Podcasts about ipod iphone

Join us for an extraordinary conversation with Philip Fierlinger, co-founder of Xero ($25B accounting software company) and current founder of Upstock. From his early days working on groundbreaking projects at General Magic alongside future Apple innovators, to revolutionsing accounting software by making it "sexy," Philip shares the untold stories behind building one of New Zealand's most successful tech companies. Discover how user testing and design-first thinking transformed the accounting industry, why Philip chose New Zealand over Silicon Valley, and the lessons he's applying to his new venture, Upstock. Get insider insights on: - The original moment that made Xero's interface revolutionary - Working alongside Tony Fadell (future iPod/iPhone creator) in the early '90s - How Philip's industrial design background shaped Xero's success - Building marketplace platforms and the challenges of B2B software - The realities of startup life, even after a multi-billion dollar exit Whether you're a founder, designer, or tech enthusiast, this episode offers rare insights into building category-defining companies and the future of B2B software. Learn from one of tech's most innovative design thinkers about what it really takes to create magical product experiences that users love.

Nick visits Everything Electric, the Duck is downed, Apple buys all the 2nm, explains (sort of) what went wrong with the “deleted” photos, ICQ is shutting down, and a 1994 PowerBook rises from the grave... All this and more on this week's Essential Apple! MISSION TO SEAFARERS APPEAL The Mission to Seafarers is looking for donations of old, but working, smartphones and internet capable tablets to give seafarers far from home a way to talk to friends and family If you have any old smartphones or tablets cluttering your cupboard and drawers why not donate them to this worthy cause? If you are in the UK contact Vicar@felixparish.com or your nearest Mission to Seafarers Centre, or if you live outside the UK simply visit missiontoseafarers.org. Why not come and join the Slack community? You can now just click on this Slackroom Link to sign up and join in the chatter! Recorded 26th May 2024 On this week's show NICK RILEY Spligosh in the Slack Sutton Park Circuit church worship on YouTube Nick's church stream videos on You Tube APPLE WWDC 2024 to reportedly showcase Project Greymatter, Apple's idea of AI for the masses – iMore Apple reportedly seeks to lock out its competitors from TSMC's 2nm process – PC Gamer macOS 15 said to bring big UI design changes – BGR Apple explains the strange iOS 17.5 bug that made photos reappear – [TechRadar]](https://www.techradar.com/phones/iphone/apple-explains-the-strange-ios-175-bug-that-made-photos-reappear) Here's why deleted iPhone photos returned to some iOS devices – The Verge TECHNOLOGY & SCIENCE Microsoft outage affects Bing, Copilot, DuckDuckGo and ChatGPT – The Verge VK announces ICQ (I Seek You) will hit end-of-support in June 26 – Windows Central SECURITY & PRIVACY Apple's Wi-Fi location data could allow people to be tracked… – iMore WORTH A CHIRP / ESSENTIAL TIPS iPhones Pause MagSafe Charging During Continuity Camera – TidBITS JUST A SNIPPET For things that are not worth more than a flypast The TinyPod turns the Apple Watch into a hybrid iPod-iPhone gadget – TechRadar Apple's 1994 PowerBook 520C rises from the grave with iPad display and i5 internals – Tom's Hardware Essential Apple Recommended Services: All Things Secured – Online security made simple by Josh Summers. Pixel Privacy – a fabulous resource full of excellent articles and advice on how to protect yourself online. Doug.ee Blog for Andy J's security tips. Ghostery – protect yourself from trackers, scripts and ads while browsing. Simple Login – Email anonymisation and disposable emails for login/registering with 33mail.com – Never give out your real email address online again. AnonAddy – Disposable email addresses Sudo – get up to 9 “avatars” with email addresses, phone numbers and more to mask your online identity. Free for the first year and priced from $0.99 US / £2.50 UK per month thereafter... You get to keep 2 free avatars though. ProtonMail – end to end encrypted, open source, based in Switzerland. Prices start from FREE... what more can you ask? ProtonVPN – a VPN to go with it perhaps? Prices also starting from nothing! Comparitech DNS Leak Test – simple to use and understand VPN leak test. Fake Name Generator – so much more than names! Create whole identities (for free) with all the information you could ever need. Wire and on the App Stores – free for personal use, open source and end to end encryted messenger and VoIP. Pinecast – a fabulous podcast hosting service with costs that start from nothing. Essential Apple is not affiliated with or paid to promote any of these services... We recommend services that we use ourselves and feel are either unique or outstanding in their field, or in some cases are just the best value for money in our opinion. Social Media and Slack You can follow us on: Twitter / Slack / EssentialApple.com / Soundcloud / Spotify / Facebook / Pinecast Also a big SHOUT OUT to the members of the Slack room without whom we wouldn't have half the stories we actually do – we thank you all for your contributions and engagement. You can always help us out with a few pennies by using our Amazon Affiliate Link so we get a tiny kickback on anything you buy after using it. If you really like the show that much and would like to make a regular donation then please consider joining our Patreon or using the Pinecast Tips Jar (which accepts one off or regular donations) And a HUGE thank you to the patrons who already do. Support The Essential Apple Podcast by contributing to their tip jar: https://tips.pinecast.com/jar/essential-apple-show This podcast is powered by Pinecast.

Welcome to Episode 410 of the Outdoor Biz Podcast. Brought to you this week by Four Wheel Campers. Today I'm talking with Kirk Ohly and Spencer T Houser about their new project Suweeka, a modular, vehicle rack system – engineered to support the lifestyle of active and passionate people in original and innovative ways. Facebook​ ​Twitter​ ​Instagram​ Love the show? Subscribe, ​rate, review, and share!​ Sign up for my Newsletter ​HERE​ I'd love to hear your feedback about the show! You can contact me here: ​rick@theoutdoorbizpodcast.com Show Notes First Bikes Spencer Takara BMX Bike First 10 speed Kirk Moxie - Monoshock First 10-speed Favorite Books Build by Tony Fadell He headed up the iPod & iPhone team and also started the NEST brand. A Trip to the Beach, By Melinda Blanchard and Robert Blanchard a husband and wife team that moved from Vermont to Anquila and followed up on their crazy idea to open a beachside restaurant. It's a great read, and it didn't dawn on me until we started Suweeka that it's a great take on starting a business.   Favorite outdoor gear purchase under $100 This hasn't changed from my last appearance, A hammock from Eagles Nest Outfitters. I think we have six of them right now   Also, a special mention for @Belmont Blankets. They are the kind of blanket our parents carried, and we didn't understand why. They are cozy and bombproof, and now my kids fight over who gets to share mine with me. https://belmontblanket.com/   Dometic water jug and rechargeable faucet https://www.dometic.com/en-us/outdoor/outdoor-drinkware/jugs?gad=1&gclid=CjwKCAjw7oeqBhBwEiwALyHLM0ocRQcdG83s_gX4fWDHkRg_Q9HhrK9sBBVouLj2HXQZzalv4TVvDxoCTRIQAvD_BwE   Knipex tools https://www.knipex-tools.com/ Follow up https://suweeka.com/ Instagram:  @suweeka_pnw @kirkohly Facebook Linkedin Kirk email Suweeka email: info@suweeka.com

iPod, iPhone & Nest co-creator Tony Fadell 

Guy Kawasaki is on a mission to make you remarkable. His Remarkable People podcast features interviews with remarkable people such as Jane Goodall, Neil deGrasse Tyson, Marc Benioff, Woz, Kristi Yamaguchi, and Bob Cialdini. Every episode will make you more remarkable.With his decades of experience in Silicon Valley as a Venture Capitalist and advisor to the top entrepreneurs in the world, Guy's questions come from a place of curiosity and passion for technology, start-ups, entrepreneurship, and marketing. If you love society and culture, documentaries, and business podcasts, take a second to follow Remarkable People.Listeners of the Remarkable People podcast will learn from some of the most successful people in the world with practical tips and inspiring stories that will help you be more remarkable.Listen to Remarkable People here: https://wavve.link/remarkablepeopleText to get notified of new episodes: https://joinsubtext.com/guyLike this show? Please leave us a review -- even one sentence helps! Consider including your Twitter handle so we can thank you personally!Thank you for your support; it helps the show!

iPod, iPhone & Nest co-creator Tony Fadell

Today's guest is Tony Fadell, Principal at Future Shape.Tony is an active investor and entrepreneur with a 30+ year history of founding companies and designing products that profoundly improve people's lives. As the Principal at Future Shape, a global investment and advisory firm coaching engineers and scientists working on foundational deep technology, he is continuing to help bring technology out of the lab and into our lives. Currently, Future Shape is coaching 200+ startups innovating game-changing technologies. Tony began his career in Silicon Valley at General Magic, the most influential startup nobody has ever heard of. He is the founder and former CEO of Nest, the company that pioneered the “Internet of Things” and created the Nest Learning Thermostat. Tony was the SVP of Apple's iPod Division and led the team that created the first 18 generations of the iPod and the first three generations of the iPhone. Throughout his career, Tony has authored more than 300 patents. In May 2016, TIME named the Nest Learning Thermostat, the iPod, and the iPhone as three of the “50 Most Influential Gadgets of All Time.” His new book is Build: An Unorthodox Guide to Making Things Worth Making. Enjoy the show!You can find me on twitter @jjacobs22 (me), @mcjpod (podcast), or @mcjcollective (company) and via email at info@mcjcollective.com, where I encourage you to share your feedback on episodes and suggestions for future topics or guests.Episode recorded April 13, 2022To learn more about Future Shape, visit: https://www.futureshape.com/To learn more about this episode, visit: https://mcjcollective.com/my-climate-journey-podcast/tony-fadell

Tony Fadell is one of the great engineers, designers, and business leaders of our time, responsible for creating the iPod, iPhone, and Nest Thermostat. He runs the investment firm Future Shape and recently released his memoir titled “Build: An Unorthodox Guide to Making Things Worth Making." In this episode, he discusses the lessons he learned at General Magic (which was building the iPhone 15 years too early) and Philips Electronics that paved the way for building some of the world's most popular devices at Apple. He explains why self-imposed constraints are essential to creating exceptional products and reveals where engineers and designers often go wrong. His passion for building is inspiring and informative for both business and everyday life.

Tony Fadell of iPod, iPhone, and Nest Fame — Stories of Steve Jobs on “Vacation,” Product Design and Team Building, Good Assholes vs. Bad Assholes, Investing in Trends Before They Become Trends, The Hydrogen Economy, The Future of Batteries, and More | Brought to you by LinkedIn Marketing Solutions marketing platform with ~770M users, LMNT electrolyte supplement, and Eight Sleep's Pod Pro Cover sleeping solution for dynamic cooling and heating. More on all three below.Tony Fadell (@tfadell) is an active investor and entrepreneur with a 30+ year history of founding companies and designing products that profoundly improve people's lives. As the principal at Future Shape, a global investment and advisory firm coaching engineers and scientists working on foundational deep technology, he is continuing to help bring technology out of the lab and into our lives. Currently, Future Shape is coaching 200+ startups innovating game-changing technologies. Tony began his career in Silicon Valley at General Magic, the most influential startup nobody has ever heard of. He is the founder and former CEO of Nest, the company that pioneered the “Internet of Things” and created the Nest Learning Thermostat. Tony was the SVP of Apple's iPod Division and led the team that created the first 18 generations of the iPod and the first three generations of the iPhone. Throughout his career, Tony has authored more than 300 patents. In May 2016, TIME named the Nest Learning Thermostat, the iPod, and the iPhone as three of the “50 Most Influential Gadgets of All Time.” His new book is Build: An Unorthodox Guide to Making Things Worth Making. Please enjoy!This episode is brought to you by LinkedIn Marketing Solutions, the go-to tool for B2B marketers and advertisers who want to drive brand awareness, generate leads, or build long-term relationships that result in real business impact.With a community of more than 770 million professionals, LinkedIn is gigantic, but it can be hyper-specific. You have access to a diverse group of people all searching for things they need to grow professionally. LinkedIn has the marketing tools to help you target your customers with precision, right down to job title, company name, industry, etc. To redeem your free $100 LinkedIn ad credit and launch your first campaign, go to LinkedIn.com/TFS!*This episode is also brought to you by Eight Sleep! Eight Sleep's Pod Pro Cover is the easiest and fastest way to sleep at the perfect temperature. It pairs dynamic cooling and heating with biometric tracking to offer the most advanced (and user-friendly) solution on the market. Simply add the Pod Pro Cover to your current mattress and start sleeping as cool as 55°F or as hot as 110°F. It also splits your bed in half, so your partner can choose a totally different temperature.And now, my dear listeners—that's you—can get $250 off the Pod Pro Cover. Simply go to EightSleep.com/Tim or use code TIM at checkout. *This episode is also brought to you by LMNT! What is LMNT? It's a delicious, sugar-free electrolyte drink mix. I've stocked up on boxes and boxes of this and usually use it 1–2 times per day. LMNT is formulated to help anyone with their electrolyte needs and perfectly suited to folks following a keto, low-carb, or Paleo diet. If you are on a low-carb diet or fasting, electrolytes play a key role in relieving hunger, cramps, headaches, tiredness, and dizziness.LMNT came up with a very special offer for you, my dear listeners. For a limited time, you can claim a free LMNT Sample Pack—you only cover the cost of shipping. For US customers, this means you can receive an 8-count sample pack for only $5. Simply go to DrinkLMNT.com/Tim to claim your free 8-count sample pack.*For show notes and past guests on The Tim Ferriss Show, please visit tim.blog/podcast.Sign up for Tim's email newsletter (5-Bullet Friday) at tim.blog/friday.For transcripts of episodes, go to tim.blog/transcripts.Discover Tim's books: tim.blog/books.Follow Tim:Twitter: twitter.com/tferriss Instagram: instagram.com/timferrissYouTube: youtube.com/timferrissFacebook: facebook.com/timferriss LinkedIn: linkedin.com/in/timferrissPast guests on The Tim Ferriss Show include Jerry Seinfeld, Hugh Jackman, Dr. Jane Goodall, LeBron James, Kevin Hart, Doris Kearns Goodwin, Jamie Foxx, Matthew McConaughey, Esther Perel, Elizabeth Gilbert, Terry Crews, Sia, Yuval Noah Harari, Malcolm Gladwell, Madeleine Albright, Cheryl Strayed, Jim Collins, Mary Karr, Maria Popova, Sam Harris, Michael Phelps, Bob Iger, Edward Norton, Arnold Schwarzenegger, Neil Strauss, Ken Burns, Maria Sharapova, Marc Andreessen, Neil Gaiman, Neil de Grasse Tyson, Jocko Willink, Daniel Ek, Kelly Slater, Dr. Peter Attia, Seth Godin, Howard Marks, Dr. Brené Brown, Eric Schmidt, Michael Lewis, Joe Gebbia, Michael Pollan, Dr. Jordan Peterson, Vince Vaughn, Brian Koppelman, Ramit Sethi, Dax Shepard, Tony Robbins, Jim Dethmer, Dan Harris, Ray Dalio, Naval Ravikant, Vitalik Buterin, Elizabeth Lesser, Amanda Palmer, Katie Haun, Sir Richard Branson, Chuck Palahniuk, Arianna Huffington, Reid Hoffman, Bill Burr, Whitney Cummings, Rick Rubin, Dr. Vivek Murthy, Darren Aronofsky, and many more.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

