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The Aretec Advantage: Unlocking Unprecedented Business Value

AI, Fintech /

INTRODUCTION In today’s rapidly evolving digital landscape, businesses are seeking innovative solutions to harness the power of their data, streamline processes, and drive growth. Aretec, in partnership with Google, is at the forefront of this transformation, offering cutting-edge AI-powered products that revolutionize the way organizations operate. This article explores the technical capabilities of Google Cloud and how Aretec leverages these technologies to deliver unparalleled value to its customers. Why Google Google Cloud is renowned for its industry-leading AI and machine learning technologies, providing businesses with the tools they need to innovate and stay ahead of the competition. With Vertex AI, Google’s end-to-end machine learning platform, organizations can accelerate the deployment and maintenance of AI models, enabling them to extract valuable insights from their data and automate complex processes. Google Cloud’s scalable, reliable, and secure infrastructure ensures that businesses can trust their most sensitive data to this powerful platform. Google received the highest scores of any vendor evaluated in both the Current Offering and Strategy categories in the Forrester Wave™: AI Infrastructure Solutions, Q1 2024 report. A visual summary of the Google services is shown in the following image. Figure 1. Google’s AI and Machine Learning Portfolio. Aretec’s Solutions Aretec has developed a suite of transformative products (Figure 2) that harness the power of Google Cloud to address critical business challenges. diSearch, our Integrated Governed Search Platform, is powered by Vertex AI and serves as the central brain of an organization. diSearch empowers teams to access, understand, and action their data with unprecedented ease. Aretec’s solutions are built with enterprise-grade security and privacy in mind, ensuring that sensitive information remains protected at all times. Aretec is currently developing and deploying complex solutions using Google Capabilities for large, global banking institutions, Regulatory Agencies, and manufacturing firms, among other customers. Figure 2. Aretec’s Product Suite Why diSearch and PensDown diSearch and PensDown are two of Aretec’s flagship products, designed to revolutionize data search and proposal generation. diSearch enables organizations to find and understand their data through advanced search and discovery capabilities, while ensuring data provenance. PensDown streamlines the proposal generation process, allowing teams to automatically create compliant outlines, collaborate effectively, and manage the end-to-end proposal lifecycle. Both products leverage Google’s state-of-the-art natural language models (e.g., Gemini) to deliver highly accurate and contextually relevant results. The Aretec product suite includes three key offerings, all powered by Vertex AI: Benefits of this Approach rapid time-to-value with pre-built prompts and frameworks, while Automated Model Tuning ensures that the platform continuously learns and improves based on each organization’s unique data. This approach empowers business users with self-serve access to data insights, reducing reliance on technical teams and accelerating decision-making. The streamlined workflows and automation capabilities of Aretec’s products save time and money, allowing teams to focus on higher-value activities and drive business growth. Key benefits include: Conclusion The partnership between Aretec and Google represents a transformative opportunity for businesses looking to harness the power of AI and data to gain a competitive edge. By leveraging Google Cloud’s technical capabilities and Aretec’s innovative products and services, organizations can unlock new levels of efficiency, insights, and growth. diSearch and PensDown, powered by Google’s AI, empower teams to work smarter, make better decisions, and drive successful outcomes. As the digital landscape continues to evolve, Aretec and Google remain committed to delivering cutting-edge solutions that help businesses thrive in the face of complex challenges. Figure 3. diSearch Platform. Learn more about Aretec Product and Services Offerings at https://Aretec.ai or contact us from https://aretec.ai/contact-us/

