Solving Modern Document Classification Challenges With Deep Learning | SOTA Document Classification Use Cases
Explore accurate classification algorithms using the latest innovations in deep learning, computer vision, and natural language processing.
The landscape for using AI in ecommerce and the retail industry 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.
These events have thrown a huge spoke in the long-term plans of both the AI in ecommerce industry and the forecasting sector alike. The previous surge of interest in facial recognition systems for retail has given way to a focus on improving the online shopping experience, leveraging AI-driven predictive analytics, and improving the management and configuration of bricks-and-mortar stores.
In the meantime, the continuity of market forecasting models has been catastrophically disrupted by a transformed business environment. With a massive increase in ecommerce sales under COVID-19, there's no doubt that huge growth in the use of artificial intelligence for ecommerce companies and online retailers is coming – but it may not be in the sectors that were anticipated just 12 months ago.
So what can we depend upon, going forward? Here are four baseline approaches to AI in ecommerce that are likely to prove resistant to the current volatile market.
A better understanding of product relationships and customer intent can transform turnover and conversion, and a really effective AI-driven inventory search system will stand out in a sector that's developed a bad reputation in recent years.
For instance, it's a common complaint that if you buy — for instance — a TV, the site you bought it from might follow up with product recommendations for non-sequitur purchases such as:
In an ideal world, you're going to need better search logic (and possibly better data) than that.
As the above examples demonstrate, the key to a successful recommender system lies not in merely finding relationships between data points and events, but in identifying associations, hidden in different strands of the data, that are both meaningful and incentivizing for the customer.
The process of developing an effective recommender system with ai technology requires a lot of initial curation, an open mind, and a willingness to experiment. A/B testing can help establish recommended weights, while offline A/B testing is another useful tool for developing optimal parameters and settings for an ecommerce product recommender algorithm.
If a single customer buys a TV and a box of chocolates in the same purchase, most supervised learning approaches will dismiss the event as an 'outlier' anomaly. Perhaps the customer was, by coincidence, buying a gift at the same time as a typical domestic fixture. Perhaps they were feeling a bit down. Who knows?
If two or more apparently unrelated customers were to place the same two items — or item types — in an order, a typical artificial intelligence framework might identify an implicit association between TV/Box Of Chocolates, and later present either as an after-market recommendation to customers who purchased the 'correlated' item.
But the system might fail to notice that the original orders were placed in the second week of February — the second-highest seasonal driver for chocolate sales.
Will the algorithm continue to associate the items throughout the summer and fall? Or will it recognize that the association is specious outside of the Easter and Valentine's Day surges, and that there is no intrinsic 'relationship' between the two items at all?
As we can see, some of the relevant data and insights necessary for an effective recommendation system will not be present in a typical customer relationship management (CRM) database, or in conventional sales analytics systems.
It's here that the deployment of machine learning analysis can really help in filtering out the noise of coincidence from the discovery of useful and exploitable data relationships.
In cases where the customer is viewing your commercial environment from a dedicated mobile app in a device with client-side neural network capabilities, it's possible to deliver truly customer-specific results, by updating the local search algorithm, and assigning dedicated weights to the customer.
In these circumstances, the algorithm can operate with almost zero latency, even for time-critical functions such as text prediction.
In a less accommodating web environment, customer inference and intent can still be fueled by targeted AI-driven analytics, depending on the server-side resources that are available for updating models for individual clients.
What do you do if you have no historical data yet, or if the customer is so new that a 'generalized' search algorithm might leave them with sub-standard matches?
One US startup grew nearly 400% in a year by using artificial intelligence to analyze its own first-party data for product engagement and platform usage metrics. In this way it was possible to develop a 'fictional' historical sales database that could be improved and refined by real-world transactions.
Research out of Harvard also recently proposed a 'First Impression Model' (FIM) capable of forecasting a customer's underlying traits and lifetime value using only the minimal information from their earliest transactions and interactions. The framework uses a probabilistic machine learning approach to feed forecast accuracy reports about new customers into the improvement of the technique over time.
In a retail setting, insights from the FIM model revealed:
The automation of useful insights like this through artificial intelligence is a recent phenomenon, due in part to the advent of GPU-based AI acceleration since 2010.
However, the analytics and forecasting marketplace remains dominated by legacy systems that apply 'static' theoretical models to real-time data, rather than the new wave of artificial intelligence approaches that are capable of suggesting entirely new sales flow models, architectures and core principles.
