在人工智能研究領域,最有前景的途徑之一是嘗試讓軟件模擬人腦的工作方式。
不過現(xiàn)在,澳大利亞的一家初創(chuàng)公司的做法更進一步。他們把真正的生物神經元嵌入到一個特殊的計算機芯片中,構成一個微型的體外大腦。
位于墨爾本的Cortical Labs希望這些合成迷你大腦能夠在消耗較少能量的同時,完成很多人工智能軟件可以執(zhí)行的任務。該公司的聯(lián)合創(chuàng)始人兼首席執(zhí)行官鐘宏文(Hon Weng Chong)說,目前,迷你大腦的處理能力已經接近蜻蜓的大腦,開發(fā)人員正嘗試著教它玩老款Atari游戲Pong。
這項測試意義重大。人工智能公司DeepMind總部位于倫敦,該公司以研究人工神經網絡(即能以某種方式模仿人類神經功能的軟件)聞名。DeepMind于2013年首次通過Atari游戲演示了其人工智能算法的性能。那次演示促使Google于次年收購了DeepMind。而Pong就是當時DeepMind演示的Atari游戲中的一種。
鐘宏文介紹說,Cortical Labs使用兩種方法來制造硬件:或從胚胎中提取小鼠神經元,或使用某種技術將人類的皮膚細胞逆向轉化為干細胞,然后誘導它發(fā)育成人類神經元。
之后,將神經元嵌入到一種特殊的金屬氧化芯片上的液態(tài)培養(yǎng)基中,芯片內含由22000個微電極組成的網格,程序員可以向神經元提供電輸入、獲得輸出結果。
眼下,Cortical Labs正在利用小鼠神經元進行Pong游戲研究。
“我們想要證明,我們可以塑造這些神經元的行為,” 鐘宏文說。
雖然Pong游戲的嘗試剛剛開始,但鐘宏文認為Cortical Labs有望在今年年底掌握這項技術。他補充說,公司設計的合成芯片將最終完成現(xiàn)今的人工智能無法企及的任務,成為解決各種復雜推理和概念理解的關鍵。
此外,人工智能深度學習還存在一個令人頭痛的問題:耗能極大。如果該公司的方法具有可拓展性,那么也將為此提供一個可能的解決方案。
AlphaGo是DeepMind為圍棋游戲開發(fā)的深度學習系統(tǒng),曾于2016年在這種古老的策略游戲中擊敗了全世界最好的人類棋手。然而,根據科技公司Ceva的估算,那場比賽消耗了1兆瓦電能,相當于100戶家庭一天的用電量。相比之下,人類大腦僅消耗了約20瓦能量,相當于AlphaGo的1/50000。
倫敦大學學院的神經科學家卡爾·弗里斯頓在大腦成像以及神經元匯集、自組織等生物系統(tǒng)理論研究上享有盛名。今年早些時候,他看了Cortical Labs的技術演示,并盛贊了該公司取得的成績。
Cortical Labs開發(fā)的這套系統(tǒng)部分借鑒了弗里斯頓及其學生的研究,但這位神經科學家與這家澳大利亞初創(chuàng)公司并無關系。
弗里斯頓說,他一直認為自己關于神經元組織方式的想法可以應用于制造更高效的神經形態(tài)計算機芯片,這種芯片處理信息的方式將比現(xiàn)有的標準計算機芯片更接近于大腦。不過,嘗試將生物神經元與半導體材料相結合的方式是他未曾預料到的。
“但令我驚訝和興奮的是,他們直奔真正的神經元。在我看來,這個團隊走上了能將這些想法付諸實現(xiàn)的正確方向,”他在談到Cortical Labs使用真正的生物神經元進行實驗時表示。
使用真正的神經元可以避免基于軟件的神經網絡遇到的若干困難。例如,為了讓人工神經網絡能夠學有成效,針對網絡處理中涉及的每種類型的數據,程序員們通常需要費時費力地人工調試初始系數或權重。此外,如何讓軟件在探索新的解決方案和依賴現(xiàn)有的有效方案之間進行權衡,也是個難題。
“如果你有一個基于生物神經元的系統(tǒng),所有這些問題都將不復存在,”弗里斯頓說。