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Large Language Models

Fintech, Others /

INTRODUCTION By now most of the world has been astonished with ChatGPT from OpenAI and its various abilities so much so that the debate of Artificial General Intelligence AGI has been restarted and given a fresh lease. Such is the impact of ChatGPT (and other similar models, stable diffusion for image generation) that various governments are already looking into incorporating AI at the same time they are also concerned about its potential or being misused and hence any regulations regarding its use are being discussed and proposed. In fact, a group of AI researchers have called for a moratorium on releasing new models (citation). Many jobs are being replaced by such models while other are transformed. In this article, we would try to understand what this amazing technology is and how various industries can use it for their own benefit. We shall start by unwrapping the term LLMs and discuss language models and some of the earlier attempts and their shortcomings. We will then discuss what powers the current LLM and how to train them. We will then discuss how to make an LLM follow human instructions and finally end with some of the use-cases where these LLMs can be used. UNWRAPING THE TERM LLM So, what does large and language model in the term Large Language Models mean? I think large is clear in its meaning, however the latter needs some introduction. Language Model is a term used by researchers to define a probabilistic model which finds the probability of a given sequence of terms. For example, what is the probability of the sentence The quick brown fox jumps over a lazy dog. In pure mathematical terms, given a sequence of words 𝑊 = (𝑤1, 𝑤2, … , 𝑤𝑘), a language model finds the probability: Before we discuss how to estimate this probability, we would like to understand why we might need to know this probability and what consequence does it have? Let’s first analyze how we humans learn and use the language. Most of us use intuition to make sentences and judge the grammatical correctness or appropriateness of a text, without necessarily being taught the rules or us remembering the grammatical rules precisely. Our brains have this amazing capability to deduce this intuition from regular use of language. Language models can be understood to be modelling this intuition from exactly the same source humans deduce it, i.e., language (in the form of text). Probabilistic likelihood is the tool that the language models use to measure it. Calculating the probability therefore gives us a measure of finding the most likely sequence versus an absurd or unlikely sequence. For example, consider the sentence, I am looking … my purse. The sequence with a preposition (at, for, towards, in, into etc.) would be most probable rather than having any other word in its place (It wouldn’t make sense to have computer/ cat/ hand etc. in the blank space). Therefore, the word sequences that are more likely will have a higher probability in contrast to those that are grammatically wrong or semantically incorrect. In fact, the next word prediction or masked word prediction is the pseudo-task used to train today’s LLMs, more on that later. Another benefit of language modelling is in machine translations since the correct translation will be more likely and hence with more probability. We can argue on similar lines on the tasks of information retrieval, speech recognition, summarization etc. HOW TO CALCULATE/ ESTIMATE THE PROBABILITY? From the product rule of probability, we know that, Therefore, equation (1) above can be expanded as below: That is, the probability of the whole word sequence is the product of the probability of each word given all the previous words in the sequence. For example, the probability, 𝑃 of the sentence “The quick brown fox jumps over the lazy dog” would be: However, calculating the exact probability this way is intractable for longer sequences because the number of terms grows exponentially. Language models therefore make simplifying assumptions, like only considering a limited context of a few words before and after the target word when calculating these conditional probabilities. They also use probability distributions over words and their contexts rather than exact values. We, therefore, have 𝑛-Grams where 𝑛 defines the number of words in the joint probability, sometimes also called the context window. Setting 𝑛 = 3, would be a trigram and equation (2) would be: We can already see the limitation of a smaller 𝑛, because it provides a too little context for the next word prediction. There have been other methods of language modelling like the Hidden Markov Model (HMM), Statistical Modelling etc., however all of them suffer from the limitation of context window. Also designing and creating the dataset is sometimes prohibitive since we need to deal with probabilities explicitly. TRANSFORMERS TO THE RESCUE Transformers are a type of neural network that are sequence-to-sequence, that is, they take as input a sequence of tokens and output another sequence of tokens. (Tokens can be understood as roughly being words but not necessarily words every time the way we understand them.) Transformer is the engine behind the extraordinary power and success of today’s LLMs, and attention is the key architectural thing in these transformers. We won’t explain what transformers are and their inner architecture here, however one can refer to the excellent blog by Jay Alammar and another one by Peter Bloem. To fully grasp the importance and formidable capabilities of transformers without delving into their intricate workings, it is crucial to retrace the origins of deep learning, which initially gained prominence in computer vision. In the pre-deep learning era, machine learning comprised two primary stages: feature engineering and model training. Domain-specific features were meticulously crafted for individual datasets and tasks, making them incompatible with different tasks. However, this paradigm shifted with the emergence of Convolutional Neural Networks (CNNs) trained on large-scale datasets like ImageNet for image classification. CNNs not only outperformed their predecessors but also introduced a new training

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The Evolving Landscape of Cyber Security Technologies

