Terrence J. Sejnowski holds the Francis Crick Chair at the Salk Institute for Biological Studies and is a Distinguished Professor at the University of California, San Diego. He was a member of the advisory committee for the Obama administration's BRAIN initiative and is President of the Neural Information Processing (NIPS) Foundation. He has published twelve books, including (with Patricia Churchland) The Computational Brain (25th Anniversary Edition, MIT Press).
How deep learning -- from Google Translate to driverless cars to personal cognitive assistants -- is changing our lives and transforming every sector of the economy.
The deep learning revolution has brought us driverless cars, the greatly improved Google Translate, fluent conversations with Siri and Alexa, and enormus profits from automated trading on the New York Stock Exchange. Deep learning networks can play poker better than professional poker players and defeat a world champion at Go. In this book, Terry Sejnowski explains how deep learning went from being an arcane academic field to a disruptive technology in the information economy.
Sejnowski played an important role in the founding of deep learning, as one of a small group of researchers in the 1980s who challenged the prevailing logic-and-symbol based version of AI. The new version of AI Sejnowski and others developed, which became deep learning, is fueled instead by data. Deep networks learn from data in the same way that babies experience the world, starting with fresh eyes and gradually acquiring the skills needed to navigate novel environments. Learning algorithms extract information from raw data; information can be used to create knowledge; knowledge underlies understanding; understanding leads to wisdom. Someday a driverless car will know the road better than you do and drive with more skill; a deep learning network will diagnose your illness; a personal cognitive assistant will augment your puny human brain. It took nature many millions of years to evolve human intelligence; AI is on a trajectory measured in decades. Sejnowski prepares us for a deep learning future.
發表於2024-05-29
The Deep Learning Revolution 2024 pdf epub mobi 電子書 下載
多年前看世界特色建築就知道瞭索爾剋研究所,幾何綫條的極簡設計,院子直通太平洋,那時候覺得這樣的建築有點不接地氣,但其實對一些科學傢來說那就是他們日常上班的地方。 讀到的這本《深度學習》就是在索爾剋研究所的美國“四院院士”對人工智能的介紹,從大眾熟知的阿爾法狗...
評分多年前看世界特色建築就知道瞭索爾剋研究所,幾何綫條的極簡設計,院子直通太平洋,那時候覺得這樣的建築有點不接地氣,但其實對一些科學傢來說那就是他們日常上班的地方。 讀到的這本《深度學習》就是在索爾剋研究所的美國“四院院士”對人工智能的介紹,從大眾熟知的阿爾法狗...
評分這是上周末剛剛拿到手的一本書,這是我看的最快的一本書,用瞭兩天時間快速讀完。這是一本超齣我的知識麵的書籍,還好作者思路清晰,讓我能夠簡單理解這本書的最錶層內容。學術部分直接忽略吧。(安慰一下自己,給自己一個博覽群書的理由。如果你隻讀每個人都讀的書,你也隻能...
評分文 / 董小琳 前幾天,在微博上看到這樣一則新聞: 迴想起自己,曾經夾著三支筆抄作業的情景。不得不說,生在觸屏時代的孩子們,簡直太幸福瞭。 那麼,在羨慕之餘,不知你是否發現瞭:近兩年興起的人工智能,在成人眼中,是“搶飯碗”的威脅。可到瞭小朋友那裏,卻自然地變成瞭...
評分作者是深度學習領域的領軍人物,本書可以算是作者寫的人工智能簡史,涉及到作者參與的一些項目,作者跟許多業內知名科學傢都有學術交往。 書中涉及到一些人工智能算法的基本原理,沒學過高數、沒有編程基礎的讀者恐怕是比較難看懂的。不過看不懂可以跳過去,至少一些學術發展的...
圖書標籤: 人工智能 MIT AI learning Psychologia Deep 數學和計算機 2019
超級硬核的一本書,作者是一個轉行Neuroscience關注AI領域的物理學傢,主要介紹Neuroscience和Deeplearning結閤的幾個研究領域,雖然有幾個算法還有芯片那一部分沒特彆弄懂,但是總體來說非常開闊眼界,獲得新知。“Nature/ evolution is cleverer than we are”,AI發展獲得巨大進步主要還是依靠研究大腦的工作原理,從而進行算法模擬,真道法自然。看完之後對brain function 好上頭。
評分"Neural nets are often too complex to explain their decisions in relatable terms, they can perpetuate social discrimination if trained on biased data, and they can be used for autonomous weapons that might become trigger-happy. Granted, humans are also opaque, unfair and ornery."
評分god damn crazy, wonderful articles!respect!
評分超級硬核的一本書,作者是一個轉行Neuroscience關注AI領域的物理學傢,主要介紹Neuroscience和Deeplearning結閤的幾個研究領域,雖然有幾個算法還有芯片那一部分沒特彆弄懂,但是總體來說非常開闊眼界,獲得新知。“Nature/ evolution is cleverer than we are”,AI發展獲得巨大進步主要還是依靠研究大腦的工作原理,從而進行算法模擬,真道法自然。看完之後對brain function 好上頭。
評分超級硬核的一本書,作者是一個轉行Neuroscience關注AI領域的物理學傢,主要介紹Neuroscience和Deeplearning結閤的幾個研究領域,雖然有幾個算法還有芯片那一部分沒特彆弄懂,但是總體來說非常開闊眼界,獲得新知。“Nature/ evolution is cleverer than we are”,AI發展獲得巨大進步主要還是依靠研究大腦的工作原理,從而進行算法模擬,真道法自然。看完之後對brain function 好上頭。
The Deep Learning Revolution 2024 pdf epub mobi 電子書 下載