Research on artificial intelligence (AI) began with the desire to understand whether and to what extent computers can solve problems in a way similar to the human mind. Many of the early works related to AI focused on creating mathematical models and algorithms capable of performing tasks requiring intelligence, such as reasoning, learning, perception, or using language.

The development of large language models is currently one of the key topics in the field of new technologies, which is why we encourage you to read further parts of this article, which include comments from the CEO of Inero Software, Andrzej Chybicki, on the evolution of this issue over the past years.

Natural Language Processing. A New Approach to Data Analysis

Techniques for processing speech, text, and images have been used in industry and science for many years. They formed the basis of products based on so-called NLP (Natural Language Processing) models. These are algorithms that could understand individual words, later map relationships between words, and combine them into sequences. They began to understand and distinguish the meaning of the same words in different sequences (in other words, they distinguished context) in order to ultimately be able to interpret the meaning of entire sentences. To say that NLP models “understand” language is somewhat of an exaggeration; nevertheless, current NLP language models are able to assign individual sentences to so-called intents (intentions), i.e., distinguish their meaning. Then, by creating appropriate software, we are able to handle simple conversations, dialogues, or respond to simple questions – this is how chatbots have worked until now.

To illustrate this number, we might compare it to reading a several-hundred-page book in a few seconds.

Words Grow in Power: Large Language Models

LLMs, or Large Language Models, which have been developed over the past few years, represent a technological breakthrough because they no longer function merely as classifiers of user intent, but by using trained sets of input and output similarities, they are able to find connections between longer sequences of words (tokens). Large language models are now capable not only of analyzing individual sentences but also significantly longer texts. This is referred to as the length of context (or context window), which currently can be as much as 128,000 tokens. To illustrate this number, we might compare it to reading a several-hundred-page book in a few seconds.

Level of Understanding: Logic in Many Ways Similar to Human Logic

Students sometimes ask, “do computers understand text?” The answer to this question is difficult, if we do not define what we mean by “understanding”. Creators of language models and researchers deal with creating various measures, criteria to be able to evaluate whether a model “understands” a given problem. Nevertheless, in the simplest approach, we can say that a model understands a given issue if the responses (sequences of words), which it generates, are consistent with how a human expert in the field would respond.

I believe that in the context of AI technology development, the difference between how humans communicate (speech, writing) and computers (protocols, databases) is gradually blurring. It seems that in the near future, AI models will support humans in an increasing number of everyday tasks. They will be able to automatically retrieve information to send a response to a customer, suggest to employees the answers to questions, or handle communication through messengers.

Models will be able to browse a whole range of information, including documents and instructions covering, for example, orders, emails, contracts, or invoices, then infer based on them, and subsequently provide the employee with ready data needed for a report, an offer, or other operational actions.

Virtual Assistants: Will Employees Gain Real Help?

Much indicates that AI assistance in work is the future for many areas, yet I still see a big problem. Despite the fact that digitization, mainly during the pandemic, has accelerated rapidly, many companies and institutions still process a large part of information in an unstructured form, i.e., in the form of documents, contracts, instructions, and email messages. The amount of information that employees deal with daily is enormous.

Ultimately, models will be able to browse a whole range of information, including documents and instructions covering, for example, orders, emails, contracts, or invoices, then infer based on them, and subsequently provide the employee with ready data needed for a report, an offer, or other operational actions. Furthermore, a potential area of application will also include validation and checking of documents, analysis of dependencies between two different documents, or several sources of knowledge.

Virtual Assistant Speeds Up Daily Work

An example might be a situation where a bank employee receives a credit application along with attachments such as account status confirmation, employment confirmation, tax statement, and others. Currently, employees often have to manually check whether the data contained in such documents are consistent, e.g., whether the number given in the credit application matches the one on the employment certificate. Whether the bank account number written in the application is correct, and whether the dates of the issued income certificate are current, among many other things. Similarly, in the insurance sector, where issuing a policy requires manually checking VIN numbers on documents, registration certificates, and comparing them with data about inspections, policies, etc.

Implementing tools based on the LLM model into organizations can bring measurable benefits in the form of automation and increasing work efficiency. It is a mistake to claim that software can replace an employee. It can help them. Analyzing the European market, we see demographic trends resulting from an aging society and the decreasing availability of skilled workers.

In summary. Natural Language Processing (NLP/LLM) models increasingly support people in everyday tasks through automating responses to customer inquiries, assisting employees, and managing communication through platforms. These technologies not only increase work efficiency through the automation of routine tasks but also enable advanced operations like document analysis or data integration, which translates into broad applications across various industries and professional levels.

Andrzej Chybicki, PhD Eng.
CEO, Inero Software Sp. z o. o.
andy@inero-software.com

Artykuł Andrzej Chybicki: Models will be able to instantly review an entire repository and then provide the employee with ready-to-use data pochodzi z serwisu Inero Software - Software Consulting.

The development of broadly understood artificial intelligence is gaining momentum. This is evidenced by the widespread interest in this topic and an increasing number of media articles and publications. What draws particular attention are the data illustrating how much financial resources are allocated to investments in semiconductor technologies, i.e., the computational capabilities and memory of new AI data centers. Everything indicates that this development will be very dynamic in the coming years.

Reports such as “The Future of Jobs Report 2020″ prepared by the World Economic Forum and research conducted by the consulting firm McKinsey & Company highlight the dynamic changes in the job market that will take place in the coming years. Both sources agree that we will witness significant transformations in the types of professions and responsibilities, resulting from technological progress, automation, and changing economic and social needs.

