software, machine learning (ML), and other effective cloud-based solutions. Combining all the different steps of the loan process into a custom processing pipeline allows for a more systematic approach, leading to faster decisions for clients looking to secure a loan.
One of the biggest bottlenecks during the loan process is information extraction. This bottleneck has two sides to it, the first being communication. We are all humans and do what we can to serve our clients and pass information effectively. But sometimes it can be a huge time commitment that requires rereviewing documents or moving through notes. Utilizing cloud-based software that is always available, information can be extracted without clients and lenders having to get on the phone. Not only does this save the lenders time and energy, but it also ensures that the information gathered is accurate and well documented.
The second side is clearing up miscommunications. Since both parties constantly exchange calls and emails, data is easily lost or overwritten with new and possibly inaccurate data. Entirely relying on an automated loan process will cut down on the human erect, the time spent re-entering data, and data disputes when things are inaccurate. While an automated loan process is something all lenders are heading towards, pulling it off is still very difficult.
The loan process can be incredibly long and requires a lot of documents to be filled in or reviewed by the parties involved. The loan process can be broken down into three key parts, application, underwriting, and loan closing.
During the first phase, you’ll work closely with a loan officer to gather the information needed to prequalify your loan.
There will be small talk, predominantly questions about getting to know each other, but most of the time will be spent answering questions like the following.
Once all of these questions are answered, and your documents are finally turned in, the bank will review and send you a proposal. After reviewing this proposal, you can move on to the underwriting process.
Next, in the underwriting phase, you will finally start working on your assigned loan. Realize up until this point, only documentation and information have been shared. This documentation still has not been verified till this step, when the underwriter will do a deep dive into the different financials of your company.
Let’s hope that in this long and extensive manual process.
Once everything is verified, the underwriter will work up a credit memo. This credit memo will be presented to the credit officer of the financial institution, who will either approve or reject the loan.
Finally, the credit officer has accepted your loan, and the initial closing has been kicked off. You’ll be signing the final paperwork and reviewing the last documents during this final stage. These documents include security agreements, Deeds of Trust, and the Note. Again, mountains of data are parsed through, checked, and verified for correctness.
Through modern ai based models we can parse the essential information out of documents to allow both parties a more effortless flow of data and information. While we know that all documents must be submitted, what if there was a way to find and extract the key information and parse it into a format that makes it easier to pass or reference in the different loan application steps?
For example, let’s say we’re reviewing a loan applicant's balance sheet. If we saw a line that the business currently has an outstanding loan that is overdue, we would probably instantly decline this candidate. However what if this was one of the last documents we reviewed for this applicant at the bank. The financial instituions would have invested hours into this application, while this candidate never had any chance of receiving a loan (once the line item on the balance sheet was found).
Now, many banks deal with these wasted hours thousands of times per year.
With recent advances in machine learning and artificial intelligence, we can leverage intelligent systems that can take in all of this information at once, and leverage it to make quick, impactful decisions that can speed up the loan process for your customers.
How does it work?
Natural Language processing is one of the fastest-growing subsets of machine learning. With the release of models like GPT-3 in June 2020 and BERT in November 2018, we’ve entered a timeline where models can understand text semantics. This earlier work wasn’t always easy to use or quick to deploy.
In 2013, a natural language processing technique was made popular called Word2Vec. The high level idea is that given some text, we can map each word to a vector. This may not seem novel, but with a word represented as a vector you can now bring in other techniques from mathematics. Some of these techniques include distance and direction that allow us to better understand word meaning and relationships. When we can represent words as vectors, figuring out the semantic meaning of our text becomes a reality when we can define words as vectors. When we take these vectors and map them into some n-dimensional space, things like euclidean distance and cosine similarity give us a decent idea of how closely related these vectors (words) are.
While (at the time) borderline revolutionary, there are pitfalls in the Word2Vec process that correlate to the strengths of NLP used in modern document processing.
Word2Vec will make one vector representation for each word, and while there may be many different usages, these will all be classified into one vector. To understand this a little more deeply, read the excerpt below.
“We went to the fair to play some games, and Billy didn’t play fair.”
In our example, we’d get a weak representative vector for the word fair as it would be the blend of the location and the way to play games.
When trained Word2Vec will create word embeddings for you to utilize on your text. However Word2Vec (like the name suggests) does not utilize the complete sentence only the exact word to get the embedding.
