Aryaman Pattnayak

Bachelor of Technology - BTech, Computer Science

AI/ML Specialist, Data science, Data Analytics

Aryaman Pattnayak is a Tech writer based in Bhubaneswar, India. His academic background is in Computer Science and Engineering. Aryaman is passionate about leveraging technology for innovation and has a keen interest in Artificial Intelligence, Machine Learning, and Data Science.

Aryaman’s work encompasses several topics, including "FinGPT" and "Financial Fraud Detection," reflecting his dedication to practical AI solutions. Outside of his professional pursuits, Aryaman finds joy in sharing insights through Medium and exploring the convergence of technology and finance. In his free time, he enjoys various hobbies, including reading and listening to music.

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Articles from Aryaman

Instilling Human Values and Moral Codes in AI Systems

This article explores the challenges and approaches to imparting human values and ethical decision-making in AI systems, with a focus on large language models like ChatGPT. It discusses techniques such as supervised fine-tuning, auxiliary models, and reinforcement learning from human feedback to imbue AI systems with desired moral stances, emphasizing the need for interdisciplinary perspectives from fields like cognitive science to align AI with human ethics.

1 Nov 2023

JudgeLM: Scalable Language Models for Evaluating Large Language Models

This research introduces JudgeLM, a scalable solution for evaluating Large Language Models (LLMs) like GPT-3 and GPT-4. By fine-tuning LLMs to judge the quality of LLM-generated responses, the study addresses the challenges of evaluating open-ended tasks while providing cost-effective, unbiased, and efficient evaluation mechanisms, promising a significant impact on the development of LLMs.

31 Oct 2023

SynthAML: A Breakthrough in Anti-Money Laundering Research

Researchers introduce SynthAML, the first publicly available synthetic dataset for studying critical challenges in anti-money laundering (AML). This dataset, created using innovative synthesis techniques, addresses issues like efficiency, effectiveness, class imbalance, concept drift, and interpretability, offering a platform for standardized assessment and academic research in the AML domain.

2 Oct 2023

Enhancing Video Captioning with a Semantic Guidance Network

Researchers have developed a "semantic guidance network" to improve video captioning by addressing challenges like redundancy and omission of information in existing methods. The approach incorporates techniques for adaptive keyframe sampling, global encoding, and similarity-based optimization, resulting in improved accuracy and generalization on benchmark datasets. This work opens up possibilities for various applications, including video content search and assistance for visually impaired users.

29 Sep 2023

Predictive Models Illuminate Wordle Gameplay Patterns and Word Difficulty

Researchers have developed robust predictive models for Wordle gameplay, forecasting the number of results and the probability distribution of guesses for specific words. These models offer valuable insights into player behavior and word attributes, paving the way for further exploration of gaming psychology and optimization of player enjoyment in linguistic puzzle games.

29 Sep 2023

Quadrupedal Robot Learns Parkour Through Deep Reinforcement Learning

Researchers at Carnegie Mellon University demonstrate that a quadrupedal robot can learn dynamic athletic behaviors like parkour directly from pixel inputs using deep reinforcement learning. Their approach, using a low-cost robot and end-to-end learning, enables the robot to perform complex athletic maneuvers, such as jumping over obstacles and crossing gaps, showcasing the potential of learning-based approaches for agile robotic locomotion.

28 Sep 2023

Unveiling the Dynamics of Data Capitalism: A New Theoretical Framework for Digital Governance

Researchers introduce the "general theory of data, artificial intelligence, and governance," offering fresh insights into the complexities of the data economy and its implications for digital governance. Their model, which incorporates data flows, knowledge concentration, and data sharing, provides a foundation for addressing the challenges of data capitalism and shaping equitable and innovative data policies in the digital age.

27 Sep 2023

ForceSight: Robust Mobile Manipulation Guided by Visual-Force Goals and Natural Language

Researchers at Georgia Tech have developed ForceSight, a system that combines deep neural networks and natural language instructions to enable robots to perform robust mobile manipulation tasks. ForceSight predicts visual-force goals and utilizes a vision transformer architecture to improve the accuracy of these predictions, significantly enhancing the robot's performance in various manipulation tasks.

26 Sep 2023

Expresso: A Benchmark and Analysis of Discrete Expressive Speech Resynthesis

Researchers from Meta AI introduce EXPRESSO, a high-quality dataset of expressive speech and a benchmark for discrete textless speech resynthesis. This dataset, comprising diverse vocal expressions like emotions, accents, and non-verbal sounds, along with a resynthesis challenge, advances the capabilities of speech synthesis systems, enabling them to capture a wide range of expressive styles.

24 Sep 2023

Advancing Air Quality Monitoring with Federated Learning and Edge Computing

Researchers propose a novel framework integrating federated learning and edge computing to revolutionize air quality monitoring systems. This review highlights the potential of these technologies in creating scalable, privacy-preserving, and collaborative monitoring systems while emphasizing the need for further research and interdisciplinary efforts to bridge theory and practice in managing urban environmental conditions.

24 Sep 2023