Celebrating 5 years with the dida-conference!🎂 We had an amazing day filled with machine learning (#ML) talks, panels, and workshops featuring top organizations. We captured some moments of it and turned them into a short video. 𝐯𝐢𝐝𝐞𝐨: youtube.com/watch?v=y3Le5T…

We are thrilled to announce the launch of our brand-new website design! We aimed to create a user-friendly, informative, and visually appealing platform for our blog readers, customers, and partners. Take a look at our new website design at dida.do

dida will be at #ICLR2024 with two papers in Vienna this year. Check them out in case you are there.

Visual Instruction Tuning Learn about "Visual Instruction Tuning": GPT-4 generates multimodal instruction tracking data. ViT-L/14 integration with Vicuna enables image comprehension and conversational applications. Read more: arxiv.org/abs/2304.08485

Choosing the Right GPT Model for Your Business For processing 4,500 documents per month: ◾ It could either spend 126 EUR for the overall task or up to 7.150 EUR ◾ GPT-3.5 is 50 times cheaper than GPT-4 ◾ GPT-4o is still 5 times cheaper than GPT-4 #AI #LLM #GPT #TECH4ALL

We recently examined MambaVision, a new backbone combining Vision Transformers (ViT) with Mamba. This integration boosts scores and performance for tasks like object detection and instance segmentation. More details here: arxiv.org/abs/2407.08083 #machinelearning #tech #ML #AI

We would like to present a paper on Physics-Informed Neural Networks (PINNs) that use physical laws to improve neural network training. It features a recent PyTorch implementation demonstrating PINNs by calculating gravity from thrown ball data. sciencedirect.com/science/articl… #ML

NVIDIA Shrinks LLama3.1 8B to 4B with Pruning and Distillation NVIDIA's latest research reduces LLama3.1 8B to 4B parameters by pruning 50% of its layers. Retrained with 40x fewer tokens, the model sees a 16% MMLU score boost. Read more: arxiv.org/pdf/2407.14679 #AI #research

OpenAI’s new o1 model boosts accuracy with chain-of-thought reasoning, excelling in complex tasks like IMO challenges (83% vs. 13% for GPT-4o) and competitive programming. Here you can read our summary for the o1 model: shorturl.at/NzjfD #AI #OpenAI #ML #innovation

In our last reading group, dida’s Machine Learning Scientists discussed the paper (arxiv.org/pdf/2501.12948) from deepseek about the DeepSeek-R1 model and their approach of using reinforcement learning directly without prior fine-tuning.