兩年前,曾經當過醫(yī)生、創(chuàng)辦過一家醫(yī)療科技公司的鐘宏文與Cortical Labs的聯(lián)合創(chuàng)始人兼首席技術官安迪·基欽攜手,開始研究創(chuàng)建生物-計算機合成人工系統(tǒng)的方法。
鐘宏文說,他們感興趣的是通用人工智能(AGI),也就是能夠像人類一樣、甚至比人類更出色地靈活完成各種任務的人工智能。“大家都在競相研制AGI,但在我們看來,真正的AGI唯有生物智能、人類智能。”他們認為,達到人類智力水平的唯一方法是使用人類神經元。
Cortical Labs也在實驗小鼠神經元。由于小鼠神經元的提取和培養(yǎng)方法已相當成熟,長期以來一直被神經科學家用作人類神經元的替代品。(利用皮膚細胞培養(yǎng)人類神經元的手段直到過去十年間才得以完善。)最近,西雅圖艾倫腦科學研究所的科學家們發(fā)現(xiàn),小鼠和人類神經元表面的蛋白質存在差異,這可能意味著它們具有不同的電學特性,也就是說,小鼠神經元未必是人類神經元的理想替代品。
鐘宏文說,他和基欽從磯村多久(Takuya Isomura)的工作中得到了啟發(fā),后者是位于東京郊區(qū)的RIKEN腦科學中心的研究員,曾師從弗里斯頓。2015年,磯村演示了覆蓋在電極網格上的人工培養(yǎng)的皮層神經元如何能夠學會克服“雞尾酒會”效應,從背景噪音中分離出單一音頻信號(比如人聲)。
去年6月剛剛正式成立的Cortical Labs已經從澳大利亞著名的風險投資公司黑鳥創(chuàng)投獲得了61萬美元的種子輪投資。
從事生物計算的公司并不只有這一家。位于加州圣拉斐爾的初創(chuàng)公司Koniku利用小鼠神經元開發(fā)出了一種64位神經元硅芯片,可以感知某些化學物質。該公司希望將這種芯片安裝在無人機上,出售給軍方和執(zhí)法部門,用于爆炸物探測。
麻省理工的研究人員則采用了另一種方法,用一種特殊的細菌來制造可以計算和存儲信息的合成芯片。(財富中文網)
譯者:胡萌琦
在人工智能研究領域,最有前景的途徑之一是嘗試讓軟件模擬人腦的工作方式。
不過現(xiàn)在,澳大利亞的一家初創(chuàng)公司的做法更進一步。他們把真正的生物神經元嵌入到一個特殊的計算機芯片中,構成一個微型的體外大腦。
位于墨爾本的Cortical Labs希望這些合成迷你大腦能夠在消耗較少能量的同時,完成很多人工智能軟件可以執(zhí)行的任務。該公司的聯(lián)合創(chuàng)始人兼首席執(zhí)行官鐘宏文(Hon Weng Chong)說,目前,迷你大腦的處理能力已經接近蜻蜓的大腦,開發(fā)人員正嘗試著教它玩老款Atari游戲Pong。
這項測試意義重大。人工智能公司DeepMind總部位于倫敦,該公司以研究人工神經網絡(即能以某種方式模仿人類神經功能的軟件)聞名。DeepMind于2013年首次通過Atari游戲演示了其人工智能算法的性能。那次演示促使Google于次年收購了DeepMind。而Pong就是當時DeepMind演示的Atari游戲中的一種。
鐘宏文介紹說,Cortical Labs使用兩種方法來制造硬件:或從胚胎中提取小鼠神經元,或使用某種技術將人類的皮膚細胞逆向轉化為干細胞,然后誘導它發(fā)育成人類神經元。
之后,將神經元嵌入到一種特殊的金屬氧化芯片上的液態(tài)培養(yǎng)基中,芯片內含由22000個微電極組成的網格,程序員可以向神經元提供電輸入、獲得輸出結果。
眼下,Cortical Labs正在利用小鼠神經元進行Pong游戲研究。
“我們想要證明,我們可以塑造這些神經元的行為,” 鐘宏文說。
雖然Pong游戲的嘗試剛剛開始,但鐘宏文認為Cortical Labs有望在今年年底掌握這項技術。他補充說,公司設計的合成芯片將最終完成現(xiàn)今的人工智能無法企及的任務,成為解決各種復雜推理和概念理解的關鍵。
此外,人工智能深度學習還存在一個令人頭痛的問題:耗能極大。如果該公司的方法具有可拓展性,那么也將為此提供一個可能的解決方案。
