Marktechpost

JANUARY 12, 2025

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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Unite.AI

AUGUST 28, 2023

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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Unite.AI

MAY 29, 2024

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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Data Extraction Neural Network Large Language Models NLP 273

Explosion

JULY 6, 2021

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 ?

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BERT Auto-complete NLP Python 52

The MLOps Blog

SEPTEMBER 26, 2024

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.

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LLM Prompt Engineering Prompt Engineer Auto-complete 72

Viso.ai

DECEMBER 22, 2023

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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Convolutional Neural Networks Computer Vision Neural Network Auto-classification 52

AWS Machine Learning Blog

NOVEMBER 1, 2023

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.

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