While this may not seem like a problem, if we trained our model on a dataset that was using the word “fair” to represent the location, and while testing our dataset, the word fair came up about cheating - our model would struggle to understand the semantic meaning of our words.
The out-of-vocabulary problem is the last and probably the worst pitfall of Word2Vec.
When a model is trained, our model will only know those words that existed in the training set. If while trying to find the semantic meaning of some text we run into a word that didn’t exist in our training - we will not have a vector for it. When this vector is missing, model accuracy falls off a cliff. This is pretty easy to understand; think of a sentence and remove one of the words from it. Whatever that word could change the whole meaning of the sentence.
In 2018, a different way of looking at text became popular with a new architecture and framework for thinking. Instead of focusing on the words what if we focused on the sentence? BERT (Bidirectional Encoder Representations from Transformers) is a state-of-the-art natural language processing algorithm. The general idea of BERT leverages many of the same ideas of Word2Vec, but instead of focusing only on that specific word, what if we focus on that word and the words around it?
BERT also allows for text to be read in any direction. This is a huge plus as before sequential reading was only allowed. This may not seem a big deal but the example below explains why it’s so impactful. During training, your model sees the sentence.
“It was nice for Billy.”
We know from Word2Vec that our model will create a word embedding from all the sequential occurrences of the word Billy up to this point. Since this is the first time seeing this word, Word2Vec figures out that Bily is probably a noun and creates the embedding. However, since BERT can read the whole sentence.
“It was nice for Billy to retrieve the stick.”
BERT can deduce that not only is Billy a noun, but it’s also actually an animal.
And if there was more to the story.
“It was nice for Billy to retrieve the stick; he’s such an awesome dog.”
BERT would be able to deduce that Billy is a male dog. While seeming simple now it’s easy to see how impactful algorithms like BERT allow us to derive deep meaning from the text we are provided.
These advances in natural language processing have opened the door for huge movements in automated document processing and information extraction. We’ve leveraged these learned NLP relationships with text extraction computer vision models to build state of the art loan automation pipelines.
With recent advances from our machine learning engineers here at Width.ai, we’ve developed a complete system that leverages state-of-the-art information extraction knowledge and text understanding models to automate various parts of the loan process.
Realize that every single document will be pushed through this state-of-the-art machine learning system, creating accurate and repeatable results for you and your clients.
Loan documents such as tax returns, bank statements, W2s, or AR & AP can be uploaded in formats such as PDF or images. Our document processing pipelines are designed to support a wide range of variance in document formats or background noise for a given document type. This means you don’t have to use specific document templates or write specific rules to support variations in client documents.
We’ve been able to use this same document processing pipeline for over 50 formats of legal document cover sheets (read)
We’ve built a custom OCR model that extracts machine & handwritten text from these specific loan documents with state of the art accuracy and the ability to understand variance in text parameters. On top of the raw text extraction is a module used to clean up and refine the text for our domain specific use case. This helps to greatly reduce errors in the NLP models downstream in common loan documents.
A key part of information extraction being able to reduce manual effort is being able to correlate extracted information with fields you need. If we’re going to extract dollar amounts, names, and other entities we want to know what they correlate to in the loan process.
We’ve built a domain specific entity recognition and matching model with the ability to not only extract key text from the documents but create correlations such as:
1. Correlate text to other text in the documents. Correlate dollar amounts to people or actions, lawyers to people, or othe information to loan seekers.
2. Correlate text from the document to form fields. Take your key information from the documents and autofill forms with the correct information. This allows you to truly automate the process of going through the loan application process.
3. Correlate text from documents to database fields. Automatically structure extracted text from the document into your database for other processes.
The model uses a mix of fine-tuned models that are domain specific to loan automation with our baseline document processing architecture underneath. We use a mix of state of the art natural language processing models to achieve the process and combination of text extraction and language understanding and fine-tune these ideas with loan documents.
Our loan automation pipelines can be deployed as a raw rest API or with a UI attached. The pathway we take mostly depends on how you plan on using the pipeline and how you plan on passing data.
When using the loan automation pipeline as a piece of an larger automation pipeline it’s most common to deploy as a rest API and add a connection. We help you define a JSON input and structure an output that fits with the rest of your pipeline. We’ve also integrated our pipelines with tools such as ZenDesk and PagerDuty that allows you to automatically send notifications to humans.
