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合成迷你大脑?一家初创公司利用人类神经元制造电脑芯片

合成迷你大脑?一家初创公司利用人类神经元制造电脑芯片

Jeremy Kahn 2020年04月04日
目前,迷你大脑的处理能力已经接近蜻蜓的大脑。

在人工智能研究领域,最有前景的途径之一是尝试让软件模拟人脑的工作方式。

不过现在,澳大利亚的一家初创公司的做法更进一步。他们把真正的生物神经元嵌入到一个特殊的计算机芯片中,构成一个微型的体外大脑。

位于墨尔本的Cortical Labs希望这些合成迷你大脑能够在消耗较少能量的同时,完成很多人工智能软件可以执行的任务。该公司的联合创始人兼首席执行官钟宏文(Hon Weng Chong)说,目前,迷你大脑的处理能力已经接近蜻蜓的大脑,开发人员正尝试着教它玩老款Atari游戏Pong。

这项测试意义重大。人工智能公司DeepMind总部位于伦敦,该公司以研究人工神经网络(即能以某种方式模仿人类神经功能的软件)闻名。DeepMind于2013年首次通过Atari游戏演示了其人工智能算法的性能。那次演示促使Google于次年收购了DeepMind。而Pong就是当时DeepMind演示的Atari游戏中的一种。

钟宏文介绍说,Cortical Labs使用两种方法来制造硬件:或从胚胎中提取小鼠神经元,或使用某种技术将人类的皮肤细胞逆向转化为干细胞,然后诱导它发育成人类神经元。

之后,将神经元嵌入到一种特殊的金属氧化芯片上的液态培养基中,芯片内含由22000个微电极组成的网格,程序员可以向神经元提供电输入、获得输出结果。

眼下,Cortical Labs正在利用小鼠神经元进行Pong游戏研究。

“我们想要证明,我们可以塑造这些神经元的行为,” 钟宏文说。

虽然Pong游戏的尝试刚刚开始,但钟宏文认为Cortical Labs有望在今年年底掌握这项技术。他补充说,公司设计的合成芯片将最终完成现今的人工智能无法企及的任务,成为解决各种复杂推理和概念理解的关键。

此外,人工智能深度学习还存在一个令人头痛的问题:耗能极大。如果该公司的方法具有可拓展性,那么也将为此提供一个可能的解决方案。

AlphaGo是DeepMind为围棋游戏开发的深度学习系统,曾于2016年在这种古老的策略游戏中击败了全世界最好的人类棋手。然而,根据科技公司Ceva的估算,那场比赛消耗了1兆瓦电能,相当于100户家庭一天的用电量。相比之下,人类大脑仅消耗了约20瓦能量,相当于AlphaGo的1/50000。

伦敦大学学院的神经科学家卡尔·弗里斯顿在大脑成像以及神经元汇集、自组织等生物系统理论研究上享有盛名。今年早些时候,他看了Cortical Labs的技术演示,并盛赞了该公司取得的成绩。

Cortical Labs开发的这套系统部分借鉴了弗里斯顿及其学生的研究,但这位神经科学家与这家澳大利亚初创公司并无关系。

弗里斯顿说,他一直认为自己关于神经元组织方式的想法可以应用于制造更高效的神经形态计算机芯片,这种芯片处理信息的方式将比现有的标准计算机芯片更接近于大脑。不过,尝试将生物神经元与半导体材料相结合的方式是他未曾预料到的。

“但令我惊讶和兴奋的是,他们直奔真正的神经元。在我看来,这个团队走上了能将这些想法付诸实现的正确方向,”他在谈到Cortical Labs使用真正的生物神经元进行实验时表示。

使用真正的神经元可以避免基于软件的神经网络遇到的若干困难。例如,为了让人工神经网络能够学有成效,针对网络处理中涉及的每种类型的数据,程序员们通常需要费时费力地人工调试初始系数或权重。此外,如何让软件在探索新的解决方案和依赖现有的有效方案之间进行权衡,也是个难题。

“如果你有一个基于生物神经元的系统,所有这些问题都将不复存在,”弗里斯顿说。

两年前,曾经当过医生、创办过一家医疗科技公司的钟宏文与Cortical Labs的联合创始人兼首席技术官安迪·基钦携手,开始研究创建生物-计算机合成人工系统的方法。

