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Analyticsindiamag
1h
100
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Should India Build a Project Stargate?
- The US’ power move to establish itself as a global leader in AI infrastructure brought together unlikely players to spearhead this mission, including Oracle chief Larry Ellison, OpenAI CEO Sam Altman, and SoftBank CEO Masayoshi Son.
- Ajai Chowdhry, HCL co-founder and chairman of the Mission Governing Board of India’s National Quantum Mission, expressed concern over the growing shift towards taking control of AI.
- Tech leaders in India are debating AI dominance, with differing opinions on building foundational models from scratch or building on top of existing models.
- Indian tech community shows eagerness to build AI models from scratch and is discussing initiatives to strengthen India's AI ecosystem.
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Medium
3h
56
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You Only Live Once: How does Single-Life Reinforcement Learning work?
- Single-life Reinforcement Learning (SLRL) aims to train robots with sub-optimal prior knowledge so that the training process more closely mirrors real-life conditions. This means if the robot is only given a single chance to complete a task, SLRL must enable it to backtrack when it gets stuck. SLRL rewards the robot for particular actions taken during the task, such as moving toward areas where there are cries for help or by opening doors. The Q-weighted adversarial learning algorithm (QWALE) which guides the robot to consistently move closer to the pathways that lead to task completion, performed better in 4 RL challenges than previous distribution matching methods.
- SLRL is based on concepts used in Reinforcement Learning (RL), where a robot makes decisions to achieve specific goals. Usually, RL resets the environment so that the robot can repeatedly learn, but SLRL needs to adapt to real-life scenarios where there is only one chance to complete a task. If the robot gets stuck, it must be able to backtrack and navigate unknown terrain. SLRL employs the Q-function, policy, state-action, reward, and entropy concepts to enable the robot to find its way to complete tasks with minimal backtracking.
- The researchers equipped the robot with sub-optimal prior knowledge to train it in disaster relief scenarios so it can rescue people from burning buildings. The robot also has a discriminator for evaluating the distance between the prior knowledge and the current state when navigating new environments. QWALE incentivizes the robot to move closer to the pathways that lead to task completion, which assists in recovery when mistakes or trapped situations occur.
- QWALE needs to be agnostic to the quality of prior experiences to help robots tackle more challenging and unknown terrains. The more similar the task or state, the quicker robots can complete a task. The researchers found that QWALE outperformed other algorithms, achieving the best results in four different RL challenges - such as arranging items on a table and using tools to achieve complex tasks in a kitchen environment.
- The study found that standard fine-tuning using RL methods doesn't account for mistakes or trapped situations, and prior data significantly improves backtracking and recovery. QWALE required 20 to 40% fewer steps on average compared to other methods, demonstrating how efficient and effective SLRL can be in evaluating prior knowledge. Future models using SLRL could enable sophisticated robots to handle new environments more effectively.
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Analyticsindiamag
4h
294
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Smriti Irani Highlights Women’s Role Being Tied to India’s Success
- Union Minister Smriti Irani called for a shift in the global narrative, urging Indians to lead discussions on pressing issues such as inclusion, climate change, and health innovation.
- She emphasized that India’s demographic dividend holds immense potential, but its success is intrinsically tied to the role of women.
- Irani highlighted the economic opportunities of gender equality, mentioning a $12 trillion global gain by 2025 if it is fully realized.
- She also addressed the impact of AI and automation on jobs, particularly concerning the potential risks faced by women.
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Medium
5h
202
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Introduction to Recurrent Neural Networks: Classic RNN, LSTM, and GRU
- Classic RNNs struggle to retain information over long sequences due to the vanishing gradient problem.
- LSTMs are powerful but computationally expensive and require significant memory.
- GRUs may not perform as well as LSTMs when dealing with extremely long or complex dependencies.
- Even the best models, like LSTMs, cannot anticipate unexpected events in the stock market.
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Medium
5h
6
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Introduction
- Software engineering ensures innovation and automation in different areas of life.
- Core principles include a systematic approach, scalability and efficiency, and quality assurance.
- Software engineering is widely used in applications like websites, mobile apps, and AI-based solutions.
- It strengthens the interface between user needs and technological advancements.
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Analyticsindiamag
5h
237
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85% Executives Are Unprepared to Implement Responsible AI: HCLTech-MIT Report
- A recent study by HCLTech and MIT Technology Review Insights reveals that 85% of business executives are not adequately prepared to implement responsible AI principles.
- Challenges hindering responsible AI adoption include complexity of implementation, lack of expertise, operational risk management difficulties, regulatory compliance hurdles, and insufficient resource allocation.
- Enterprises plan to increase investments in responsible AI over the next twelve months.
- Recommendations for enterprises include establishing a robust responsible AI framework, leveraging partnerships, and setting up a dedicated Center of Excellence for AI initiatives.
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Analyticsindiamag
5h
261
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Will India’s AI Regulations Stunt GCCs’ Growth?
- The proposal of Artificial Intelligence guidelines by the government could harm the scalability prospects of Global Capability Centres (GCCs).
- GCCs in India are focussed on accelerating their time-to-market primarily by building advanced skills, capabilities, and cost-efficiency.
- According to a report, the concentration of computational power and data infrastructure in a few hands has led to concerns of unfair trade practices.
- Assessing “market dominance” and “abuse of dominance” in the AI environment would require regulators to continuously monitor its shifting dynamics.
- Initiatives like mandatory reporting, liability management across the AI lifecycle, and tools such as watermarking can lead to increasing overhead and compliance requirements for GCCs, notably data protection, accountability, and bias mitigation.
- GCCs require agility and a non-restrictive regulatory environment to remain efficient and competitive in the AI race, according to Arijit Mitra, MD at BEAT, Acuity Knowledge Partners.
