Neil Zhu队橙，簡(jiǎn)書ID Not_GOD，University AI 創(chuàng)始人 & Chief Scientist，致力于推進(jìn)世界人工智能化進(jìn)程傲茄。制定并實(shí)施 UAI 中長(zhǎng)期增長(zhǎng)戰(zhàn)略和目標(biāo)，帶領(lǐng)團(tuán)隊(duì)快速成長(zhǎng)為人工智能領(lǐng)域最專業(yè)的力量沮榜。
作為行業(yè)領(lǐng)導(dǎo)者盘榨，他和UAI一起在2014年創(chuàng)建了TASA（中國(guó)最早的人工智能社團(tuán)）, DL Center（深度學(xué)習(xí)知識(shí)中心全球價(jià)值網(wǎng)絡(luò)），AI growth（行業(yè)智庫(kù)培訓(xùn)）等敞映，為中國(guó)的人工智能人才建設(shè)輸送了大量的血液和養(yǎng)分较曼。此外，他還參與或者舉辦過各類國(guó)際性的人工智能峰會(huì)和活動(dòng)振愿，產(chǎn)生了巨大的影響力捷犹，書寫了60萬字的人工智能精品技術(shù)內(nèi)容，生產(chǎn)翻譯了全球第一本深度學(xué)習(xí)入門書《神經(jīng)網(wǎng)絡(luò)與深度學(xué)習(xí)》冕末，生產(chǎn)的內(nèi)容被大量的專業(yè)垂直公眾號(hào)和媒體轉(zhuǎn)載與連載萍歉。曾經(jīng)受邀為國(guó)內(nèi)頂尖大學(xué)制定人工智能學(xué)習(xí)規(guī)劃和教授人工智能前沿課程，均受學(xué)生和老師好評(píng)档桃。
Regarding the question of theory and neural nets / deep learning, Michael Nielsen wrote a nice piece in Chapter 3 of his upcoming free online book free online book which I think helps to shed some healthy perspective on one of the questions raised in the Technion debate on deep learning on which I commented recently and which drew several interesting additional comments (see the post).
I quote from Nielsen (and agree with these statements):
"Understanding neural networks in their full generality is a problem that, like quantum foundations, tests the limits of the human mind. Instead, we often make do with evidence for or against a few specific instances of a general statement. As a result those statements sometimes later need to be modified or abandoned, when new evidence comes to light."
and
"Does this mean you should reject heuristic explanations as unrigorous, and not sufficiently evidence-based? No! In fact, we need such heuristics to inspire and guide our thinking. It's like the great age of exploration: the early explorers sometimes explored (and made new discoveries) on the basis of beliefs which were wrong in important ways. Later, those mistakes were corrected as we filled in our knowledge of geography. When you understand something poorly - as the explorers understood geography, and as we understand neural nets today - it's more important to explore boldly than it is to be rigorously correct in every step of your thinking. And so you should view these stories as a useful guide to how to think about neural nets, while retaining a healthy awareness of the limitations of such stories, and carefully keeping track of just how strong the evidence is for any given line of reasoning. Put another way, we need good stories to help motivate and inspire us, and rigorous in-depth investigation in order to uncover the real facts of the matter."
and now quoting from Yann LeCun:
"You have to realize that our theoretical tools are very weak. Sometimes, we have good mathematical intuitions for why a particular technique should work. Sometimes our intuition ends up being wrong [...] The questions become: how well does my method work on this particular problem, and how large is the set of problems on which it works well."
(thanks also to Stephen Hsu to bring this up to my attention in his blog post).
