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Data Science & AI

cutting-edge AI-powered products

The Aretec Advantage: Unlocking Unprecedented Business Value

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

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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