3. Hands-on Projects: I led the development of "MedicLLM," an AI assistant for psychiatric diseases, which was tested by a renowned hospital . This project involved creating an argumentative multi-agent framework and XAI which improved clinician trust.
2. I built a Part-of-Speech (POS) tagger for an under-resourced language. For this, I trained and compared a range of models, including classic SVM and HMM, as well as deep learning models. I also fine-tuned modern SLM deploying the final solution as a live Streamlit app.
A few things I've worked on:
1. Professional Experience : At a previous organization, I developed a web mining application and fine-tuned Stable Diffusion models, which improved image generation. I also engineered RAG-based LLM services and built a conversational AI chatbot
Was this the only thing left? Now even research papers are done by AI
In this new arXiv paper, “C. Opus” co-authors a rebuttal defending AI reasoning by blaming token limits and bad prompts.
Basically: AI just explained why humans were wrong about AI 😅
Self-defense: On
Attended Techfest today and by IIT Bombay:
Udaan – enabling translation of learning materials across Indian languages with Project LeapOver bridging language barriers.
BharatGen – building Generative AI tailored to India’s needs with homegrown LLMs like BharatGen2B.
#techfest
In a mere 24 months, large language models have catalyzed an AI renaissance. Groundbreaking systems like LLMs.
Their ability to understand, analyze, and generate.This swift paradigm shift demonstrates AI's potential to reshape industries and redefine the boundaries of A.I
"Today, I had the pleasure of meeting a statue of the great Alan Turing. Even though he's cast in stone, his genius has a way of Turing people into computer enthusiasts like me! 🌟🤖 His legacy continues to inspire and shape the world of technology." #AlanTuring
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Falcon 180B was trained on 3.5 trillion tokens on up to 4096 GPUs simultaneously, using Amazon SageMaker for a total of ~7,000,000 GPU hours. This means Falcon 180B is 2.5 times larger than Llama 2.
Stands between GPT 3.5 and 4.
Blog Post hugging face:- https://t.co/czTuakWhu3
Many LLMs sprint ahead, pursuing general intelligence. Many strives to keep up the pace. Though the finish line is still far off, the leading LLMs run with optimism, hoping their progress will benefit humanity.
@OpenAI@AnthropicAI@MosaicML@huggingface#LLMs#AI
Calling all @langchain developers
There are no Langchain communities in Twitter
Since there is no community
I created one for developers
To interact and learn from each other
Plan to keep a small and focused LangChain group
More details below ↓
The ability to reduce training parameters by x1000. With just a couple of datasets, it is now possible to achieve desirable outcomes by your own data to a pre-trained model. It's remarkable how a different computational approach can achieve impressive results with minimal effort