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> But that leaves large and important areas that GPTs are entirely unfit for: Real-time problem solving in dynamic environments, understanding and reacting to c
by rvz 3y ago
> But that leaves large and important areas that GPTs are entirely unfit for: Real-time problem solving in dynamic environments, understanding and reacting to current events or spatial reasoning and coordination in the physical world.
It is important to recognize the limitations of these deep neural networks and especially GPTs which both have been hyped to the point where most see it as the solution to everything; since many deep-learning supporters continue to compare it to the human brain, despite the severely limited explainability in these models to the point where not even researchers know what it is doing.
It's one of the reasons why there's very low trust in deep neural networks in general. Humans can be held to account for their actions but with almost all these neural network systems cannot be held to account when something goes wrong and accept the output as the answer whilst 'it is thinking' or 'reasoning'.
> The mechanism behind real world complex systems requires both simple rulesets and a communicative fabric that allows for real time feedback loops. For flocks of starlings this can be as easy as keeping eye contact with the next bird. For Termites, it is pheromone trails. For societies, it is language, reputation and status. For capital markets, it is money. And for the internet, its the physical wiring and network architecture that makes it all possible.
As long as this solution provides transparency and explainable results rather than giving an answer from a black box system then I'm looking forward to this approach.
- why_only_15 3y agoHuman explainability/interpretability is terrible -- it takes years to understand a person well enough to model their actions well in novel situations, and there's tons of literature that backs up that people consistently invent bullshit explanations when asked why they did things. Humans can only be held to account by telling them to change, which we can do with neural networks too.
- rvz 3y ago> Humans can only be held to account by telling them to change, which we can do with neural networks too. Even if humans invent bullshit explanations, in serious cases of accountability is done in the courts system which humans investigate about the whole timeline of events of a dispute which there is very little room for perverting the course of justice and making everything up. Hence this scenario, lawyers would liked to have known as to why did an AI system give hallucinated citations when it was used in a legal proceeding? It's even worse that legal experts knowingly trusted it and failed to reason with the results from this AI system; because fundamentally it cannot explain why that issue happened. [0] It even goes beyond basic citations, with autonomous cars without humans behind the wheel [1] with the company (Cruise) being unable to convince the regulators or even explain the crashes and had to pull the vehicles off the road due to this high amount of risk. So yet again, explainability in AI with neural networks is still far worse than humans, even when these systems cannot be trusted in high risk and novel situations. [0] https://www.theguardian.com/technology/2023/jun/23/two-us-lawyers-fined-submitting-fake-court-citations-chatgpt https://www.theguardian.com/technology/2023/jun/23/two-us-la... [1] https://www.theguardian.com/us-news/2023/oct/24/driverless-car-self-driving-california-cruise-gm https://www.theguardian.com/us-news/2023/oct/24/driverless-c...