钟宏文说,他们感兴趣的是通用人工智能(AGI),也就是能够像人类一样、甚至比人类更出色地灵活完成各种任务的人工智能。“大家都在竞相研制AGI,但在我们看来,真正的AGI唯有生物智能、人类智能。”他们认为,达到人类智力水平的唯一方法是使用人类神经元。

Cortical Labs也在实验小鼠神经元。由于小鼠神经元的提取和培养方法已相当成熟,长期以来一直被神经科学家用作人类神经元的替代品。(利用皮肤细胞培养人类神经元的手段直到过去十年间才得以完善。)最近,西雅图艾伦脑科学研究所的科学家们发现,小鼠和人类神经元表面的蛋白质存在差异,这可能意味着它们具有不同的电学特性,也就是说,小鼠神经元未必是人类神经元的理想替代品。

钟宏文说,他和基钦从矶村多久(Takuya Isomura)的工作中得到了启发,后者是位于东京郊区的RIKEN脑科学中心的研究员,曾师从弗里斯顿。2015年,矶村演示了覆盖在电极网格上的人工培养的皮层神经元如何能够学会克服“鸡尾酒会”效应,从背景噪音中分离出单一音频信号(比如人声)。

去年6月刚刚正式成立的Cortical Labs已经从澳大利亚著名的风险投资公司黑鸟创投获得了61万美元的种子轮投资。

从事生物计算的公司并不只有这一家。位于加州圣拉斐尔的初创公司Koniku利用小鼠神经元开发出了一种64位神经元硅芯片,可以感知某些化学物质。该公司希望将这种芯片安装在无人机上,出售给军方和执法部门,用于爆炸物探测。

麻省理工的研究人员则采用了另一种方法,用一种特殊的细菌来制造可以计算和存储信息的合成芯片。(财富中文网)

译者:胡萌琦

在人工智能研究领域,最有前景的途径之一是尝试让软件模拟人脑的工作方式。

不过现在,澳大利亚的一家初创公司的做法更进一步。他们把真正的生物神经元嵌入到一个特殊的计算机芯片中,构成一个微型的体外大脑。

位于墨尔本的Cortical Labs希望这些合成迷你大脑能够在消耗较少能量的同时,完成很多人工智能软件可以执行的任务。该公司的联合创始人兼首席执行官钟宏文(Hon Weng Chong)说,目前,迷你大脑的处理能力已经接近蜻蜓的大脑,开发人员正尝试着教它玩老款Atari游戏Pong。

这项测试意义重大。人工智能公司DeepMind总部位于伦敦,该公司以研究人工神经网络(即能以某种方式模仿人类神经功能的软件)闻名。DeepMind于2013年首次通过Atari游戏演示了其人工智能算法的性能。那次演示促使Google于次年收购了DeepMind。而Pong就是当时DeepMind演示的Atari游戏中的一种。

钟宏文介绍说,Cortical Labs使用两种方法来制造硬件:或从胚胎中提取小鼠神经元,或使用某种技术将人类的皮肤细胞逆向转化为干细胞,然后诱导它发育成人类神经元。

之后,将神经元嵌入到一种特殊的金属氧化芯片上的液态培养基中,芯片内含由22000个微电极组成的网格,程序员可以向神经元提供电输入、获得输出结果。

眼下,Cortical Labs正在利用小鼠神经元进行Pong游戏研究。

“我们想要证明,我们可以塑造这些神经元的行为,” 钟宏文说。

虽然Pong游戏的尝试刚刚开始,但钟宏文认为Cortical Labs有望在今年年底掌握这项技术。他补充说,公司设计的合成芯片将最终完成现今的人工智能无法企及的任务,成为解决各种复杂推理和概念理解的关键。

此外,人工智能深度学习还存在一个令人头痛的问题:耗能极大。如果该公司的方法具有可拓展性,那么也将为此提供一个可能的解决方案。

AlphaGo是DeepMind为围棋游戏开发的深度学习系统,曾于2016年在这种古老的策略游戏中击败了全世界最好的人类棋手。然而,根据科技公司Ceva的估算,那场比赛消耗了1兆瓦电能,相当于100户家庭一天的用电量。相比之下,人类大脑仅消耗了约20瓦能量,相当于AlphaGo的1/50000。

伦敦大学学院的神经科学家卡尔·弗里斯顿在大脑成像以及神经元汇集、自组织等生物系统理论研究上享有盛名。今年早些时候,他看了Cortical Labs的技术演示,并盛赞了该公司取得的成绩。

