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? Natural Language Processing (NLP) is a subfield of artificial intelligence.

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Heartbeat

MARCH 19, 2024

Introduction Large language models (LLMs) have emerged as a driving catalyst in natural language processing and comprehension evolution. As the need for more powerful language models grows, so does the need for effective scaling techniques. Popular LLMs include Falcon 40B, GPT-4, LLaMa 2, and BERT.

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Unite.AI

AUGUST 8, 2023

These advanced AI deep learning models have seamlessly integrated into various applications, from Google's search engine enhancements with BERT to GitHub’s Copilot, which harnesses the capability of Large Language Models (LLMs) to convert simple code snippets into fully functional source codes.

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Unite.AI

SEPTEMBER 20, 2023

Rust aids in this process by speeding up the execution time, ensuring that the tokenization process is not just accurate but also swift, enhancing the efficiency of natural language processing tasks. We choose a BERT model fine-tuned on the SQuAD dataset.

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Towards AI

JANUARY 30, 2024

GPT4, Stable Diffusion, Llama, BERT, Gemini Large Language Models (LLMs) Foundation models, trained on the “Transformer Architecture”, that can perform a wide array of Natural Language Processing (NLP) tasks like text generation, classification, summarisation etc. Examples: GPT 3.5,

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ODSC - Open Data Science

NOVEMBER 8, 2023

Prompt Engineering Another buzzword you’ve likely heard of lately, prompt engineering means designing inputs for LLMs once they’re developed. You can even fine-tune prompts to get exactly what you want. Don’t go in aimlessly expecting it to do everything. Plan accordingly!

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Flipboard

DECEMBER 6, 2023

Tools like LangChain , combined with a large language model (LLM) powered by Amazon Bedrock or Amazon SageMaker JumpStart , simplify the implementation process. Tools like LangChain , combined with a large language model (LLM) powered by Amazon Bedrock or Amazon SageMaker JumpStart , simplify the implementation process.

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The MLOps Blog

FEBRUARY 15, 2024

Due to the rise of LLMs and the shift towards pre-trained models and prompt engineering, specialists in traditional NLP approaches are particularly at risk. The rapid advancements of Large Language Models (LLMs) are changing the day-to-day work of ML practitioners and how company leadership thinks about AI.

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deepsense.ai

FEBRUARY 6, 2024

Illustration of a few-shot segmentation process. Segment Anything Model (SAM) Inspired by the success of prompting techniques utilized in the field of natural language processing, researchers from Meta AI proposed the Segment Anything Model (SAM), which aims to perform image segmentation based on segmentation prompts.

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IBM Journey to AI blog

FEBRUARY 13, 2024

They are now capable of natural language processing ( NLP ), grasping context and exhibiting elements of creativity. The quality of outputs depends heavily on training data, adjusting the model’s parameters and prompt engineering, so responsible data sourcing and bias mitigation are crucial.

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Unite.AI

SEPTEMBER 25, 2023

However, as technology advanced, so did the complexity and capabilities of AI music generators, paving the way for deep learning and Natural Language Processing (NLP) to play pivotal roles in this tech. Initially, the attempts were simple and intuitive, with basic algorithms creating monotonous tunes.

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Heartbeat

AUGUST 2, 2023

Large language models, such as GPT-3 (Generative Pre-trained Transformer 3), BERT, XLNet, and Transformer-XL, etc., Overall, transformer architecture has revolutionized the field of natural language processing by enabling efficient and effective modeling of long-range dependencies and capturing complex contextual relationships in text.

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ODSC - Open Data Science

SEPTEMBER 18, 2023

Getting started with natural language processing (NLP) is no exception, as you need to be savvy in machine learning, deep learning, language, and more. Large Language Models Finally, the course concludes with a look at large language models, such as BERT, ELMo, GPT, and ULMFiT.

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Heartbeat

JANUARY 9, 2024

Large language models have emerged as ground-breaking technologies with revolutionary potential in the fast-developing fields of artificial intelligence (AI) and natural language processing (NLP). Open-Source Models: Open-source models are large language models made available to the public with their source code.

