評論
打印
One is that we should avoid an arms race and lethal autonomous weapons.
第一，我們需要避免軍備競賽，以及致命的自動化武器出現。
The idea here is that any science can be used for new ways of helping people or new ways of harming people.
其中的想法是，任何科學都可以用新的方式來幫助人們，同樣也可以以新的方式對我們造成傷害。
For example, biology and chemistry are much more likely to be used for new medicines or new cures than for new ways of killing people,
例如，生物和化學更可能被用來制造新的醫藥用品，而非帶來新的殺人方法，
because biologists and chemists pushed hard -- and successfully -- for bans on biological and chemical weapons.
因為生物學家和化學家很努力，也很成功地，在推動禁止生化武器的出現。
And in the same spirit, most AI researchers want to stigmatize and ban lethal autonomous weapons.
基于同樣的精神，大部分的人工智能研究者也在試圖指責和禁止致命的自動化武器。
Another Asilomar AI principle is that we should mitigate AI-fueled income inequality.
另一條阿西洛瑪人工智能會議的原則是，我們應該要減輕由人工智能引起的收入不平等。
I think that if we can grow the economic pie dramatically with AI
我認為，我們能夠大幅度利用人工智能發展出一塊經濟蛋糕，
and we still can't figure out how to divide this pie so that everyone is better off, then shame on us.
但卻沒能相處如何來分配它才能讓所有人受益，那可太丟人了。
Alright, now raise your hand if your computer has ever crashed.
那么問一個問題，如果你的電腦有死機過的，請舉手。
Wow, that's a lot of hands. Well, then you'll appreciate this principle that we should invest much more in AI safety research
哇，好多人舉手。那么你們就會感謝這條準則，我們應該要投入更多以確保對人工智能安全性的研究，
because as we put AI in charge of even more decisions and infrastructure,
因為我們讓人工智能在主導更多決策以及基礎設施時，
we need to figure out how to transform today's buggy and hackable computers into robust AI systems that we can really trust,
我們要了解如何將會出現程序錯誤以及有漏洞的電腦，轉化為可靠的人工智能，
because otherwise, all this awesome new technology can malfunction and harm us, or get hacked and be turned against us.
否則的話，這些了不起的新技術就會出現故障，反而傷害到我們，或被黑入以后轉而對抗我們。
And this AI safety work has to include work on AI value alignment,
這項人工智能安全性的工作必須包含對人工智能價值觀的校準，
because the real threat from AGI isn't malice, like in silly Hollywood movies, but competence
因為AGI會帶來的威脅通常并非出于惡意，就像是愚蠢的好萊塢電影中表現的那樣，而是源于能力，
AGI accomplishing goals that just aren't aligned with ours.
AGI想完成的目標與我們的目標背道而馳。
For example, when we humans drove the West African black rhino extinct,
例如，當我們人類促使了西非的黑犀牛滅絕時，
we didn't do it because we were a bunch of evil rhinoceros haters, did we?
并不是因為我們是邪惡且痛恨犀牛的家伙，對吧？
We did it because we were smarter than them and our goals weren't aligned with theirs.
我們能夠做到只是因為我們比它們聰明，而我們的目標和它們的目標相違背。
But AGI is by definition smarter than us,
但是AGI在定義上就比我們聰明，
so to make sure that we don't put ourselves in the position of those rhinos if we create AGI,
所以必須確保我們別讓自己落到了黑犀牛的境遇，如果我們發明AGI，
we need to figure out how to make machines understand our goals, adopt our goals and retain our goals.
首先就要解決如何讓機器明白我們的目標，選擇采用我們的目標，并一直跟隨我們的目標。