※ 本文章发布于订阅号：百车全说，订阅号阅读更加方便，欢迎关注今天是埃隆·马斯克故事的第五期，也是完结篇。上回咱们说到SpaceX公司第四次火箭发射成功，公司账上资金基本已经亏空，这时候美国NASA出手相救，一下甩出16亿美元的合同，让SpaceX起死回生。马斯克一战成名，吸引了两位研究电动飞机的工程师来找他融资，结果老马兴趣不大。临别前两位工程师提到了他们曾经是研究电动汽车项目的，老马顿时来了精神，其中一位叫斯特劳贝尔的工程师，赶忙引荐AC propulsion电动汽车公司的老板盖奇给马斯克认识，结果盖奇只想提供技术，不愿意开发整车。但是盖奇又介绍了两位准备造电动车的工程师，艾伯哈德和塔彭宁给老马认识。这两位就是特斯拉真正的创始人。很快在2004年马斯克投资650万美元，成为了特斯拉的大股东。可是4年后，由于公司遇到财务危机，马斯克不满CEO艾伯哈德的管理，于是把他从CEO的位置上换下，艾伯哈德一怒之下选择离职。不久，塔彭宁也离开了特斯拉。至此之后，马斯克正式成为特斯拉的新主人。可是特斯拉第一辆车Roadster开发得并不顺利，变速器问题一直无法解决，成本也是居高不下。关键时候，马斯克又后院失火，妻子生完二胎得了严重的抑郁症，闹着要离婚。马斯克在媒体的渲染下，成了霸占企业的骗子，不负责任的老公。那么，马斯克是如何度过2008年的难关的呢？之后他又是如何一路高歌猛进的呢？今天这期节目，咱们接着聊。2008年6月，马斯克被离婚事件和特斯拉Roadster项目进展不利，搅得心烦意乱，这时候一个哥们打电话给他，想带他出去转转，放空一下自己。两个老爷们出去转转，能干什么呢？肯定是去美女如云的地方，做点能让自己开心的事情。打电话的哥们叫比尔·李，也投资了马斯克的公司一点钱。但这都不是重点，重点是小李子老婆的爸爸是美国前副总统艾伯特·戈尔，他老婆的爷爷是国际著名环境学家，主要研究全球气候变暖问题。所以说，交际圈决定了你的事业高度。小李子带马斯克先是去到阿斯顿·马丁工厂转了一圈，阿斯顿·马丁的CEO全程陪同。但在他眼里，马斯克就是个外行，充其量算是个汽车爱好者，特斯拉在他眼里就是个玩具，根本不算车。当天晚上，小李子带马斯克去到伦敦一个高端俱乐部，直奔vip包间。马斯克一进门，心中的雾霾瞬间散去，男同胞们都懂，这是怎样的一个画面。在这间屋里，有一位就是马斯克的下一任妻子，英国演员露拉·莱莉。认识不到三天，俩人就跨越了普通朋友的友谊，马斯克还想带她去美国，莱莉有些犹豫。但是没多久，莱莉还是从英国飞去美国找了马斯克。俩人从2008年一直谈恋爱到2010年正式结婚，那时候马斯克39岁，莱莉24岁。老牛吃嫩草的婚姻普遍不被外界看好，这段婚姻显然也得不到莱莉父母的支持。果不其然，2年后俩人离婚。但是18个月后俩人又复婚了，可是复婚不到1年，俩人又离婚了。莱莉第一次离婚分得420万美元，第二次离婚分得1600万美元。上期节目里咱们提到，2008年2月，特斯拉做了一次Roadster新车交付仪式，实际上这次交付只有7台车，是作为“创始人系列”提供给了马斯克和其他投资人，包括谷歌创始人佩奇和布林、eBay创始人斯科尔、还有特斯拉真正的创始人艾伯哈德等等。大家是不是非常眼熟这种操作模式，2017年蔚来EP9也办过一次交付仪式，也是定制了6台给了投资人。在众多硅谷名人车主的带货下，各大媒体开始吹嘘Roadster是他们见过最棒的电动车，性能媲美911，外形秒杀法拉利。但只有特斯拉内部员工知道，变速箱问题还是没有解决。一直拖到2008年年底，变速箱问题终于解决了，但是每台车的制造成本从10万美元上升到了12万美元。因此，马斯克决定把车价从9.2万美元提升到10.9万美元。这一举动，让400多位订购特斯拉的客户异常愤怒，在洛杉矶的一次客户见面会上，马斯克差点被打。马斯克其实也很委屈，不仅Roadster卖一台亏一台，而且要维持特斯拉公司的运营，每个月需要400多万的费用。而当时公司账上资金最少的时候，只有不到100万。用马斯克的话说，1周内见不到钱进账，公司就百分之百关门大吉了。还好马斯克的朋友比较多，前面说到的比尔·李借了200万给他救急，谷歌创始人布林也掏了50万，这都是私人关系借的钱，也没指望马斯克能还回来。马斯克的亲兄弟金巴尔也卖了好几处房产，来给马斯克救急。2008年年底，SpaceX突然接到16亿美元的NASA订单，马斯克赶忙跟NASA协商，能否拆借一部分资金给特斯拉救急，竟然NASA同意了这个决定。这时候，马斯克卖了一些自己持有的太阳能公司SolarCity的股票，继续投资特斯拉。然后又让他表兄弟林登和拉斯的软件公司Everdream拆借了1500万美元。东拼西凑，也有了个2000多万。马斯克迅速找到投资人，告诉他们自己把最后的老本都拿出来拼了，能不能大家也多少凑一点帮帮忙，但是投资人还是不为所动。这时候，马斯克还想通过政府借款来挽救特斯拉公司。有人可能要问了，马斯克到处借钱，是为了继续研发Roadster吗？不是的，实际上这个时候，马斯克已经很清楚，这个所谓的电动超跑Roadster就是个赔钱玩意儿，指望它能赚钱，几乎是天方夜谭。但是前期靠Roadster炒作名气的目的已经达到了，这台车的使命已经完成。接下来融到的钱，必须投入开发新车型Model S。如果大家注意看ModelS右侧的换挡杆，左侧的雨刮控制拨杆，会发现跟奔驰车上的一模一样。而实际上特斯拉在2008年遇到危机的时候，是戴姆勒公司救了特斯拉。之后戴姆勒奔驰的工程师还帮助特斯拉公司开发了ModelS，戴姆勒在2009年5月还收购了特斯拉近10%的股份，给了后者一笔5000万美元的救命钱。我估计戴姆勒公司现在肯定很后悔当初的这个决定，2014年戴姆勒不看好特斯拉未来的发展，出售了所持的股份。戴姆勒的领导在特斯拉最穷的时候进场，在特斯拉即将暴富的时候离场，活活地培养了一个颠覆自己的对手，真的是一波骚操作。要说马斯克真的运气超级好，在2007年次贷危机爆发前，他飞了一趟德国，拜访了戴姆勒全球研发负责人韦伯。当时Roadster还在研发阶段，马斯克就想去戴姆勒取取经，顺带忽悠忽悠德国人，看看他们是否对电动车感兴趣，可以帮他们也改一改电动车，给公司赚点小钱。刚好2007年，戴姆勒刚刚发布了Smart电动版，他们也在物色供应商。马斯克当着韦伯的面，把自己公司的技术优势添油加醋地吹嘘了一番。实际上特斯拉Roadster是个什么货色，他自己心里太清楚了。结果2007年12月，戴姆勒高管真的决定1个月后来硅谷考察特斯拉公司。马斯克一看有戏，立刻要求公司全体员工把手头的事都放一放，他有个大胆的决定，在戴姆勒高管来之前，造一台电动smart给他们瞅瞅。当时，就有一哥们提出了疑问，老大，咱们美国没有smart卖啊。马斯克问，离我们最近的卖smart的店在哪里?那哥们回答，在墨西哥。马斯克说，那不得了，就去墨西哥给我弄一台回来，马上立刻就去。于是不到1个月的时间，特斯拉的工程师改造了一台电动版的Smart，当戴姆勒的高层来到特斯拉公司的时候，马斯克给了他们一个惊喜，当时戴姆勒的高管科勒上车后，一脚电门踩下去，车嗖的一下就窜出了车库。科勒当时非常震惊，觉得电动Smart才是未来的方向。当然，如果他当时多看看美国新闻媒体的报道，知道Roadster的变速箱问题，估计就没有后面的故事了。戴姆勒高管回去后没多久，就决定向特斯拉采购1000个用于Smart的电池包，订单总价值4000万美元。当年5月，戴姆勒又投资了特斯拉5000万美元。当时丰田公司听说这事，决定也跟投一笔，2010年丰田也投资5000万买了特斯拉3.15%的股份，同时与特斯拉共同开发RAV4电动版，后来丰田还以4200万美元的白菜价，把加州的工厂卖给了特斯拉。但是丰田与特斯拉的蜜月期非常短暂，2012年RAV4电动版上市，卖得非常惨。丰田公司对电动车的看法开始有180°大反转，到了2016年，丰田把特斯拉的股票全部出售。熬过了2008年的危机，2009年的特斯拉可以说在戴姆勒和丰田两位大佬的背书下，风光无限。不仅如此，特斯拉还拿到了美国能源部提供的4.65亿美元低息贷款。这家公司甚至开始有了政府背书，腰杆子一下子就硬了起来。终于在2010年6月29日，特斯拉成功在纳斯达克上市。这时候的特斯拉，一共才卖了1000多台车。这不仅是1956年福特汽车IPO以来第一家上市的美国汽车制造商，也是目前唯一一家在美国上市的纯电动汽车独立制造商。你可以说马斯克运气好，但是运气都是给有准备的人的。2008年马斯克决定开发第二款量产车Model S，实际上他心里也没底，因为量产数万台车的工厂他都没找到，这个车放哪里造都是个问题。结果2009年傍上了丰田这个大佬，还连哄带骗地拿下了丰田在旧金山湾区的弗雷蒙特工厂，这个工厂有88个足球场那么大，接下来的几年，数十万台Model S和Model X在这个工厂下线。特斯拉Roadster在2011年停产，在同一年，有个中国人把Roadster开回了中国，成为了中国第一位特斯拉车主。我在2016年的67期节目里，曾经采访过他，大家感兴趣可以移步2016年专辑重温一遍那期节目。对于特斯拉来说，Roadster只是小试牛刀，之后量产的Model S才是特斯拉开创辉煌历史的标志性车型。这款车一上市就成为了爆款，但是开发这款车的过程也是历尽磨难。在2007年，马斯克就在找设计师开发新车型Model S，他希望这台车必须是从无到有的设计，是那种让大家看到就觉得哇塞的轿车。他找了一圈汽车设计界的大拿，最终选定了一位叫菲斯克的哥们，让他负责设计Model S的外观。他跟菲斯克说，这款车定价5-7万美元，我希望2年内就上市，而且看起来这车远远比定价贵很多的样子。菲斯克拍着胸脯保证，这个任务对他来说小菜一碟，因为他之前在宝马和福特等传统车企中担任设计师近十年，阿斯顿马丁DB9、阿斯顿马丁V8 Vantage和宝马Z8均是其代表作。马斯克拍着菲斯克的肩膀说，老兄，我相信你的实力。可是，后来的事实证明，实力可以相信，人品不能全信。马斯克后来形容菲斯克的设计，就像黄鼠狼下耗子，一窝不如一窝。菲斯克拙劣的设计严重影响了特斯拉团队的工作进程。马斯克也百思不得其解，他给的钱不算少，菲斯克之前的作品也是有目共睹，怎么到了我这儿就变得这么糟糕透顶呢？马斯克形容早期菲斯克设计的Model S就像个硕大无比的鸡蛋，一点儿美感都没有。菲斯克直到这时候还在狡辩，说特斯拉给他的限制条件太多，太严格。半年后，事情的真相才水落石出，原来菲斯克早在2005年就成立了自己的公司，菲斯克汽车，并且于2008年1月推出了卡玛混动车，这台车棱角分明，性感迷人。马斯克看完之后，气得要吐血，他明白自己之前与菲斯克的沟通和建议，都被他用在了这台的设计上，而给自己的设计方案只是敷衍了事而已。马斯克觉得自己忽悠别人几十年，这次终于是被人忽悠了。08年4月，特斯拉起诉菲斯克公司，但是最终败诉。不过，菲斯克汽车公司最终的结局也是烧光了14亿美元后，在2013年走向了破产。但是菲斯克破产后，中国的万向集团和李泽楷的公司跑来收购，最终万向集团以1.492亿美元收购菲斯克汽车，但是菲斯克本人保留了Fisker的商标。万向集团把公司改名Karma，Karma车型改名Revero继续运作。但是故事到这里还没有完，菲斯克汽车竟然死灰复燃，开始了二次创业。而且在2020年以特殊目的收购SPAQ公司，借壳上市成功，一台车都没造，公司市值就暴增到了29亿美元。大家是不是耳熟“特殊目的上市”这个词？对的，贾跃亭的FF前不久也是以这种方式上市成功的。菲斯克汽车之所以能东山再起，与菲斯克的媳妇有着非常大的关系。菲斯克的媳妇吉塔，号称硅谷女版钢铁侠，剑桥大学生物技术博士学位，之后一直在银行和基金会做投资顾问。2012年与菲斯克结婚，2016年开始与老公一起联手复活菲斯克汽车，先是拉来“汽车界的富士康”麦格纳公司加入合作，然后真的富士康公司也跑来站台。目前菲斯克汽车混的也是风生水起。马斯克估计看着菲斯克新闻，摇头苦笑，这种人品极差的人都能混成这样，简直是咱们电动车圈的笑话。再回到特斯拉这里来聊，取消了与菲斯克的合作，马斯克开始物色下一个设计师，一开始他准备高薪聘请当时名声大噪的苹果设计师法戴尔，这哥们设计了iPod和iPhone，但是聊完之后马斯克发现，除非让他当特斯拉CEO，否则人家根本不会动心。之后马斯克终于又物色了一个设计师，他是菲斯克的校友，叫做霍兹豪森。这哥们曾经在大众公司待了8年，之后跳槽到了通用，又跳槽到了马自达。可以说他把德系，美系和日系车的设计风格都学了一遍。所以，你现在看特斯拉Model S车型，的确看不出他有着哪国车型的浓烈风格，非常的混搭。但是你仔细看，这款车还是有一些奔驰CLS的影子。没办法，在设计Model S之初，参考最多的车型就是奔驰CLS，完全从零打造，是无稽之谈。霍兹豪森的加入让Model S的项目进展走上了快车道，但是马斯克还是经常会来指手画脚，一会儿要求车内的屏幕得再大一点，一会儿要求整车得用全铝车身，一会又要门把手得隐藏，靠近就能自动弹出来。终于在2009年3月份，特斯拉拿出了4辆Model S和20辆Roadster，在SpaceX总部举办了一场交付仪式。老规矩，马斯克又是请了一大批的明星和政商两届的名人来到现场造势。