If you’re looking to have a human pass documents into the pipeline or don’t have upstream automation than a UI to upload documents works well. A simple one touch upload and run allows you to kick off the pipeline when you want to.
Our baseline document processing pipeline has reached 94% accuracy on a standard document processing and information extraction dataset with over 11,000 examples. The dataset has over 20 classes labels and is a standard in the deep learning world. Not only does this dataset evaluate accuracy for text extraction tasks but post-OCR tasks such as named entity recognition.
Fine-tuning our loan automation pipeline on your specific use case boosts the accuracy even further. Fine-tuning allows our models to understand your specific task and language cues that affect the target variables. Once the models have seen real examples of the loan documents you want to process and relationships between key fields and text that relationship is learned.
By utilizing loan automation pipelines you will spend less time reviewing loan documents, finding key information, and completing the loan process.
The repeatable nature of automation software allows you to standardize steps in the loan process. The fields you receive when processing a document are the same every time and don’t change unless you want them to.
The benefits of loan automated loan processing are seen quickly when considering the costs of manual loan processing. Cutting down the time required for manual resources in the legal industry is always a great way to lower costs. With the current hourly rate in this industry the ROI you can get by automating any amount of manual processing is going to be large.
On top of the reduction of required manual labor by expensive professionals software also allows you to reduce the time required. Many document processing tasks take as little as 2.7 seconds to complete and allow you to move through loan review processes quicker.
Want to learn more about how we can deploy a custom loan automation pipeline with over 94% accuracy for your business? Let’s talk about how we can automate your processes -> Width.ai
Discover the power of text-guided open-vocabulary segmentation using large language models like GPT-4 & ChatGPT for automating image and video processing tasks.
Learn how CLIPSeg segmentation, in combination with GPT-4 and ChatGPT, can enable diverse applications from medical image diagnosis to remote sensing.
Can GPT-4 make your life as a finance or banking employee easier? Learn how GPT-4 and NLP can be used in finance to increase revenues and streamline workflows.
A deep dive into how we reached SOTA accuracy in product similarity matching through a custom fine-tuning pipeline that refines the CLIP model for image similarity.
Boost your conversions and sales numbers with NLP in sales using OpenAI's GPT-3 and GPT-4. You can use chatbots to improve customer experience and loyalty.
Explore the use of GPT for opinion summarization through innovative pipeline methods, evaluation metrics like ROUGE and BERTScore, and human evaluation insights. Dive into novel entailment-based evaluation tools for a comprehensive understanding of model performance in capturing diverse user opinions.
Come aboard the large language model revolution with our deep dive on AI21 vs. GPT-3 for business use cases like ad copy generation and math proof generation.
A technical guide to using BERT for extractive summarization on lectures that outperforms other NLP models
Discover how prompt based LLMs like GPT-3 & GPT-4 are transforming news summarization with its zero-shot capabilities and adaptability to specialized tasks like keyword-based summarization. Learn about the limitations of current evaluation metrics and the potential future directions in text summarization research.
Discover the PEZ method for learning hard prompts through optimization, a powerful technique that enhances generative models for image generation and language tasks, improves transferability, and enables few-shot learning
Take a look at how Width.ai built 17 generative ai pipelines for use in the Keap.com marketing copy generation product
A deep look at how recurrent feature reasoning outperforms other image inpainting methods for difficult use cases and popular datasets.
See a comparison of GPT-3 vs. GPT-J, a self-hosted, customizable, open-source transformer-based large language model you can use for your business workflows.
Discover how transformer networks are revolutionizing image and video segmentation, and get insights on modern semantic segmentation vs. instance segmentation.
Discover how the state-of-the-art mask-aware transformer produces visually stunning and semantically meaningful images and how it stacks up against Stable Diffusion & DALL-E for large-hole inpainting
Unlock the full potential of spaCy with this guide to building production-grade text classification pipelines for business data.
We compare 12 AI text summarization models through a series of tests to see how BART text summarization holds up against GPT-3, PEGASUS, and more.
Let’s take a look at what intent classification is in conversational ai and how you can build a GPT-3 intent classification model for conversational ai and chatbot pipelines.