Sometimes the data will not bend to the theory, and only machine learning can dynamically address this. Even fairly recent analytics luminaries such as Hadoop and Apache Spark are now ceding ground to TensorFlow, and to the rise in more flexible neural networks.
This is the ideal time to integrate ai solutions into product search, because:
The advent of the COVID-19 pandemic greatly accelerated, uptake of Ai-enabled chatbots across all industries and in the public sector. Consequently the chatbot sector is currently estimated to rise from its current value of $2.6 billion USD to $9.4 billion by 2024, a compound annual growth rate (CAGR) of 29.7%.
Some of the many sectors that have increased use cases for AI-centered chatbots since the outbreak include:
Examples of innovative chatbots include:
In 2020 Microsoft acquired exclusive licensing rights for OpenAI's revolutionary GPT-3 text-generation system, which had already astonished the tech press, with its insight and capability earlier in the year, and which can operate as a chatbot if required.
It's been argued that GPT-3 finally demonstrates that a generalized knowledge system can conduct useful and informative discourse without needing to be trained with domain-specific datasets, or years of historical customer interactions.
Though it isn't yet a perfect conversational partner, GPT-3 does demonstrate the rise in public confidence and expectations for AI discourse agents over the next five years.
Companies and individuals licensing GPT-3 are using it for purposes as diverse as Python code generation, resume building, content generation and chat response automation. This indicates that chatbot deployments may, in the future, represent only one utilization of a central business-focused AI system.
Facebook's own research into 'open domain' chatbots such as GPT-3 indicates that the near future will remain dominated by domain-specific AI agents, powered by insights from a company's own customer data.
Therefore the immediate value of chatbot development lies in leveraging historical (proprietary) data to ensure apposite responses in areas such as:
The shopping experience is becoming noticeably more driven by data and ai enabled algorithms, with computer vision a crucial element in a new ecommerce & online retailers revolution.
The general global image recognition market is set to rise from $30.28 Billion USD in 2020 to $98.47 Billion by 2027, while the retail sector alone is estimated to rise from $1.4 billion USD to $3.7 billion by 2025.
Additionally, Gartner has forecast that by 2022 70% of retail interactions will involve ai solutions and their ancillary technologies.
In the current 'masked' age, the locus of AI research and investment has shifted from in-store facial recognition to image-based analysis of stock items in a retail context:
Image recognition can also help in evaluating how paid influencer campaigns are disseminating images of your product (through image recognition), and in gauging the type of responses that the product is creating (through NLP and associated techniques).
Though the need for customers to wear masks has impacted the usefulness of facial recognition in retail, computer vision can be deployed in various ways in a store without requiring facial identification:
You might be looking forward to running your historical customer data or site traffic statistics through a neural network in order to:
However, nearly all of these activities are made possible by tracking methods that are increasingly coming up for legislative review around the world, or else being suppressed by the makers of the most popular web-browsers and manufacturers of mobile devices.
The major technology in decline is cross-domain tracking. The past five years of scandals around AI-enabled political social engineering have brought the use of supercookies and other 'omniscient' web tracking techniques far enough into the public consciousness to effect real change:
The message here is that the 'wild west' era of populating ecommerce datasets with cross-tracked data is coming to an end,.
In this transitional period, it's unclear whether the new generation of privacy-focused frameworks — such as Google's Privacy Sandbox initiative, LiveRamp's Authenticated Traffic Solution, and offerings from Neustar and Project Rearc, — may eventually fall foul of later privacy initiatives.
Therefore it's prudent to:
The restrictions that come with a growing emphasis on privacy, and with the privations of the pandemic, are also unique opportunities for using artificial intelligence in ecommerce and online retail space to continue to grow.
The massive growth of consumer data under coronavirus restrictions has led to a corresponding boom in the Big Data Analytics (BDA) sector, leading in turn to an unexpected acceleration in machine-driven analytics that most believe is here to stay.
In the meantime, the prospect of well-defined privacy laws promises, at last, a stable legal environment and a relatively level playing field for ecommerce analytics and artificial intelligence systems, furnishing the business confidence necessary to invest in a new generation of compliant and performant AI-driven analysis frameworks.
Interested in seeing what Ai models can do for your ecommerce business? Want to start collecting data that gives you ROI your competition would beg to have? Let's talk about how we can use Ai to increase conversions and find potential high value customers hiding in plain site.
Contact us to learn more
Don't let your niche competitors find these Ai models first.
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
Using ai for document information extraction to automate various parts of the loan process.
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 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.
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