施瓦辛格继续为马斯克吆喝，克林顿的办公室主任麦克拉蒂也对着镜头直呼哇塞，美国国务院政治军事事务参谋长奥坎奈尔，甚至还问马斯克公司缺不缺人？后来，他还真的成了特斯拉业务发展副总裁，带来了大批高端客户资源。在一波名人带货的背景之下，特斯拉Model S日产量还是个位数的时候，订单已经突破了1万张。直到2012年6月份，Model S才正式交付到消费者手中。当时特斯拉虽然风头正劲，但是传统车企仍然像是看小孩子过家家一样的看着特斯拉，因为这家车企即使一年销售3.5万辆车，在整个汽车市场中，这家车企也占不到1个百分点，几乎可以忽略不计。然而随着2015年的Model X，2017年的Model 3上市，局面变的一发不可收拾。特斯拉以势如破竹的节奏冲击着传统车企看似坚不可摧的市场。2013年1月，马斯克突然宣布要进入中国市场，并且宣称要在中国投资数亿美元建设充电站，未来3-4年实现本土化生产，还会在中国设立工程研发中心。说干就干，特斯拉公司先是在2013年的全球开店计划里，把25家新店中的1家放在了北京的朝阳区芳草地，在这个寸土寸金的地方，拿了个740平米的展厅。这个面积相当于美国展厅的3倍大小。看来特斯拉是仔细调研过中国消费者的消费习惯的。但是芳草地的展厅迟迟没能开业，大家就很奇怪特斯拉到底唱的哪出戏？后来特斯拉中国区销售总监说明了实情，原来是特斯拉的商标被人注册了，特斯拉公司正在与这个商标持有人商量转让的事宜。这个非常有头脑的中国人叫占宝生，江西九江人，1999年在南京读完大学，就开始从事国际贸易。2006年9月，占宝生以个人名义，首次对Tesla商标进行注册。随后，他又分别于2007年，2009年对特斯拉和Tesla Motors申请注册。回想一下，特斯拉公司2003年成立，2004年马斯克成为大股东，2006年7月，特斯拉Roadster正式亮相，马斯克请来了施瓦辛格，小李子，谷歌创始人等等社会名流为他的公司造势。似乎这个时间点就对上了。2个月后，中国人占宝生注册了特斯拉的商标。这件事告诉我们，要学好英文，多思考，眼光放长远。占宝生不仅拿下了特斯拉的商标，tesla.com、tesla.cn、teslamotors.cn这三个域名也在他公司旗下。特斯拉美国总部急于解决这个问题，于是在硅谷面试了一位中国人，郑顺景，他原是宾利中国区总经理。马斯克当面表示，如果你能解决商标问题，中国区总裁的位置就是你的了。2012年11月，郑顺景火速回国，找到占宝生面谈，结果他以为对方很好忽悠，第一次见面竟然开了个5万美元的价格。殊不知，占宝生2004年就开始倒卖商标和域名，是个资深倒爷，郑顺景想忽悠他，也不调查调查他的背景。占宝生当场翻脸，说TMD有多远滚多远，5万美元，打发要饭花子啊？过了半年，郑顺景再次找占宝生面谈，这次开价到了200万人民币。占宝生一脸鄙视的看着他说道，你当我是屌丝呢，我分分钟就能掏出200万。后来马斯克透露，占宝生的心理价位是3000万美元。但是占宝生的回复是，我从来没开过价，后来我自己开始融资，准备造电动车，就叫特斯拉。郑顺景因为搞不定此事，2014年3月31日黯然离场。2013年7月，特斯拉副总裁，那位美国国务院政治军事事务参谋长奥坎奈尔，带队来到中国，专程为了解决此事。奥坎奈尔先让中国区销售负责人继续推进筹备公司事宜，特斯拉三个字不能用，就改成拓速乐。拓速乐是香港人对于Tesla的音译，就像Lexus在香港叫凌志，后来进入大陆也是因为凌志商标被抢注，所以才被迫改名雷克萨斯。但是奥坎奈尔来中国的任务，还是要拿回特斯拉商标。于是他通过多方走访，了解到在中国想要从别人手里夺回商标，只有三种途径。一是证明自己为“驰名商标”，因为中国对“驰名商标”有特殊保护，可是特斯拉当时在中国没人知道，显然这条路走不通。第二条路，是提出连续三年停止使用撤销申请。也就是说，占宝生只要3年内没拿特斯拉这个商标去造车，就可以申请撤销。结果，人家不仅有个特斯拉的网站，一直用这个商标对外宣传自己在筹备造车。而且占宝生还说有人愿意投资他10几亿。只要马斯克愿意，他可以花2亿买他的技术。如果这两条路都走不通，那就只能走第三条路了，和解。2014年8月5日，北京市第三中级人民法院官方微博发布消息称：特斯拉之争和平落幕，双方当事人握手言和。特斯拉与占宝生之间涉及商标、著作权、不正当竞争等一系列知识产权案件被北京三中院在不到一个月的时间内顺利调解。8月29日，占宝生在机场被民警带走，理由是涉嫌虚报注册资本。在看守所关了5个月，才重获自由。占宝生在一次采访里说，是郑顺景在背后搞他，因为他的不配合，让郑顺景丢了工作，这是报复。从我手头有限的资料来看，我觉得这哥们把事情想简单了。这里特斯拉的商标问题终于得以解决，另一边又传来了好消息。2014年1月22日，特斯拉官方微博发了一篇文章《一个公道的价格》，文章里详细描述了Model S在中国定价为73.4万元的原因，美国售价81070美元，运输与装卸3600美元，关税和其他税19000美元，增值税17700美元，合计734000元（汇率为6.05元/美元），当时中国消费者震惊了，他们从来没见过这么坦诚的车企，别的车企都把买进口车的人当韭菜宰，特斯拉竟然把价格说的明明白白。一下子，特斯拉在中国的人气暴涨，大家口口相传，称赞特斯拉是个不割韭菜的老实人。说实话，在2013年，特斯拉定这个价格确实是个老实人。因为当年浙江一个小老板，因为特别喜欢特斯拉Model S这款车，大陆没有销售，他只好去香港订购，前后花了200多万才落地上牌。在特斯拉说要进入中国市场的时候，很多汽车媒体也是猜测这款车定价应该在150万左右。所以，当年这个定价，可以说的确是非常良心。特斯拉的直营模式也是一种创新，但是这也导致后来的二级市场倒卖订单的情况，当时一张Model S的订单甚至可以加价10几万出手。2014年4月，马斯克首次造访中国，每到一处都受到了明星版的待遇，粉丝尖叫，媒体追捧。陪在马斯克身边的是小个子美女，是刚上任的中国区总裁，吴碧瑄。吴碧瑄毕业于耶鲁大学，先后在麦肯锡、摩托罗拉和苹果公司工作，苹果公司中国区业务主要是她带团队负责开拓，马斯克需要一个熟悉中国市场的总裁，他觉得小吴非常合适。吴碧瑄也是不负众望，立刻来到上海找相关部门洽谈。她提出四点需求：一是希望上海给免费牌照，二是希望帮助特斯拉建超级充电站，三是税收优惠，四是希望拿到财政补贴。对方只回了一句话，你要把工厂开在这里，什么都好谈。2014年4月21日，马斯克第二次来到中国，在吴碧瑄的引荐下，马斯克见到了科技部的领导，口直心快的马斯克一见面就问，能否给咱的电动车减免一些进口关税？没想到领导当面表示，这件事我肯定会办的。旁边的吴碧瑄听得冷汗直冒。4月22日，在北京芳草地展厅，马斯克将参与中国首批车主交车仪式，实际上这个所谓交车仪式，也是挑选了一批名人来合作宣传的广告而已。特斯拉Model S从2013年8月开始接受预定，大多数客户交了25万定金，却一直被特斯拉通知延迟交付。第一批拿到车的客户有重庆力帆集团董事长尹喜地，汽车之家总裁李想、UC优视董事长兼CEO俞永福、时代集团执行副总裁潘燕明等等。这些人非富即贵。普通消费者觉得特斯拉不守规矩，插队安排订单。当天的交车仪式非常混乱，现场有不少来维权的特斯拉车主，怒吼着表示抗议。当天，有7位车主从马斯克手里拿到了钥匙，包括新浪CEO曹国伟、携程网创始人梁建章、诺亚财富首席战略官谭文清、一号店董事长于刚、SMC中国基金执行合伙人邵庆晓、紫辉创始人郑刚、一嗨租车董事长兼CEO章瑞平，其中3位车主由夫人出席代领。虽然场面有点混乱，但是马斯克觉得自己受到了这么多人追捧，心里还是挺得意。4月23日，马斯克又抵达上海，参加了上海金桥的交车仪式，他跟当地领导说，你们的办事效率太高了，据说金桥的超充桩10天就建成了，这要放在美国一个月都完不成。不久之后，上海发放了3000个进口新能源车牌照，这一政策最大的受益方，当然是特斯拉。这3000多张牌照，价值2.7-3.6亿。马斯克的中国首秀可以说是收获颇丰，这也坚定了他要在中国本土化生产特斯拉的决心。可是2014年特斯拉表面上四面开花，实际上投入大产出低。2014年年底，上任不到年的吴碧瑄离职，新任中国区总裁换成了朱晓彤。有人分析，吴碧瑄的离职，跟销量完成率不足计划的1/4，库存接近4位数，不在补贴范围之内，直营模式与政策相悖有很大关系。而朱晓彤的上位，跟他的充电桩建设成绩出色，有很大关系。特斯拉从供不应求，到库存大增。2014年的特斯拉经历了过山车般的待遇。很多人纳闷，这一年特斯拉到底遇到啥事了？首先是一把手的交接问题，2014年3月底中国区总裁郑顺景离职，由吴碧瑄接任。而实际上2013年11月，美国总部就把吴碧瑄调安排过来了，然后给了郑顺景一个全球副总裁的虚职，并且要求郑顺景向吴碧瑄汇报工作。所以这4个月的时间，特斯拉中国区等于说有两个一把手，内部斗争不说，也能猜到有多激烈。可惜吴碧瑄的好日子也没过久，2014年9月份，美国总部又派来了一位全球副总裁，大中华区CTO，金俊。由他负责特斯拉在大中华区的市场营销、公关传播及品牌推广等工作。金俊可以绕开中国区总裁，直接向美国总部汇报工作。这一人事安排，也直接导致后来的吴碧瑄离职。金俊在特斯拉也就待了半年，就离职了。因为他在任期间，销量无任何起色。2014年，特斯拉刚在中国销售的时候，美国总部给中国区的年度任务是3000台，没想到2月份春节刚过，3000辆车的订单就全部完成了。于是，美国总部下发了新的目标5000台。这时候吴碧瑄和郑顺景的内斗开始，吴碧瑄先后成立了电话销售、大宗订单和上门试驾三个新的销售部门，把郑顺景的权利限制在芳草地门店的日常事务，门店订单直线下滑。随后，线上订单一直被黄牛加价炒作，吴碧瑄启动了客户“背景调查”，要求那些提交4辆车以上订单的个人买家提供更多的身份、职业和购车意图等信息，防止“黄牛”转售。4月份马斯克中国首秀，见识了中国消费者的热情，当即决定把全年任务定到1万台。可是，4月底特斯拉的销量就开始下滑，由于采取了“背景调查”，黄牛无利可图，特斯拉订单火爆的虚假繁荣瞬间熄火。不仅如此，很多黄牛看订单转不出手，干脆恶意“逃单”，因为刚开始特斯拉的订金要求只有1.5万人民币。由于逃单过多，特斯拉中国区开始囤积了大量库存车。也正是因为这件事，才导致特斯拉改变订金规则，从1.5万调整到5万。可是交了这么多订金，特斯拉一再延迟交付，这也导致了后来的客户不满，大闹马斯克交车现场的事件。也正是因为被库存车和销售任务的困扰，2014年9月吴碧瑄当时还找到阿里，想要合作一次双11促销活动。当时阿里对特斯拉并不感兴趣，一是对于这种电动车新品牌，到底质量和服务靠谱不靠谱，他们心里没有数。二是当时苹果和LV都在天猫开了直营店，他们并不缺特斯拉这一个商户。当时跟吴碧瑄一起造访阿里的，还有马斯克，但是马斯克只顾着跟马云吹牛皮，没有关心双11的事。吴碧瑄以为双11的事，是马斯克默许的，他就继续推进了这件事，沟通了很久，特斯拉天猫旗舰店终于上线，美国总部的副总裁，吴碧瑄的直接领导古里安也认可这件事。2014年10月20日，特斯拉开始宣传入驻天猫商城，参加双11这件事。这是特斯拉中国在自身官网销售渠道之外，首次尝试与第三方平台联手销售合作。特斯拉原本为“双11当天购”准备了18辆Model S现车，消费者在下单及付清全款后，最快5天就能“闪电提车”。此外，特斯拉还将为“双11当天购”的客户免费安装家用充电桩，作为特殊优惠。特斯拉与天猫的合作原本设想得非常美好。在之前特斯拉披露的合作细节中，消费者在特斯拉天猫旗舰店选购自己喜欢的车型，冻结5万元余额宝资金当作订金，在提车城市的特斯拉网点付完尾款，最快5天即可提车。提车前订金还将享受余额宝收益。如果效果和反响良好，那么特斯拉将在天猫上开店，把天猫当做一个直销渠道，有助于缓解在2014年下半年已经越来越严重的现车积压问题，把订单真正变成销量。结果宣传刚开始，美国总部就发来邮件要求立即停止与天猫的合作。吴碧瑄一脸懵逼。2014年10月30日，特斯拉官方单方面宣布解除与天猫之间的合作。在主管全球销售和服务的古里安都同意与天猫合作的大前提下，能叫停它的只有一个人。马斯克对此事极为震怒，他认为这是天猫在借势特斯拉的品牌进行营销，并且不符合特斯拉一贯的销售流程。他再次强调：与所有跨国公司一样，全球需要有统一政策，来保持品牌的统一性！特斯拉中国与天猫的合作破坏了原有的车辆交付规则，对此前下订单的消费者不公平。而实际上，只有吴碧瑄知道，天猫活动使用的部分车辆是来自客户的退订车辆。所以，这件事也让特斯拉与阿里结下了梁子。好了，埃隆·马斯克的聊到这里，总算是接近尾声了。我们一共花了5期节目，我累计写了4万多字，录制了大家别忘了，2008年这一年对马斯克来说还有一件大事，他的猎鹰1号火箭第三次发射失败。两个月后的第四次发射能否成功，他心里也没有底。如果第四次发射失败的话，马斯克的SpaceX就彻底关门大吉了。再联系我们今天聊的特斯拉的进展，2008年可以说也是命悬一线。这也难怪马斯克催着SpaceX的员工尽快进行第四次发射，结果这一把真的赌赢了，很快就拿到了16亿美元你的NASA订单，这么多钱，有没有拆借一点去用于特斯拉公司解燃眉之急，我想只有马斯克自己知道吧。

