Entries by Team Acumentica

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Chain of Thought (COT) in AI: Enhancing Decision-Making and Reasoning

By Team Acumentica   Chain of Thought (COT) in Artificial Intelligence (AI) is a concept that aims to improve the decision-making and reasoning capabilities of AI systems by emulating human-like thought processes. This approach involves breaking down complex problems into simpler, sequential steps that the AI can follow to arrive at a solution. By incorporating

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An Overview of Liquid Neural Networks: Types and Applications

By Team Acumentica   Abstract   Liquid neural networks represent a dynamic and adaptive approach within the broader realm of machine learning. This article explores the various types of liquid neural networks, their unique characteristics, and their potential applications across different fields. By examining the distinctions and commonalities among these networks, we aim to provide

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Seizing Big Opportunities in the Stock Market: The Art of Taking Calculated Risks

By Team Acumentica   In the world of investing, the ability to identify and act on significant opportunities can define the success of an investor’s portfolio. Known colloquially as “taking big swings,” this approach involves making substantial investments when exceptional opportunities arise. This strategy can lead to substantial returns but also comes with heightened risks.

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Emerging Deep Learning Architectures

By Team Acumentica   Emerging Deep Learning Architectures Before focusing on some of the emerging developments AI architecture, let’s revisit the current transformer architecture and explain its etymology. The Transformer is a type of deep learning model introduced in a paper titled “Attention Is All You Need” by Vaswani et al., published by researchers at

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Liquid Neural Networks: Transformative Applications in Finance, Manufacturing, Construction, and Life Sciences

By Team Acumentica   Abstract Liquid neural networks represent an advanced paradigm in machine learning, characterized by their dynamic architecture and adaptive capabilities. This paper explores the theoretical foundation of liquid neural networks, their distinct features, and their burgeoning applications across four pivotal sectors: finance, manufacturing, construction, and life sciences. We discuss the advantages of

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The Role of Mixed-Mode of Action (MOA) in AI Agents

By Team Acumentica    Introduction   The rise of artificial intelligence (AI) has revolutionized numerous fields, from healthcare and finance to entertainment and transportation. AI agents, designed to perform specific tasks or provide services, are increasingly becoming integral to various applications. These agents can leverage mixed-mode of action (MOA) strategies to enhance their performance, reliability,

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Deep Reinforcement Learning: An Overview

By Team Acumentica   Introduction   Deep Reinforcement Learning (DRL) combines the principles of reinforcement learning (RL) with deep learning to create powerful algorithms capable of solving complex decision-making problems. This field has gained significant attention due to its success in applications such as game playing, robotics, and autonomous driving.   Basics of Reinforcement Learning

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Integrating Monetarist Theory into AI-Driven Stock Predictive Systems Part 2. Exploring the Insights of Money Supply and Inflation

By Team Acumentica   Introduction   In today’s fast-paced financial markets, predicting stock prices accurately is a formidable challenge that has drawn the interest of economists, technologists, and investors alike. The advent of artificial intelligence (AI) has opened new horizons in the field of stock market prediction, enabling sophisticated analysis and forecasting techniques. However, the

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Voice Mode: Transforming Human-Computer Interaction

By Team Acumentica   Abstract   Voice mode, a term encapsulating voice-based user interfaces, is revolutionizing the way humans interact with computers. This article delves into the theoretical underpinnings, technological advancements, and practical applications of voice mode. Emphasis is placed on the benefits, challenges, and future prospects of this burgeoning field.   Introduction   The

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Learning Self-Attention with Neural Networks

By Team Acumentica   Self-attention, a mechanism within the field of neural networks, has revolutionized the way models handle and process data. It allows models to dynamically weigh the importance of different parts of the input data, thereby improving their ability to learn and make predictions. This capability is particularly powerful in tasks that involve

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Understanding Non-Efficient Markets: Dynamics, Implications, and Strategies

By Team Acumentica   In the realm of finance, the Efficient Market Hypothesis (EMH) posits that at any given time, asset prices fully reflect all available information. However, in reality, many markets are not perfectly efficient. Non-efficient markets exhibit discrepancies between market prices and intrinsic values, often due to a variety of factors such as

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Comparing the Human Brain with AI Neural Networks(ANNs): Solving Complex Problems

By Team Acumentica   Introduction   The quest to replicate the human brain’s complex processes in machines has led to the development of artificial neural networks (ANNs). Both the human brain and ANNs rely on interconnected neurons (biological or artificial) and synapses (or connections) to process and transmit information. This article explores the similarities and

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