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Topbots

JUNE 6, 2023

Autoencoding models, which are better suited for information extraction, distillation and other analytical tasks, are resting in the background — but let’s not forget that the initial LLM breakthrough in 2018 happened with BERT, an autoencoding model. Developers can now focus on efficient prompt engineering and quick app prototyping.[11]

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Unite.AI

AUGUST 28, 2023

With advancements in deep learning, natural language processing (NLP), and AI, we are in a time period where AI agents could form a significant portion of the global workforce. Systems like ChatGPT by OpenAI, BERT, and T5 have enabled breakthroughs in human-AI communication.

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Heartbeat

OCTOBER 5, 2023

The emergence of Large Language Models (LLMs) like OpenAI's GPT , Meta's Llama , and Google's BERT has ushered in a new era in this field. These LLMs can generate human-like text, understand context, and perform various Natural Language Processing (NLP) tasks.

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AWS Machine Learning Blog

MAY 31, 2023

Large language models In recent years, language models have seen a huge surge in size and popularity. In 2018, BERT-large made its debut with its 340 million parameters and innovative transformer architecture, setting the benchmark for performance on NLP tasks. Q: What solutions come pre-built with Amazon SageMaker JumpStart?

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Snorkel AI

MAY 25, 2023

A brief history of large language models Large language models grew out of research and experiments with neural networks to allow computers to process natural language. In the 2010s, this research intersected with the then-bustling field of neural networks, setting the ground for the first large language model.

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Snorkel AI

MAY 25, 2023

A brief history of large language models Large language models grew out of research and experiments with neural networks to allow computers to process natural language. In the 2010s, this research intersected with the then-bustling field of neural networks, setting the ground for the first large language model.

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Chatbots Life

MAY 12, 2023

In practice, we can ask a language model to continue the sequence with the word it thinks is the most probable. So from a high-level point of view, ML engineers are also translators as they also translate business requirements into code. In the future, prompt engineering can be both a useful skill and a form of artistic expression.

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Pickl AI

SEPTEMBER 21, 2023

The early days of language models can be traced back to programs like ELIZA , a rudimentary chatbot developed in the 1960s, and continued with ALICE in the 1990s. These early language models laid the foundation for natural language processing but were far from the human-like conversational agents we have today.

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deepsense.ai

NOVEMBER 14, 2023

In short, EDS is the problem of the widespread lack of a rational approach to and methodology for the objective, automated and quantitative evaluation of performance in terms of generative model finetuning and prompt engineering for specific downstream GenAI tasks related to practical business applications. Garrido-Merchán E.C.,

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Applied Data Science

DECEMBER 23, 2022

In our review of 2019 we talked a lot about reinforcement learning and Generative Adversarial Networks (GANs), in 2020 we focused on Natural Language Processing (NLP) and algorithmic bias, in 202 1 Transformers stole the spotlight. The debate was on again: maybe language generation is really just a prediction task?

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AWS Machine Learning Blog

NOVEMBER 1, 2023

accuracy on the development set, while its counterpart bert-base-uncased boasts an accuracy of 92.7%. His research interests are in the area of natural language processing, explainable deep learning on tabular data, and robust analysis of non-parametric space-time clustering. This model achieves a 91.3%

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Explosion

MAY 17, 2023

A lot of people are building truly new things with Large Language Models (LLMs), like wild interactive fiction experiences that weren’t possible before. But if you’re working on the same sort of Natural Language Processing (NLP) problems that businesses have been trying to solve for a long time, what’s the best way to use them?

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Topbots

JUNE 29, 2023

While you will absolutely need to go for this approach if you want to use Text2SQL on many different databases, keep in mind that it requires considerable prompt engineering effort. 4] In the open-source camp, initial attempts at solving the Text2SQL puzzle were focussed on auto-encoding models such as BERT, which excel at NLU tasks.[5,

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