Этот выпуск в YouTube: https://youtu.be/jSdYAEnWbic Дима и Вячеслав обсуждают концепцию "jobs to be done" и кроме iPhone и Netflix пытаются применить ее к корпоративному обучению английскому языку. * Большое количество людей не готовы прививаться от COVID-19. Как это связано (и связано ли) с пониманием математики? * Vaccine hesitancy is putting progress against covid-19 at risk (https://www.economist.com/briefing/2021/02/13/vaccine-hesitancy-is-putting-progress-against-covid-19-at-risk) * Бита и мяч стоят $1.10. Бита стоит на $1 дороже, чем мяч. Сколько стоит бита? * В пруду площадь его поверхности покрытая лилиями увеличивается в 2 раза каждый день. С момента появления первой лилии до момента, когда пруд был полностью покрыт ими прошло 40 дней. На какой день лилиями была покрыта ровно половина пруда? * Прекрасная диаграмма (https://upload.wikimedia.org/wikipedia/commons/6/65/Cognitive_bias_codex_en.svg), которая категоризирует все когнитивные искажения * Димын опыт поиск материалов по теории хаоса * Курс по нелинейным динамическим системам и теории хаоса (https://youtube.com/playlist?list=PLbN57C5Zdl6j_qJA-pARJnKsmROzPnO9V) * Эпизод Cortex (https://www.relay.fm/cortex/111), в котором Grey рассказывает о наблюдениях за работой алгоритмов YouTube * Что если интернет вместо масштабирования одного преподавателя на огромное количество студентов, он позволит иметь огромное количество узко-компетентных преподавателей-менторов? * Видео "This Will Revolutionize Education" (https://youtu.be/GEmuEWjHr5c) "Это разные масс-маркеты" * Clayton Christinsen (https://en.wikipedia.org/wiki/Clayton_Christensen) * Disruptive innovation theory (https://en.wikipedia.org/wiki/Disruptive_innovation) * Know Your Customers’ “Jobs to Be Done” (https://hbr.org/2016/09/know-your-customers-jobs-to-be-done) * The Innovator's Dilemma (https://www.amazon.co.uk/Innovators-Dilemma-Technologies-Management-Innovation/dp/142219602X) * The Innovator's Solution (https://www.amazon.co.uk/Innovators-Solution-Creating-Sustaining-Successful/dp/1422196577/) * iPod и iPhone * В чем разница между потребностью и job to be done? * Как Netflix может в принципе конкурировать со сном * Алгоритмы рекомендаций и их job to be done * Кто нанимает и какой job to be done у подкаста Biweekly * Выбирать из 2-х вариантов нужно не так выбирают из 2000 вариантов * Как приложить jobs to be done к обучению английскому * Сакральный вопрос: "где это применяется в производстве?" * Этот выпуск перевалил за 1 час не в последнюю очередь благодаря длинному фоллоу-апу

