they definitely are not.
I suspect that you are not only ignoring the existing safeguards that have already come of those discussions, but I suspect you’re also ignoring or pretending like those public discussions never happened in the first place.
Furthermore, I suspect you’re also trivializing what is and is not in contention with moral issues as these companies are trying to compete against each other.
I also think you’re probably assuming the slower options are the safer options because you haven’t really considered the risks of ceding power/investment to a less scrupulous competitor.
I’m not claiming any of these men are moral upstanding people or that they’ve done enough.
I think people should be very critical, but they should at least make the effort to ENGAGE in the moral issues and consequences.
Your cheap four word response only adds cheap rhetoric to the conversation.
If you really care about the moral issues, start typing.
Even with continuous backpropagation and "learning", enriching the training data, so called online-learning, the limitations will not disappear. The LLMs will not be able to conclude things about the world based on fact and deduction. They only consider what is likely from their training data. They will not foresee/anticipate events, that are unlikely or non-existent in their training data, but are bound to happen due to real world circumstances. They are not intelligent in that way.
Whether humans always apply that much effort to conclude these things is another question. The point is, that humans fundamentally are capable of doing that, while LLMs are structurally not.
The problems are structural/architectural. I think it will take another 2-3 major leaps in architectures, before these AI models reach human level general intelligence, if they ever reach it. So far they can "merely" often "fake it" when things are statistically common in their training data.
Our training data is a lot more diverse than an LLMs. We also leverage our senses as a carrier for communicating abstract ideas using audio and visual channels that may or may not be grounded in reality. We have TV shows, video games, programming languages and all sorts of rich and interesting things we can engage with that do not reflect our fundamental reality.
Like LLMs, we can hallucinate while we sleep or we can delude ourselves with untethered ideas, but UNLIKE LLMs, we can steer our own learning corpus. We can train ourselves with our own untethered “hallucinations” or we can render them in art and share them with others so they can include it in their training corpus.
Our hallucinations are often just erroneous models of the world. When we render it into something that has aesthetic appeal, we might call it art.
If the hallucination helps us understand some aspect of something, we call it a conjecture or hypothesis.
We live in a rich world filled with rich training data. We don’t magically anticipate events not in our training data, but we’re also not void of creativity (“hallucinations”) either.
Most of us are stochastic parrots most of the time. We’ve only gotten this far because there are so many of us and we’ve been on this earth for many generations.
Most of us are dazzled and instinctively driven to mimic the ideas that a small minority of people “hallucinate”.
There is no shame in mimicking or being a stochastic parrot. These are critical features that helped our ancestors survive.