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Advancing AI trust with new responsible AI tools, capabilities, and resources

AWS Machine Learning Blog

As generative AI continues to drive innovation across industries and our daily lives, the need for responsible AI has become increasingly important. At AWS, we believe the long-term success of AI depends on the ability to inspire trust among users, customers, and society.

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Five machine learning types to know

IBM Journey to AI blog

Machine learning (ML) technologies can drive decision-making in virtually all industries, from healthcare to human resources to finance and in myriad use cases, like computer vision , large language models (LLMs), speech recognition, self-driving cars and more. However, the growing influence of ML isn’t without complications.

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The Rise and Fall of Data Science Trends: A 2018–2024 Conference Perspective

ODSC - Open Data Science

The next wave of advancements, including fine-tuned LLMs and multimodal AI, has enabled creative applications in content creation, coding assistance, and conversational agents. However, with this growth came concerns around misinformation, ethical AI usage, and data privacy, fueling discussions around responsible AI deployment.

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Breaking down the advantages and disadvantages of artificial intelligence

IBM Journey to AI blog

AI operates on three fundamental components: data, algorithms and computing power. Data: AI systems learn and make decisions based on data, and they require large quantities of data to train effectively, especially in the case of machine learning (ML) models.

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Step-by-step guide: Generative AI for your business

IBM Journey to AI blog

Organizations in which AI developers or software engineers are involved in the stage of developing AI use cases are much more likely to reach mature levels of AI implementation. Data Scientists and AI experts: Historically we have seen Data Scientists build and choose traditional ML models for their use cases.

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Google AI Introduces Croissant: A Metadata Format for Machine Learning-Ready Datasets

Marktechpost

When building machine learning (ML) models using preexisting datasets, experts in the field must first familiarize themselves with the data, decipher its structure, and determine which subset to use as features. So much so that a basic barrier, the great range of data formats, is slowing advancement in ML.

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Build generative AI–powered Salesforce applications with Amazon Bedrock

AWS Machine Learning Blog

SageMaker endpoints can be registered with Salesforce Data Cloud to activate predictions in Salesforce. Requests and responses between Salesforce and Amazon Bedrock pass through the Einstein Trust Layer , which promotes responsible AI use across Salesforce. Follow him on LinkedIn.