https://kotatsuxu.com/2021/01/30/clubhouse_android/ ※例外的に、電話機能付きのiPadであれば招待可能の場合もあるかもしれません(確認中) ※古すぎるiPhone(iPhone4)はclubhouseのアプリがインストールできなかった報告をフォロワーさんからもらいました

欢迎收听雪球和喜马拉雅联合出品的财经有深度，雪球，3500万投资者在线交流，一起探索投资投资的智慧，听众朋友们大家好，我是主播匪石-34，今天分享的稿件名字叫做百度估值为啥就是上不去？来自于刘志超。 跟百度管理层交流时，对方提了个问题，为啥投资者给百度的估值“就是上不去”呢？ 其实，曾几何时，百度的估值“就是下不来”。大小利空，都被投资者看作上车的机会。百度股价也总会涨起来，从不辜负。 为啥呢？因为当时百度垄断了PC时代中国互联网入口。就是CCTV来批，全国网民们痛骂百度赚昧良心的钱，转过身打开电脑还得先百度一下。垄断了互联网入口，这句话你会给什么估值？无论问老鸟还是菜鸟，答案都下不来。 那为啥百度的估值现在“就是上不去”呢？发生过什么大利空吗。 有人说，锅在文化，也有人说，锅在管理。其实这些都不重要，也没有什么了不起的利空事件。根源在于，人们使用互联网的方式改变了。 智能手机普及后，大家使用互联网的习惯发生了彻底改变，再也不需要一个导航页了，而是直接进入自己感兴趣的app里刷刷看看。 我之前发过一条球文，“生意兴衰的根源，是人类生活方式与习惯的变化”。时代抛弃了你，跟你做了什么无关。这场大掉队里，怪百度做错了什么吗？也没啥可怪的，腾讯在搞出微信前也是岌岌可危，甚至被逼的要网民在QQ和360里二选一，现在谁还记得360？ 当百度激情十足地大举投入AI，投资者却显得很冷静。原因也很好理解，公司的引擎从一个垄断又赚钱的业务，切换到一个无法垄断又在不停投入的业务，你会给它一个怎样的估值？ 当然，往远看，AI的赛道足够宽，往实看，智能汽车的故事及其衍生的车联网故事也可以足够性感。 但问题是，这个性感还只存在于剪影阶段，甚至是个素描版剪影。能让投资者兴奋的性感，就是没有成片，也得有一张高清照片吧。如果要拿iPhone的故事做比喻，就起码得有个一代原型机吧。 否则的话，就像第一次iphone发布会，乔布斯循环地放iPod、手机和电脑的图片，如果最后不出现iPhone，让你去想象，你可能会设计出一个要你命3000。 现在的智能车，更像iPhone出现前那种配一根手写笔的商务手机。车联网，则更像是一个科幻场景。我是一个科幻迷，相信美好的科幻都会实现。 但问题是，即便最后这些美好真的会实现，投资者可能会觉得未必一定是百度来实现，即便是由百度实现了，投资者可能觉得未必会被一家公司独占。 百度由“估值下不来”变成“估值上不去”，其实不怪它做错了什么，如果一定要怪，就怪它没在合适的时候做对些什么。想做些什么让估值上去，也的确没啥可做的。 不如别管大家怎么看，专心搞业务，等风来。 作者：刘志超链接：https://xueqiu.com/3386153330/159473125来源：雪球著作权归作者所有。商业转载请联系作者获得授权，非商业转载请注明出处。风险提示：本文所提到的观点仅代表个人的意见，所涉及标的不作推荐，据此买卖，风险自负。

欢迎收听雪球和喜马拉雅联合出品的财经有深度，雪球，3500万投资者在线交流，一起探索投资投资的智慧，听众朋友们大家好，我是主播匪石-34，今天分享的稿件名字叫做百度估值为啥就是上不去？来自于刘志超。 跟百度管理层交流时，对方提了个问题，为啥投资者给百度的估值“就是上不去”呢？ 其实，曾几何时，百度的估值“就是下不来”。大小利空，都被投资者看作上车的机会。百度股价也总会涨起来，从不辜负。 为啥呢？因为当时百度垄断了PC时代中国互联网入口。就是CCTV来批，全国网民们痛骂百度赚昧良心的钱，转过身打开电脑还得先百度一下。垄断了互联网入口，这句话你会给什么估值？无论问老鸟还是菜鸟，答案都下不来。 那为啥百度的估值现在“就是上不去”呢？发生过什么大利空吗。 有人说，锅在文化，也有人说，锅在管理。其实这些都不重要，也没有什么了不起的利空事件。根源在于，人们使用互联网的方式改变了。 智能手机普及后，大家使用互联网的习惯发生了彻底改变，再也不需要一个导航页了，而是直接进入自己感兴趣的app里刷刷看看。 我之前发过一条球文，“生意兴衰的根源，是人类生活方式与习惯的变化”。时代抛弃了你，跟你做了什么无关。这场大掉队里，怪百度做错了什么吗？也没啥可怪的，腾讯在搞出微信前也是岌岌可危，甚至被逼的要网民在QQ和360里二选一，现在谁还记得360？ 当百度激情十足地大举投入AI，投资者却显得很冷静。原因也很好理解，公司的引擎从一个垄断又赚钱的业务，切换到一个无法垄断又在不停投入的业务，你会给它一个怎样的估值？ 当然，往远看，AI的赛道足够宽，往实看，智能汽车的故事及其衍生的车联网故事也可以足够性感。 但问题是，这个性感还只存在于剪影阶段，甚至是个素描版剪影。能让投资者兴奋的性感，就是没有成片，也得有一张高清照片吧。如果要拿iPhone的故事做比喻，就起码得有个一代原型机吧。 否则的话，就像第一次iphone发布会，乔布斯循环地放iPod、手机和电脑的图片，如果最后不出现iPhone，让你去想象，你可能会设计出一个要你命3000。 现在的智能车，更像iPhone出现前那种配一根手写笔的商务手机。车联网，则更像是一个科幻场景。我是一个科幻迷，相信美好的科幻都会实现。 但问题是，即便最后这些美好真的会实现，投资者可能会觉得未必一定是百度来实现，即便是由百度实现了，投资者可能觉得未必会被一家公司独占。 百度由“估值下不来”变成“估值上不去”，其实不怪它做错了什么，如果一定要怪，就怪它没在合适的时候做对些什么。想做些什么让估值上去，也的确没啥可做的。 不如别管大家怎么看，专心搞业务，等风来。 作者：刘志超链接：https://xueqiu.com/3386153330/159473125来源：雪球著作权归作者所有。商业转载请联系作者获得授权，非商业转载请注明出处。风险提示：本文所提到的观点仅代表个人的意见，所涉及标的不作推荐，据此买卖，风险自负。