Fintech, Others /

Introduction In today’s hyperconnected world, where digital transformation is revolutionizing every aspect of our lives, the need for robust cyber security technologies has never been more critical. Malicious actors are constantly evolving their tactics, making it imperative for organizations to stay ahead by leveraging advances in cyber security technologies. In this blog post, we will explore some of the key advancements in this field that are helping organizations secure their digital assets and protect against cyber threats. Assessment and Training Programs for Cybersecurity Professionals The first step towards bolstering cyber security is establishing a formalized assessment and training program for cybersecurity professionals. This program should align with industry standards such as the National Institute of Standards and Technology (NIST) Cybersecurity Workforce guidance. By incentivizing certifications in critical need areas through salary incentives, organizations can attract and retain top talent in the field of cyber security. This helps build a highly skilled workforce equipped with up-to-date knowledge and expertise to tackle emerging threats effectively. Standard Operating Procedures and Knowledge Management Developing robust Standard Operating Procedures (SOPs) and implementing a comprehensive knowledge management capability is crucial in ensuring organizational compliance with security policies, procedures, standards, and guidelines. Employees and stakeholders need to be well-informed on how to engage with the organization’s IT department and adhere to security protocols. By implementing a digital system, such as ClickUp or Atlassian’s Jira, organizations can efficiently manage and disseminate information in real-time, enabling seamless collaboration and streamlined workflows. Technology Architecture, Governance, and Deployment For organizations to effectively combat cyber threats, they must enhance their technology architecture, governance, and deployment strategies. The AretecSBD team advises the Office of Information Technology (OIT) to evaluate, elevate, and strengthen the agency’s cybersecurity posture. By assessing current IT operations and identifying gaps, recommendations can be made for improvement and optimization in line with industry best practices such as the NIST Cybersecurity Framework. Additionally, implementing a new cybersecurity Zero-Trust architecture and a standard set of controls can minimize risks and fortify the organization’s defenses against evolving threats. Real-time Cyber Event Detection In today’s threat landscape, it is crucial to detect and respond rapidly to cybersecurity events to minimize their impact on organizational systems and data. By leveraging advanced monitoring tools, organizations can identify potential vulnerabilities and cyber threats in real-time. These tools provide insights into system and network behavior, enabling early detection of malicious activities and timely response. It is essential to prioritize vulnerabilities based on severity and potential impact, promptly address them, and continuously test and verify fixes to ensure their effectiveness. In-Depth Security Assessment and Data Governance To ensure compliance with cybersecurity standards, it is essential to conduct comprehensive security assessments, such as PCPE penetration testing and vulnerability testing. These assessments help identify potential security risks and provide actionable insights to safeguard the organization’s overall infrastructure, systems, and data. Additionally, organizations must address challenges related to data documentation and cataloging. By establishing a comprehensive inventory of all data assets, organizations can improve data governance, compliance efforts, and overall cybersecurity posture. Leveraging Technology for Secure Application Development The secure development of applications is paramount in today’s digital landscape. By integrating scheduled dynamic security scans into the software development lifecycle using tools like GitLab, organizations can proactively identify and address potential vulnerabilities, minimizing the risk of security breaches. This approach ensures that the application remains secure for users and protects sensitive data from unauthorized access. Conclusion In conclusion, cyber security technologies have come a long way in helping organizations combat the ever-evolving threat landscape. By establishing formalized assessment and training programs, implementing SOPs and knowledge management capabilities, enhancing technology architectures, and leveraging tools for real-time detection, organizations can significantly strengthen their cyber defenses. Additionally, in-depth security assessments, data governance, and secure application development practices are vital in building a resilient cyber security framework. Embracing these advances and staying proactive is crucial to protecting digital assets and maintaining cybersecurity in today’s dynamic digital environment.

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Why are Records Management Systems So Important in 2022?

