四个行业专家谈论什么变化,速度,和人工智能的极限在哪里。
接下来将推动半导体革命?
当你问别人几十年的经验在半导体制造和软件开发,答案包括神经形态架构从人工智能和材料。
费德里科•Faggin,世界上第一个微处理器的设计;总裁兼首席执行官特里•布鲁尔布鲁尔科学;Sanjay Natarajan公司副总裁应用材料;凑说,“Mayberry迈克尔首席技术官英特尔,坐下来谈谈下一步作为半的产业战略研讨会的一部分,由Dan Hutcheson主持,超大规模的研究。
唐森:费德里科•Faggin,特里•布鲁尔凑说。“Mayberry Sanjay Natarajan,迈克尔
问:你看到相似性与人工智能和英特尔的4004年发展吗?
Faggin:4004年是显示,你可以把一个CPU芯片上。为此,硅栅技术是基础,因为它提供了至少10倍的速度和密度的2 x因素。这只是介绍第一个微处理器。但直到光刻技术和8080年的性能在一个足够的水平,有足够数量的应用程序真正向世界展示,微处理器已经到来。这也是我的一个项目和我的想法在英特尔。然后我在神经网络工作回到新思国际,我与Carver Mead成立的公司,我们所做的计算与计算机通过使用模拟与数字,因为模拟使用更少的力量。你可以把每一个处理器。它有极大的单位的权力更多的功能,你可以得到的。我已经看到了这些增长。现在有令人难以置信的进步。cpu提供zettaflops鳞次栉比。这种技术是在我们理解什么是真正需要创建具有成本效益和权力有效的解决方案。
问:你在做TCAD晶体管,提供的微控制器和扩展增量收益。现在,我们试图模拟大脑,如果你看看现代的人工智能,有很多问题。
Faggin:最根本的问题是,因为最近的成功,人们推断和有前途的事情是不可能的。人工智能仍然是基于神经网络。事实上,这是基于计算机的模拟,它遵循一个数学模型,试图捕捉大脑这是完全不同于我们所做的计算。这是两个步骤从大脑中发生的实际操作。但最重要的是,我们意识到人类和机器人和电脑不是有意识的。和我有一个好理论为什么永远不会发生。但是我们承诺有意识的机器人在四年四年——有意识的电脑。这是不可能的。甚至没有关闭。没有神经网络可以理解任何东西。所以你离开的时刻某一组数据,它可以学习和数据的相关性,机械结构是完全不知道该做什么。 Even self-driving cars require there to be this common sense sort of low-level comprehension.
问:当你看行业的发展,你认为需要解决的问题吗?
布鲁尔:我是一个真正的粉丝人工智能。我们是一个用户和AI的推动者。在这两种情况下,我们将持续改进的价值。我们已经完成了摩尔定律。我们做了更多的晶体管,我们正在做的事情3 d。它是更昂贵的。问题是,我们已经在这个行业持续改进途径非常,非常,非常长的时间。我们需要一个破坏者,AI可以作为破坏者。我们的行业,我认为这是非常重要的和非常有价值,我们持续改进的路径移动。未来到底在哪呢?一个在它的使用。现在我们的材料供应商。在过去,客户想要的56钠的部分在我们的材料。这是一个可辨认的实体。随着时间的推移,现在变成了十亿分之五。好吧,我可以看到十亿分之五。它可能确实存在。但是我们惹麻烦,因为我们描述它作为一个单点的路径,我们越来越接近那些点没有。所以现在如果你坚持持续改进,这将是一万亿分之五。我们谈论现实或概率?我们谈论曲线或面吗?是时候摆脱持续改进潮流和颠覆性的差异。 We have to do our role, but we can’t do it by looking at 120 or 200 points. You have to start integrating it by way of fingerprints. So we fingerprint the data. And then we have to analyze the fingerprints. We’re really close to wanting AI in the simplest forms do the analysis so we can provide the product that is reliable. We’re getting outside of point definitions for quality and for defects. We also are enablers for the AI, because for the last 10 or 20 years we have been developing carbon sensor technology. And carbon sensors represent quite a big change in capability from other types of sensors. There are a lot of the benefits. It’s supposed to be easy to work with. It isn’t easy, but it’s workable. You can make parts and devices with them. You can put in flexible substrates. And so we’re building sensor arrays that can do five or six different properties thus saving time.
