John Snow Labs

SEPTEMBER 5, 2023

Topics Covered Include Large Language Models, Semantic Search, ChatBots, Responsible AI, and the Real-World Projects that Put Them to Work John Snow Labs , the healthcare AI and NLP company and developer of the Spark NLP library, today announced the agenda for its annual NLP Summit, taking place virtually October 3-5.

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John Snow Labs

NOVEMBER 27, 2024

Achieving this status reflects John Snow Labs’ ongoing engineering, scientific, and operational efforts to minimize the environmental impact of AI technologies. The rapid evolution of Artificial Intelligence comes with immense potential — and responsibility,” said David Talby, CTO, John Snow Labs.

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Unite.AI

FEBRUARY 7, 2024

LLMs are deep neural networks that can generate natural language texts for various purposes, such as answering questions, summarizing documents, or writing code. LLMs, such as GPT-4 , BERT , and T5 , are very powerful and versatile in Natural Language Processing (NLP).

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Unite.AI

JULY 4, 2024

Google Open Source LLM Gemma In this comprehensive guide, we'll explore Gemma 2 in depth, examining its architecture, key features, and practical applications. Responsible Use : Adhere to Google's Responsible AI practices and ensure your use of Gemma 2 aligns with ethical AI principles.

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AWS Machine Learning Blog

NOVEMBER 14, 2024

Core benefits of Amazon Bedrock and Amazon Location Service Amazon Bedrock provides capabilities to build generative AI applications with security, privacy, and responsible AI practices. Being serverless, it allows secure integration and deployment of generative AI capabilities without managing infrastructure.

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Unite.AI

JULY 11, 2023

This course explores LLMs (Large Language Models) – AI models trained on large amounts of textual data. Google’s Bard AI ” is an excellent example of an LLM that makes advanced human-machine interaction possible. Understand how LLMs are used for sentiment analysis. What will AI enthusiasts learn?

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Unite.AI

DECEMBER 14, 2023

What Are Large Language Models? LLMs have gained significant popularity and are widely used in various applications, including natural language processing and machine translation. LLMs are designed to learn from vast amounts of data and can generate text, answer questions, and perform other tasks.

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Unite.AI

APRIL 26, 2024

Large Language Models (LLMs) have revolutionized the field of natural language processing (NLP) by demonstrating remarkable capabilities in generating human-like text, answering questions, and assisting with a wide range of language-related tasks.

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Marktechpost

APRIL 21, 2024

This microlearning module is perfect for those curious about how AI can generate content and innovate across various fields. Introduction to Responsible AI : This course focuses on the ethical aspects of AI technology. It introduces learners to responsible AI and explains why it is crucial in developing AI systems.

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Marktechpost

JANUARY 16, 2024

Large Language Models (LLMs) signify a remarkable advance in natural language processing and artificial intelligence. These models, exemplified by their ability to understand and generate human language, have revolutionized numerous applications, from automated writing to translation.

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AWS Machine Learning Blog

MARCH 14, 2025

Finally, metrics such as ROUGE and F1 can be fooled by shallow linguistic similarities (word overlap) between the ground truth and the LLM response, even when the actual meaning is very different.

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Topbots

DECEMBER 5, 2023

Generated with DALL-E 3 In the rapidly evolving landscape of Natural Language Processing, 2023 emerged as a pivotal year, witnessing groundbreaking research in the realm of Large Language Models (LLMs). Top LLM Research Papers 2023 1. Where to learn more about this research?

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LLM Large Language Models Natural Language Processing Chatbots 110

Marktechpost

MAY 30, 2024

Natural Language Processing on Google Cloud This course introduces Google Cloud products and solutions for solving NLP problems. It covers how to develop NLP projects using neural networks with Vertex AI and TensorFlow. It also introduces Google’s 7 AI principles.

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Unite.AI

FEBRUARY 21, 2025

Large Language Models (LLMs) have significantly advanced natural language processing (NLP), excelling at text generation, translation, and summarization tasks. Traditional LLMs, designed to predict the next word, rely on statistical pattern recognition rather than structured reasoning.

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AWS Machine Learning Blog

FEBRUARY 12, 2025

For a demonstration on how you can use a RAG evaluation framework in Amazon Bedrock to compute RAG quality metrics, refer to New RAG evaluation and LLM-as-a-judge capabilities in Amazon Bedrock. Responsible AI Implementing responsible AI practices is crucial for maintaining ethical and safe deployment of RAG systems.

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Marktechpost

AUGUST 31, 2024

Large Language Models (LLMs) have demonstrated remarkable capabilities in various natural language processing tasks. However, they face a significant challenge: hallucinations, where the models generate responses that are not grounded in the source material.

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Marktechpost

MAY 6, 2024

In the quickly changing field of Natural Language Processing (NLP), the possibilities of human-computer interaction are being reshaped by the introduction of advanced conversational Question-Answering (QA) models. Recently, Nvidia has published a competitive Llama3-70b QA/RAG fine-tune. The Llama3-ChatQA-1.5

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AWS Machine Learning Blog

JUNE 25, 2024

However, the implementation of LLMs without proper caution can lead to the dissemination of misinformation , manipulation of individuals, and the generation of undesirable outputs such as harmful slurs or biased content. Introduction to guardrails for LLMs The following figure shows an example of a dialogue between a user and an LLM.

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IBM Journey to AI blog

OCTOBER 16, 2023

Foundation models: The power of curated datasets Foundation models , also known as “transformers,” are modern, large-scale AI models trained on large amounts of raw, unlabeled data. Generative AI chatbots have been known to insult customers and make up facts. But how trustworthy is that training data?

