Machine Learning

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OpenAI sidesteps Nvidia with unusually fast coding model on plate-sized chips

ai, AI (Artificial Intelligence), AI Agents, AI chips, AI coding, AI development tools, AI speed, Artificial Intelligence, Biz & IT, Cerebras, code agents, Machine Learning, nvidia, OpenAI, Sam Altman, tokens

On Thursday, OpenAI released its first production AI model to run on non-Nvidia hardware, deploying the new GPT-5.3-Codex-Spark coding model on chips from Cerebras. The model delivers code at more than 1,000 tokens (chunks of data) per second, which is reported to be roughly 15 times faster than its predecessor. To compare, Anthropic’s Claude Opus […]

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Attackers prompted Gemini over 100,000 times while trying to clone it, Google says

ai, AI (Artificial Intelligence), AI language models, AI security, Artificial Intelligence, Biz & IT, ChatGPT, chatgtp, deepseek, Distillation, Gemini, Google, Google Gemini, Large Language Models, LLMs, Machine Learning, OpenAI

On Thursday, Google announced that “commercially motivated” actors have attempted to clone knowledge from its Gemini AI chatbot by simply prompting it. One adversarial session reportedly prompted the model more than 100,000 times across various non-English languages, collecting responses ostensibly to train a cheaper copycat. Google published the findings in what amounts to a quarterly

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Accelerating science with AI and simulations

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For more than a decade, MIT Associate Professor Rafael Gómez-Bombarelli has used artificial intelligence to create new materials. As the technology has expanded, so have his ambitions.Now, the newly tenured professor in materials science and engineering believes AI is poised to transform science in ways never before possible. His work at MIT and beyond is

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Expert-vetted reasoning datasets for reinforcement learning: why they lift model performance

ai, AI (Artificial Intelligence), ai training data, Artificial Intelligence, Machine Learning, Shaip Blogs

Reinforcement learning (RL) is great at learning what to do when the reward signal is clean and the environment is forgiving. But many real-world settings aren’t like that. They’re messy, high-stakes, and full of “almost right” decisions. That’s where expert-vetted reasoning datasets become a force multiplier: they teach models the why behind an action—not just

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What Is Sociophonetics and Why It Matters for AI

ai, AI (Artificial Intelligence), ai training data, Artificial Intelligence, Audio Collection, Conversational AI, Data Collection, Machine Learning, Shaip Blogs, Speech Recognition, TTS

You’ve probably had this experience: a voice assistant understands your friend perfectly, but struggles with your accent, or with your parents’ way of speaking. Same language. Same request. Very different results. That gap is exactly where sociophonetics lives — and why it suddenly matters so much for AI. Sociophonetics looks at how social factors and

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AI vs ML vs LLM vs Generative AI: What’s the Difference and Why It Matters

ai, AI (Artificial Intelligence), Artificial Intelligence, Generative AI, Large Language Models, LLM, Machine Learning, Shaip Blogs

In today’s AI-driven world, buzzwords like AI, Machine Learning (ML), Large Language Models (LLMs), and Generative AI are everywhere—but often misunderstood. They’re used interchangeably, though each has a distinct role and impact. In this blog, we won’t just define them in silos. Instead, we’ll pit them against each other, clarify how they’re related, how they

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How to Build a Matryoshka-Optimized Sentence Embedding Model for Ultra-Fast Retrieval with 64-Dimension Truncation

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In this tutorial, we fine-tune a Sentence-Transformers embedding model using Matryoshka Representation Learning so that the earliest dimensions of the vector carry the most useful semantic signal. We train with MatryoshkaLoss on triplet data and then validate the key promise of MRL by benchmarking retrieval quality after truncating embeddings to 64, 128, and 256 dimensions.

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AI For Image Recognition: What It Is, How It Works & Examples

ai, AI (Artificial Intelligence), Artificial Intelligence, Computer vision, Machine Learning, Shaip Blogs

Human beings have the innate ability to distinguish and precisely identify objects, people, animals, and places from photographs. Artificial intelligence is the underlying technology that powers image recognition, enabling computers to analyze and interpret visual data. However, computers don’t come with the capability to classify images. Yet, they can be trained to interpret visual information

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AI Models & Ethical Data: Building Trust in Machine Learning

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In the rapidly evolving landscape of artificial intelligence, one fundamental truth remains constant: the quality and ethics of your training data directly determine the trustworthiness of your AI models. As organizations race to deploy machine learning solutions, the conversation around ethical data collection and responsible AI development has moved from the periphery to the center

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What is Named Entity Recognition (NER) – Example, Use Cases, Benefits & Challenges

ai, AI (Artificial Intelligence), Artificial Intelligence, Machine Learning, NER, NLP(Natural Language Processing), Shaip Blogs

Every time we hear a word or read a text, we have the natural ability to identify and categorize the word into people, place, location, values, and more. Humans can quickly recognize a word, categorize it and understand the context. For example, when you hear the word ‘Steve Jobs,’ you can immediately think of at

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