Lifelong Learning

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• Learning Techniques
  • Artificial Intelligence (AI) ... Generative AI ... Machine Learning (ML) ... Deep Learning ... Neural Network ... Reinforcement ... Learning Techniques
  • Transfer Learning
• Artificial General Intelligence (AGI) to Singularity ... Curious Reasoning ... Emergence ... Moonshots ... Explainable AI ... Automated Learning
• Lifelong Learning Machines (L2M) | DARPA
• Progress on Lifelong Learning Machines Shows Potential for Bio-Inspired Algorithms | USC & DARPA
• Researchers Selected to Develop Novel Approaches to Lifelong Machine Learning | DARPA
• Conversational AI ... ChatGPT | OpenAI ... Bing/Copilot | Microsoft ... Gemini | Google ... Claude | Anthropic ... Perplexity ... You ... phind ... Ernie | Baidu
• Memory

In recent years, researchers have developed deep neural networks that can perform a variety of tasks, including visual recognition and natural language processing (NLP) tasks. Although many of these models achieved remarkable results, they typically only perform well on one particular task due to what is referred to as "catastrophic forgetting." Essentially, catastrophic forgetting means that when a model that was initially trained on task A is later trained on task B, its performance on task A will significantly decline. A new approach to overcome multi-model forgetting in deep neural networks and A generative memory approach to enable lifelong reinforcement learning | Ingrid Fadelli

• A new developmental reinforcement learning approach for sensorimotor space enlargement | Ingrid Fadelli

Forgetting

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In the quest to build AI that goes beyond today's single-purpose machines, scientists are developing new tools to help AI remember the right things — and forget the rest. Saving AI from catastrophic forgetting | Kaveh Waddell - Axios

• Special report: The future of forgetting | Alison Snyder - Axios
• To Remember, the Brain Must Actively Forget | Toma Vagner - Quanta Magazine
• Can We Get Better at Forgetting? Some things aren’t worth remembering. Science is slowly working out how we might let that stuff go. | Benedict Carey - The New York Times
• OpenAI’s Jeff Clune on deep learning’s Achilles’ heel and a faster path to artificial general intelligence (AGI) - Khari Johnson - VentureBeat
• Catastrophic Interference
• Machine unlearning: The critical art of teaching AI to forget | Matthew Duffin - VentureBeat ... outlines several different methods for machine unlearning, including sharding and slicing, incremental training, and data deletion. Sharding and slicing divides the data into smaller subsets, which can then be unlearned independently. Incremental training trains the model on a new dataset while keeping the old data in place, gradually removing the old data as it becomes outdated. Data deletion removes specific data points from the model, either manually or automatically. The article concludes by discussing the challenges and opportunities of machine unlearning. One challenge is that it can be computationally expensive to unlearn large models. Another challenge is that it can be difficult to ensure that the unlearning process does not damage the model's accuracy.