In the article ” Most of 2030’s Jobs Haven’t Been Invented Yet” we can read:

“The IFTF, a nonprofit that seeks to identify emerging trends and their impacts on global society, forecasts that many of the tasks and duties of the jobs that today’s young people will hold in 2030 don’t exist right now.”

source:  https://www.voanews.com/a/most-of-2030-s-jobs-haven-t-been-invented-yet/4778002.html

From the management perspective, this presents a substantial challenge. The way relationships and duties among employees of different departments evolve will significantly change the operation of many organizations. Identifying these areas and any potential consequences arising from the AI revolution will be crucial for success in the coming years.

“In my opinion, understanding the capabilities that new technologies offer and assessing how they can improve a given organization will be a key managerial skill in the coming decade”- emphasizes the CEO of Inero Software, Andrzej Chybicki.

He points out two ways for businesses to develop their technological awareness in the area of AI:

The first is to explore products or services available on the market that can be applied as ready-made solutions. The decision to implement them will depend on the extent to which these solutions can be tailored to specific processes that require optimization and automation. However, it’s important to be aware that such software is delivered in a certain “bulk” that needs to not only be implemented but also integrated with internal organizational processes to effectively cooperate with the newly implemented product.

The alternative is creating in-house AI-based solutions, which large companies are already opting for. This includes, for example, designing their own language models tailored to the individual needs and requirements of a particular enterprise. These are usually multi-million dollar projects, but some can be completed within a smaller budget.

One of the global brands that decided to create its own LLM model is Amazon. The purpose of the “Rufus” model is to support potential customers at every stage of the purchasing path. Its implementation aims to assist users and optimize the order placement process.

“At Inero, we apply a product-service approach. Having knowledge on how to solve certain problems, using our proprietary systems like DocQuality or DeliverM8 – Freight Matching Platform, we aim to present a certain solution template and its potential. Then, we tailor it to the specific needs and processes carried out within the client company’s structure, rather than the other way around. Our products provide a template of possibilities that new technologies offer.” – highlights Andrzej Chybicki.

This solution combines the good features of a product approach with the flexibility of a service approach, while the cost is more affordable. For example, the DocsQuality application utilizes machine-learning techniques for document categorization and the recognition of illegible sections of files. This program can be easily and quickly integrated with a client’s internal infrastructure, optimizing the document circulation process by detecting problematic documents.

In summary, the development of artificial intelligence in business management opens up space for innovation. The challenges associated with adopting new technologies require a strategic approach, both in the context of implementing ready-made solutions and creating internal AI systems. Success in the coming decade will depend on managerial abilities to understand and utilize technological potential, as well as skills in managing a changing team that will require the implementation of new tools, tailored to new tasks.

Artykuł The possibilities brought by the development and popularization of AI in business management pochodzi z serwisu Inero Software - Software Consulting.

Optimization of Data Collection Process Thanks to AI Algorithms

In an era of progress and numerous achievements in natural language processing, we see increasing possibilities in the analysis and inference based on data collected in unstructured textual archives and documents. Therefore, streamlining business operations and implementing NLP (Natural Language Processing) and LLM (Large Language Models) tools become essential. The digitization of business processes moves many tasks into the virtual world, and controlling and analyzing a large volume of collected data becomes a significant challenge.

Save time

Of course, time is crucial. In the age of constant haste, laborious analytical tasks requiring focus, precision, and diligence become a burden for efficient and often dispersed teams. The modern business operates swiftly. Many industries, such as accounting, law firms, insurance, or transportation, place significant emphasis on the quality of documentation, which significantly impacts group efficiency.

Let’s take the transportation sector as an example. TSL companies handle dozens of freight shipments daily, each generating a large amount of documentation, including invoices, contracts, policies, delivery notes, and driver logs. When added to the internal administrative workflow, it’s easy to overlook an unreadable document (e.g., hastily scanned), which can disrupt the document circulation process at a later stage and require corrections, thus significantly extending the task list. This could affect cash flow. Therefore, optimizing the data collection process with the right quality becomes crucial.

Traditional methods are becoming less effective

Traditional software development methods are becoming less effective when dealing with large databases. They often rely solely on structurally processed data and predefined rules, which may not be suitable for working with unstructured text and the subtleties of natural writing. The classical approach to software development faces challenges when dealing with large knowledge bases.

Working on DocsQuality, we lower the barrier for teams to access NLP (Neuro-Linguistic Programming) and LLM (Large Language Model) tools, enabling them to easily retrieve data from unstructured texts and documents.

The role of DocsQuality software is to assess document attributes before they are imported into the accounting workflow. It evaluates real-time factors such as readability, clarity, and file quality. The software immediately highlights issues, for example, with email attachments, signaling that the system should not accept the document into the workflow at this stage.

NLP and LLM models

Combining and applying NLP and LLM models is an innovative method for assessing document quality. During software development, NLP can be used to extract text from image documents, utilizing Optical Character Recognition (OCR) techniques to transform image text into editable text, which can then be evaluated for quality. On the other hand, LLM models can read and understand text in PDF documents, allowing for assessment of readability, grammar, syntax, and spelling correctness, followed by content analysis, inference, and understanding.

The engineers at Inero Software are continuously developing the functionalities offered by DocsQuality. Creating new software in such a dynamic environment requires constant improvement and searching for optimal solutions. The potential of integrating NLP and LLM tools into this software will enable even more precise documentation analysis in the future.

DocsQuality software

If you are interested in DocsQuality software, we invite you to visit our website at https://docsquality.com/ and contact us via email at hi@docs-quality.com. We would be happy to answer all your questions and provide information about its functionalities and implementation. DocsQuality can be successfully integrated with an existing ERP system, expanding the offered functionalities to include effective monitoring of document quality, and allowing the identification of problematic files before they are introduced into the accounting workflow.

Artykuł Optimization of Data Collection Process Thanks to AI Algorithms pochodzi z serwisu Inero Software - Software Consulting.