Unite.AI

MARCH 14, 2024

This includes AI systems used for indiscriminate surveillance, social scoring, and manipulative or exploitative purposes. In the realm of high-risk AI, the legislation imposes obligations for risk assessment, data quality control, and human oversight.

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AWS Machine Learning Blog

JANUARY 28, 2025

Furthermore, evaluation processes are important not only for LLMs, but are becoming essential for assessing prompt template quality, input data quality, and ultimately, the entire application stack. It consists of three main components: Data config Specifies the dataset location and its structure.

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Unite.AI

JANUARY 23, 2025

In practical terms, this means standardizing data collection, ensuring accessibility, and implementing robust data governance frameworks. Responsible AI Companies that embed responsible AI principles on a robust, well-governed data foundation will be better positioned to scale their applications efficiently and ethically.

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AWS Machine Learning Blog

FEBRUARY 21, 2025

By implementing this technique, organizations can improve response accuracy, reduce response times, and lower costs. Whether youre new to AI development or an experienced practitioner, this post provides step-by-step guidance and code examples to help you build more reliable AI applications.

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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

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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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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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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Pickl AI

JUNE 23, 2023

Monitoring and Evaluation Data-centric AI systems require continuous monitoring and evaluation to assess their performance and identify potential issues. This involves analyzing metrics, feedback from users, and validating the accuracy and reliability of the AI models. Governance Emphasizes data governance, privacy, and ethics.

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Pickl AI

APRIL 27, 2023

Hence, introducing the concept of responsible AI has become significant. Responsible AI focuses on harnessing the power of Artificial Intelligence while complying with designing, developing, and deploying AI with good intentions. By adopting responsible AI, companies can positively impact the customer.

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John Snow Labs

OCTOBER 19, 2023

One reason for this bias is the data used to train these models, which often reflects historical gender inequalities present in the text corpus. To address gender bias in AI, it’s crucial to improve the data quality by including diverse perspectives and avoiding the perpetuation of stereotypes.

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Viso.ai

DECEMBER 18, 2023

Organizations can easily source data to promote the development, deployment, and scaling of their computer vision applications. 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.

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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.

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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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AWS Machine Learning Blog

JANUARY 26, 2024

After your generative AI workload environment has been secured, you can layer in AI/ML-specific features, such as Amazon SageMaker Data Wrangler to identify potential bias during data preparation and Amazon SageMaker Clarify to detect bias in ML data and models.

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Unite.AI

SEPTEMBER 26, 2024

Additionally, AI models require ongoing updates and monitoring to remain accurate and effective, which can be costly for businesses without specialized AI teams. The desire to cut costs could compromise the quality of AI solutions. AI can also increase biases if trained on biased data, leading to unfair outcomes.

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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. involved several critical stages, beginning with extensive pre-training on a diverse dataset.

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Pickl AI

DECEMBER 10, 2024

As the global AI market, valued at $196.63 from 2024 to 2030, implementing trustworthy AI is imperative. This blog explores how AI TRiSM ensures responsible AI adoption. Key Takeaways AI TRiSM embeds fairness, transparency, and accountability in AI systems, ensuring ethical decision-making.

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Dlabs.ai

DECEMBER 6, 2022

launched an initiative called ‘ AI 4 Good ‘ to make the world a better place with the help of responsible AI. So if you’re looking for a high-quality, ethical team, they’re a solid choice.

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AWS Machine Learning Blog

NOVEMBER 25, 2024

ISO/IEC 42001 is an international management system standard that outlines requirements and controls for organizations to promote the responsible development and use of AI systems. Responsible AI is a long-standing commitment at AWS. At Snowflake, delivering AI capabilities to our customers is a top priority.

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