Unite.AI

MAY 21, 2024

Amidst Artificial Intelligence (AI) developments, the domain of software development is undergoing a significant transformation. Traditionally, developers have relied on platforms like Stack Overflow to find solutions to coding challenges. Finally, ethical considerations are also integral to future strategies.

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IBM Journey to AI blog

JANUARY 9, 2024

Companies still often accept the risk of using internal data when exploring large language models (LLMs) because this contextual data is what enables LLMs to change from general-purpose to domain-specific knowledge. In the generative AI or traditional AI development cycle, data ingestion serves as the entry point.

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Snorkel AI

DECEMBER 2, 2024

The integration between the Snorkel Flow AI data development platform and AWS’s robust AI infrastructure empowers enterprises to streamline LLM evaluation and fine-tuning, transforming raw data into actionable insights and competitive advantages. Here’s what that looks like in practice.

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AWS Machine Learning Blog

DECEMBER 4, 2024

Engineers need to build and orchestrate the data pipelines, juggle the different processing needs for each data source, manage the compute infrastructure, build reliable serving infrastructure for inference, and more. Together, Tecton and SageMaker abstract away the engineering needed for production, real-time AI applications.

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Snorkel AI

MARCH 4, 2025

Misaligned LLMs can generate harmful, unhelpful, or downright nonsensical responsesposing risks to both users and organizations. This is where LLM alignment techniques come in. LLM alignment techniques come in three major varieties: Prompt engineering that explicitly tells the model how to behave.

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Marktechpost

APRIL 1, 2024

Addressing this challenge requires a solution that is scalable, versatile, and accessible to a wide range of users, from individual researchers to large teams working on the state-of-the-art side of AI development. Existing research emphasizes the significance of distributed processing and data quality control for enhancing LLMs.

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Towards AI

FEBRUARY 18, 2025

That said, Ive noticed a growing disconnect between cutting-edge AI development and the realities of AI application developers. AI agents, on the other hand, hold a lot of promise but are still constrained by the reliability of LLM reasoning. AI Revolution is Losing Steam? Take, for example, the U.S.

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Unite.AI

OCTOBER 22, 2024

There are major growth opportunities in both the model builders and companies looking to adopt generative AI into their products and operations. We feel we are just at the beginning of the largest AI wave. Data quality plays a crucial role in AI model development.

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Marktechpost

JUNE 15, 2024

NVIDIA has recently unveiled the Nemotron-4 340B , a groundbreaking family of models designed to generate synthetic data for training large language models (LLMs) across various commercial applications. They are optimized for inference using the NVIDIA TensorRT-LLM library, enhancing their efficiency and scalability.

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Towards AI

APRIL 2, 2024

As we have discussed, there have been some signs of open-source AI (and AI startups) struggling to compete with the largest LLMs at closed-source AI companies. This is driven by the need to eventually monetize to fund the increasingly huge LLM training costs. This would be its 5th generation AI training cluster.

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TheSequence

DECEMBER 15, 2024

Google emphasizes its commitment to responsible AI development, highlighting safety and security as key priorities in building these agentic experiences. Command R7B: Command R7B, developed by Cohere, is the smallest model in their R series, focusing on speed, efficiency, and quality for building AI applications. .

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Snorkel AI

DECEMBER 2, 2024

The integration between the Snorkel Flow AI data development platform and AWS’s robust AI infrastructure empowers enterprises to streamline LLM evaluation and fine-tuning, transforming raw data into actionable insights and competitive advantages. Heres what that looks like in practice.

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AWS Machine Learning Blog

NOVEMBER 15, 2024

The rapid advancement of generative AI promises transformative innovation, yet it also presents significant challenges. Concerns about legal implications, accuracy of AI-generated outputs, data privacy, and broader societal impacts have underscored the importance of responsible AI development.

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Pickl AI

DECEMBER 25, 2024

By understanding its significance, readers can grasp how it empowers advancements in AI and contributes to cutting-edge innovation in natural language processing. Key Takeaways The Pile dataset is an 800GB open-source resource designed for AI research and LLM training. Who Created the Pile Dataset and Why?

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AWS Machine Learning Blog

NOVEMBER 7, 2024

Prompt chaining – Generative AI developers often use prompt chaining techniques to break complex tasks into subtasks before sending them to an LLM. A centralized service that exposes APIs for common prompt-chaining architectures to your tenants can accelerate development.

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TheSequence

OCTOBER 23, 2023

What’s more, these models aren’t always cheaper than data labeling with human annotators. But we’ve found that it is possible to elevate data quality by using an optimal mix of human and LLM labeling. To get a clear picture of LLM performance, we need to compare output on real-world projects as well.

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Snorkel AI

JANUARY 9, 2024

Snorkel offers a full suite of third-party data connectors, making data stored in popular cloud repositories like Databricks quickly and easily accessible for data-centric AI development with Snorkel Flow. Register for the next Enterprise LLM Virtual Summit! During this free 3-hour virtual summit on Jan.

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ODSC - Open Data Science

OCTOBER 2, 2024

Below you’ll find a selection of AI slide decks from Europe’s hands-on deep dives, immersive talks, and more! Unlocking Unstructured Data: Bridging Social (Survey) Sciences and NLP/LLM Research Through Open Science Prof. billion customer interactions.