Fintech, Others /

Why are Records Management Systems So Important in 2022? There is no doubt about the fact that information is at the heart of every organization or federal agency. In today’s digital age, information is the key to everything; be it for strategy management, research, and development, federal compliance, or keeping up-to-date with the latest industry dynamics. But how does an organization manage any particular information or its corporate records, and rely on its ability to compete, comply with regulations, recover from any disastrous situation, or in simple words; operate with efficiency? Knowing More about Records Management Systems The systematic control of information or records throughout their life cycle and stages can be known as records management. Records are evidence of what the organization does or how it operates. It can also include their business activities, transactions, details regarding their contract negotiations, business correspondence, employee files, financial statements, confidential information, just to name a few. Records can come in many formats such as: In more complicated situations, if there is a lawsuit then all of this information – including copies or backups of the information that individuals might have retained may come in handy to safeguard the organization. Why Records Management is So Important for Federal Agencies in the US Records are useful information assets, and they necessarily hold value for the organization. As a federal agency in the US, all stakeholders must manage information effectively to maximize control, and ensure the vitality of the organization. It is a federal mandate for any federal agency operating in the US, to effectively implement a state-of-the-art records management system so that they can make sure that their confidential information is retrievable, authentic, and accurate. This follows a procedure, which is as follows: Employees, management, or stakeholders within the organization can utilize records management system daily to: Everyone in the organization is responsible for managing the records and information of the organization. Each employee can play an important role to protect the future of the organization by creating, using, retrieving, and maintaining records in a records management system, in line with the organization’s policies and procedures. Perfect Recordkeeping Systems A flawless recordkeeping system is a shared filing system, which can be easily implemented in an organization, provided that the recordkeeping system has the technical capabilities for records to be captured, organized, accessed, protected, retrieved, retained, and disposed of, in compliance with the approved record schedules. A perfect recordkeeping system is more than just a software tool. It can be used for managing digital records of the information necessary for the organization. However, a records management system must be used in conjunction with governing policies, procedures, and it must have defined roles and responsibilities for the stakeholders involved. There are various types of recordkeeping systems, which can also be known as records systems, which integrate various Artificial Intelligence (AI) tools to manage records, with different functionalities. These systems may range from manual to automated processes. The goal for a perfect record management system is to: Implementing an Effective Records Management System Aretec, Inc. is a data science firm specializing in customer experience, automation, and tailored digital solutions that transform data into actionable insights to aid government decision-makers. Aretec Inc. harnesses the power of emerging technology and ingenuity to help enable clients to meet their mission objectives. As a part of their AI solutions, Aretec has developed Context AI, to solve a multitude of problems using digital out-of-the-box capabilities and customization. With their Document AI, they can configure optical character recognition, document classification, text extraction, content summarization, keyword detection, and structured data extraction, just to name a few of their Document AI functionalities. With Context Document AI, records can be easily be created in context, managed or migrated, or exported across platforms. Context also helps maintain record authenticity, reliability, integrity, and usability. Using Context, it would become easier to retain and dispose of records in a systematic, auditable, authorized way. Consequences of Not Maintaining Records Management System Records that are not properly maintained, or kept outside of a records management system, can cause serious operational risks and consequences. This may include loss of data, privacy or security breaches for the organization, unreliability for the organization, loss of evidence for information, inaccessibility, and business disruption. Unacceptable records storage systems include: It is important for federal agencies in the US, to implement a state-of-the-art records management system, which is designed to systematically control records. A records management system can help manage records in any format, as it has the advanced capability for managing digital records. The records management system that you choose should fit the size and complexity of your organization. Knowing what you need, and how the records management system should be used, Context AI, with its Document AI can meet the requirements of today’s fast-paced technological environment, and resolve complicated records management processes. Context is an automated records management system, with robust searching capabilities, to help systematically organize your records, allowing you to capture, manipulate and manage sophisticated data that the records contain. With Context AI, your organization can have increased productivity, decreased paperwork, greater operational efficiency, better management, improved customer services, and information support.

