Summary of Development Of An Adaptive Multi-domain Artificial Intelligence System Built Using Machine Learning and Expert Systems Technologies, by Jeremy Straub
Development of an Adaptive Multi-Domain Artificial Intelligence System Built using Machine Learning and Expert Systems Technologies
by Jeremy Straub
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a step towards creating artificial general intelligence (AGI) by developing a mechanism for an AI to reason and make decisions in unknown domains. The approach combines expert systems with gradient descent trained expert systems (GDTES), utilizing generative artificial intelligence (GAI) to create networks and training datasets. This system can be trained using available sources or the AI’s own pre-trained model, optimizing decision-making through learning. While not meeting traditional AGI standards, this approach provides a similar capability that requires learning before use. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper takes us closer to creating artificial general intelligence (AGI), an AI that can learn and make decisions like humans. Right now, AI is great at doing specific tasks, but it needs training for each new problem. The authors want to change this by giving the AI a way to figure out new problems on its own. They combine old ideas with newer techniques, using “generative” AI to create networks and train the AI. This helps the AI make better decisions by learning from experience. |
Keywords
» Artificial intelligence » Gradient descent