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Snorkel AI

AUGUST 24, 2023

Machine learning to identify emerging patterns in complaint data and solve widespread issues faster. However, banks may encounter roadblocks when integrating AI into their complaint-handling process. Banks cannot send their sensitive customer data to crowd labelers or to third-party models without compromising security.

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Snorkel AI

JUNE 2, 2023

We’re excited to announce this new connector in conjunction with our upcoming The Future of Data-Centric AI virtual event. On June 7, the first day of the conference, Databricks Chief Technologist and Co-founder Matei Zaharia will discuss “Making LLM Applications Production Grade” at 1:30 PM PDT.

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Snorkel AI

JUNE 2, 2023

We’re excited to announce this new connector in conjunction with our upcoming The Future of Data-Centric AI virtual event. On June 7, the first day of the conference, Databricks Chief Technologist and Co-founder Matei Zaharia will discuss “Making LLM Applications Production Grade” at 1:30 PM PDT.

Machine Learning 52

Machine Learning Data Science Large Language Models ML 52

Snorkel AI

AUGUST 24, 2023

Machine learning to identify emerging patterns in complaint data and solve widespread issues faster. However, banks may encounter roadblocks when integrating AI into their complaint-handling process. Banks cannot send their sensitive customer data to crowd labelers or to third-party models without compromising security.

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Snorkel AI

AUGUST 24, 2023

Machine learning to identify emerging patterns in complaint data and solve widespread issues faster. However, banks may encounter roadblocks when integrating AI into their complaint-handling process. Banks cannot send their sensitive customer data to crowd labelers or to third-party models without compromising security.

Large Language Models 52

Large Language Models Natural Language Processing Artificial Intelligence Artificial Intelligence 52

Snorkel AI

AUGUST 24, 2023

Machine learning to identify emerging patterns in complaint data and solve widespread issues faster. However, banks may encounter roadblocks when integrating AI into their complaint-handling process. Banks cannot send their sensitive customer data to crowd labelers or to third-party models without compromising security.

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Large Language Models Natural Language Processing Artificial Intelligence Artificial Intelligence 52

NVIDIA

JULY 13, 2023

Financial Transformers , or “FinFormers,” can learn context and understand the meaning of unstructured financial data. They can power Q&A chatbots, summarize and translate financial texts, provide early warning signs of counterparty risk, quickly retrieve data and identify data-quality issues.

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ODSC - Open Data Science

SEPTEMBER 27, 2024

Open Data Science AI News Blog Recap DOD Urged to Accelerate AI Adoption Amid Rising Global Threats ( Source ) Anthropic Eyes $40 Billion Valuation in New Funding Round ( Source ) Meta to Launch AI Celebrity Voices from Judi Dench, John Cena, and Other Celebrities ( Source ) Celebrities Fall Victim to ‘Goodbye Meta AI’ Hoax as Fake Privacy Message (..)

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Snorkel AI

OCTOBER 4, 2023

Some may choose to experiment with non-traditional data sources like digital footprints or recurring streaming payments to predict repayment behavior. How foundation models jumpstart AI development Foundation models (FMs) represent a massive leap forward in AI development. See what Snorkel option is right for you.

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Bugra Akyildiz

AUGUST 3, 2024

model size and data volume are significantly different as well as various strategies for data sampling. Articles Meta has announced the release of Llama 3.1 , latest and most capable open-source large language model (LLM) collection to date. Today, we had a special issue with Llama3.1

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Neural Network Prompt Engineer Prompt Engineering Large Language Models 52

Topbots

DECEMBER 19, 2023

Presenters from various spheres of AI research shared their latest achievements, offering a window into cutting-edge AI developments. In this article, we delve into these talks, extracting and discussing the key takeaways and learnings, which are essential for understanding the current and future landscapes of AI innovation.

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Topbots

SEPTEMBER 11, 2023

While each of them offers exciting perspectives for research, a real-life product needs to combine the data, the model, and the human-machine interaction into a coherent system. AI development is a highly collaborative enterprise. From this data, your model will learn about the structure, flow, and style of successful articles.

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AWS Machine Learning Blog

JANUARY 26, 2024

Many customers are looking for guidance on how to manage security, privacy, and compliance as they develop generative AI applications. This post provides three guided steps to architect risk management strategies while developing generative AI applications using LLMs.

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Unite.AI

SEPTEMBER 4, 2023

However, the world of LLMs isn't simply a plug-and-play paradise; there are challenges in usability, safety, and computational demands. In this article, we will dive deep into the capabilities of Llama 2 , while providing a detailed walkthrough for setting up this high-performing LLM via Hugging Face and T4 GPUs on Google Colab.

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Marktechpost

SEPTEMBER 8, 2024

Training the Model: A Focus on Quality and Compliance The training of EXAONE 3.0 This dataset was carefully curated to include web-crawled data, publicly available resources, and internally constructed corpora. s Outstanding Performance on Rigorous English and Korean Benchmarks and Standing on the Open LLM Leaderboard 2 EXAONE 3.0

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ODSC - Open Data Science

OCTOBER 3, 2024

AI Development Lifecycle: Learnings of What Changed with LLMs Noé Achache | Engineering Manager & Generative AI Lead | Sicara Using LLMs to build models and pipelines has made it incredibly easy to build proof of concepts, but much more challenging to evaluate the models. billion customer interactions.

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