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Knowing about AI and Automation for Digital Transformations

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  Knowing about AI and Automation for Digital Transformations Artificial intelligence and automation are transforming businesses in the United States, and contribute to economic growth via to the ability to enhance productivity. More and more businesses and federal agencies are moving towards digital transformations. At the same time, artificial intelligence (AI) and automation are transforming the nature of work and the workplace itself. Machines and AI-integrated technologies will be able to carry out tasks seamlessly, and even perform more tasks done by humans. Organizations that are unable to transition themselves, or are unable to integrate AI and automation, will eventually decline, while others will grow and change with AI and automation. Today, organizations need to grapple with significant business challenges, or federal mandates. Organizations need to tackle the challenges of automation and using AI, they can help to resolve some of the critical issues of digitalization, keeping up with Internet of Things (IoT) technologies and making insightful decisions based on analytical information. Accelerating Progress in AI and Automation Automation in AI is not something new. For example, automation and AI are being integrated for document and records management systems. Recent technological innovations are pushing the frontier of what machines can do. Today, federal agencies in the US need to provide value to the customers by digitalizing their customer experience. These types of improvements can provide value for the business, streamline operations, enable business efficiency, contribute to economic growth, and make unimaginable progress on some of the most difficult challenges. With the rapid digitization of Document Management, Records Management and a looming deadline in most lines of businesses to hybrid the management of paper and electronic information, it is essential to find a solution that provides not only an electronic copy of your paper record but an insight into and tool for finding the information you seek. Aretec’s platform, Context, can help you unify those worlds in a seamless fashion. Context Automation Nowadays, AI and automation goes beyond traditional approaches in business. With automation and advanced AI, new generations of more capable systems are appearing in the business world to make work easier. For example automated check-out processes, workflow automations, custom integrations with cloud solutions, video AI solutions for object detections, video summarization, document AI for text extraction, content summarization, keyword detection, and structured data extraction, to name a few. Much of the progress is driven by improvements in AI and automation systems, which includes mechanics, sensors, and software tools. One of such software AI tools is Context AI, which has the unique capability to integrate AI and automation for a variety of tasks. As automation and AI have become more sophisticated, and use faster and more efficient computing available in cloud environments, it is driving exponential growth to recognize data structures, and extract meaning information and key insights. Context is a thoughtfully designed data science platform ideal for rigorous Records Management projects that can ingest Records in a variety of data formats (documents, images, videos, etc.) from a variety of data sources (cloud & on-prem, SaaS, etc.). Context AI Capabilities Context AI automates your data and processes your documents at scale. Context has been built with the features of providing a generic document analysis and retrieval, and supports specific formats of the document to often be processed in large batches by organizations. Context AI allows you to automate and validate documents to streamline workflows, reduce guesswork, and keep data accurate and compliant. Context AI matches a parser with your provided document and sees the quality of extraction. The output will be in the form of JSON format that will save in the context database to apply further analysis and generate the report from it. Context AI’s Human in the Loop feature suggests mistakes within the document retrieval that allow you to fix or change the data. Context AI learns from your changes and fixes to improve their document retrieval AI process day by day. How AI and Automation can Help Organizations for their Document Management or Record Management Processes Context can help organizations build an end-to-end records management solution that starts from the raw document and moves through our pipeline to extract high-value information by converting the unstructured data into structured data. Along with document parser integration, our custom pipeline has a user-friendly interface so that documents and extracted information can be interfaced with the existing business processes. In simple words, text, key-value pairs, and tables are all features from the PDF or any type of document that could be detected via Context AI. Traditional OCR-driven records management solutions do not offer the ability to automatically extract key-value pairs, a key differentiator for Context. Natural language processing (NLP) techniques allow context to learn and determine the output of the next event based on not only the current input but also previous input along with the time-series data you have uploaded in the context. Transforming Businesses with the Help of AI and Automation AI and automation can generate value in various sectors of the businesses. AI and automation in applications can be used for a different array of processes, to personalize their customer experiences. The technology can also help to detect anomalies in production, identify fraudulent transactions in financial institutions, and offer AI capabilities to address classifications, estimations, and resolve clustering problems. Context Document Types Context has added the support of multiple types of documents in their application. You can use those types of documents to retrieve your data and conduct the analysis on your data in the context. The type of documents parsers Context currently supports are listed below. Our team can also build custom parsers for any form in a matter of hours. 1-General Processors  2-Contract Processors  3-Lending Processors 4-Procurement Processors  5-Other Processors Some of the benefits of using Context AI are:

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Records Management Roadmap ‚ AI for Documents Content and Records Management