Discover the capabilities of zero-shot object detection, which enables anyone to use a model out-of-the-box without any training and generate production-grade results.
What is facial expression recognition and what SOTA models are being used today in production
Get a simple TensorFlow facial recognition model up & running quickly with this tutorial aimed at using it in your personal spaces on smartphones & IoT devices.
Explore accurate classification algorithms using the latest innovations in deep learning, computer vision, and natural language processing.
Learn what human activity recognition means, how it works, and how it’s implemented in various industries using the latest advances in artificial intelligence.
What is the the SetFit architecture and how does it outperform GPT-3 and other few shot large language models
What is image classification and how we build production level TensorFlow image classification systems for recognizing various products on a retail shelf.
Explore the application of intelligent document processing (IDP) in different industries and dive in-depth on intelligent document pipelines.
How to build an image classification model in PyTorch with a real world use case. How you can perform product recognition with image classification
Let's build a custom CTA generator that you'll actually want to use for your website copy
We’re going to look at how we built a state of the art NLP pipeline for blended summarization and NER to process master service agreements (MDAs) that vary the outputs based on the input document and what is deemed important information.
Get a comprehensive overview of a purchase order vs. invoice, including when businesses use each, what information goes in them, and more.
Learn what Google Shopping categories are used for and how you can automate fitting products to this taxonomy using ai.
Automatically categorize your Shopify store products to the Shopify Product Taxonomy instantly with ai based PIM software
Dive deep into 3-way invoice matching, including how it works, eight benefits for your business, and the problems with doing it manually.
Smart farming using computer vision and deep learning provides the most promising path forward in the slow-moving industry of agriculture.
How we leveraged large language models to build a legal clause rewriting pipeline that generates stronger language and more clarity in legal clauses
Apply AI to your favorite sport with this guide. Learn how automated ball tracking can change the game for coaches and players.
Categorize your ecommerce products to the 2021 google product taxonomy tree instantly with our Ai software
Surveying the current landscape of ecommerce automation and how you can use ai to automate huge chunks of your product management.
Classify your product data against an existing product category database or generate categories and tags in seconds using artificial intelligence
Warehouse automation plays a crucial role across your supply chain. Learn about how machine learning and ai software can be integrated into your warehouse automation stack.
4 different NLP methods of summarizing longer input text into different methods such as extractive, abstractive, and blended summarization
iscover an invoice OCR tool that will revolutionize the way you handle invoices. There’s no human intervention needed & a dramatically lower per-invoice cost.
Instead of invoice matching taking upwards of a week, it could take mere seconds with the proper automation solution. Learn more here.
Manual and template-based invoicing are riddled with low accuracy and required human intervention. Learn how to systematically eliminate these issues with the right invoice data capture software.
A complete walkthrough guide on how to use visual search in ecommerce stores to create more sales and real examples of companies already using it.
Automating the extraction of data from invoices can reduce the stress of your accountants by finding inaccuracies, digitizing paper invoices, and more.
How you can optimize email marketing campaigns with machine learning based models that improve conversion & click-through rates.
How you can use machine learning based data matching to compare data features in a scalable architecture for deduping, record merging, and operational efficiency
Learn how lifetime value or LTV prediction can improve your marketing strategies. Then, discover the best statistical & machine learning models for your predictions.
A deep understanding of how we use gpt-3 and other NLP processes to build flexible chatbot architectures that can handle negotiation, multiple conversation turns, and multiple sales tactics to increase conversions.
The popular HR company O.C. Tanner, which has been in business since 1927 and has over 1500 employees, was looking to research and design two GPT-3 software products to be used as internal tools with their clients. GPT-3 based products can be difficult to outline and design given the sheer lack of publicly available information around optimizing and improving these systems to a production level.
We’ll compare Tableau vs QlikView in terms of popularity, integrations, ease of use, performance, security, customization, and more.
With a context-aware recommender system, you can plan ways to recreate some of the contextual conditions that persuade them to buy more from you.
We’re going to walk through building a production level twitter sentiment analysis classifier using GPT-3 with the popular tweet dataset Sentiment140.
Find out how machine learning in medical imaging is transforming the healthcare world and making it more efficient with three use cases.
Discover ways that machine learning in health care informatics has become indispensable. Review the results of two case studies and consider two key challenges.
