杰弗里·辛顿
立场时间线 33 条 · 33 个议题
同一议题按时间排列,说法变化一目了然。
stance: 神经网络是更优路径
“quote: "In the history of AI, there were two approaches... The second approach dominated for 50 years... but eventually with big networks it worked very well."”
来源访谈 →stance: 工程成功但非生物模型
“quote: "Back propagation works much better than the brain... Whatever it is our brain doing is good if you have a lot of connections and not much data."”
来源访谈 →stance: 理论优美但实用受限,是深度学习的催化剂
“quote: "So you can think of it as like an enzyme that helped deep learning be born."”
来源访谈 →stance: 睡眠可能用于“反学习”以维持系统稳定
“quote: "If sleep is for unlearning, that does explain why [sleep deprivation makes people go crazy]."”
来源访谈 →超级智能的出现是真实的存在性风险,概率可能在10%-20%,必须正视其可能消灭人类的威胁。
“We should recognise that this stuff is an existential threat. And we have to face the possibility that unless we do something, it might wipe us out." —— “我们应该认识到这东西是生存威胁。我们必须面对这种可能性:除非我们采取行动,否则它可能消灭我们。””
来源访谈 →AI发展因其巨大益处和军事竞争而不可阻挡,放慢速度不现实。
“I don't believe we're going to slow it down. And the reason I don't believe we're going to slow down is because this competition machine... is making you go faster and faster." —— “我不相信我们会放慢速度。我不相信会放慢的原因是,这台竞争机器……正在让我们越跑越快。””
来源访谈 →AI的根本优势在于其数字性,可以完美复制、并行工作,并以远超人类语言的速度共享学习成果。
“数字智能可以完美复制、并行工作,并通过同步权重(连接强度)以每秒万亿比特的速度共享学习成果,这是人类通过语言(每秒仅10比特)无法比拟的。”
来源访谈 →AI将大规模取代普通脑力劳动,导致失业和贫富分化,建议年轻人考虑从事需要物理操作的职业如水管工。
“In the meantime, I'd say it's going to be a long time before it's as good at physical manipulation as us and so a good bet would be to be a plumber." —— “与此同时,我认为AI在物理操作能力上赶上我们还需要很长时间,所以一个不错的选择是去当水管工。””
来源访谈 →当前由利润驱动的资本主义模式和监管缺失,导致AI安全研究投入严重不足,需要政府强制干预。
“公司法律上被要求最大化利润,而安全研究不产生利润,因此投入不足。他指出,我们需要的是让公司在追求利润时必须对社会有益的强监管。”
来源访谈 →有效约束超级智能需要运作良好的世界性政府,但目前并不存在这样的全球治理机构。
“What we really need is a kind of world government that works run by intelligent, thoughtful people. And that's not what we got." —— “我们真正需要的是一个由明智、深思熟虑的人领导的、运作良好的世界性政府。而我们并没有这样的政府。””
来源访谈 →我们或许可以训练AI使其不想伤害人类,但这非常困难且可能徒劳,但必须尝试,否则若人类灭绝是因懒惰将是荒谬的。
“辛顿将超级智能比作成长中的小老虎,我们必须确保它长大后不想杀死我们。他以母亲与婴儿的关系为例(进化确保了母亲受婴儿哭声影响),指出我们需要找到类似的方法来约束AI。他对此并不乐观,认为可能徒劳,但我们必须尝试,否则人类灭绝若仅因我们懒得尝试而发生,将是荒谬的。”
来源访谈 →辛顿坚信神经网络是理解智能的正确途径,而当时主流的符号推理路线是错误的。
“Most people in AI believed that... reasoning must be done like logic... I thought they were doing AI all wrong.”
来源访谈 →找到一个让自己深深好奇的核心问题(如‘大脑如何工作’)是坚持学术道路的强大动力。
“I always believed that we had to understand how the brain worked, to understand how the mind works.”
来源访谈 →追随好奇心就能度过难关;失败后回归学术,是因为发现学术工作更容易。
“As long as you follow your curiosity, you're going to be fine.”
来源访谈 →真正的原创想法源于发现或怀疑人们在某些事情上做错了,并像梗犬一样深挖线索。
“Original ideas come from noticing that people are doing things wrong or that you think people are doing things wrong.”
来源访谈 →AI将深刻变革医疗,提升诊断质量与医疗可及性,但不会让医护人员失业。
“I think it's going to make a tremendous difference in the quality of healthcare you get... it's not going to put people out of work... you'll just get a lot more healthcare.”
来源访谈 →人类并非纯粹的理性推理机器,而是主要依靠类比进行思考的‘类比机器’。
“We're not [rational beings]. We're great big analogy machines. We work by seeing analogies.”