Cortical Labs开发的这套系统部分借鉴了弗里斯顿及其学生的研究,但这位神经科学家与这家澳大利亚初创公司并无关系。

弗里斯顿说,他一直认为自己关于神经元组织方式的想法可以应用于制造更高效的神经形态计算机芯片,这种芯片处理信息的方式将比现有的标准计算机芯片更接近于大脑。不过,尝试将生物神经元与半导体材料相结合的方式是他未曾预料到的。

“但令我惊讶和兴奋的是,他们直奔真正的神经元。在我看来,这个团队走上了能将这些想法付诸实现的正确方向,”他在谈到Cortical Labs使用真正的生物神经元进行实验时表示。

使用真正的神经元可以避免基于软件的神经网络遇到的若干困难。例如,为了让人工神经网络能够学有成效,针对网络处理中涉及的每种类型的数据,程序员们通常需要费时费力地人工调试初始系数或权重。此外,如何让软件在探索新的解决方案和依赖现有的有效方案之间进行权衡,也是个难题。

“如果你有一个基于生物神经元的系统,所有这些问题都将不复存在,”弗里斯顿说。

两年前,曾经当过医生、创办过一家医疗科技公司的钟宏文与Cortical Labs的联合创始人兼首席技术官安迪·基钦携手,开始研究创建生物-计算机合成人工系统的方法。

钟宏文说,他们感兴趣的是通用人工智能(AGI),也就是能够像人类一样、甚至比人类更出色地灵活完成各种任务的人工智能。“大家都在竞相研制AGI,但在我们看来,真正的AGI唯有生物智能、人类智能。”他们认为,达到人类智力水平的唯一方法是使用人类神经元。

Cortical Labs也在实验小鼠神经元。由于小鼠神经元的提取和培养方法已相当成熟,长期以来一直被神经科学家用作人类神经元的替代品。(利用皮肤细胞培养人类神经元的手段直到过去十年间才得以完善。)最近,西雅图艾伦脑科学研究所的科学家们发现,小鼠和人类神经元表面的蛋白质存在差异,这可能意味着它们具有不同的电学特性,也就是说,小鼠神经元未必是人类神经元的理想替代品。

钟宏文说,他和基钦从矶村多久(Takuya Isomura)的工作中得到了启发,后者是位于东京郊区的RIKEN脑科学中心的研究员,曾师从弗里斯顿。2015年,矶村演示了覆盖在电极网格上的人工培养的皮层神经元如何能够学会克服“鸡尾酒会”效应,从背景噪音中分离出单一音频信号(比如人声)。

去年6月刚刚正式成立的Cortical Labs已经从澳大利亚著名的风险投资公司黑鸟创投获得了61万美元的种子轮投资。

从事生物计算的公司并不只有这一家。位于加州圣拉斐尔的初创公司Koniku利用小鼠神经元开发出了一种64位神经元硅芯片,可以感知某些化学物质。该公司希望将这种芯片安装在无人机上,出售给军方和执法部门,用于爆炸物探测。

麻省理工的研究人员则采用了另一种方法,用一种特殊的细菌来制造可以计算和存储信息的合成芯片。(财富中文网)

译者:胡萌琦

One of the most promising approaches to artificial intelligence is to try to mimic how the human brain works in software.

But now an Australian startup has gone a step further. It’s actually building miniature disembodied brains, using real, biological neurons embedded on a specialized computer chip.

Cortical Labs, based in Melbourne, is hoping to teach these hybrid mini-brains to perform many of the same tasks that software-based artificial intelligence can, but at a fraction of the energy consumption. Currently, the company is working to get its mini-brains—which so far are approaching the processing power of a dragonfly brain—to play the old Atari arcade game Pong, Hon Weng Chong, the company’s cofounder and chief executive officer, said.

The benchmark is significant because Pong was among the early Atari games that DeepMind—the London-based A.I. company known for its work with artificial neural networks, software that in some ways mimics the functioning of human neurons—first used to demonstrate the performance of its A.I. algorithms in 2013. That demonstration helped lead to Google’s purchase of DeepMind the following year.

Cortical Labs uses two methods to create its hardware: It either extracts mouse neurons from embryos or it uses a technique in which human skin cells are transformed back into stem cells and then induced to grow into human neurons, Chong said.

