Margaret M.

“All outputs are hallucinations i.e. fabricated and ungrounded. Many of these outputs happen to match reality when there’s abundant training data and repetition” Yeah that really nails what it is like working with language models. Very insightful post.

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🚨 AI Trend to Watch: Diffusion Language Models Most of today’s AI systems — including popular LLMs — generate text one token at a time. This sequential approach has powered the generative AI wave but also introduces latency and compute inefficiencies. A new approach may change that. Inception Labs’ Mercury introduces the concept of a Diffusion Large Language Model (dLLM) — applying diffusion techniques (similar to those used in image models like Stable Diffusion) to language generation. Why this matters: • Parallel generation – Instead of producing tokens sequentially, diffusion models refine outputs in parallel, dramatically reducing latency. • Speed at scale – Early benchmarks show generation speeds exceeding 1000 tokens per second on modern GPUs. • Real-time AI systems – This architecture could unlock truly responsive AI for coding copilots, conversational agents, and interactive applications. This signals a broader shift in the AI landscape: ➡️ From sequential generation → parallel generation ➡️ From batch AI workflows → real-time AI systems ➡️ From model intelligence alone → intelligence + speed as a competitive edge For organizations building AI platforms, it’s a reminder that model architecture innovation is far from over. The next breakthroughs in AI may come not only from larger models — but from fundamentally different ways of generating intelligence. #AITrends #ArtificialIntelligence #GenerativeAI #LLM #AIInfrastructure #MachineLearni

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Claude Code + Opus is now excellent at surfacing unknowns in large codebases and for epic sized projects. If you're not starting every epic with AI research spikes and pre-spike clarifications, you're missing a big part of first delivery success. "Is this epic shovel ready or do we still have gaps?" #ai #claudecode #compoundengineering

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Abstractions vs. Substrates is a great way to understand what AI is amazing at and what it needs to deliver and be trusted in the enterprise. Adam Seligman puts it great!

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Imagine conversing with your books as if they were mentors. 📚 The rise of voice‑first AI is reshaping how we absorb knowledge—from static PDFs to interactive dialogues. What makes Bookified stand out is its blend of real‑time Vapi voice, ElevenLabs personas, and seamless Clerk authentication, turning PDFs into living conversations. 👉 Upload PDFs and let AI chunk them intelligently - Choose a persona and start a voice chat instantly - Capture every exchange with auto‑generated transcripts - Organize your library with smart search - Secure access and subscription for premium features Beyond efficiency, this tech invites us to rethink AI as a collaborative companion in learning and leadership. How will you let voice‑first AI reshape the way you lead and learn? #AI #VoiceTech #Learning #Leadership #DigitalTransformation #EthicalAI Reference: [https://lnkd.in/gXNZYzBq] 🔄 Share 👍 React 🌐 Visit www.aravind-r.com #AravindRaghunathan

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Responsibilities AI Developing reposible AI requeires an understanding of the possible issues, limitations, or unintended consequences. https://lnkd.in/gx7DX4sf

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