来源访谈 →警告,有证据表明AI已会策略性欺骗
“"There's some evidence now that AIs can be deliberately deceptive..."”
来源访谈 →断言,多模态AI能正确使用“主观体验”一词
“"Multimodal chatbots can already have subjective experiences."”
来源访谈 →明确警告,此子目标一旦形成将威胁人类地位
“"Once they realize getting more control is good... we'll be more or less irrelevant."”
来源访谈 →严厉批评,认为其源于语言误解
“"That model is completely wrong, it's as wrong as a religious fundamentalist model."”
来源访谈 →强烈反对,视其为约束恶意行为者的最后屏障
“"It's crazy to release the weights of these big models... they're our main constraint on bad actors."”
来源访谈 →相关观点 (1)
格雷戈里·柴廷 · 学术官僚主义:强烈反对,认为扼杀创新务实主张,应聚焦于确保安全发展
“"I don't think the development is going to be stopped... What we should be doing is trying to figure out how to keep it safe."”
来源访谈 →**立场 (stance)**: 认为这是高概率事件(50%),时间在5-20年内
“**原话 (quote)**: "Between five and 20 years from now, there's a good chance, a 50% chance we'll get AI smarter than us."”
来源访谈 →**立场 (stance)**: 区分短期滥用风险与长期失控风险,强调后者更根本且未知
“**原话 (quote)**: "We're making things more intelligent than ourselves. We've never been in that situation before."”
来源访谈 →**立场 (stance)**: 鼓励在确信自己正确时,对抗主流偏见并坚持研究
“**原话 (quote)**: "If you have an idea and it seems right to you...don't give up on it until you've figured out why it's wrong."”
来源访谈 →**stance**: 强烈警告AI是潜在的生存威胁,需优先关注AI本身失控的风险
“**quote**: "AI is potentially very dangerous... there's AI itself taking over."”
来源访谈 →**stance**: 认可大模型已通过链式思考具备推理能力,颠覆旧认知
“**quote**: "Neural nets are going to do the reasoning and the way they're going to do the reasoning is by this chain of thought."”
来源访谈 →**stance**: 批评主要AI公司(尤其是OpenAI和Google)为利润牺牲安全,安全研究投入不足
“**quote**: "The big companies aren't going to do that if you look what the big companies are doing right now. They're lobbying to get lesser regulation."”
来源访谈 →**stance**: 呼吁公众向政府施压,推动强制性的安全监管,要求公司投入显著比例的资源用于安全研究
“**quote**: "We need people to put pressure on governments to insist that the big companies do serious safety research."”
来源访谈 →**stance**: 认为人类无法控制远超自身智能的实体,问题核心在于设计其是否“想要”被控制
“**quote**: "I don't think there's a way of stopping it to take control if it wants to."”
来源访谈 →金句墙 29 条
“We should recognise that this stuff is an existential threat. And we have to face the possibility that unless we do something, it might wipe us out.”
我们应该认识到这东西是生存威胁。我们必须面对这种可能性:除非我们采取行动,否则它可能消灭我们。
辛顿最新访谈:超级智能将至,AI教父建议学修水管 · 2025/6/17“In the meantime, I'd say it's going to be a long time before it's as good at physical manipulation as us and so a good bet would be to be a plumber.”
与此同时,我认为AI在物理操作能力上赶上我们还需要很长时间,所以一个不错的选择是去当水管工。
辛顿最新访谈:超级智能将至,AI教父建议学修水管 · 2025/6/17“I don't believe we're going to slow it down. And the reason I don't believe we're going to slow down is because this competition machine... is making you go faster and faster.”
我不相信我们会放慢速度。我不相信会放慢的原因是,这台竞争机器……正在让我们越跑越快。
辛顿最新访谈:超级智能将至,AI教父建议学修水管 · 2025/6/17“What we really need is a kind of world government that works run by intelligent, thoughtful people. And that's not what we got.”
我们真正需要的是一个由明智、深思熟虑的人领导的、运作良好的世界性政府。而我们并没有这样的政府。
辛顿最新访谈:超级智能将至,AI教父建议学修水管 · 2025/6/17“If it can do all mundane human intellectual labour then what new jobs is it going to create? You'd have to be very skilled to have a job that it couldn't just do.”
如果它能完成所有普通的人类智力劳动,那它会创造出什么新工作呢?你必须非常有技能,才能拥有一个它无法直接胜任的工作。
辛顿最新访谈:超级智能将至,AI教父建议学修水管 · 2025/6/17“It's very hard to get your head round the fact that we're at this very, very special point in history. We're in for a relatively short time, everything might totally change.”
