Remove Auto-complete Remove BERT Remove Categorization
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Researchers from Fudan University and Shanghai AI Lab Introduces DOLPHIN: A Closed-Loop Framework for Automating Scientific Research with Iterative Feedback

Marktechpost

Researchers want to create a system that eventually learns to bypass humans completely by completing the research cycle without human involvement. Fudan University and the Shanghai Artificial Intelligence Laboratory have developed DOLPHIN, a closed-loop auto-research framework covering the entire scientific research process.

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Beyond ChatGPT; AI Agent: A New World of Workers

Unite.AI

Systems like ChatGPT by OpenAI, BERT, and T5 have enabled breakthroughs in human-AI communication. Current Landscape of AI Agents AI agents, including Auto-GPT, AgentGPT, and BabyAGI, are heralding a new era in the expansive AI universe. Their primary focus is to minimize the need for human intervention in AI task completion.

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Making Sense of the Mess: LLMs Role in Unstructured Data Extraction

Unite.AI

Named Entity Recognition ( NER) Named entity recognition (NER), an NLP technique, identifies and categorizes key information in text. Source: A pipeline on Generative AI This figure of a generative AI pipeline illustrates the applicability of models such as BERT, GPT, and OPT in data extraction.

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Introducing spaCy v3.1

Explosion

For example, you’ll be able to use the information that certain spans of text are definitely not PERSON entities, without having to provide the complete gold-standard annotations for the given example. spacy-dbpedia-spotlight Use DBpedia Spotlight to link entities ✍️ contextualSpellCheck Contextual spell correction using BERT ?

BERT 52
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LLM Hallucinations 101: Why Do They Appear? Can We Avoid Them?

The MLOps Blog

Self-attention is the mechanism where tokens interact with each other (auto-regressive) and with the knowledge acquired during pre-training. In extreme cases, certain tokens can completely break an LLM. Others, like Gary Marcus, argue strongly that transformer-based LLMs are completely unable to eliminate hallucinations.

LLM 72
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Segment Anything Model (SAM) Deep Dive – Complete 2024 Guide

Viso.ai

This leap forward is due to the influence of foundation models in NLP, such as GPT and BERT. Full-Auto: SAM independently predicts segmentation masks in the final stage, showcasing its ability to handle complex and ambiguous scenarios with minimal human intervention.

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Dialogue-guided visual language processing with Amazon SageMaker JumpStart

AWS Machine Learning Blog

Key strengths of VLP include the effective utilization of pre-trained VLMs and LLMs, enabling zero-shot or few-shot predictions without necessitating task-specific modifications, and categorizing images from a broad spectrum through casual multi-round dialogues. To mitigate the effects of the mistakes, the diversity of demonstrations matter.