欢迎收听雪球和喜马拉雅联合出品的财经有深度，雪球，3500万投资者在线交流，一起探索投资投资的智慧，听众朋友们大家好，我是主播匪石-34，今天分享的稿件名字叫做百度估值为啥就是上不去？来自于刘志超。 跟百度管理层交流时，对方提了个问题，为啥投资者给百度的估值“就是上不去”呢？ 其实，曾几何时，百度的估值“就是下不来”。大小利空，都被投资者看作上车的机会。百度股价也总会涨起来，从不辜负。 为啥呢？因为当时百度垄断了PC时代中国互联网入口。就是CCTV来批，全国网民们痛骂百度赚昧良心的钱，转过身打开电脑还得先百度一下。垄断了互联网入口，这句话你会给什么估值？无论问老鸟还是菜鸟，答案都下不来。 那为啥百度的估值现在“就是上不去”呢？发生过什么大利空吗。 有人说，锅在文化，也有人说，锅在管理。其实这些都不重要，也没有什么了不起的利空事件。根源在于，人们使用互联网的方式改变了。 智能手机普及后，大家使用互联网的习惯发生了彻底改变，再也不需要一个导航页了，而是直接进入自己感兴趣的app里刷刷看看。 我之前发过一条球文，“生意兴衰的根源，是人类生活方式与习惯的变化”。时代抛弃了你，跟你做了什么无关。这场大掉队里，怪百度做错了什么吗？也没啥可怪的，腾讯在搞出微信前也是岌岌可危，甚至被逼的要网民在QQ和360里二选一，现在谁还记得360？ 当百度激情十足地大举投入AI，投资者却显得很冷静。原因也很好理解，公司的引擎从一个垄断又赚钱的业务，切换到一个无法垄断又在不停投入的业务，你会给它一个怎样的估值？ 当然，往远看，AI的赛道足够宽，往实看，智能汽车的故事及其衍生的车联网故事也可以足够性感。 但问题是，这个性感还只存在于剪影阶段，甚至是个素描版剪影。能让投资者兴奋的性感，就是没有成片，也得有一张高清照片吧。如果要拿iPhone的故事做比喻，就起码得有个一代原型机吧。 否则的话，就像第一次iphone发布会，乔布斯循环地放iPod、手机和电脑的图片，如果最后不出现iPhone，让你去想象，你可能会设计出一个要你命3000。 现在的智能车，更像iPhone出现前那种配一根手写笔的商务手机。车联网，则更像是一个科幻场景。我是一个科幻迷，相信美好的科幻都会实现。 但问题是，即便最后这些美好真的会实现，投资者可能会觉得未必一定是百度来实现，即便是由百度实现了，投资者可能觉得未必会被一家公司独占。 百度由“估值下不来”变成“估值上不去”，其实不怪它做错了什么，如果一定要怪，就怪它没在合适的时候做对些什么。想做些什么让估值上去，也的确没啥可做的。 不如别管大家怎么看，专心搞业务，等风来。 作者：刘志超链接：https://xueqiu.com/3386153330/159473125来源：雪球著作权归作者所有。商业转载请联系作者获得授权，非商业转载请注明出处。风险提示：本文所提到的观点仅代表个人的意见，所涉及标的不作推荐，据此买卖，风险自负。

Tony Fadell — On Building the iPod, iPhone, Nest, and a Life of Curiosity | Brought to you by SuperFat and Four Sigmatic. “Get bored. Just put away all of your things. Maybe go clean up the garage or whatever it is. Right? Through that, you're going to start to think differently. You're going to act slightly differently and your mind might open up to other sources of inspiration, other problems...” — Tony FadellTony Fadell (@tfadell), sometimes called "the father of the iPod," is an active investor and entrepreneur with a 30+ year history of founding companies and designing products that profoundly improve people's lives. As the Principal at Future Shape, a global investment and advisory firm coaching engineers and scientists working on foundational deep technology, he is continuing to help bring technology out of the lab and into our lives. Currently, Future Shape is coaching 200+ startups innovating game-changing technologies.Tony founded Nest Labs, Inc. in 2010 and served as its Chief Executive Officer until his resignation in 2016. He joined Apple Computer Inc. in 2001 and, as the SVP of Apple's iPod division, led the team that created the first 18 generations of the iPod and the first three generations of the iPhone. Tony founded the Mobile Computing Group at Philips Electronics and served as its Chief Technology Officer and Director of Engineering 1995 to 1998, responsible for all aspects of business and product development, including the award-winning Philips Velo and Nino PDAs. From 1998 to 1999, he served as Vice President for Philips Strategy & Venture focused on building out its digital media strategy and investment portfolio. From 1992 to 1995, he served as a Hardware and Software Architect at General Magic, which created the precursor to the iPhone 15 years earlier.Tony has filed more than 300 patents for his work. In May 2016, Time named the Nest Learning Thermostat, the iPod, and the iPhone three of the "50 Most Influential Gadgets of All Time." Tony graduated with a BS degree in Computer Engineering from the University of Michigan in 1991.Please enjoy!This episode is also brought to you by SuperFat Nut Butters. These little beauties are great. I’ve been using them as quick mini-breakfasts and on-the-go fuel for a few months now. They’re 200–300 calories each, depending on which ingredient cocktail you eat (MCT, protein, macadamia, caffeine, etc.); 3–5g of net carbs per pouch; keto- and Paleo-friendly; and easy to throw in a backpack or pocket. The first time I tried SuperFat, I finished the entire box in a few days, so watch your portion control.I suggest ordering the Variety Box and you can try all 5 SuperFat flavors in one box, and it has 2 pouches of each flavor. Get 15% off your order by going to SuperFat.com/tim.This podcast is also brought to you by Four Sigmatic. I reached out to these Finnish entrepreneurs after a very talented acrobat introduced me to one of their products, which blew my mind (in the best way possible). It is mushroom coffee featuring Lion's Mane. It tastes like coffee, but there are only 40 milligrams of caffeine, so it has less than half of what you would find in a regular cup of coffee. I do not get any jitters, acid reflux, or any type of stomach burn. It put me on fire for an entire day, and I only had half of the packet.You can try it right now by going to foursigmatic.com/tim and using the code Tim to get 20 percent off your first order. If you are in the experimental mindset, I do not think you'll be disappointed.***If you enjoy the podcast, would you please consider leaving a short review on Apple Podcasts/iTunes? It takes less than 60 seconds, and it really makes a difference in helping to convince hard-to-get guests.For show notes and past guests, please visit tim.blog/podcast.Sign up for Tim’s email newsletter (“5-Bullet Friday”) at tim.blog/friday.For transcripts of episodes, go to tim.blog/transcripts.Interested in sponsoring the podcast? Please fill out the form at tim.blog/sponsor.Discover Tim’s books: tim.blog/books.Follow Tim:Twitter: twitter.com/tferriss Instagram: instagram.com/timferrissFacebook: facebook.com/timferriss YouTube: youtube.com/timferriss

Support these videos: http://pgbovine.net/support.htmhttp://pgbovine.net/PG-Vlog-398-iphone-11-pro-unboxing.htm- [http://pgbovine.net/PG-Vlog-146-macbook-air-unboxing.htm](http://pgbovine.net/PG-Vlog-146-macbook-air-unboxing.htm)Recorded: 2019-11-22 (2)

iPhoneができて10年。iPodがiPhoneに吸収されて10年。音楽自体もCDからファイルそのものになり、今はユーザーがファイルも所有しなくていいようになってきています。いま、どうやって音楽を聴いているか、温悪自体は所有するかしないか、そんなことについて喋っています。

承接上期内容，17苹果发布会的二号位当属数据版本的Apple Watch 3，前段时间苹果停售了 iPod 里的两款纯播放器，另部分果粉唏嘘不已，我台也是疑虑重重，随着Apple Watch 3的发布，谜底揭开，并非停售，而是重生，原先口袋中可以装得下千首歌曲的 iPod移至腕上。同时Apple Watch 3终于开拓出流量数据的功能，在与iPhone的比拼中给自己加上一块筹码，另自身的使用场景得到扩展，也许将来Apple Watch 真的能挤掉iPhone，但肯定不是现在，时间也不会短。

承接上期内容，17苹果发布会的二号位当属数据版本的Apple Watch 3，前段时间苹果停售了 iPod 里的两款纯播放器，另部分果粉唏嘘不已，我台也是疑虑重重，随着Apple Watch 3的发布，谜底揭开，并非停售，而是重生，原先口袋中可以装得下千首歌曲的 iPod移至腕上。同时Apple Watch 3终于开拓出流量数据的功能，在与iPhone的比拼中给自己加上一块筹码，另自身的使用场景得到扩展，也许将来Apple Watch 真的能挤掉iPhone，但肯定不是现在，时间也不会短。

Victor and Neil talk about the future of the iPod, Apple Watch, and manufacturing in modern America.

Horace explains why he was wrong about the BMW i3. Also, more Q&A.

Horace explains why he was wrong about the BMW i3. Also, more Q&A.

果然，2006年，魅族可与苹果iPod的媲美的产品——miniplayer横空出世，这款产品采用了当时最好的芯片、最好的屏幕，搭配性价比最高的耳机，价格却十分合理。miniPlayer一举击败了当时国内国外所有的MP3随身听产品，很快就成为销量冠军。此名扬天下。乔布斯对产品的品质，有一种狂热的永无止境的追求。所以，凡是由乔布斯创意的产品，都是享誉世界的经典产品。早年的苹果机、麦金托什机不用提起，近年来的iPod随身听，更是工业产品中的极品。苹果公司的iPod，与其说是产品，更不如说是艺术品。连英国女皇，都专门为iPod的设计，颁发了王室的最高奖励。乔布斯的iPod随身听，在全球已销售一亿多台，靠一件产品就使苹果公司一举翻身，股价爆涨几十倍。乔布斯借iPod的成功，顺势推出的iPhone手机， 魅族的团队研发了大约30歉产品。许多部已经可以上市，但是黄章坚决不同意上市销售。不同意的理由只有一个。他觉得不够好用。怕消费者会不满意。 从2006年起，黄章带领的魅族，成了当仁不让的“国内MP3第一品牌”，年销售额超过10亿元。 在MP3行业，魅族取得了巨大的成功。但算起来，却一共只有lO款产品。其实，魅族的团队研发了大约30款产品，许多都已经可以上市，但是黄章坚决不同意上市销售。不同意的理由只有一个，他觉得不够好用，怕消费者会不满意。所以，魅族MP3历史上留下的lO款产品，全是经典。黄章对产品尽善尽美的要求，与苹果公司的乔布斯如出一门。苹果公司的iPod也只有几种型号，却风靡全球，卖出上亿台。 魅族MP3产品的取得了巨大成功，但在黄章眼里，这只不过是与苹果公司的一场前哨站。

乔布斯是如何对苹果进行颠覆的（上） 乔布斯在1997年重返苹果之后，在最初的三年也曾经在热门的个人电脑上进行微创新。比如，它设计了一些苹果机彩壳，一时间争取到了眼球，但并没有成功，卖个人电脑卖不过戴尔，卖系统卖不过微软。没办法，乔布斯只好从大公司看不上的MP3开始。 我认为，从2001年做IPOD开始，乔布斯带领苹果重新踏上了创业的道路。IPOD是一款MP3播放器，当时MP3已经满街都是。对于像微软、戴尔这样的大公司来说，MP3没有前途、没有价值。以马后炮的方式来看，乔布斯做IPOD，实际上是打了一场侧翼战，避开了当时的主流竞争对手的主战场，通过微创新，达到了颠覆市场的目标。 有人说，乔布斯的一生是神一样的传奇，但我觉得苹果的成功并不是高瞻远瞩、缜密规划的结果。从IPOD到IPHONE，中间究竟发生了什么？这是一个值得琢磨的问题。 乔布斯二次创业，是从一个普遍需求开始的，这是他成就一项伟大事业的基础。有人说，乔布斯善于创造需求，我觉得这是扯淡。没有人能够创造需求，对音乐的需求是人类与生俱来的，乔布斯所做的，只不过是通过IPOD把音乐的体验做到了极致，满足了人们的需求。 IPOD之所以能够流行，首先在于它一流的设计，跟其他MP3相比，IPOD鹤立鸡群。再一个微创新，是里面的东芝小硬盘，号称可以存储1万首歌，一辈子都听不完。从IPOD开始，每一个微小的创新持续改变，都成就了一款伟大的产品。在IPOD中加入一个小屏幕，就有了IPOD TOUCH的雏形，有了IPOD TOUCH，任何人都会想到，如果加上一个通话模块打电话会怎么样呢？于是，就有了IPHONE；有了IPHONE，把它的屏幕一下子拉大，不就变成了IPAD了吗？ 然而，一切看似眼花缭乱、万象丛生的东西，无一不是从那个“一”开始，那个“一”就是IPOD。要知道，当苹果推出IPHONE的时候，IPOD在全球的销量已经超过1亿部。这1亿多部IPOD不仅为苹果创造了口碑，创造了品牌，而且也为苹果捕捉了不少消费者的体验。没有这个台阶，如果乔布斯一下子上来就做IPHONE，也不见得会成功。