These neurons are then embedded in a nourishing liquid medium on top of a specialized metal-oxide chip containing a grid of 22,000 tiny electrodes that enable programmers to provide electrical inputs to the neurons and also sense their outputs.

Right now, Cortical Labs is using mouse neurons for its Pong research.

“What we are trying to do is show we can shape the behavior of these neurons,” Chong said.

Although it is starting with Pong, a task Chong said he thinks Cortical Labs will be able to master by the end of the year, he added that the company’s hybrid chips could eventually be the key to delivering the kinds of complex reasoning and conceptual understanding that today’s A.I. can’t produce.

The company’s method, if it proves scalable, also offers a potential solution to one of the most vexing problems facing deep learning: It is extremely energy intensive.

AlphaGo, the deep-learning system DeepMind created to play Go and which beat the world’s best human player in that ancient strategy game in 2016, consumed one megawatt of power while playing the game, enough to power about 100 homes for a day, according to an estimate by technology company Ceva. By contrast, the human brain consumes about 20 watts of power, or 50,000 times less energy than AlphaGo used.

Karl Friston, a neuroscientist at University College London renowned for his work on brain imaging, as well as the theoretical underpinnings of how biological systems, including collections of neurons, self-organize, saw a demonstration of Cortical Labs’ technology earlier this year and said he is impressed with the company’s work.

Aspects of Cortical Labs’ system are based on Friston’s work and the research of some of his students, but the neuroscientist has no affiliation with the Australian startup.

Friston said he always assumed his ideas about how neurons organize would be used to build more efficient neuromorphic computer chips—hardware that tries to mimic how the brain processes information much more closely than today’s standard computer chips do. The idea of trying to integrate biological neurons with semiconductors is not, Friston said, an idea he’d anticipated.

“But to my surprise and delight they have gone straight for the real thing,” he said of Cortical Labs’ use of real biological neurons. “What this group has been able to do is, to my mind, the right way forward to making these ideas work in practice.”

Using real neurons avoids several other difficulties that software-based neural networks have. For instance, to get artificial neural networks to start learning well, their programmers usually have to engage in a laborious process of manually adjusting the initial coefficients, or weights, that will be applied to each type of data point the network processes. Another challenge is to get the software to balance how much it should be trying to explore new solutions to a problem versus relying on solutions the network has already discovered that work well.

“All these problems are completely eluded if you have a system that is based on biological neurons to begin with,” Friston said.

Chong, a former medical doctor who had founded a previous health technology company, began researching ways to create hybrid biologic-computer intelligence systems about two years ago, along with his cofounder and chief technology officer, Andy Kitchen.

Chong said the pair were interested in the idea of artificial general intelligence (AGI for short)—A.I. that has the flexibility to perform almost any kind of task as well or better than humans. “Everyone is racing to build AGI, but the only true AGI we know of is biological intelligence, human intelligence,” Chong said. He noted the pair figured the only way to get human-level intelligence was to use human neurons.

Mouse neurons, which Cortical Labs is also experimenting with, have long been used as proxies for human neurons by neuroscientists because there were long-established methods for extracting and culturing them. (The ability to culture engineer human neurons from skin cells has only been perfected in the past decade.) Recently scientists at the Allen Institute for Brain Science in Seattle have found differences in the proteins that coat mouse and human neurons, which may mean they have different electrical properties and that mouse neurons may not actually be good stand-ins for human ones.

Chong said he and Kitchen took inspiration from the work of Takuya Isomura, a researcher at the RIKEN Center for Brain Science outside Tokyo who has studied under Friston. Isomura had shown in 2015 how cultured cortical neurons overlaid on an electrode grid could learn to overcome the “cocktail party” effect, separating an individual audio signal, such as a person’s voice, from the cacophony of background noise.

Cortical Labs, which was founded formally only last June, has received about $610,000 in seed funding from Blackbird Ventures, a prominent Australian venture capital firm.

It is not the only company working on biological computing. A startup called Koniku, based in San Rafael, Calif., has developed a 64-neuron silicon chip, built using mouse neurons, that can sense certain chemicals. The company wants to use the chips in drones that it will sell to militaries and law enforcement for detecting explosives.

Meanwhile, researchers at the Massachusetts Institute of Technology have taken a different approach—using a specialized strain of bacteria in a hybrid chip to compute and store information.

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