AlphaGo是DeepMind為圍棋游戲開發(fā)的深度學習系統(tǒng),曾于2016年在這種古老的策略游戲中擊敗了全世界最好的人類棋手。然而,根據科技公司Ceva的估算,那場比賽消耗了1兆瓦電能,相當于100戶家庭一天的用電量。相比之下,人類大腦僅消耗了約20瓦能量,相當于AlphaGo的1/50000。
倫敦大學學院的神經科學家卡爾·弗里斯頓在大腦成像以及神經元匯集、自組織等生物系統(tǒng)理論研究上享有盛名。今年早些時候,他看了Cortical Labs的技術演示,并盛贊了該公司取得的成績。
Cortical Labs開發(fā)的這套系統(tǒng)部分借鑒了弗里斯頓及其學生的研究,但這位神經科學家與這家澳大利亞初創(chuàng)公司并無關系。
弗里斯頓說,他一直認為自己關于神經元組織方式的想法可以應用于制造更高效的神經形態(tài)計算機芯片,這種芯片處理信息的方式將比現(xiàn)有的標準計算機芯片更接近于大腦。不過,嘗試將生物神經元與半導體材料相結合的方式是他未曾預料到的。
“但令我驚訝和興奮的是,他們直奔真正的神經元。在我看來,這個團隊走上了能將這些想法付諸實現(xiàn)的正確方向,”他在談到Cortical Labs使用真正的生物神經元進行實驗時表示。
使用真正的神經元可以避免基于軟件的神經網絡遇到的若干困難。例如,為了讓人工神經網絡能夠學有成效,針對網絡處理中涉及的每種類型的數據,程序員們通常需要費時費力地人工調試初始系數或權重。此外,如何讓軟件在探索新的解決方案和依賴現(xiàn)有的有效方案之間進行權衡,也是個難題。
“如果你有一個基于生物神經元的系統(tǒng),所有這些問題都將不復存在,”弗里斯頓說。
兩年前,曾經當過醫(yī)生、創(chuàng)辦過一家醫(yī)療科技公司的鐘宏文與Cortical Labs的聯(lián)合創(chuàng)始人兼首席技術官安迪·基欽攜手,開始研究創(chuàng)建生物-計算機合成人工系統(tǒng)的方法。
鐘宏文說,他們感興趣的是通用人工智能(AGI),也就是能夠像人類一樣、甚至比人類更出色地靈活完成各種任務的人工智能。“大家都在競相研制AGI,但在我們看來,真正的AGI唯有生物智能、人類智能。”他們認為,達到人類智力水平的唯一方法是使用人類神經元。
Cortical Labs也在實驗小鼠神經元。由于小鼠神經元的提取和培養(yǎng)方法已相當成熟,長期以來一直被神經科學家用作人類神經元的替代品。(利用皮膚細胞培養(yǎng)人類神經元的手段直到過去十年間才得以完善。)最近,西雅圖艾倫腦科學研究所的科學家們發(fā)現(xiàn),小鼠和人類神經元表面的蛋白質存在差異,這可能意味著它們具有不同的電學特性,也就是說,小鼠神經元未必是人類神經元的理想替代品。
鐘宏文說,他和基欽從磯村多久(Takuya Isomura)的工作中得到了啟發(fā),后者是位于東京郊區(qū)的RIKEN腦科學中心的研究員,曾師從弗里斯頓。2015年,磯村演示了覆蓋在電極網格上的人工培養(yǎng)的皮層神經元如何能夠學會克服“雞尾酒會”效應,從背景噪音中分離出單一音頻信號(比如人聲)。
去年6月剛剛正式成立的Cortical Labs已經從澳大利亞著名的風險投資公司黑鳥創(chuàng)投獲得了61萬美元的種子輪投資。
從事生物計算的公司并不只有這一家。位于加州圣拉斐爾的初創(chuàng)公司Koniku利用小鼠神經元開發(fā)出了一種64位神經元硅芯片,可以感知某些化學物質。該公司希望將這種芯片安裝在無人機上,出售給軍方和執(zhí)法部門,用于爆炸物探測。
麻省理工的研究人員則采用了另一種方法,用一種特殊的細菌來制造可以計算和存儲信息的合成芯片。(財富中文網)
譯者: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.