Unite.AI

MAY 29, 2024

This advancement has spurred the commercial use of generative AI in natural language processing (NLP) and computer vision, enabling automated and intelligent data extraction. Typically, the generative AI model provides a prompt describing the desired data, and the ensuing response contains the extracted data.

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Unite.AI

AUGUST 2, 2023

These limitations are a major issue why an average human mind is able to learn from a single type of data much more effectively when compared to an AI model that relies on separate models & training data to distinguish between an image, text, and speech. They require a high amount of computational power.

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ODSC - Open Data Science

JUNE 9, 2023

Once a set of word vectors has been learned, they can be used in various natural language processing (NLP) tasks such as text classification, language translation, and question answering. This allows BERT to learn a deeper sense of the context in which words appear. or ChatGPT (2022) ChatGPT is also known as GPT-3.5

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Towards AI

OCTOBER 18, 2024

How Retrieval-Augmented Generation (RAG) Can Boost NLP Projects with Real-Time Data for Smarter AI Models This member-only story is on us. With models like GPT-3 and BERT, it feels like we’re able to do things that were once just sci-fi dreams, like answering complex questions and generating all kinds of content automatically.

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NLP Natural Language Processing BERT ChatGPT 111

IBM Journey to AI blog

AUGUST 1, 2023

True to their name, generative AI models generate text, images, code , or other responses based on a user’s prompt. But what makes the generative functionality of these models—and, ultimately, their benefits to the organization—possible? An open-source model, Google created BERT in 2018.

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Marktechpost

DECEMBER 5, 2023

Large Language Models (LLMs), like GPT, PaLM, LLaMA, etc., Their ability to utilize the strength of Natural Language Processing, Generation, and Understanding by generating content, answering questions, summarizing text, and so on have made LLMs the talk of the town in the last few months.

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Unite.AI

OCTOBER 22, 2024

From recommending products online to diagnosing medical conditions, AI is everywhere. As AI models become more complex, they demand more computational power, putting a strain on hardware and driving up costs. For example, as model parameters increase, computational demands can increase by a factor of 100 or more.

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Neural Network Convolutional Neural Networks Deep Learning Natural Language Processing 147

Unite.AI

NOVEMBER 6, 2023

In recent years, Generative AI has shown promising results in solving complex AI tasks. Modern AI models like ChatGPT , Bard , LLaMA , DALL-E.3 Moreover, Multimodal AI techniques have emerged, capable of processing multiple data modalities, i.e., text, images, audio, and videos simultaneously.

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AWS Machine Learning Blog

FEBRUARY 6, 2024

We address this skew with generative AI models (Falcon-7B and Falcon-40B), which were prompted to generate event samples based on five examples from the training set to increase the semantic diversity and increase the sample size of labeled adverse events.

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Large Language Models BERT NLP Data Scientist 137

AssemblyAI

MAY 3, 2023

The problem of how to mitigate the risks and misuse of these AI models has therefore become a primary concern for all companies offering access to large language models as online services. This concept is not exclusive to natural language processing, and has also been employed in other domains.

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Marktechpost

JULY 22, 2024

Authorship Verification (AV) is critical in natural language processing (NLP), determining whether two texts share the same authorship. With deep learning models like BERT and RoBERTa, the field has seen a paradigm shift. Existing methods for AV have advanced significantly with the use of deep learning models.

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Topbots

APRIL 11, 2023

In the rapidly evolving field of artificial intelligence, natural language processing has become a focal point for researchers and developers alike. We’ll start with a seminal BERT model from 2018 and finish with this year’s latest breakthroughs like LLaMA by Meta AI and GPT-4 by OpenAI.

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NVIDIA

FEBRUARY 11, 2025

Like the prolific jazz trumpeter and composer, researchers have been generating AI models at a feverish pace, exploring new architectures and use cases. Google released BERT as open-source software , spawning a family of follow-ons and setting off a race to build ever larger, more powerful LLMs.

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Marktechpost

MAY 8, 2024

Traditional models often require substantial resources, which hinders practical application and scalability. Existing research in large language models (LLMs) includes foundational frameworks like GPT-3 by OpenAI and BERT by Google, utilizing traditional Transformer architectures. Check out the Paper and Github.

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Artificial Corner

JULY 30, 2023

Photo by Shubham Dhage on Unsplash Introduction Large language Models (LLMs) are a subset of Deep Learning. Image by YouTube video “Introduction to large language models” on YouTube Channel “Google Cloud Tech” What are Large Language Models? What are large language models used for?

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Unite.AI

SEPTEMBER 18, 2023

Artificial Intelligence is a very vast branch in itself with numerous subfields including deep learning, computer vision , natural language processing , and more. Using blockchain frameworks to deploy AI models to achieve decentralization services among models, and enhancing the scalability and stability of the system.

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Unite.AI

FEBRUARY 23, 2024

Prompt engineering is the art and science of crafting inputs (or “prompts”) to effectively guide and interact with generative AI models, particularly large language models (LLMs) like ChatGPT. But what exactly is prompt engineering, and why has it become such a buzzword in the tech community?

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Marktechpost

JANUARY 15, 2025

These limitations are particularly significant in fields like medical imaging, autonomous driving, and natural language processing, where understanding complex patterns is essential. This gap has led to the evolution of deep learning models, designed to learn directly from raw data. What is Deep Learning?

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Marktechpost

APRIL 25, 2024

They use deep learning techniques to process and produce language in a contextually relevant manner. The development of LLMs, such as OpenAI’s GPT series, Google’s Gemini, Anthropic AI’s Claude, and Meta’s Llama models, marks a significant advancement in natural language processing.

