Facebook的首席人工智能科學家楊立昆（Yann LeCun）曾推動深度學習崛起，成為谷歌和亞馬遜之類巨頭紛紛應用的尖端人工智能技術，可迅速翻譯并識別圖片中的物品。

深度學習的核心是被稱為神經網絡的軟件，可過濾海量數據，從而比人類更快地掌握數據中的模式。但該項技術需要巨大的計算能力，促使英特爾和硬件初創公司等半導體制造商努力設計全新的計算機芯片，降低能耗的同時提升一些人工智能計算任務效率。

星期一，楊立昆在舊金山舉辦的國際固態電路會議上發表新研究論文，概述他對未來人工智能的看法，著重關注芯片和硬件的發展前景。

以下是他談到的幾個要點：

1. 從翻譯語言到管理內容

雖然Facebook、谷歌和微軟等公司都打著降低能耗的口號研究專門的電腦芯片，不過楊立昆直接挑明了此類創新為何重要，因為新款芯片可以幫助各公司在自家數據中心應用更多神經網絡。

因此，在線語音翻譯等任務可能進一步完善，實現秒完成。與此同時，人工智能系統可以分析視頻里的每一幀并識別人或物體，不僅僅是識別靜態圖像，由此大大提高準確性。

楊立昆還認為，使用性能更佳的計算機芯片可以提升內容管控效率，比如掃描文本中的攻擊性語言或虛假新聞。對于努力刪除平臺上不良宣傳或濫用行為的Facebook來說，相關技術進步越快越好。

2. 到處是“更智能”吸塵器和割草機的世界

楊立昆也在密切關注可供日常電器使用的電腦芯片，例如可以安裝在吸塵器和割草機上的新產品。想象一下未來配置神經網絡的割草機，可以輕松識別雜草和玫瑰，他解釋說。

楊立昆還設想今后研發出性能更好的移動計算芯片，可以直接在移動設備上運行神經網絡，不必再將信息傳回數據中心計算。一些智能手機已經內置人工智能功能，比方說識別用戶面部解鎖設備，但如果想完成更復雜的任務，更先進的芯片必不可少。

他表示，人工智能的另一障礙是現在的電池續航能力。人工智能技術耗電量比較大，意味著一些較小的設備上使用人工智能會比較受限制。

3. 讓電腦懂一些常識

盡管深度學習領域進步顯著，但電腦仍然缺乏常識。現在的電腦要瀏覽數千張大象的照片才能在其他照片里獨立識別出來。

相比之下，兒童只要對動物有了基本的理解，就可以迅速認識各種大象。即使增加認識的難度，孩子們還是能推斷出大象只是一種體型巨大的動物。

楊立昆認為，人們終將開發出新型神經網絡，可以通過篩選大量數據獲得常識。過程類似于先傳授基本技術，以后可用來參考，就像百科全書一樣。然后，人工智能從業者可以進一步訓練神經網絡實現識別，并執行比現在更高級的任務。

但只有更先進的計算機芯片才可能實現，楊立昆希望盡快研發成功。（財富中文網）

譯者：Pessy

審校：夏林

Facebook’s chief artificial intelligence scientist Yann LeCun helped spearhead the rise of deep learning, the cutting-edge AI technology used by companies like Google and Amazon to quickly translate languages and identify objects in photos.

At the core of deep learning is software called a neural network, which sifts through enormous amounts of data so that it can notice patterns more quickly than humans. But this technology requires tremendous computing power, prompting semiconductor makers like Intel and hardware startups to explore radical new computer chip designs for the job that gobble less energy and improve the efficiency of certain AI computational tasks.

LeCun present a new research paper on Monday at the International Solid State Circuits Conference in San Francisco that will outline his vision for AI’s future. In particular, he’ll focus on how the chips and hardware that makes it possible must evolve.

Here are a few highlights from his talk:

1. From translating languages to policing content

Although companies like Facebook, Google, and Microsoft are exploring specialized computer chips that reduce energy consumption, LeCun is blunt about why such innovation is important—new computer chips will allow companies to use even more neural nets inside their data centers than what’s possible today.

As a result, tasks like online speech translation could be supercharged so that they could be done in real time. Meanwhile, AI systems would be able to analyze every frame in a video in an effort to identify people or objects rather than just a few stills—thereby significantly boosting accuracy.

LeCun also believes that content moderation, like scanning text for offensive language or fake news, could be improved using better computer chips. For a company like Facebook that struggles with deleting propaganda and abusive behavior from its service, those advancements couldn’t come soon enough.

2. A world of “smarter” vacuum cleaners and lawnmowers

One trend LeCun is closely watching is computer chips that can fit in everyday devices like vacuum cleaners and lawnmowers. Imagine a futuristic lawnmower loaded with neural networks that could recognize the difference between weeds and garden roses, he explains.

LeCun also envisions even more sophisticated mobile computing chips that can run neural networks directly on the devices themselves rather than having to send information back to data centers for processing. Already, some smartphones are designed with AI built in that can recognize a user’s face to unlock the device, but improved computer chips will be necessary for more advanced tasks.

Another hurdle to AI are today’s batteries, he says. The technology eats a lot of energy, which means that using AI on some smaller devices is limited.

3. Giving computers some common sense

Despite advances in deep learning, computers still lack common sense. They would need to review thousands of images of an elephant to independently identify them in other photos.

In contrast, children quickly recognize elephants because they have a basic understanding about the animals. If challenged, they can extrapolate that an elephant is merely a different kind of animal—albeit a really big one.

LeCun believes that new kinds of neural networks will eventually be developed that gain common sense by sifting through a smorgasbord of data. It would be akin to teaching the technology basic facts that it can later reference, like an encyclopedia. AI practitioners could then refine these neural networks by further training them to recognize and carry out more advanced tasks than modern versions.

But it would only be possible using more powerful computer chips—ones that LeCun hopes are just around the corner.