- However, experts like Tushar Bisht, senior principal software architect at Sabre, look at the AI governance guidelines as a commendable step towards promoting safe and responsible AI integration.
- The GCC sector in India is aiming to shape the future by creating jobs through innovative AI-based solutions.
- GCCs must adopt proactive strategies, stay informed about evolving global tech regulations, and foster collaboration with regulatory bodies to shape compliance standards, according to a report by Nasscom Community.
- The GCC sector is expected to see growth in the coming years with the sector likely to go from 1,580 in 2023 to 2,400 GCCs in 2030 while creating over 4.5 million jobs in India.
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Medium
5h
182
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The Evolution of Experimental Design
- The field of Design of Experiments (DoE) has undergone a remarkable transformation over the past century, evolving from manually constructed designs to sophisticated computer-generated experimental plans.
- Classical designs of this era were characterized by their elegant simplicity. Their construction relied on systematic rules: circular permutations and combinatorial principles that could be executed with just paper and pencil.
- The 1970s marked a transformative period in experimental design, driven by the rapid growth of computing power and the emergence of algorithmic optimization methods.
- The concept of optimality criteria became central to this revolution. Different criteria were developed to address specific experimental objectives.
- New optimality criteria and algorithmic developments continue to expand the capabilities of computer-generated designs.
- This convergence has created unprecedented flexibility in experimental design.
- Modern innovations continue to emerge from both traditions. This suggests that the future of DoE will likely involve further synthesis of traditional principles with advanced computational methods.
- Definitive Screening Designs (DSDs), Orthogonal Minimally Aliased Response Surface (OMARS) designs, and Orthogonal Main Effects Screening Designs for Mixed-Level Factors (OML designs) represent advances in classical design construction principles.
- Current research explores the intersection of model-based and model-agnostic frameworks, promising even greater flexibility and efficiency in experimental design.
- These developments enhance our ability to design efficient and effective experiments across diverse fields of application.
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Fourweekmba
6h
65
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Emerging Trends In AI
- The current AI paradigm is based on neural-net-based, Generative AI, which will expand the existing software industry.
- Microsoft views AI enhancing sustainability, scientific breakthroughs, and responsible AI practices, including better testing and customization.
- According to NVIDIA's leadership team, generative AI and agentic AI will transform industries in 2025, driving innovation in robotics, quantum computing, and data analytics.
- a16z highlighted what to expect as key ideas for 2025, with tech innovation spanning nuclear energy for AI demands, healthcare breakthroughs like CAR-T therapy, and AI copilots for white-collar roles.
- Many discussions around AI focus on the technical aspect but miss the broader picture.
- The next cycle of development of this current AI paradigm may play out in the next 10-30 years.
- AI will transform work and daily life with more capable models, autonomous agents, and personalized companions.
- AI adoption jumped to 75% among business leaders in 2024, with 2025 set to see broader integration across work and home.
- Generative AI adoption surged across industries, forecasted to generate $1.3 trillion in revenue by 2032.
- Enterprises build AI factories to transform raw data into business intelligence for applications like supply chain optimization and market forecasting.
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Medium
3h
293
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Generative AI Career Guide 2025: Essential Skills, MLOps, and Job Market Transformation
- Generative AI is a cultural shift that is transforming jobs and industries.
- Understanding generative AI, MLOps, and AI ethics is crucial for career growth by 2025.
- Generative AI goes beyond mimicking patterns and focuses on creating new ideas and possibilities.
- It has the potential to reshape various fields including art, marketing, and innovation.
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Analyticsindiamag
3h
171
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Oracle’s Larry Ellison is the ‘CEO of Everything’
- Oracle's Larry Ellison is being referred to as the 'CEO of Everything' by US President Donald Trump.
- Oracle has partnered in a $500 billion investment in US AI infrastructure and has a stake in TikTok Global.
- Oracle is assisting the US government in building data centers for AI infrastructure and collaborating with OpenAI and NVIDIA.
- Ellison discussed AI's potential in healthcare, including early cancer detection and personalized vaccines.
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Medium
4h
61
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Cramér’s V: How to Quantify Strength Between Categories
- Cramér’s V is a statistic that quantifies the association strength between categorical variables.
- Understanding the relationship between categorical variables is crucial in data science and machine learning.
- Cramér’s V can be used to guide decision-making in personalized marketing and product design.
- This guide provides a deep dive into Cramér’s V, including its definition, computation, and real-world applications.
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Medium
4h
68
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Build Retrieval Augmented Generation (RAG) From Scratch
- The RAG pipeline integrates several components.
- The RAGPipeline class initializes with the path to the PDF file and creates a vector database (vec_db) from the document.
- Embeddings are generated for each chunk using the SentenceTransformer model.
- The retrieved chunks are passed to OpenAI’s GPT model to generate a coherent response.
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Analyticsindiamag
5h
239
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AWS Commits ₹60,000 Crore for Data Centres in Telangana
- Amazon Web Services (AWS) has committed to investing ₹60,000 crore to establish new data centres in Telangana.
- The investment aligns with AWS’s broader expansion plans in India, investing $4.4 billion by 2030.
- Telangana government is facilitating the expansion by providing additional land for AWS’s new projects.
- AWS's investments are expected to strengthen Telangana's position in India's digital and cloud infrastructure growth.
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Medium
6h
138
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Writing computer programs IS FOR Everybody
- Writing computer programs is not only for programmers, but for everybody.
- Learning programming can benefit individuals in various ways, from improving marketing messages to adding analysis to research.
- Programming is important for participating in the modern world, affecting the products we buy, the policies we vote for, and the data we share online.
- Previously, only a few people knew how to code, but now programming is accessible to everyone.
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