Fintech, Others /

Records Management Roadmap – AI for Documents Content and Records Management The Journey Begins – Records Management System Paper has long been the key way of storing documents. All types of documents have existed, and today we generate documents in electronics applications as well like messaging, emails, chats, and others. Although the documents or communications exist in paper and media, the media forms are dealt with in different ways, so they may be available to the end users, but the information is nonexistent in a cumulative format. The Challenge for Records Management The challenge with records management is the combination of paper plus electronic documents, even if they do exist in soft media, the various formats make it extremely difficult to manage them with any one application. The federal and other governments have a broad mix of document types but there is no system to manage these documents consistently, in a systematic way over long period of times. The White House Directive In 2019, White House OMB (Office of Management and Budgeting) issued a directive to convert all types of documents that are generally provided to NARA (National Authority of Records Administration) in paper form. All government agencies’ documents have to be provided to NARA in electronic form (with standardized associated metadata) by the end of 2022. One of the key Paragraph states what the documents are. Maintain permanent electronic records (e.g., emails, IMs, text messages, electronic documents, spreadsheets, presentations, images, maps, videos, blogs and other social media tools that generate communications) electronically, and if applicable, in an approved electronic records system. The OMB-19-22 memo lists the types of documents that need to be stored in a way that take advantage of capabilities that are to be provided in one Document Management System that can collect, convert, transform, parse, analyze, and present in a way that ordinary users can search and review the documents. It is definitely a very complex task and there are no systems that can perform all these tasks or perform them in a secure, reliable, and distributed manner to a large audience in a collaborative manner. Artificial Intelligence (AI) for Documents, Content and Records Management Aretec saw the document space as an opportunity and created a platform called Context, which interfaces to Google’s AI-driven Document AI as the technology to perform all these functions. Google Document AI and the other AI functions like the Records AI and the ability to convert records or forms into a document format will help in organizing all records, paper document, and various content types into an electronic form for records management system. These document types can be collected, converted, transformed, parsed, analyzed, and presented as one document type.  This uses all the latest AI algorithms to convert a scan of the form into a record or an electronic document with a high degree of accuracy. Aretec also created a Human in the Loop function so that the Document or Record AI software learns from a human in the loop and its machine learning and AI capabilities improve with time. An array of all the document types mentioned earlier can be taken as a pilot and various types of documents identified as candidates for creating an all-encompassing content management platform that helps in creating the compliance required by current and future needs of all Federal, State, County or City governments to serve their communities. Context AI is a system that provides all the capabilities, and Aretec is willing to work with any organization to provide them a demonstration of these capabilities. It will take a group of people or experts and teams to prepare the plans, deploy the infrastructure, and eventually get to the point where you can start to get some traction with the record management project. This effort may go on for a year or two before any tangible progress will show. This team is typically look like below. Luckily there is a ray of light in the tunnel. Aretec looked at the problem and while we were all fighting with Covid for the past two years, and hired a large team that set out to take Google’s AI technology for documents and applied it to Content and Records Management. We developed the Context content platform that takes almost all the Google technology infrastructure and software AI assets and adds custom code to deliver a fully Self Serviced Context Platform. The plan was to build Context Self-Service to solve customer experience issues, and provide an easy-to-use interface for the citizen analyst. Digitalization Process in Records Management System The process first step is to use a paper Digitalization process that takes all the paper forms and documents and scans them into a large repository. This technology area of scanning has made a lot of progress in the past few years. Although you can scan any form or document with your smartphone or any tabletop scanner, that is only applicable to individuals. Government agencies whether they are federal, state, county or city governments contains thousands or millions of document forms. The only caveat is to use someone who can deliver on scanning reliably. Aretec partners with the industry leading digitization providers with the equipment and expertise to deliver document and forms in a reliable manner. Once the paper records or document or record forms are scanned, they can be loaded into Context with a simple drag and drop. Context also has an ever-growing list of pre-built connectors to whatever platform or system you use to store your records. Customers can upload thousands of documents or content in one go. Once the documents are loaded, Context takes over and it starts converting and indexing them. A few documents are done first with a human in the loop so that the Context AI gets trained and as the confidence improves, the number of documents increases substantially. Document Artificial Intelligence Function for Records Management As the Document AI scans the documents, it also creates all the ‘key’ and ‘value’ pairs that simple OCR

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