Emmimal Alexander

As an AI expert based in India with a focus on global tech trends, I analyzed the intensifying US-China AI race in my article China vs. USA: The Race for AI Supremacy and Where India Stands (published Nov 2025): https://emitechlogic.com/china-vs-usa-the-race-for-ai-supremacy-and-where-india-stands/. The piece examines America's private-sector innovation (e.g., OpenAI, NVIDIA) versus China's state-driven scale (e.g., AIDP investments, surveillance tech), while spotlighting India's strengths as a talent hub (IITs, IndiaAI Mission) and potential neutral player in ethical AI development and outsourcing. I offer balanced, forward-looking insights on geopolitical implications, job impacts, and pathways for collaboration—ideal for stories on AI power dynamics, emerging markets, or global ethics

In this Article I explored the historical 'AI Winters' and their modern lessons in my in-depth article AI Winter Explained: How Funding Cuts, Failed Promises, and Market Shifts Shaped the Future of Artificial Intelligence (Dec 2025): https://emitechlogic.com/ai-winter-explained-how-funding-cuts-failed-promises-and-market-shifts-shaped-the-future-of-artificial-intelligence/. Covering the 1974–1980 (e.g., ALPAC Report ending $20M in translation funding) and 1987–1993 (e.g., Lisp Machine collapse, expert systems failures) busts, it details causes like overpromising, scalability issues, and funding crashes. I contrast this with today's more resilient AI—driven by real revenue and.production-scale deployment—while highlighting risks of market corrections from high costs and hype. Practical sections offer advice for sustainable AI careers (e.g., master fundamentals, diversify skills) and businesses (e.g., prioritize ROI, avoid dependencies). Ideal for commentary on AI project failures, hype cycles, investment risks, or whether another 'Winter' looms.

As an AI engineer specializing in deep learning history and applications, I detailed the 70-year rise of neural networks in my comprehensive article Rise of Neural Networks: Historical Evolution, Practical Understanding and Future Impact (Dec 2025): https://emitechlogic.com/rise-of-neural-networks-historical-evolution-practical-understanding-and-future-impact-on-modern-ai-systems/. Tracing milestones from McCulloch-Pitts (1943) and Perceptron hype/collapse (1957–1969) to backpropagation (1986), AlexNet's GPU-fueled breakthrough (2012), and Transformers (2017), it explains core mechanics (e.g., attention, LSTMs) with interactive demos and tables. I highlight lessons from failures—driving rigor over hype—while exploring modern impacts: foundation models, multimodal systems, agentic AI, and emergent capabilities from scale. Balanced insights on why neural nets dominate (vs. symbolic AI) and future challenges (e.g., data bottlenecks). Perfect for commentary on AI evolution, deep learning fundamentals, hype cycles, or the road to agentic systems.

As an AI engineer with hands-on deployment experience, I traced 750 years of AI engineering history—focusing on why systems fail predictably—in my article The Engineering History of AI: Why Modern Systems Fail in Predictable Ways (Dec 2025): https://emitechlogic.com/the-engineering-history-of-ai/. From Ramon Llull's 13th-century combinatorial machine and Turing's Universal Machine to symbolic AI winters, backpropagation, Transformers, and modern LLMs, it highlights recurring constraints (e.g., combinatorial explosion causing hallucinations, tokenization breaking math, context truncation). Practical sections explain production pitfalls—like inference costs and stochastic outputs—and solutions: grounding with RAG/tools, hybrid neurosymbolic integration, and deterministic verification. Balanced insights on building reliable AI, drawing from real deployments. Ideal for commentary on LLM failures, engineering reliability, neurosymbolic trends, or historical lessons for today's hype.

As an AI engineer bridging classical and modern systems, I authored a complete guide to expert systems—the rule-based AI pioneers of the 1970s–1980s—in Expert Systems: A Complete Guide (Dec 2025): https://emitechlogic.com/expert-systems-a-complete-guide/. Covering core components (knowledge base, inference engines with forward/backward chaining), iconic examples (MYCIN's diagnostics, XCON's millions in savings), and hands-on Python code for building your own (e.g., laptop troubleshooting system), it explains their decline (knowledge bottlenecks, maintenance) while highlighting lessons for today: superior explainability vs. black-box LLMs, and the rise of hybrids/neurosymbolic AI. Practical insights on when rules outperform ML (e.g., compliance, transparency). Ideal for commentary on trustworthy AI, explainable systems, neurosymbolic trends, or historical roots of modern challenges.

I created a complete hands-on guide to developing an AI-powered tutor using Retrieval-Augmented Generation and vector databases in Building an AI-Powered Tutor with RAG and Vector Databases (Dec 2025): https://emitechlogic.com/building-an-ai-powered-tutor-with-rag-and-vector-databases/. Using LangChain, Pinecone, Groq API (Llama/Mixtral), HuggingFace embeddings, and Streamlit, it walks through data ingestion from PDFs, chunking, semantic retrieval, grounded prompting, and a live chat UI—with full code, GitHub repo (github.com/Emmimal/ai-powered-tutor-rag-vector-db), interactive RAG simulator, and evaluation metrics (Ragas/TruLens). Practical tips on reducing hallucinations, optimizing latency (caching/HNSW), and ethical deployment for personalized education. Ideal for insights on AI tutors, reliable RAG applications, EdTech innovation, or overcoming LLM limitations in learning tools.