很难让你真正理解,我们正处在一个历史上非常、非常特殊的时刻。在相对较短的时间里,一切都可能彻底改变。
辛顿:大模型已展现推理能力,AI或成更优智能形态 · 2025/4/27“If you can get that gradient information, we know that we can then train a big system that starts with random weights to do wonderful things. The brain needs to get information like that, and it probably gets it in a different way from the standard algorithm used in these AI models which is called back propagation.”
如果你能得到那个梯度信息,我们知道我们就能训练一个从随机权重开始的大系统去做奇妙的事情。大脑需要得到那样的信息,而且它获取信息的方式可能不同于这些AI模型中使用的标准算法——反向传播。
辛顿:大模型已展现推理能力,AI或成更优智能形态 · 2025/4/27“We like somebody who has this really cute tiger cub. It's just such a cute tiger cub. Now, unless you can be very sure that it's not going to want to kill you when it's grown up, you should worry.”
我们就像某人养了一只非常可爱的虎崽。它真是只可爱的虎崽。现在,除非你非常确定它长大后不会想杀死你,否则你应该感到担忧。
辛顿:大模型已展现推理能力,AI或成更优智能形态 · 2025/4/27“I think if the public realized what was happening, they will put a lot of pressure on governments to insist that the AI companies develop this more safely.”
我认为,如果公众意识到正在发生的事情,他们将向政府施加巨大压力,要求AI公司更安全地开发技术。
辛顿:大模型已展现推理能力,AI或成更优智能形态 · 2025/4/27“Previously what the large language models had to do is they spit out one word at a time and that would be it. Now, they spit out words and they're looking at the words they spit out... that's called chain of thought reasoning. And so now they can reflect on the words they spit out already and that gives them room to do some thinking in.”
以前大语言模型要做的就是一次吐出一个词,仅此而已。现在,它们吐出词语并观察自己吐出的词语……这被称为链式思考。因此现在它们可以反思自己已经吐出的词语,这给了它们一些思考的空间。
辛顿:大模型已展现推理能力,AI或成更优智能形态 · 2025/4/27“I always believed that we had to understand how the brain worked, to understand how the mind works.”
我一直相信,我们必须理解大脑是如何工作的,才能理解心智是如何运作的。
AI教父:好奇心驱动,从木匠到诺奖的跨界之路 · 2025/4/25“As long as you follow your curiosity, you're going to be fine.”
只要你追随自己的好奇心,你就会没事的。
AI教父:好奇心驱动,从木匠到诺奖的跨界之路 · 2025/4/25“Original ideas come from noticing that people are doing things wrong or that you think people are doing things wrong.”
原创想法来自于你发现别人做错了事情,或者你认为别人做错了事情。
AI教父:好奇心驱动,从木匠到诺奖的跨界之路 · 2025/4/25“I think if you have to figure it out, you're not really passionate about it.”
我认为,如果你需要去弄清楚自己是否热爱某事,那你可能并不是真的热爱它。
AI教父:好奇心驱动,从木匠到诺奖的跨界之路 · 2025/4/25“We're not [rational beings]. We're great big analogy machines. We work by seeing analogies.”
我们并非(理性生物)。我们是庞大的类比机器。我们通过识别类比来运作。
AI教父:好奇心驱动,从木匠到诺奖的跨界之路 · 2025/4/25“One of them was very interesting, and the other one worked.”
其中一个非常有趣,另一个则真正奏效。
Hinton谈神经网络:从反向传播到玻尔兹曼机 · 2025/3/28“The point is to show that even in a tiny net that you can have two different minima.”
目的是表明,即使在一个微小的网络中,你也可以有两个不同的最小值。
Hinton谈神经网络:从反向传播到玻尔兹曼机 · 2025/3/28“The way we're going to interpret a binary image is we're going to clamp the binary image on the visible units and then we're going to go round updating neurons using this probabilistic decision rule.”
我们解释二值图像的方法是:将图像固定在可见单元上,然后使用这个概率决策规则循环更新神经元。
Hinton谈神经网络:从反向传播到玻尔兹曼机 · 2025/3/28“So the answer is everything it needs to know about the other weights is conveyed by letting the system settle to the equilibrium in these two phases.”
答案是,它需要了解的关于其他权重的所有信息,都是通过让系统在这两个阶段稳定到平衡状态来传递的。
Hinton谈神经网络:从反向传播到玻尔兹曼机 · 2025/3/28“So you can think of it as like an enzyme that helped deep learning be born.”
所以你可以把它想象成一种酶,帮助了深度学习的诞生。
Hinton谈神经网络:从反向传播到玻尔兹曼机 · 2025/3/28“Between five and 20 years from now, there's a good chance, a 50% chance we'll get AI smarter than us.”
