Google may be the most popular search engine, but it isn’t always the best for academic and scientific research. Many valuable scholarly resources are buried under commercial results or hidden behind paywalls. Fortunately, there are specialized search engines designed to help researchers, students, and professionals find high-quality, peer-reviewed content. Here’s a selection of the best alternatives to Google, focusing on science, technology, medicine, and economics. 1. RefSeek ( www.refseek.com ) Best for: General academic research. RefSeek indexes over a billion documents, including research papers, encyclopedias, and books. Unlike Google, it prioritizes educational content and filters out commercial websites. 2. WorldCat ( www.worldcat.org ) Best for: Finding books and research materials in libraries worldwide. WorldCat allows users to search for books, articles, and historical archives in over 20,000 libraries. It’s ideal for tracking down rare or specialized academic ma...
Comparison of FFN Fusion with Other Approaches & Suitable Use Cases FFN Fusion (NVIDIA) FFN Fusion optimizes transformers by identifying feed-forward layers that can be executed in parallel. By analyzing dependencies and fusing low-interaction FFN layers, it achieves significant reductions in inference latency and computational cost. Unlike traditional techniques that modify numerical precision or prune parameters, this approach restructures the model while preserving accuracy. Best Use Cases High-throughput AI applications : Ideal for AI assistants, chatbots, and large-scale LLM-based systems that need rapid multi-token generation. Enterprise-level LLM deployments : Works well where cost efficiency is important without compromising model performance. Real-time scientific research tools : Can enhance inference speed in AI-driven analytics, simulations, and predictive modeling. Quantization Quantization reduces the precision of numerical calculations (e.g., from 32-...
A detailed analysis of tentative timelines and the extent of change due to AI and robotics across key sub-sectors in India, focusing on the period from 2040 to 2055, with insights drawn from current trends, government initiatives, and industry projections. Analysis is tailored to reflect India’s unique socioeconomic landscape, including its large informal economy, youthful workforce, and ongoing digital transformation. Where relevant, Key Assumptions Technological Progress : By 2040–2055, AI and robotics will advance significantly, with improved natural language processing (NLP) supporting regional languages, cost reductions in hardware, and scalable mobile-based solutions overcoming infrastructure barriers. India-Specific Factors : India’s large youth population, growing IT sector, and government initiatives (e.g., IndiaAI Mission, Digital India) will drive adoption, but uneven infrastructure and skill gaps will moderate the pace in rural areas and informal sectors. Extent of Ch...
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