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AWS Machine Learning Blog

JUNE 25, 2024

From customer service and ecommerce to healthcare and finance, the potential of LLMs is being rapidly recognized and embraced. Businesses can use LLMs to gain valuable insights, streamline processes, and deliver enhanced customer experiences. The raw data is processed by an LLM using a preconfigured user prompt.

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Automation Prompt Engineer Prompt Engineering Categorization 126

ODSC - Open Data Science

AUGUST 24, 2023

Evolving Trends in Prompt Engineering for Large Language Models (LLMs) with Built-in Responsible AI Practices Editor’s note: Jayachandran Ramachandran and Rohit Sroch are speakers for ODSC APAC this August 22–23. are harnessed to channel LLMs output. Auto Eval Common Metric Eval Human Eval Custom Model Eval 3.

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Towards AI

JULY 27, 2024

We will also discuss how it differs from the most popular generative AI tool ChatGPT. Claude AI Claude AI is developed by Anthropic, an AI startup company backed by Google and Amazon, and is dedicated to developing safe and beneficial AI. ChatGPT vs. Claude AI: How do they differ? Let’s compare.

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AWS Machine Learning Blog

JANUARY 4, 2024

Generative AI has opened up a lot of potential in the field of AI. One such area that is evolving is using natural language processing (NLP) to unlock new opportunities for accessing data through intuitive SQL queries. You can use supervised fine-tuning based on your LLM to improve the effectiveness of text-to-SQL.

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Allen AI

FEBRUARY 1, 2024

With OLMo we hope to work against this trend and empower the research community to come together to better understand and engage with language models in a scientific way, leading to more responsible AI technology that benefits everyone.”

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John Snow Labs

DECEMBER 2, 2024

Today, we are excited to announce that John Snow Labs’ Medical LLM – Small and Medical LLM – Medium large language models (LLMs) are now available on Amazon SageMaker Jumpstart. Medical LLM in SageMaker JumpStart is available in two sizes: Medical LLM – Small and Medical LLM – Medium.

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AWS Machine Learning Blog

FEBRUARY 12, 2025

You can create synthetic training data using a larger language model and use it to fine-tune a smaller model, which has the benefit of a quicker turnaround time. In this post, we explore how to use Amazon Bedrock to generate synthetic training data to fine-tune an LLM. The following chart summarizes the judges decisions.

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Marktechpost

DECEMBER 12, 2023

Thanks to the success in increasing the data, model size, and computational capacity for auto-regressive language modeling, conversational AI agents have witnessed a remarkable leap in capability in the last few years. Thus, the semantic difference between the user and agent responsibilities can be captured by Llama Guard.

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AWS Machine Learning Blog

MARCH 4, 2024

Alida’s customers receive tens of thousands of engaged responses for a single survey, therefore the Alida team opted to leverage machine learning (ML) to serve their customers at scale. Alida built the topic and sentiment classification as a service with survey response analysis as its first application.

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AWS Machine Learning Blog

AUGUST 11, 2023

Natural language processing (NLP) is the field in machine learning (ML) concerned with giving computers the ability to understand text and spoken words in the same way as human beings can. Due to their inherent complexity, training an LLM from scratch is a very challenging task that very few organizations can afford.

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Unite.AI

JANUARY 23, 2024

Large language models (LLMs) have exploded in popularity over the last few years, revolutionizing natural language processing and AI. From chatbots to search engines to creative writing aids, LLMs are powering cutting-edge applications across industries.

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AWS Machine Learning Blog

MAY 22, 2024

Using machine learning (ML) and natural language processing (NLP) to automate product description generation has the potential to save manual effort and transform the way ecommerce platforms operate. BLIP-2 consists of three models: a CLIP-like image encoder, a Querying Transformer (Q-Former) and a large language model (LLM).

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AWS Machine Learning Blog

MARCH 14, 2024

We formulated a text-to-SQL approach where by a user’s natural language query is converted to a SQL statement using an LLM. This data is again provided to an LLM, which is asked to answer the user’s query given the data. The relevant information is then provided to the LLM for final response generation.

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AWS Machine Learning Blog

APRIL 24, 2024

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon using a single API, along with a broad set of capabilities you need to build generative AI applications with security, privacy, and responsible AI.

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ODSC - Open Data Science

AUGUST 29, 2023

As AI models become more sophisticated and versatile, the demand for tailored, context-aware interactions grows. Natural Language Processing Engineer Natural Language Processing Engineers who specialize in prompt engineering are linguistic architects when it comes to AI communication.

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AWS Machine Learning Blog

MAY 13, 2024

In part 1 of this blog series, we discussed how a large language model (LLM) available on Amazon SageMaker JumpStart can be fine-tuned for the task of radiology report impression generation. You can securely integrate and deploy generative AI capabilities into your applications using the AWS services you are already familiar with.

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Marktechpost

JULY 4, 2023

For the unaware, ChatGPT is a large language model (LLM) trained by OpenAI to respond to different questions and generate information on an extensive range of topics. It can translate multiple languages, generate unique and creative user-specific content, summarize long text paragraphs, etc.

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ODSC - Open Data Science

MARCH 11, 2024

Introducing the Topic Tracks for ODSC East 2024 — Highlighting Gen AI, LLMs, and Responsible AI ODSC East 2024 , coming up this April 23rd to 25th, is fast approaching and this year we will have even more tracks comprising hands-on training sessions, expert-led workshops, and talks from data science innovators and practitioners.

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