问:我们摆脱考虑增量变化去一个全新的思维方式。
布鲁尔:这是破坏性的。我们正处于一个地方我们可以看到晶片作为增量块。事实上,它比这更糟糕的是。有些人做微处理器。有些人的记忆。我们将回顾有一天在一个简单的,陈旧的世界观。我希望AI是颠覆性的作品,将有助于改变我们的行业。
问:摩尔定律正在放缓。这有什么影响吗?
Natarajan:40年来我们理所当然摩尔定律给我们送来了。1970年,一个芯片有一千个晶体管。2010年,艺术的状态有10亿个晶体管。你有40年来一百万倍。如果你数学,我们每两年有1.997倍的晶体管。你也有一个推论的好处更节能计算每年约15%。世界上大多数理所当然。和他们的利益理所当然的在人工智能中启用。人工智能在1956年开始。这是第一次使用这个词在达特茅斯学院,创造了这个词的人工智能。所以它已经存在很长时间了。 Even in the ’70s and ’80s, you had self-driving cars that kind of sort of drive to work, but not really. The concept for Alexa was around 40 years ago. It’s just that computing wasn’t powerful enough. But if you extend that to 2010 up to now, you see a pretty ominous situation. Computing power has significantly stalled in the past seven or eight years. That is really going to have severe consequences for the future of things like AI, which are really just getting started. Alexa is more or less just annoying me at the moment. Hopefully, it’ll do something truly useful 10 or 15 years. But the key to doing something useful is really to continue driving Moore’s Law, or some version of Moore’s Law, where you reliably get more efficient computing every year. And you get more dense computation per square inch. We’re in trouble on those counts.
问:在设备级别发生变化在应用所谓的综合材料平台。这怎么不同?
Natarajan:如果你看看整个堆AI,和你接受的前提是遇到了麻烦,那么你不得不质疑假设栈。我现在操作的是底部。在过去的60年里,人工智能是一个传统的CPU。最初的版本是在计算机中实现。这就是我们在采取什么现成的和写代码。我们有点聪明的最高建筑水平。今天,我们只是做矩阵数学。那么为什么不使用图形处理器,因为这是更快呢?这是一个进化的步骤以更高效的方式实现人工智能。今天我们看到的下一个步骤,我们所做的更有目的的电脑。 Looking ahead to a traditional von Neumann machine, where you separate computation and memory, doesn’t make a whole lot of sense. That’s not how our brain works. To the extent we can understand it, instead of simulating neural networks, maybe we should really be implementing that. Those paradigms are getting smartly challenged at the top of the pyramid for AI. If you go down to the bottom of the pyramid, where the fabrication is being done at the equipment level, at the integration of the equipment in a fab, the assumption has not been challenged sufficiently. We still fundamentally build those chips the same way. Think about depositing, modifying, removing and measuring materials. We still think about some of the discrete functions that happen in discrete tools. That’s broken moving forward. There’s a fundamental need to continue to making progress and to rethink that assumption. That’s where the concepts and the tone of integrated material solutions comes from. Let’s stop thinking about those things we do as different things, because we can unlock a lot of capability if we are willing to be much more intellectually creative at that level.
问:那么计算将会怎样改变?
Mayberry:六年前当AI开幕,正是在符号、规则和逻辑,这是很好的一个数学的人。我们可以构造离散级别的东西。有确定性。可预测性。我们可以隐藏方差,可以这么说。但这结果不是特别好。我认为它没有规模很好因为它很难提取专家的专业知识。任何人完成新闻采访应该感激。你问一些问题,如果你不提出正确的问题你没有得到你所需要的信息。我会给一个现实的例子,其实发生在我们的一些测试自动车辆。 The vehicle said, ‘The traffic has moved to the right and there’s an opening. I’m going to merge into traffic.’ But maybe there was an ambulance coming down the road. The concept of an ambulance was not something that had been encapsulated in the rules and the symbols. It’s not, per se, a failure of the system. It’s a failure of the imaginers of the system to comprehend all this. So the expert system failed mainly from an ability to extract data. We sold a lot of CPUs that went into those workloads, and we continue to sell a lot of CPUs that do things like that—even though we mostly don’t call that AI. The second area is really around learning of data—pattern matching, statistical learning, a bunch of things. That’s not new to us either, because fabs are an enormous generator of data. So we’ve been doing机器学习现在,建模和几十年来。我们不称之为人工智能。真正的崛起引发了发明带安全标签的数据。没有互联网,没有足够的数据来创建这些有用的模型。人类专家标记数据,允许人们建立数据文件。是的,你需要更快的硬件和正确的统计算法。所以我们去哪里吗?模式匹配是自下而上的一部分或多或少,和象征意义是最高的部分。两者之间有一个巨大的海湾,这是一个有趣的领域为研究有趣的领域需要的数据是什么样子的。这是一个有趣的区域物理硬件需要什么样…人类善于把知识从一个域,说,“嘿,这类似于我以前见过的东西。再次,有有趣的事情。 But part of the answer to that is how you represent knowledge. If you represent knowledge in a very static structure, then you don’t have that cross-domain ability to transfer, mapping of one problem into another area. And then finally, humans are good at creating knowledge out of nothing. Yes, they’re building on the stuff that they’ve done already, but so far machines are completely unable to do that unless you bound the problem so much like a game where you have perfect knowledge of the current conditions. You have a perfect knowledge of the rules. And then you can create new knowledge out of that. So that piece is the furthest away. It’s the hardest to do. But it’s the most interesting of them all. And that’s of course what scares people. If machines are to create knowledge, are they going to create knowledge that we would consider good or bad?