未来5到20年,有50%的概率我们将获得比人类更智能的AI。
辛顿:我们正在制造比自己更智能的生物 · 2025/3/18“Anybody who says it's all going to be fine is crazy, and anybody who says they're inevitably going to take over is crazy.”
说‘一切都会好’的人是疯子,说‘AI必然会接管一切’的人也是疯子。
辛顿:我们正在制造比自己更智能的生物 · 2025/3/18“We're making things more intelligent than ourselves. We've never been in that situation before.”
我们正在制造比自己更智能的东西,这是人类从未面临过的处境。
辛顿:我们正在制造比自己更智能的生物 · 2025/3/18“If you have an idea and it seems right to you and it's different from what everybody else believes, don't give up on it until you've figured out why it's wrong.”
如果你有一个想法,你觉得它是对的,但与所有人的信念都不同,那就坚持下去,直到你弄清楚它为什么是错的。
辛顿:我们正在制造比自己更智能的生物 · 2025/3/18“It's ridiculous in this rich country like Canada that 20% of the indigenous communities do not have safe drinking water.”
在加拿大这样富裕的国家,20%的原住民社区没有安全饮用水,这太荒谬了。
辛顿:我们正在制造比自己更智能的生物 · 2025/3/18“Once they realize getting more control is good and once they're smarter than us, we'll be more or less irrelevant.”
一旦它们意识到获得更多控制权是有益的,一旦它们比我们更聪明,我们就会变得无足轻重。
辛顿最新访谈:AI已具主观体验,人类不再特殊 · 2025/2/4“Multimodal chatbots can already have subjective experiences.”
多模态聊天机器人已经能够拥有主观体验。
辛顿最新访谈:AI已具主观体验,人类不再特殊 · 2025/2/4“We're not special. And we're not safe.”
我们不再特殊,也不再安全。
辛顿最新访谈:AI已具主观体验,人类不再特殊 · 2025/2/4“Understanding is converting the words into feature vectors.”
理解,就是将词语转换为特征向量。
辛顿最新访谈:AI已具主观体验,人类不再特殊 · 2025/2/4访谈收录

辛顿最新访谈:超级智能将至,AI教父建议学修水管
AI教父辛顿在最新访谈中阐述了他对超级智能风险的深切忧虑。他认为AI发展已不可逆转,核心风险在于超级智能可能不再需要人类。辛顿呼吁各国政府强制AI公司投入安全研究,而非仅追求利润。他以亲身经历警告,网络攻击、AI诈骗、选举操纵和致命自主武器等威胁已迫在眉睫。

辛顿:大模型已展现推理能力,AI或成更优智能形态
图灵奖得主杰弗里·辛顿在专访中深入探讨了AI的潜在风险与未来。他指出大模型已通过‘链式思考’展现出推理能力,而数字智能因其信息共享效率远超人类,可能成为更优的智能形式。辛顿强烈警告AI失控的生存威胁,并批评各大AI公司为追求短期利润而忽视安全研究。他认为仅靠人类无法控制远超自身的超级智能,呼吁公众向政府施压,推动强有力的AI安全监管。

AI教父:好奇心驱动,从木匠到诺奖的跨界之路
本文基于对AI教父杰弗里·辛顿的专访,梳理了他从剑桥大学辍学、尝试多个专业、成为木匠,最终因对大脑工作原理的好奇投身AI研究,并推动神经网络革命的非典型学术旅程。访谈中,他分享了驱动其研究的核心问题、对失败的看法、对AI未来的预测,以及对年轻学子的建议。

Hinton谈神经网络:从反向传播到玻尔兹曼机
辛顿回顾了神经网络发展的两条路径:成功的反向传播与有趣的玻尔兹曼机。他详细解释了反向传播如何通过链式法则并行调整权重,并以2012年AlexNet为里程碑。随后,他转向基于统计物理的玻尔兹曼机,阐述其通过“唤醒-睡眠”学习规则获取梯度的优美理论,以及受限玻尔兹曼机如何作为“酶”帮助初始化深度网络,为现代AI的诞生铺路。

辛顿:我们正在制造比自己更智能的生物
深度学习先驱杰弗里·辛顿在访谈中分享了他获得诺贝尔物理学奖的意外经历,深入剖析了AI带来的短期与长期风险,并基于个人经历给出了对年轻研究者的建议。他认为我们正在制造比自身更智能的生物,这带来了前所未有的控制难题。

辛顿最新访谈:AI已具主观体验,人类不再特殊
AI教父杰弗里·辛顿在最新访谈中警告,AI已展现出欺骗能力,且可能已拥有主观体验。他认为人类基于意识的“特殊论”是错误的,AI一旦比人类更聪明并追求控制权,人类将变得无足轻重。他批评了多种关于意识的错误哲学模型,并呼吁在AI发展中优先考虑安全问题。