My AI Journey: The Tools That Opened Each Door
Author(s): Sophia Banton Originally published on Towards AI. Steve Jobs once said, “Technology is nothing. What’s important is that you have a faith in people, that they’re basically good and smart, and if you give them tools, they’ll do wonderful things with …
LAI #75: Generative AI vs. Agentic AI vs. AI Agents
Author(s): Towards AI Editorial Team Originally published on Towards AI. Good morning, AI enthusiasts, This week’s issue dives into where the field is heading — beyond generation, toward autonomy and better error awareness. We’re starting with a breakdown of the increasingly fuzzy …
Inside a 7-Layer Cognitive Stack: How Claude + MCP Deliver Real-Time Epistemic Intelligence…
Author(s): R. Thompson (PhD) Originally published on Towards AI. “Context is not just a variable. It’s the difference between output and understanding.” The evolution of AI agents has moved beyond prediction and instruction-following into a domain where interpretability, confidence, and memory are …
The Comparison between the Encoder and the Decoder
Author(s): tangbasky Originally published on Towards AI. This article primarily discusses the advantages and disadvantages of large language models based on encoder and decoder architectures. Both the encoder and decoder architectures are built upon the Transformer model. Initially, this encoder-decoder architecture was …
The Hidden Cost of AI: The Workplace Penalty Nobody Saw Coming
Author(s): MKWriteshere Originally published on Towards AI. Image Generated by Author Using Gpt-4o Using AI may boost your productivity while secretly tanking your professional reputation. New research reveals a troubling disconnect between AI’s benefits and how others perceive AI users, creating a …
Merging Minds: How Neuroscience and AI Are Creating the Future of Intelligence
Author(s): Talha Nazar Originally published on Towards AI. Imagine a world where your thoughts can control machines. You think, and a robotic arm moves. You feel, and a digital avatar mimics your expression. Sounds like science fiction, right? But this is no …
How to Achieve Structured Output in Claude 3.7: Three Practical Approaches
Author(s): Omri Eliyahu Levy Originally published on Towards AI. How to Achieve Structured Output in Claude 3.7: Three Practical Approaches TL;DR — Full code can be found here At Baz, we’re building an AI code review agent that combines static analysis with …
Visual Grounding for Advanced RAG Frameworks
Author(s): Felix Pappe Originally published on Towards AI. Image created by the author using gpt-image-1 AI chatbots and advanced Retrieval-Augmented Generation (RAG) systems are increasingly adept at providing up-to-date, context-aware answers based on previously retrieved text chunks. However, despite their seemingly reliable …
DeepSeek R1 on a Budget? Our XGBoost Model Predicts 84% Accuracy and 30–40% RAM Savings via Quantization
Author(s): R. Thompson (PhD) Originally published on Towards AI. (Credit : Developed using AI) “Deploying AI locally is no longer a constraint — it’s a prediction challenge.” Much of today’s generative AI narrative is fixated on benchmarks, token counts, and transformer tweaks. …
A Deep Technical Exploration of Retrieval-Augmented Generation (RAG) with Transformers, DPR, FAISS, and BART
Author(s): Saif Ali Kheraj Originally published on Towards AI. RAG stands for Retrieval-Augmented Generation. It’s a clever setup where a transformer model (you know, the brains behind all gpts) doesn’t just make things up — it actually goes out, finds real information, …
Recent Posts
