A Large Language Model (LLM) is a type of artificial intelligence model designed to understand and generate human language. Large language models can answer questions, write essays, summarise texts, translate languages, generate creative content, and even engage in conversation. Their ability to generate coherent and contextually relevant text is what makes them powerful tools for language-based applications.
DeepVisionTech combines automatic sign-language interpretation, workplace inclusion tools, and education-focused solutions into an integrated, end-to-end platform.
India’s ambitious proposal for a single mandatory AI training licence faces feasibility, legal and innovation concerns.
The partnership aims to enable CAMB.AI’s multilingual voice models to run on standard CPUs.
Tinker is an API service that handles compute and infrastructure needs for fine-tuning models.
An SLM should be small and efficient enough to run locally on a laptop, smartphone, or personal GPU, while still being fast and useful for real-world AI agent tasks.
The reasoning capabilities in vision-based and multimodal systems still lag in abstract problem-solving tasks.
The study challenges the claim that reflection in AI models emerges only after fine-tuning or reinforcement learning.
Jyothirlatha explained how the company’s Saksham AI platform is redefining business operations, customer service, and decision-making processes.
Microsoft chief Satya Nadella recently said that traditional SaaS companies will collapse in the AI agent era.
The researchers explain that Transformer2 can adapt like a living brain.
Databricks spent $10 million developing DBRX, yet only recorded 23 downloads on Hugging Face last month.
The core idea behind RevThink is rooted in how humans approach complex problems.
Models like GPT-4 and Claude-1 may be more robust in handling emotional shifts, possibly due to their training.
If the future is all about communication in voice and giving it to the millions of UPI users in the country, speech models are necessary.
NotebookLM started as an experiment by Google Labs in July 2023.
“There are many teams within Meesho who are successfully deploying LLMs in their applications,” says Meesho’s AI chief Debdoot Mukherjee.
Meta’s Llama models are steadily closing the gap with OpenAI’s GPT-4o and o1, pushing towards autonomous machine intelligence with advancements in real-time reasoning and adaptability.
Adds Intel ARC dGPU and Core Ultra iGPU support for Linux and Windows, bringing broader compatibility and performance optimisation to Intel GPUs in AI workloads.
With DIFF Transformer, you can achieve 30% accuracy improvement and 10-20% accuracy gain in many-shot in-context learning across datasets.
“LLMs, with their ever-expanding knowledge base, offer the potential for cross-pollination of ideas.”
Perhaps the most critical challenge that LLM developers face is the lack of robust methods for verifying the outputs of these models.
GPUs are optimised for parallel processing, making them much faster than CPUs for tasks like training deep learning models, which involve extensive matrix calculations. In this Video you will find why you don’t need a GPU to run AI models.
MoE and MoA are two methodologies designed to enhance the performance of large language models (LLMs) by leveraging multiple models.
BNP Paribas has announced a multi-year partnership agreement with Mistral AI to leverage it’s commercial models.
Armand Ruiz has revealed the entirety of its comprehensive 6.48 TB dataset used to train Granite 13B.
“If you’re a student or an academic dreaming of making LLMs for Indian languages, stop wasting your time. You’re not going to make it.”
Perplexity AI had raised $15 million in its seed funding round when it was just a six-month old company, which it could have done in India.
Though there is definitely a need to work on other types of AI.
India needs lot of electricity in the future for AI? No, it just needs 1-bit LLMs.
Moreover, the recent Indian chatbot Hanooman, released by SML is also powered by IIT Bombay projects.
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