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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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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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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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Archana Joshi, Head – Strategy (BFS and EnterpriseAI), LTIMindtree – Interview Series

Unite.AI

This shift is also leading to new types of work in IT services, such as developing custom models, data engineering for AI needs and implementing responsible AI. The evolution of AI is promising but also brings many corporate challenges, especially around ethical considerations in how we implement it.

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Why Enterprises Need AI Query Engines to Fuel Agentic AI

NVIDIA

Its a critical component of agentic AI , as it serves as a bridge between an organizations knowledge base and AI-powered applications, enabling more accurate, context-aware responses. AI agents form the basis of an AI query engine, where they can gather information and do work to assist human employees.

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