Publicación de Perplexity

Learn about Caption-to-Phrase Grounding in Florence 2: Discover how this technique bridges captions with specific image regions for precise understanding and retrieval. #captiontophrasegrounding #florence2 #computervision #ai #machinelearning #deeplearning #imageunderstanding #semanticsegmentation #visualperception #imageprocessing #datascience #artificialintelligence #computerscience #algorithm #objectdetection #imageanalysis #neuralnetworks #datavisualization #learnai

GPT-4 killer in the market🔥 Anthropic has just dropped a bomb in the AI landscape with the groundbreaking Claude 3 AI Model. Claude Opus, the apex model, is flexing near-human comprehension and fluency, dominating tasks like analysis, code generation, and multilingual conversations. Check out our latest blog about Claude 3 and discover how it is reshaping the AI landscape 👉 https://lnkd.in/dzQWHXgk #Anthropic #Claude3 #GPT4Killer #ChatGPT #AI

Mistral AI's Groundbreaking Mixtral 8x7B: Revolutionizing Machine Learning with Sparse Mixture of Experts (SMoE) Language Model #32ktokencontextsize #AI #AItechnology #artificialintelligence #benchmarks #costandlatencycontrols #feedforwardblock #llm #machinelearning #MistralAI #Mixtral8X7B #parametergroups #Pretraining #SparseMixtureofExperts

LLaVA-NeXT: Pioneering Progress in Multimodal Understanding and Video Comprehension In the dynamic pursuit of Artificial General Intelligence (AGI), the significance of Language and Multimodal Models (LLMs and LMMs) cannot be overstated. They serve as the cornerstone of cutting-edge AI, mimicking the cognitive abilities of human minds across a spectrum of tasks. However, amidst this progress, evaluating the capabilities of these models remains a challenge, with fragmented datasets spread across various platforms like Google Drive and Dropbox. https://is.gd/SsV703 #AI #AnyRestechnique #artificialintelligence #benchmarksurpassing #LLaVANeXT #llm #machinelearning #multimodalunderstanding #OCRcapabilities #opensourceLMM #reasoning #videocomprehension

Claude 3, introduced by Anthropic, features models like Haiku, Sonnet, and Opus with distinct balances of speed, intelligence, and cost. While GPT-4's pricing is based on prompt and sampled tokens, Claude 3's pricing varies across its models based on input and output token usage. Each model excels in different areas, with Claude 3 focusing on multi-language support, vision capabilities, and reduced biases, and GPT-4 offering extensive capabilities in language understanding and generation with varying context lengths. Both platforms emphasize the development of responsible AI, aiming to minimize biases and ensure ethical use of technology. Try Claude 3: https://claude.ai/chat Release: https://lnkd.in/gcVthRmm #generatieveai #claude3 #anthropic #responsibleai

Mitigating LLMs hallucinations is one of the major challenges we face in our custom generative AI Solutions. Found the following Nature paper titled "Detecting hallucinations in large language models using semantic entropy" by Sebastian et al to be interesting. Here, are few highlights of the paper. 🕹 Implements an entropy-based uncertainty estimator that identifies confabulations, which are arbitrary and incorrect outputs 🕹Calculates uncertainty at the level of meaning rather than word sequences 🕹Adaptable across different datasets and tasks 🕹Does not require task-specific data 🕹Helps users recognize when an LLM's response might be unreliable, potentially increasing the safe and effective use of LLMs Looking forward to try it in our solutions. #AI #Hallucinations #LLMs #OpenAI #AISolutions #EthicalAI #Safety #aiconsulting

Using YOLO_NAS, A computer vision model DECI AI, the model was trained on a sign language dataset provided from RoboFlow to be able to detect and predict sign language Further improvements can be done to the model Be sure to add suggestions and corrections to the code, Thank you :) #AI #ComputerVision #YOLO_NAS

Fascinating, but predictable trends in AI model performance! 📊 This chart from Maxime Labonne shows the rapid progress of open-weight language models compared to closed-source ones, updated with #Llama3.1 Key takeaways: - Llama 3.1 405B has narrowed the gap significantly, performing on par with top closed-source models like GPT-4 and Claude 3.5 Sonnet. - Open-source models have made remarkable strides since 2022, with performance jumping from ~54% to over 85%. - The rate of improvement for open models appears steeper than closed ones. - More accessibility and transparency in powerful AI capabilities - Potential for faster innovation through collaborative open-source efforts #ArtificialIntelligence #MachineLearning #OpenSource #AITrends

Computer Vision with 🦝 Lenny! Learned Image Classification (how computers see pics) In 1 min #lennytheraccoon #computervision #AI #Imageclassification

Google's Gemini: A Controversial Yet Unmatched Milestone in AGI. Despite criticisms over its demo presentation, Gemini sets a new standard in AI, eclipsing GPT-4 with its advanced multimodal functions with reasoning capabilities and readiness for mobile deployment. This development marks a significant moment in the generative AI landscape. #Gemini #AGI #LargeLanguageModels #GenerativeAI #TechInnovation #SynacalAI https://lnkd.in/diM78xvM