Cathryn Carson is an Assoc Prof of History, and the Ops Lead of the Social Sciences D- Lab at UC Berkeley. Fernando Perez is a research scientist at the Henry H. Wheeler Jr. Brain Imaging Center at U.C. Berkeley. Berkeley Institute for Data Science.TranscriptSpeaker 1: Spectrum's next. Speaker 2: Mm MM. Speaker 3: Uh Huh [inaudible]. Speaker 4: [00:00:30] We'll come to spectrum the science and technology show on Katie l x Berkeley, a biweekly 30 minute program bringing you interviews featuring bay area scientists and technologists as well as a calendar of local events. Speaker 3: [inaudible].Speaker 1: Hello and good afternoon. My name is Renee Rao and I'll be hosting today's show this week [00:01:00] on spectrum present part two of our two part series on big data at cal. The Berkeley Institute for data science bids is only four months old. Two people involved with shaping the institute are Catherine Carson and Fernando Perez. They are today's guest Catherine Carson is an associate professor of history and associate dean of social sciences and the operational lead of the social sciences data lab at UC Berkeley for Nana Perez is a research scientist at the Henry H. Wheeler [00:01:30] Jr Brain imaging center at UC Berkeley. He created the iPod iPhone project while he was a graduate student in 2001 and continues to lead the project today. In part two they talk about teaching data science. Brad Swift conducts the interview Speaker 5: on the teaching side of things. Does data science just fold into the domains in the fields and some faculty embrace it, others don't. How does the teaching of data science move [00:02:00] forward at an undergraduate level? Yeah, there there've been some really interesting institutional experiments in the last year or two here at Berkeley. Thinking about last semester, fall of 2013 stat one 57 which was reproducible collaborative data science pitched at statistics majors simply because you have to start with the size that can fit in a classroom [00:02:30] and training students in the practices of scientific collaboration around open source production of software tools or to look at what was Josh Bloom's course, so that's astro four 50 it's listed as special topics in astrophysics just because Josh happens to be a professor in the astronomy department and so you have to list it somewhere. The course is actually called Python for science Speaker 6: [00:03:00] and it's a course that Josh has run for the last, I think this is, this was its fourth iteration and that course is a completely interdisciplinary course that it's open to students in any field. The examples really do not privilege and the homework sets do not privilege astronomy in any way and we see students. I liked her a fair bit in that course as a guest lecture and we see students from all departments participating. This last semester it was packed to the gills. We actually had problems because we couldn't find a room large enough to accommodate. So word of mouth is working. In terms of students finding these [00:03:30] courses, Speaker 5: it's happening. I wouldn't say it's working in part because it's very difficult to get visibility across this campus landscape. I am sure there are innovations going on that even the pis and bids aren't aware of and one of the things we want to do is stimulate more innovation in places like the the professional schools. We'll be training students who need to be able to use these tools as well. What do they have in mind or there [00:04:00] are other formats of instruction beyond traditional semester courses. What would intensive training stretched out over a much shorter time look like? What gaps are there in the undergraduate or graduate curriculum that can effectively be filled in that way? The Python bootcamp is another example of this that's been going on for Speaker 6: for about four years. Josh and I teach a a bootcamp on also python for data science that is immediately before the beginning of the fall semester. Literally the weekend before [00:04:30] and it's kind of, it's a prerequisite for the semester long course, but it's three days of intensive hands-on scientific bite on basically programming and data analysis and computing for three days. We typically try to get a large auditorium and we got 150 to 200 people. A combination of undergrads, Grad Students, postdocs, folks from LVL campus faculty and also a few folks from industry. We always leave, leave a few slots available for people from outside the university to come and that one a has been very popular at [00:05:00] tends to, it's intense to have very good attendance be, it serves as an on ramp for the course because we advertise the in the semester course during the bootcamp and that one has been fairly successful so far and I think it has worked well. Speaker 6: We see issues with it too. That would be that we would like to address three days is probably not enough. Um, it means because it's a single environment, it means that we have to have examples that are a little bit above that can accommodate everyone, but it means they're not particularly interesting for any one group. It would be, I think it would be great to have [00:05:30] things of this nature that might be a little bit better focused at the life sciences and the social sciences that the physical sciences, so that the examples are more relevant for a given community that may be better targeted at the undergraduate and the graduate level so that you can kind of select a little bit in tune the requirements or the methodological base a little bit better to the audience. But so far we've had to kind of bootstrapping with what we have. Speaker 6: There's another interesting course on campus offered by the ice school by Raymond Lecture at the high school called working with open data [00:06:00] that is very much aimed at folks who are the constituency of the high school that have an intersection of technical background with a broader interdisciplinary kind of skills that are the hallmark of the high school and they work with openly available data sets that are existing on the Internet to create basically interesting analysis projects out of them and that's of course that that I've seen come up with some very, very successful and compelling projects at the end of the semester Speaker 7: about the teaching and preparation in universities. In [00:06:30] the course of doing interviews on spectrum, a number of people have said that really the only way to tackle sciences interdisciplinary, the big issues of science is with an interdisciplinary approach, but that that's not being taught in universities as the way to do science. Sarah way to break that down using data science as a vehicle. Speaker 5: I can speak about that as a science and technology studies scholar. The practice of interdisciplinarity, what makes it actually work is one of the [00:07:00] the most challenging social questions that can be asked of contemporary science and adding into that the fact that scientists get trained inside this existing institution that we've inherited from let's roughly say the Middle Ages with a set of disciplines that have been in their current form since roughly the late 19th century. That is the interface where I expect in the next oh two to five decades major transformations in research universities. [00:07:30] We don't yet know what an institution or research institution will look like that does not take disciplines as it sort of zero order ground level approximation to the way to encapsulate truth. But we do see, and I think bids is like data science in general and an example of this. We do see continual pressure to open up the existing disciplines and figure out how to do connections across them. It's [00:08:00] not been particularly easy for Berkeley to do that in part because of the structure of academic planning at our institution and in part because we have such disciplinary strengths here, but I think the invitation for the future that that word keeps coming back invitation. The invitation for the future for us is to understand what we mean by practicing interdisciplinarity and then figure out how to hack the institution so that it learns how to do it better. [inaudible] Speaker 8: [inaudible] [00:08:30] you're listening to structure fun. K A, l ex Berkeley Fasten Kirsten and Fernando Perez are our guests. They're part of the Berkeley Institute for Data Science for Bids [inaudible] Oh, Speaker 6: it seems that data science has an almost unlimited [00:09:00] application. Are there, are you feeling limits? I don't know about limits specifically because I think in principle almost any discipline can have some of its information and whatever the concepts and constructs of that discipline can probably be represented in a way that is amicable to quantitative analysis of some sort. In that regard, probably almost any discipline can have a data science aspect to it. I think it's important not to sort of [00:09:30] over fetishize it so that we don't lose sight of the fact that there's other aspects of intellectual work in all disciplines that are still important. That theory still has a role. That model building still has a role that, uh, knowing what questions to ask, it's still important that hypotheses still matter. I'm not so sure that it's so much an issue of drawing arbitrary limits around it, but rather of being knowledgeable and critical users of the tools and the approaches that are offered. Speaker 6: Because in terms of domain [00:10:00] applications, I actually recently saw at the strata conference, which is one of these more industry oriented big data conferences that took place a few weeks ago in Silicon Valley. It's in Santa Clara. One of the best talks that I saw at the conference was an analysis half the poem, if I told him that Gertrude Stein wrote about Picasso After Picasso painted this very famous portrait of her. And that poem has a very, very repetitive rhythmic structure. It has very few words and it's a long poem with a very peculiar linguistic structure. And [00:10:30] this hardest, I, I'm blanking on his name right now, but he's an artist who works kind of at the intersection of digital arts in, in linguistics wrote basically a custom one off visual analysis and visualization tool to work on the structure of this poem to visualize it, to turn it into music. Speaker 6: And it was a beautiful talk. It was a beautiful and very interesting talk and this was kind of the exact opposite of this was tiny data. This was one poem and in fact during the Q and a they asked him and he said, well I've tried to use the tool [00:11:00] on a few other things and there's a few songs in hip hop that it works well with, but it's almost, it's almost custom made for this one poem, right? So this was sort of tiny data, completely non generalizable and yet I thought it was fascinating and beautiful talk. So that's kind of an example that I would have never have thought of as as data science. Any examples of misapplication? Speaker 5: I think we can admit that data science is a buzzword that is [00:11:30] exactly through, it's almost indefinable nature creates space for people to do methodologically problematic and in many cases also uninteresting work. Just throwing data into an analysis without asking is this the right analysis will get you stupid or misleading answers. It's the garbage in out principle. So yeah, like any intellectual tool in the toolkit, [00:12:00] there are misleading conclusions that can be drawn and one of the powers that Berkeley brings to this effort in data science is a focus on the methodology, the intelligent development of methodology along with just building things that look like tools on their own. I think that's going to be the place with the sweet spot for universities because of the emphasis on rigor and stringency and reasoning [00:12:30] along with just getting out results that look good and are attractive Speaker 7: with data science. Are there infrastructure challenges that are worth talking about either in industry or at an academic institution? Because I know that computing power now through Amazon, Google organizations like that are enormous and so industry is sort of giving up the idea of having their own [00:13:00] computational capacity and they're using cloud virtual universities I would think are following suit. Speaker 6: Yes, there is work being done already on campus in that regard. We've had some intersection with those teams. The university right now, uh, we've had since last year a new CIO on campus, Larry Conrad, who's been spearheading an effort to sort of reimagine what the research computing infrastructure for campus should look like. [00:13:30] Considering these questions precisely of what is happening in industry, what are the models that are successfully being used at other institutions to provide larger scales off competitional resources across all disciplines and beyond the disciplines that have been traditionally the ones that have super computers. Well, there's a long history of departments, again, like physics, like competition, fluid dynamics, teams like quantum chemistry teams that have had either their own clusters or that have large budgets who have access to the supercomputing centers at [00:14:00] the doe labs and things of that nature. But as we've been saying today, all of a sudden those needs are exploding across all disciplines and the usage patterns are changing and that often what is the bottleneck is maybe not the amount of raw compute power, but the ability to operate over a very large data sets, so maybe storage is the issue or maybe throughput biologists often end up buying computers that look really weird. Speaker 6: Too many supercomputing centers because they, the actual things that they need are skewed in a different way and so there are certainly [00:14:30] challenges in that regard when we do know that Berkeley is right now at least in the midst of making a very concerted and serious attempt at at least taking a step forward on this problem. Speaker 7: A lot of data is derived from personal information. Are there privacy concerns that you have [inaudible] Speaker 5: they're all quite definitely in so many different ways that the input of experts who have thought about questions of consent, of privacy, [00:15:00] of the challenges around keeping de identified data d identified when it is possible through analytics to understand what patterns are emerging from them that is going to be so key. Especially to working with social data. And so one of the still open questions for all of us working with data that is about people is how to develop the practices that will do the protections necessary [00:15:30] in order to avoid the kinds of catastrophic misuses and violations of privacy that many of us do. Fear will be coming our way as so much data becomes available so fast with so many invitations to just make use of it and worry about the consequences later. That's not the responsible way forward. And I would like to see bids and Berkeley take on that challenge as part of its very deliberate agenda. Speaker 8: [00:16:00] Okay. Spectrum is a public affairs show on k a l ex Berkeley. Our guests are Cathryn Carson and Fernando Perez. In the next segment they talk about institutional reactions to bids. Oh, Speaker 7: are there any impediments that you've run into within the bids process [00:16:30] of getting up and running? Cause it's been going since, uh, Speaker 5: it's not been going on that long as it, it's only December of 2013. Pretty recent, but I'm sure there's gotta be some institutional pushback or no, it's, it's been incredible actually how much support the institution has given. What bids is though, is a laboratory for the kind of collaboration that we're trying to instantiate. And so you have 13 brilliant Co-pi eyes each with their own vision and figuring out where [00:17:00] the intersection is and how to get the different sets of expertise and investments where they, where those intersections lie and how to get them aligned. I mean, that's, that's one of the fascinating challenges in front of beds as a laboratory in the small, for the process at large that we're trying to do Speaker 7: on the tools and programming side. How would you break up what languages are providing, what kind of capability, [00:17:30] and are there new languages that are ascendent and other languages that are languages that are losing their grip? I'm sort of curious. It's a, it's another trivia questions that I think might have some interest for people. No, I think there's, there's clearly an ascendance. I think naturally the expansion of the surface of people interested in these problems Speaker 6: is naturally driving the growth and importance of high level languages that are immediately usable by domain scientists. We're not full time programmers [00:18:00] and professional programmers. Traditionally a lot of the high end computing had been done in languages like c, c plus plus for trend and some Java that are languages that tend to be more the purview of, of people who do lots of software development. And a lot of that did happen in departments like physics and chemistry and computer science, but not so much in other disciplines. And so we're seeing the rise of open source languages like Python and r that are immediately applicable and easy to use for data analysis where a few commands [00:18:30] can load a file, compute some statistics on it, produce a few visualizations, and you can do that in five lines of code, not having to write a hundred or 500 lines of c plus plus. Speaker 6: Right. And so the languages like that are, they're not new. Both I think are came out in the late eighties early nineties python came out in 1991 but they're seeing a huge amount of growth in recent years for this reason. There's also a growth of either new tools to extend these languages [00:19:00] or new languages as well. Tools for example, that connect these languages to databases or extensions to these languages to couple them to databases in better ways so that people don't have to only write raw sequel, which SQL is not the classic language for interacting with databases, so extensions to couple existing languages to database back ends. A lot of work is being done in that direction and there are some novel languages. For example, there's a team at MIT that about two years ago started [00:19:30] a project for a new language called Julia that is aimed at numerical computing, but it's sort of re-imagining. Speaker 6: What would you do if you wanted to create a language like python with the strengths of language like python or Ruby or r, but if you were doing that today with the lessons of the last 20 years, that would be good for numerical computing, but it would be easy to use for domain scientists. That would be high level, that would be interactive, that would feel like a scripting tool, but that would also give you very high performance. [00:20:00] If you had the the last 20 years of lessons and the advances in some of the underlying technology and improved compiler machinery that we have today, how would you go about that problem? And I think the Giulia team at MIT is making rapid progress and it has caught the intention of people in the statistics community of people in the numerical analysis and algorithms community. Some prominent people have become very interested in how to become active participants in its development. Speaker 6: So we're seeing both mature tools like python and are growing in their strength and and their importance. At the latest Strada Conference, [00:20:30] for example, there was a an analysis of kind of the the abstracts submitted that had r and python in their names versus things like excel or sequel or Java and Python and are clearly dominating that space, but also these, these kinds of more novels, sort of research level languages that whose futures still not clear because they're very, very young, but at least they're exploring sort of the frontier of what will we do in the next five or 10 years. And is this an area that's ripe for a commercial software creators who develop [00:21:00] a tool that would be specific to data science and sort of the same way that Mat lab is kind of specific now it's kind of a generic tool for mathematics. Obviously my answer here is extremely biased, but I'm, I sort of think that the space for a, the window to create a proprietary data science language is closed already. Speaker 6: I think the community simply would not adopt a new one. There are some existing successful ones such as mat lab, IDL, which is smaller than Madlib. It is widely used in the astronomy and astrophysics. [00:21:30] And Physics Communities Mathematica, which is a project that came out of the mathematics and physics world and that is very, very sophisticated and interesting. Maple, which is also a mathematics language. Those are successful existing proprietary languages. I think the mood has changed to these are products that came out in the eighties and the nineties. I think the, the window for that, uh, as a purely proprietary offer has closed. I think what we're going to see is the continued growth and the rise potential. You have new entrants that are fundamentally [00:22:00] open source, but yet that maintain, as I said earlier, a healthy dialogue with industry because it doesn't mean, for example, in the art world there are companies that build very successful commercial products around are there is a product called r studio that is a development environment for analysis in our, and that's a company, there's a company called I think revolution analytics. Speaker 6: I think they built some sort of sort of large scale backend high-performance version of our, I don't know the details, I don't use it, but I've seen their website. I think they're a large company that builds kind of our for the enterprise. So I think [00:22:30] that's what we're going to see moving forward at the base. People want the base technology, the base language to be open source. And I think for us as universities and for me as a scientist, I think that's a Tenet I'm not willing to compromise on because I do not want a result that I obtain or result that I published or a tool that I educate my students with to have a black box that I'm legally prevented from opening and to tell my student, well, this is a result about nature, but you can't understand how it was achieved because you are legally prevented from opening the box. [00:23:00] I think that is fundamentally unacceptable. But what is, I think a perfectly sensible way forward, is to have these base layers that are open on top of which domain specific tools can be created by industry that add value for specific problems, for specific domains that may be add performance, whatever. Catherine Carson and Fernando Perez. Thanks very much for coming on spectrum. Thanks for having us here. Thanks much. Speaker 8: [inaudible]Speaker 9: [00:23:30] all spectrums. Past shows are archived on iTunes university. We've created a simple link for you. The link is tiny url.com/k Speaker 1: a l x Speaker 8: spectrum Speaker 1: Rick Curtis Skin. I will present a few of the science and technology events [00:24:00] happening locally over the next two weeks. Speaker 10: Counter culture, labs and pseudo room present gravitational waves, results and implications with Bicep to collaborator Jamie Tolan at the pseudo room, hackerspace to one 41 Broadway in Oakland on Sunday, April 27th at 7:00 PM recently, scientists from the Bicep to experiment recorded their data findings demonstrating [00:24:30] evidence of gravitational waves that may imply cosmic inflation. The bicep to experiment is an international collaboration of research and technology from many institutions including a team at Stanford University work. Jamie Tolan works. Jamie will discuss the results of the bicep two experiment and its scientific contribution to current theories that attempt to explain the why, what and how of our universe. The event will be free. Speaker 1: On April 30th UCLA professor [00:25:00] of geography, Jared diamond will give this year's Horace m Albright Lecture in conversation. Diamond is best known for his Pulitzer Prize winning book, guns, germs and steel and this lecture he will discuss his newest book, the world until yesterday, what we can learn from traditional societies. The book is about how traditional peoples differ from members of modern industrial societies and their reactions to danger. He will then produce B in a question answer session with the audience doors open at 6:00 PM [00:25:30] the event is free and open to the public on a first come first served basis will be held Wednesday, April 30th from seven to 8:30 PM in the International House Auditorium at two two nine nine Piedmont Avenue Berkeley. Speaker 10: The theme of Mays science at the theater is science remix. Joined Berkeley lab scientists at the East Bay Center for the Performing Arts in Richmond, California on May 1st at 7:00 PM they'll discuss how discovery [00:26:00] happens. Help you show what science means to you and reveal why science can be as personal as you want it to be. Light refreshments will be served, but bring your imagination and participate at this free event. Speaker 1: A feature spectrum is to present new stories about science that we find particularly interesting. Rick Carnesi joins me in presenting the news. Speaker 10: Nature News reported on April 13th that a team of scientists from [00:26:30] Caltech have estimated that Mars's atmosphere was probably never thick enough to keep temperatures on the planet surface above freezing for very long. Edwin kite now at Princeton used from the Mars reconnaissance orbiter to catalog more than 300 craters and an 84,000 square kilometer area near the planets equator. The sizes of the creators were compared to computer models with varying atmospheres. Dance [00:27:00] or atmospheres would have broken up small objects as they do on earth, but the high frequency of smaller craters on Mars suggest the upper limit of atmospheric pressure on Mars was only one or two bar. This most likely means a temperatures on Mars have typically been below freezing. Did the team notes that their findings do allow the possibility of scenarios of Mars having a slightly thicker atmosphere at times. Do you perhaps to volcanic activity or gas is released by the large impact events and these could have [00:27:30] made Mars warmer for decades or centuries at a time, allowing water to flow. Then Speaker 1: science daily reports one of the first social science experiments to rest on. Big Data has been published in plus one. A chair of investigators from Simon Fraser University analyzed when humans start to experience and age-related decline in cognitive motor skills. The researchers analyze the digital performances of over 3000 starcraft two players, age 16 to 44 starcraft two is a ruthless intergalactic computer [00:28:00] game that players often undertake to win serious money. Their performance records, which can be easily accessed, represent thousands of hours worth of strategic real time. Cognitive based moves performed at various skill levels using complex statistical modeling. Researchers distilled meaning from this colossal compilation of information about how players responded to their opponents and more importantly, how long they took to react after around 24 years of age, players show slowing and a measure of cognitive speed that is known to be important for performance. [00:28:30] Explains Joe Thompson lead author of the study. This cognitive performance decline is present even at higher levels of skill, but there's a silver lining in this earlier than expected slippery slope into old age. Thompson says older players, those slower seem to compensate by employing simpler strategies and using the games interface more efficiently. The younger players enabling them to retain their skill despite cognitive motor speed losses. These findings says Thompson suggests that our cognitive motor capabilities are not stable across our adulthood, but are constantly [00:29:00] in flux and that our day to day performance is a result of the constant interplay between change and adaptation. Speaker 2: [inaudible]Speaker 11: and music heard during this show was written and produced by Alex Simon. Today's interview was edited by Rene Rau. Thank you for listening to spectrum. If you have comments about the show, please send them to us via email or email [00:29:30] address is spectrum dot kalx@yahoo.com join us in two weeks at this same tone. [inaudible]. See acast.com/privacy for privacy and opt-out information.