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John Snow Labs

JUNE 27, 2023

Are you curious about the groundbreaking advancements in Natural Language Processing (NLP)? Prepare to be amazed as we delve into the world of Large Language Models (LLMs) – the driving force behind NLP’s remarkable progress. What are Large Language Models (LLMs)?

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Mlearning.ai

APRIL 8, 2023

Charting the evolution of SOTA (State-of-the-art) techniques in NLP (Natural Language Processing) over the years, highlighting the key algorithms, influential figures, and groundbreaking papers that have shaped the field. Evolution of NLP Models To understand the full impact of the above evolutionary process.

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Topbots

OCTOBER 4, 2023

Predictive AI is used to predict future events or outcomes based on historical data. For example, a predictive AI model can be trained on a dataset of customer purchase history data and then used to predict which customers are most likely to churn in the next month. a social media post or product description).

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Marktechpost

OCTOBER 29, 2024

PII Masker is an advanced open-source tool designed to protect sensitive data by leveraging state-of-the-art artificial intelligence (AI) models. Developed by HydroXai, PII Masker is available on GitHub and aims to streamline the process of identifying and masking PII within data sets.

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Snorkel AI

MARCH 1, 2023

Table of contents What are foundation models? Foundation models are large AI models trained on enormous quantities of unlabeled data—usually through self-supervised learning. The field of foundation models is developing fast, but here are some of the most noteworthy entries as of this page’s most recent update.

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Towards AI

AUGUST 2, 2024

One of the pillars of this transformation has been the adoption of large language models(LLM) and we cannot imagine the development of AI without them. From GPT-3 to BERT, these models are revolutionizing natural language processing, developing machines that understand, generate, and interact in human languages.

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Towards AI

JANUARY 30, 2024

To alleviate this… abhinavkimothi.gumroad.com Types of Models Foundation Models Large AI models that have millions/billions of parameters and are trained on terabytes of generalized and unlabelled data. Designed to be general-purpose, providing a foundation for various AI applications. Examples: GPT 3.5,

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AWS Machine Learning Blog

JUNE 25, 2024

Businesses can use LLMs to gain valuable insights, streamline processes, and deliver enhanced customer experiences. Advantages of adopting generative approaches for NLP tasks For customer feedback analysis, you might wonder if traditional NLP classifiers such as BERT or fastText would suffice.

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Viso.ai

MARCH 20, 2024

Foundation models are recent developments in artificial intelligence (AI). Models like GPT 4, BERT, DALL-E 3, CLIP, Sora, etc., are at the forefront of the AI revolution. Use Cases for Foundation Models Applications in Pre-trained Language Models like GPT, BERT, Claude, etc.

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Towards AI

SEPTEMBER 5, 2024

Understanding the Transformer Architecture by Greg Postalian-Yrausquin Are you curious about how modern AI models like GPT and BERT work? This article provides a comprehensive overview of the Transformer Architecture, breaking down its key components and mechanisms that have revolutionized natural language processing.

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Unite.AI

SEPTEMBER 13, 2024

These techniques allow TensorRT-LLM to optimize inference performance for deep learning tasks such as natural language processing, recommendation engines, and real-time video analytics. Triton is an open-source software that supports dynamic batching, model ensembles, and high throughput.

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ODSC - Open Data Science

JANUARY 17, 2025

Implicit Learning of Intent : LLMs like GPT, BERT, or other transformer-based models learn to predict the next word or fill in missing text based on surrounding context. The primary goal of RAG is to enhance the quality and accuracy of generated text by incorporating relevant external information during the generation process.

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John Snow Labs

OCTOBER 19, 2023

Natural language processing (NLP) is a critical branch of artificial intelligence devoted to understanding and generating natural language. LangTest supports Hugging Face ( HF ) models for this test, and it loads them with fill-mask as selected task. Some example samples.

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Snorkel AI

SEPTEMBER 21, 2023

Natural language processing ( NLP ) and computer vision can capture values specific to the trial subject that help identify or exclude potential participants, creating alignment across different systems and document types. The language for eligibility criteria is not standardized, and data sources can be dense and unstructured.

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Viso.ai

JULY 18, 2024

LLMs are pre-trained on extensive data on the web which shows results after comprehending complexity, pattern, and relation in the language. LLMs apply powerful Natural Language Processing (NLP), machine translation, and Visual Question Answering (VQA). Selecting Model : Choose an appropriate pre-trained model (e.g.,

Large Language Models 52

Large Language Models Neural Network Natural Language Processing BERT 52

Snorkel AI

SEPTEMBER 21, 2023

Natural language processing ( NLP ) and computer vision can capture values specific to the trial subject that help identify or exclude potential participants, creating alignment across different systems and document types. The language for eligibility criteria is not standardized, and data sources can be dense and unstructured.

BERT 59

BERT Data Scientist Natural Language Processing Data Science 59

John Snow Labs

OCTOBER 3, 2023

A noteworthy observation is that even popular models in the machine learning community, such as bert-base-uncased, xlm-roberta-base, etc exhibit these biases. These models, trained on vast amount of data, inevitably inherit the prejudices present in the datasets they’re trained on.

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John Snow Labs

NOVEMBER 6, 2023

On the other hand, LangTest has emerged as a transformative force in the realm of Natural Language Processing (NLP) and Large Language Model (LLM) evaluation. The `transformers` library by Hugging Face offers a multitude of pretrained models, including those for natural language processing tasks.

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