问:在设备层面,英特尔推出了有趣的新技术将不断飙升的神经网络。怎么这些改变游戏然后试图采取传统的CPU和加载神经网络。
Mayberry让我澄清一下。第一个模拟神经网络芯片是1989年。这是65个神经细胞,大约10000个突触。这是基于eepm技术。没有去任何地方。还为时过早。人们不知道如何使用它。我们今天的建筑是什么样子,但我们实际上有一个飙升的数字实现神经网络。它不是一个定时系统,一切都是同步的。它是不同步的,能够被编程递归神经网络,因此它带来的时间到今天的神经网络技术。 That opens up new avenues, because we don’t train it with back propagation. You don’t have to be able to do mathematical derivatives on it. Now we have to figure out the best way to train it, but it’s showing very promising results that are in some ways unexpected. And we’re finding that you can use optimization around lots of multi-interactions and variables.
问:很多你们都谈论什么是系统设计提高抽象层次。做的如何,你期待什么样的问题?
Natarajan:有技术挑战和非技术的挑战。本书初版,这里的成功,让我们不会帮助我们前进。我们形成了一个金字塔,坦白地说这个行业非常好工作了。我们从材料、设备制造和向上移动堆栈。很难破坏,因为它有如此惊人的成功。我无法想象另一个行业发展这么远,这么快,在很短的时间内。但我相信,特别是在人工智能的环境中,它不会让我们下一个高原,我们需要。有很多人类挑战分解这些范例和想出一个完全不同的方式处理这个问题。和你不能最小化技术挑战和发明新的方法来控制材料在原子水平。一个简单的例子是一种不同的开关。 I can do this by the end of the day in PowerPoint, but actually making it is going to take thousands and thousands of human years of blood, sweat, and tears.
Faggin:生命系统的例子是我们需要学习的。你如何保持信息处理?生命系统是精致的信息处理系统。这些都不是经典系统。他们的系统我们仍在试图找出一种。所以对我来说,下一步是不会来自硅。从我们的理解系统有机体,它是一个生物材料的流动的细胞。两天后,细胞的原子和分子并不存在的原子和分子。这是发生在电脑吗?不。 The atoms and molecules of the computer are the ones which it was built with. So you use that as a scaffold and run signals over it. Life doesn’t work that way. Why are we smart? Because we are based on the stuff of life, and that’s next frontier. In AI, you force it to understand the difference between a living system and a mechanical system. We have convinced ourselves that our minds are like computers. That’s not even close. We have emotions. Where are the emotions in computers? We have thoughts. Then we have sensations. When I smell something, I don’t have a simulator to tell me it’s a rose. I’m actually smelling the rose. It is a sensation. That’s what consciousness does. I urge you to look beyond the way we do things. The real stuff is living systems.
布鲁尔:我同意,但我们必须把它分阶段。我希望有人工智能的第一步。如果我们可以做第一步,我们应该很兴奋。我不知道关于复制生物系统在不久的将来。但AI可能是第一步,我们应该兴奋。我们处理一个,两个,三个部分的数据,但是我们的大脑处理成千上万块的数据,很快得出结论。人工智能只是开始,途径。
Mayberry:你越无知,越确定你的意见。我了解更多关于人工智能,我学习我的无知的界限越来越多。和最初的问题是,你要怎么解决这些问题呢?我承认我不知道。但我开始知道我不知道,所以我们可以和工作在这些碎片。如果你有一个盲点并没有意识到它,你永远不会知道它可以在这些特定的事情工作。所以我们一起把我们的路线图,我们描绘boxes-what很好理解中间是什么?有什么事情我们可以开始做那个不容易理解?
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