IBM Journey to AI blog

MAY 8, 2023

Risk and limitations of AI The risk associated with the adoption of AI in insurance can be separated broadly into two categories—technological and usage. Technological risk—data confidentiality The chief technological risk is the matter of data confidentiality.

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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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Flipboard

FEBRUARY 19, 2025

Additionally, we discuss some of the responsible AI framework that customers should consider adopting as trust and responsible AI implementation remain crucial for successful AI adoption. But first, we explain technical architecture that makes Alfred such a powerful tool for Andurils workforce.

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Towards AI

FEBRUARY 11, 2025

Author(s): Richie Bachala Originally published on Towards AI. Beyond Scale: Data Quality for AI Infrastructure The trajectory of AI over the past decade has been driven largely by the scale of data available for training and the ability to process it with increasingly powerful compute & experimental models.

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Snorkel AI

MARCH 4, 2025

Data quality dependency: Success depends heavily on having high-quality preference data. When choosing an alignment method, organizations must weigh trade-offs like complexity, computational cost, and data quality requirements. Learn how to get more value from your PDF documents! Sign up here!

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Unite.AI

JUNE 10, 2024

Josh Wong is the Founder and CEO of ThinkLabs AI. ThinkLabs AI is a specialized AI development and deployment company. Its mission is to empower critical industries and infrastructure with trustworthy AI aimed at achieving global energy sustainability. Josh Wong attended the University of Waterloo.

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Towards AI

APRIL 2, 2024

However, the AI community has also been making a lot of progress in developing capable, smaller, and cheaper models. This can come from algorithmic improvements and more focus on pretraining data quality, such as the new open-source DBRX model from Databricks. Why should you care?

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Marktechpost

DECEMBER 11, 2024

Image Source : LG AI Research Blog ([link] Responsible AI Development: Ethical and Transparent Practices The development of EXAONE 3.5 models adhered to LG AI Research s Responsible AI Development Framework, prioritizing data governance, ethical considerations, and risk management.

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Marktechpost

OCTOBER 7, 2024

However, challenges such as false positives, data quality, and limited treatment options prompted the integration of AI and IoMT technologies. These technologies enhance early detection and personalized care but face obstacles like data privacy, device reliability, and model generalizability. Check out the Paper.

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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. Sign up here!

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Snorkel AI

MARCH 4, 2025

Data quality dependency: Success depends heavily on having high-quality preference data. When choosing an alignment method, organizations must weigh trade-offs like complexity, computational cost, and data quality requirements. Learn how to get more value from your PDF documents! Sign up here!

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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.

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Pickl AI

DECEMBER 16, 2024

With the global AI market exceeding $184 billion in 2024a $50 billion leap from 2023its clear that AI adoption is accelerating. This blog aims to help you navigate this growth by addressing key enablers of AI development. Key Takeaways Reliable, diverse, and preprocessed data is critical for accurate AI model training.

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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.

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Snorkel AI

JANUARY 31, 2023

Snorkel AI provides a data-centric AI development platform for AI teams to unlock production-grade model quality and accelerate time-to-value for their investments. Seldon is a deployment solution that helps teams serve, monitor, explain, and manage their ML models in production.

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Snorkel AI

JANUARY 31, 2023

Snorkel AI provides a data-centric AI development platform for AI teams to unlock production-grade model quality and accelerate time-to-value for their investments. Seldon is a deployment solution that helps teams serve, monitor, explain, and manage their ML models in production.

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O'Reilly Media

NOVEMBER 28, 2023

People with AI skills have always been hard to find and are often expensive. While experienced AI developers are starting to leave powerhouses like Google, OpenAI, Meta, and Microsoft, not enough are leaving to meet demand—and most of them will probably gravitate to startups rather than adding to the AI talent within established companies.

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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.

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ODSC - Open Data Science

FEBRUARY 11, 2025

What is AI Engineering? Chip Huyen began by explaining how AI engineering has emerged as a distinct discipline, evolving out of traditional machine learning engineering. While machine learning engineers focus on building models, AI engineers often work with pre-trained foundation models, adapting them to specific use cases.

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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

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.

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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

MARCH 14, 2023

Users are able to rapidly improve training data quality and model performance using integrated error analysis and model-guided feedback to develop highly accurate and adaptable AI applications. Oil companies generate massive volumes of unstructured data in daily drilling reports, well maintenance logs, and other files.

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Snorkel AI

MARCH 14, 2023

Users are able to rapidly improve training data quality and model performance using integrated error analysis and model-guided feedback to develop highly accurate and adaptable AI applications. Oil companies generate massive volumes of unstructured data in daily drilling reports, well maintenance logs, and other files.

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Dlabs.ai

SEPTEMBER 24, 2024

Instead of applying uniform regulations, it categorizes AI systems based on their potential risk to society and applies rules accordingly. This tiered approach encourages responsible AI development while ensuring appropriate safeguards are in place. Also, be transparent about the data these systems use.

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Snorkel AI

FEBRUARY 27, 2023

Alex Ratner, CEO and co-founder of Snorkel AI, presented a high-level introduction to data-centric AI at Snorkel’s Future of Data-Centric AI virtual conference in 2022. From there, the key part, of course, is iterating as quickly as possible.

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Snorkel AI

FEBRUARY 27, 2023

Alex Ratner, CEO and co-founder of Snorkel AI, presented a high-level introduction to data-centric AI at Snorkel’s Future of Data-Centric AI virtual conference in 2022. From there, the key part, of course, is iterating as quickly as possible.

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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. Well, in AI products, you can pause this fight and use both to your advantage.

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Viso.ai

DECEMBER 18, 2023

This allows for: Developing Robust and Generalizable AI Models. Training AI models on synthetic data exposes them to a wider range of variations and edge cases. Rapid AI Development. Using generative models for synthetic data can be much faster and cheaper than collecting real-world data.

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Viso.ai

JUNE 24, 2024

With content summarization, we can describe scenes, explain text, and give sentiment analysis. Let’s see how these technologies can be used within AI solutions for the blind. First, accuracy should be the top priority, this means better data and model training or fine-tuning.

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Unite.AI

SEPTEMBER 4, 2023

Llama 2 isn't just another statistical model trained on terabytes of data; it's an embodiment of a philosophy. One that stresses an open-source approach as the backbone of AI development, particularly in the generative AI space. Data quality and diversity are just as pivotal as volume in these scenarios.

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Pickl AI

DECEMBER 10, 2024

Businesses face fines and reputational damage when AI decisions are deemed unethical or discriminatory. Socially, biased AI systems amplify inequalities, while data breaches erode trust in technology and institutions. Broader Ethical Implications Ethical AI development transcends individual failures.

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AWS Machine Learning Blog

NOVEMBER 25, 2024

From the outset, AWS has prioritized responsible AI innovation and developed rigorous methodologies to build and operate our AI services with consideration for fairness, explainability, privacy and security, safety, controllability, veracity and robustness, governance, and transparency.

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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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