Accelerate your growth by pivoting key areas of your business to AI. Your business outcomes will be achieved quicker & you’ll see benefits you didn’t plan for.
We built a GPT-3 based software solution to automate raw data processing and data classification. Our model handles keyword extraction, named entity recognition, text classification | Case Study
We built a custom GPT-3 pipeline for key topic extraction for an asset management company that can be used across the financial domain | Case Study
How you can use GPT-3 to create higher order product categorization and product tagging from your ecommerce listings, and how you can create a powerful product taxonomy system with ai.
5 ways you can use product matching software in ecommerce to create real value that raises your sales metrics and improves your workflow operations.
Data mining and machine learning in cybersecurity enable businesses to ensure an acceptable level of data security 24/7 in highly dynamic IT environments. Learn how data security is getting increasingly automated.
Product recognition software has tremendous potential to improve your profits and slash your costs in your retail business. Find out just how useful it is.
Big data has evolved from hype to a crucial part of scaling your organization in every modern industry. Learn more about how big data is transforming organizations and providing business impacts.
Learn how natural language processing can benefit everybody involved in education from individual students and teachers to entire universities and mass testing agencies.
Here’s how automated data capture systems can benefit your business in some key ways and some real-life examples of what it looks like in practice.
Use these power ai and machine learning tools to create business intelligence in your marketing that pushes your business understanding and analytics past your competition.
We built a custom ML pipeline to automate information extraction and fine tuned it for the legal document domain.
In this practical guide, you'll get to know the principles, architectures, and technologies used for building a data lake implementation.
Find out how machine learning in biology is accelerating research and innovation in the areas of cancer treatment, medical devices, and more.
An enterprise data warehouse (EDW) is a repository of big data for an enterprise. It’s almost exclusive to business and houses a very specific type of data.
Dlib is a versatile and well-diffused facial recognition library, with perhaps an ideal balance of resource usage, accuracy and latency, suited for real-time face recognition in mobile app development. It's becoming a common and possibly even essential library in the facial recognition landscape, and, even in the face of more recent contenders, is a strong candidate for your computer vision and facial recognition or detection framework.
Learn how to utilize machine learning to get a higher customer retention rate with this step-by-step guide to a churn prediction model.
Machine learning algorithms are helping the oil and gas industry cut costs and improve efficiency. We'll show you how.
We’ll show you the difference between machine learning vs. data mining so you know how to implement them in your organization.
Here’s why you should use deep learning algorithms in your business, along with some real-world examples to help you see the potential.
Beam search is an algorithm used in many NLP and speech recognition models as a final decision making layer to choose the best output given target variables like maximum probability or next output character.
Best Place For was looking for an image recognition based software solution that could be used to detect and identify different food dishes, drinks, and menu items in images sourced from blogs and Instagram. The images would be pulled from restaurant locations on Instagram and different menu items would be identified in the images. This software solution has to be able to handle high and low quality images and still perform at the highest production level, while accounting for runtime as well as accuracy.
Deep learning recommendation system architectures make use of multiple simpler approaches in order to remediate the shortcomings of any single approach to extracting, transforming and vectorizing a large corpus of data into a useful recommendation for an end user.
Let's take a look at the architecture used to build neural collaborative filtering algorithms for recommendation systems
GPT-3 is one of the most versatile and transformative components that you can include in your framework, application or service. However, sensational headlines have obscured its wide range of capabilities since its launch. Let’s take a look at the ways that companies and researchers are achieving real-world results with GPT-3, and examine the untapped potential of this 'celebrity AI'.
How to get started with machine learning based dynamic pricing algorithms for price optimization and revenue management
Let's take a look at how you can use spaCy, a state of the art natural language processing tool, to build custom software tools for your business that increase ROI and give you data insights your competitors wish they had.
The landscape for AI in ecommerce has changed a lot recently. Some of the most popular products and approaches have been compromised or undermined in a very short time by a new global impetus for privacy reform, and by the way that the COVID-19 pandemic has transformed the nature of retail.
Extremely High ROI Computer Vision Applications Examples Across Different Industries
Building Data Capture Services To Collect High ROI Business Data With Machine Learning and AI
Software packages and Inventory Data tools that you definitely need for all automated warehouse solutions
Inventory automation with computer vision - how to use computer vision in online retail to automate backend inventory processes