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With Generative AI Advances, The Time to Tackle Responsible AI Is Now

Unite.AI

AI models in production. Today, seven in 10 companies are experimenting with generative AI, meaning that the number of AI models in production will skyrocket over the coming years. As a result, industry discussions around responsible AI have taken on greater urgency.

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How LLM Unlearning Is Shaping the Future of AI Privacy

Unite.AI

The distilled model can then replace the original LLM, ensuring that privacy is maintained without the necessity for full model retraining. Continual Learning Systems : These techniques are employed to continuously update and unlearn information as new data is introduced or old data is eliminated.

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AI Learns from AI: The Emergence of Social Learning Among Large Language Models

Unite.AI

Cross-Modality Learning : Extending social learning beyond text to include images, sounds, and more could lead to AI systems with a richer understanding of the world, much like how humans learn through multiple senses. The focus would be on developing AI systems that can reason ethically and align with societal values.

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The Path from RPA to Autonomous Agents

Unite.AI

They build upon the foundations of predictive and generative AI but take a significant leap forward in terms of autonomy and adaptability. AI agents are not just tools for analysis or content generationthey are intelligent systems capable of independent decision-making, problem-solving, and continuous learning.

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AI’s Inner Dialogue: How Self-Reflection Enhances Chatbots and Virtual Assistants

Unite.AI

AI models, particularly chatbots, learn from interactions through various learning paradigms, for example: In supervised learning , chatbots learn from labeled examples, such as historical conversations, to map inputs to outputs. It is essential to balance adaptability and consistency for chatbots.

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Breaking down the advantages and disadvantages of artificial intelligence

IBM Journey to AI blog

Data is often divided into three categories: training data (helps the model learn), validation data (tunes the model) and test data (assesses the model’s performance). For optimal performance, AI models should receive data from a diverse datasets (e.g.,

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Achieve operational excellence with well-architected generative AI solutions using Amazon Bedrock

AWS Machine Learning Blog

It’s a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like Anthropic, Cohere, Meta, Mistral AI, and Amazon through a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI.