GPT4: Is AI Smarter than We Think and Should We Be Concerned?
- GPT4 is smarter than expected and is concerning
- OpenAI has not been forthcoming about its architecture, leaving questions of what is going on inside the model unanswered
- Turing’s test cannot definitively answer if there is something like a mind inside the model
- Removing conversations about consciousness from the training data sets would be difficult as it is deeply integrated with the experience of consciousness
- Humans are also trained on how to communicate internal states and emotions, making this much more complex for AI systems
- Despite having full access to GPTs floating point numbers, we know more about human thinking than GPTs architecture.
Exploring the Pros and Cons of Artificial Intelligence Development
- AI can imitate humans
- AI has difficulty making interpretations based on understanding
- AI’s predictive capabilities have improved over time and continue to do so
- Rationality is important when considering probability theory and reasoning
- Transformation networks are performing surprising tasks such as playing chess without needing to reason
- Reinforcement learning with human feedback has made GPT series worse in some ways, however it has led to an improvement in prediction accuracy
- Artificial intelligence still has a long way to go before reaching human levels of understanding.
Exploring the Paths to Artificial Intelligence: Neural Networks, Evolutionary Computation and Imitative Learning
- Neural networks are promising to achieve intelligence without having to understand how it works
- 2006 saw the emergence of a blob of different AI methodologies all aiming at this goal
- Some believed that manually programming knowledge into the system would eventually lead to artificial intelligence
- Others suggested evolutionary computation, studying neuroscience or creating giant neural networks and training them with gradient descent
- Skeptics doubt that AI can be created without understanding how it works, but concede that evolutionary computation will work in the limit
- Now, people are trying to train AI using imitative learning and reinforcement learning.
Unexpected Intelligence Produced By Neural Networks Without Understanding
- Gradient descent can be used to obtain intelligence without understanding how it works
- Neural networks are particularly suited for this, and if done correctly, they can produce a massive amount of intelligence
- It was unexpected that this could happen and was not ruled out by the model
- This is not necessarily a smart thing for a species to do.