Cathryn Carson is an Assoc Prof of History, and the Ops Lead of the Social Sciences D- Lab at UC Berkeley. Fernando Perez is a research scientist at the Henry H. Wheeler Jr. Brain Imaging Center at U.C. Berkeley. Berkeley Institute for Data Science.TranscriptSpeaker 1: Spectrum's next. Speaker 2: Mm MM. Speaker 3: Uh Huh [inaudible]. Speaker 4: [00:00:30] We'll come to spectrum the science and technology show on Katie l x Berkeley, a biweekly 30 minute program bringing you interviews featuring bay area scientists and technologists as well as a calendar of local events. Speaker 3: [inaudible].Speaker 1: Hello and good afternoon. My name is Renee Rao and I'll be hosting today's show this week [00:01:00] on spectrum present part two of our two part series on big data at cal. The Berkeley Institute for data science bids is only four months old. Two people involved with shaping the institute are Catherine Carson and Fernando Perez. They are today's guest Catherine Carson is an associate professor of history and associate dean of social sciences and the operational lead of the social sciences data lab at UC Berkeley for Nana Perez is a research scientist at the Henry H. Wheeler [00:01:30] Jr Brain imaging center at UC Berkeley. He created the iPod iPhone project while he was a graduate student in 2001 and continues to lead the project today. In part two they talk about teaching data science. Brad Swift conducts the interview Speaker 5: on the teaching side of things. Does data science just fold into the domains in the fields and some faculty embrace it, others don't. How does the teaching of data science move [00:02:00] forward at an undergraduate level? Yeah, there there've been some really interesting institutional experiments in the last year or two here at Berkeley. Thinking about last semester, fall of 2013 stat one 57 which was reproducible collaborative data science pitched at statistics majors simply because you have to start with the size that can fit in a classroom [00:02:30] and training students in the practices of scientific collaboration around open source production of software tools or to look at what was Josh Bloom's course, so that's astro four 50 it's listed as special topics in astrophysics just because Josh happens to be a professor in the astronomy department and so you have to list it somewhere. The course is actually called Python for science Speaker 6: [00:03:00] and it's a course that Josh has run for the last, I think this is, this was its fourth iteration and that course is a completely interdisciplinary course that it's open to students in any field. The examples really do not privilege and the homework sets do not privilege astronomy in any way and we see students. I liked her a fair bit in that course as a guest lecture and we see students from all departments participating. This last semester it was packed to the gills. We actually had problems because we couldn't find a room large enough to accommodate. So word of mouth is working. In terms of students finding these [00:03:30] courses, Speaker 5: it's happening. I wouldn't say it's working in part because it's very difficult to get visibility across this campus landscape. I am sure there are innovations going on that even the pis and bids aren't aware of and one of the things we want to do is stimulate more innovation in places like the the professional schools. We'll be training students who need to be able to use these tools as well. What do they have in mind or there [00:04:00] are other formats of instruction beyond traditional semester courses. What would intensive training stretched out over a much shorter time look like? What gaps are there in the undergraduate or graduate curriculum that can effectively be filled in that way? The Python bootcamp is another example of this that's been going on for Speaker 6: for about four years. Josh and I teach a a bootcamp on also python for data science that is immediately before the beginning of the fall semester. Literally the weekend before [00:04:30] and it's kind of, it's a prerequisite for the semester long course, but it's three days of intensive hands-on scientific bite on basically programming and data analysis and computing for three days. We typically try to get a large auditorium and we got 150 to 200 people. A combination of undergrads, Grad Students, postdocs, folks from LVL campus faculty and also a few folks from industry. We always leave, leave a few slots available for people from outside the university to come and that one a has been very popular at [00:05:00] tends to, it's intense to have very good attendance be, it serves as an on ramp for the course because we advertise the in the semester course during the bootcamp and that one has been fairly successful so far and I think it has worked well. Speaker 6: We see issues with it too. That would be that we would like to address three days is probably not enough. Um, it means because it's a single environment, it means that we have to have examples that are a little bit above that can accommodate everyone, but it means they're not particularly interesting for any one group. It would be, I think it would be great to have [00:05:30] things of this nature that might be a little bit better focused at the life sciences and the social sciences that the physical sciences, so that the examples are more relevant for a given community that may be better targeted at the undergraduate and the graduate level so that you can kind of select a little bit in tune the requirements or the methodological base a little bit better to the audience. But so far we've had to kind of bootstrapping with what we have. Speaker 6: There's another interesting course on campus offered by the ice school by Raymond Lecture at the high school called working with open data [00:06:00] that is very much aimed at folks who are the constituency of the high school that have an intersection of technical background with a broader interdisciplinary kind of skills that are the hallmark of the high school and they work with openly available data sets that are existing on the Internet to create basically interesting analysis projects out of them and that's of course that that I've seen come up with some very, very successful and compelling projects at the end of the semester Speaker 7: about the teaching and preparation in universities. In [00:06:30] the course of doing interviews on spectrum, a number of people have said that really the only way to tackle sciences interdisciplinary, the big issues of science is with an interdisciplinary approach, but that that's not being taught in universities as the way to do science. Sarah way to break that down using data science as a vehicle. Speaker 5: I can speak about that as a science and technology studies scholar. The practice of interdisciplinarity, what makes it actually work is one of the [00:07:00] the most challenging social questions that can be asked of contemporary science and adding into that the fact that scientists get trained inside this existing institution that we've inherited from let's roughly say the Middle Ages with a set of disciplines that have been in their current form since roughly the late 19th century. That is the interface where I expect in the next oh two to five decades major transformations in research universities. [00:07:30] We don't yet know what an institution or research institution will look like that does not take disciplines as it sort of zero order ground level approximation to the way to encapsulate truth. But we do see, and I think bids is like data science in general and an example of this. We do see continual pressure to open up the existing disciplines and figure out how to do connections across them. It's [00:08:00] not been particularly easy for Berkeley to do that in part because of the structure of academic planning at our institution and in part because we have such disciplinary strengths here, but I think the invitation for the future that that word keeps coming back invitation. The invitation for the future for us is to understand what we mean by practicing interdisciplinarity and then figure out how to hack the institution so that it learns how to do it better. [inaudible] Speaker 8: [inaudible] [00:08:30] you're listening to structure fun. K A, l ex Berkeley Fasten Kirsten and Fernando Perez are our guests. They're part of the Berkeley Institute for Data Science for Bids [inaudible] Oh, Speaker 6: it seems that data science has an almost unlimited [00:09:00] application. Are there, are you feeling limits? I don't know about limits specifically because I think in principle almost any discipline can have some of its information and whatever the concepts and constructs of that discipline can probably be represented in a way that is amicable to quantitative analysis of some sort. In that regard, probably almost any discipline can have a data science aspect to it. I think it's important not to sort of [00:09:30] over fetishize it so that we don't lose sight of the fact that there's other aspects of intellectual work in all disciplines that are still important. That theory still has a role. That model building still has a role that, uh, knowing what questions to ask, it's still important that hypotheses still matter. I'm not so sure that it's so much an issue of drawing arbitrary limits around it, but rather of being knowledgeable and critical users of the tools and the approaches that are offered. Speaker 6: Because in terms of domain [00:10:00] applications, I actually recently saw at the strata conference, which is one of these more industry oriented big data conferences that took place a few weeks ago in Silicon Valley. It's in Santa Clara. One of the best talks that I saw at the conference was an analysis half the poem, if I told him that Gertrude Stein wrote about Picasso After Picasso painted this very famous portrait of her. And that poem has a very, very repetitive rhythmic structure. It has very few words and it's a long poem with a very peculiar linguistic structure. And [00:10:30] this hardest, I, I'm blanking on his name right now, but he's an artist who works kind of at the intersection of digital arts in, in linguistics wrote basically a custom one off visual analysis and visualization tool to work on the structure of this poem to visualize it, to turn it into music. Speaker 6: And it was a beautiful talk. It was a beautiful and very interesting talk and this was kind of the exact opposite of this was tiny data. This was one poem and in fact during the Q and a they asked him and he said, well I've tried to use the tool [00:11:00] on a few other things and there's a few songs in hip hop that it works well with, but it's almost, it's almost custom made for this one poem, right? So this was sort of tiny data, completely non generalizable and yet I thought it was fascinating and beautiful talk. So that's kind of an example that I would have never have thought of as as data science. Any examples of misapplication? Speaker 5: I think we can admit that data science is a buzzword that is [00:11:30] exactly through, it's almost indefinable nature creates space for people to do methodologically problematic and in many cases also uninteresting work. Just throwing data into an analysis without asking is this the right analysis will get you stupid or misleading answers. It's the garbage in out principle. So yeah, like any intellectual tool in the toolkit, [00:12:00] there are misleading conclusions that can be drawn and one of the powers that Berkeley brings to this effort in data science is a focus on the methodology, the intelligent development of methodology along with just building things that look like tools on their own. I think that's going to be the place with the sweet spot for universities because of the emphasis on rigor and stringency and reasoning [00:12:30] along with just getting out results that look good and are attractive Speaker 7: with data science. Are there infrastructure challenges that are worth talking about either in industry or at an academic institution? Because I know that computing power now through Amazon, Google organizations like that are enormous and so industry is sort of giving up the idea of having their own [00:13:00] computational capacity and they're using cloud virtual universities I would think are following suit. Speaker 6: Yes, there is work being done already on campus in that regard. We've had some intersection with those teams. The university right now, uh, we've had since last year a new CIO on campus, Larry Conrad, who's been spearheading an effort to sort of reimagine what the research computing infrastructure for campus should look like. [00:13:30] Considering these questions precisely of what is happening in industry, what are the models that are successfully being used at other institutions to provide larger scales off competitional resources across all disciplines and beyond the disciplines that have been traditionally the ones that have super computers. Well, there's a long history of departments, again, like physics, like competition, fluid dynamics, teams like quantum chemistry teams that have had either their own clusters or that have large budgets who have access to the supercomputing centers at [00:14:00] the doe labs and things of that nature. But as we've been saying today, all of a sudden those needs are exploding across all disciplines and the usage patterns are changing and that often what is the bottleneck is maybe not the amount of raw compute power, but the ability to operate over a very large data sets, so maybe storage is the issue or maybe throughput biologists often end up buying computers that look really weird. Speaker 6: Too many supercomputing centers because they, the actual things that they need are skewed in a different way and so there are certainly [00:14:30] challenges in that regard when we do know that Berkeley is right now at least in the midst of making a very concerted and serious attempt at at least taking a step forward on this problem. Speaker 7: A lot of data is derived from personal information. Are there privacy concerns that you have [inaudible] Speaker 5: they're all quite definitely in so many different ways that the input of experts who have thought about questions of consent, of privacy, [00:15:00] of the challenges around keeping de identified data d identified when it is possible through analytics to understand what patterns are emerging from them that is going to be so key. Especially to working with social data. And so one of the still open questions for all of us working with data that is about people is how to develop the practices that will do the protections necessary [00:15:30] in order to avoid the kinds of catastrophic misuses and violations of privacy that many of us do. Fear will be coming our way as so much data becomes available so fast with so many invitations to just make use of it and worry about the consequences later. That's not the responsible way forward. And I would like to see bids and Berkeley take on that challenge as part of its very deliberate agenda. Speaker 8: [00:16:00] Okay. Spectrum is a public affairs show on k a l ex Berkeley. Our guests are Cathryn Carson and Fernando Perez. In the next segment they talk about institutional reactions to bids. Oh, Speaker 7: are there any impediments that you've run into within the bids process [00:16:30] of getting up and running? Cause it's been going since, uh, Speaker 5: it's not been going on that long as it, it's only December of 2013. Pretty recent, but I'm sure there's gotta be some institutional pushback or no, it's, it's been incredible actually how much support the institution has given. What bids is though, is a laboratory for the kind of collaboration that we're trying to instantiate. And so you have 13 brilliant Co-pi eyes each with their own vision and figuring out where [00:17:00] the intersection is and how to get the different sets of expertise and investments where they, where those intersections lie and how to get them aligned. I mean, that's, that's one of the fascinating challenges in front of beds as a laboratory in the small, for the process at large that we're trying to do Speaker 7: on the tools and programming side. How would you break up what languages are providing, what kind of capability, [00:17:30] and are there new languages that are ascendent and other languages that are languages that are losing their grip? I'm sort of curious. It's a, it's another trivia questions that I think might have some interest for people. No, I think there's, there's clearly an ascendance. I think naturally the expansion of the surface of people interested in these problems Speaker 6: is naturally driving the growth and importance of high level languages that are immediately usable by domain scientists. We're not full time programmers [00:18:00] and professional programmers. Traditionally a lot of the high end computing had been done in languages like c, c plus plus for trend and some Java that are languages that tend to be more the purview of, of people who do lots of software development. And a lot of that did happen in departments like physics and chemistry and computer science, but not so much in other disciplines. And so we're seeing the rise of open source languages like Python and r that are immediately applicable and easy to use for data analysis where a few commands [00:18:30] can load a file, compute some statistics on it, produce a few visualizations, and you can do that in five lines of code, not having to write a hundred or 500 lines of c plus plus. Speaker 6: Right. And so the languages like that are, they're not new. Both I think are came out in the late eighties early nineties python came out in 1991 but they're seeing a huge amount of growth in recent years for this reason. There's also a growth of either new tools to extend these languages [00:19:00] or new languages as well. Tools for example, that connect these languages to databases or extensions to these languages to couple them to databases in better ways so that people don't have to only write raw sequel, which SQL is not the classic language for interacting with databases, so extensions to couple existing languages to database back ends. A lot of work is being done in that direction and there are some novel languages. For example, there's a team at MIT that about two years ago started [00:19:30] a project for a new language called Julia that is aimed at numerical computing, but it's sort of re-imagining. Speaker 6: What would you do if you wanted to create a language like python with the strengths of language like python or Ruby or r, but if you were doing that today with the lessons of the last 20 years, that would be good for numerical computing, but it would be easy to use for domain scientists. That would be high level, that would be interactive, that would feel like a scripting tool, but that would also give you very high performance. [00:20:00] If you had the the last 20 years of lessons and the advances in some of the underlying technology and improved compiler machinery that we have today, how would you go about that problem? And I think the Giulia team at MIT is making rapid progress and it has caught the intention of people in the statistics community of people in the numerical analysis and algorithms community. Some prominent people have become very interested in how to become active participants in its development. Speaker 6: So we're seeing both mature tools like python and are growing in their strength and and their importance. At the latest Strada Conference, [00:20:30] for example, there was a an analysis of kind of the the abstracts submitted that had r and python in their names versus things like excel or sequel or Java and Python and are clearly dominating that space, but also these, these kinds of more novels, sort of research level languages that whose futures still not clear because they're very, very young, but at least they're exploring sort of the frontier of what will we do in the next five or 10 years. And is this an area that's ripe for a commercial software creators who develop [00:21:00] a tool that would be specific to data science and sort of the same way that Mat lab is kind of specific now it's kind of a generic tool for mathematics. Obviously my answer here is extremely biased, but I'm, I sort of think that the space for a, the window to create a proprietary data science language is closed already. Speaker 6: I think the community simply would not adopt a new one. There are some existing successful ones such as mat lab, IDL, which is smaller than Madlib. It is widely used in the astronomy and astrophysics. [00:21:30] And Physics Communities Mathematica, which is a project that came out of the mathematics and physics world and that is very, very sophisticated and interesting. Maple, which is also a mathematics language. Those are successful existing proprietary languages. I think the mood has changed to these are products that came out in the eighties and the nineties. I think the, the window for that, uh, as a purely proprietary offer has closed. I think what we're going to see is the continued growth and the rise potential. You have new entrants that are fundamentally [00:22:00] open source, but yet that maintain, as I said earlier, a healthy dialogue with industry because it doesn't mean, for example, in the art world there are companies that build very successful commercial products around are there is a product called r studio that is a development environment for analysis in our, and that's a company, there's a company called I think revolution analytics. Speaker 6: I think they built some sort of sort of large scale backend high-performance version of our, I don't know the details, I don't use it, but I've seen their website. I think they're a large company that builds kind of our for the enterprise. So I think [00:22:30] that's what we're going to see moving forward at the base. People want the base technology, the base language to be open source. And I think for us as universities and for me as a scientist, I think that's a Tenet I'm not willing to compromise on because I do not want a result that I obtain or result that I published or a tool that I educate my students with to have a black box that I'm legally prevented from opening and to tell my student, well, this is a result about nature, but you can't understand how it was achieved because you are legally prevented from opening the box. [00:23:00] I think that is fundamentally unacceptable. But what is, I think a perfectly sensible way forward, is to have these base layers that are open on top of which domain specific tools can be created by industry that add value for specific problems, for specific domains that may be add performance, whatever. Catherine Carson and Fernando Perez. Thanks very much for coming on spectrum. Thanks for having us here. Thanks much. Speaker 8: [inaudible]Speaker 9: [00:23:30] all spectrums. Past shows are archived on iTunes university. We've created a simple link for you. The link is tiny url.com/k Speaker 1: a l x Speaker 8: spectrum Speaker 1: Rick Curtis Skin. I will present a few of the science and technology events [00:24:00] happening locally over the next two weeks. Speaker 10: Counter culture, labs and pseudo room present gravitational waves, results and implications with Bicep to collaborator Jamie Tolan at the pseudo room, hackerspace to one 41 Broadway in Oakland on Sunday, April 27th at 7:00 PM recently, scientists from the Bicep to experiment recorded their data findings demonstrating [00:24:30] evidence of gravitational waves that may imply cosmic inflation. The bicep to experiment is an international collaboration of research and technology from many institutions including a team at Stanford University work. Jamie Tolan works. Jamie will discuss the results of the bicep two experiment and its scientific contribution to current theories that attempt to explain the why, what and how of our universe. The event will be free. Speaker 1: On April 30th UCLA professor [00:25:00] of geography, Jared diamond will give this year's Horace m Albright Lecture in conversation. Diamond is best known for his Pulitzer Prize winning book, guns, germs and steel and this lecture he will discuss his newest book, the world until yesterday, what we can learn from traditional societies. The book is about how traditional peoples differ from members of modern industrial societies and their reactions to danger. He will then produce B in a question answer session with the audience doors open at 6:00 PM [00:25:30] the event is free and open to the public on a first come first served basis will be held Wednesday, April 30th from seven to 8:30 PM in the International House Auditorium at two two nine nine Piedmont Avenue Berkeley. Speaker 10: The theme of Mays science at the theater is science remix. Joined Berkeley lab scientists at the East Bay Center for the Performing Arts in Richmond, California on May 1st at 7:00 PM they'll discuss how discovery [00:26:00] happens. Help you show what science means to you and reveal why science can be as personal as you want it to be. Light refreshments will be served, but bring your imagination and participate at this free event. Speaker 1: A feature spectrum is to present new stories about science that we find particularly interesting. Rick Carnesi joins me in presenting the news. Speaker 10: Nature News reported on April 13th that a team of scientists from [00:26:30] Caltech have estimated that Mars's atmosphere was probably never thick enough to keep temperatures on the planet surface above freezing for very long. Edwin kite now at Princeton used from the Mars reconnaissance orbiter to catalog more than 300 craters and an 84,000 square kilometer area near the planets equator. The sizes of the creators were compared to computer models with varying atmospheres. Dance [00:27:00] or atmospheres would have broken up small objects as they do on earth, but the high frequency of smaller craters on Mars suggest the upper limit of atmospheric pressure on Mars was only one or two bar. This most likely means a temperatures on Mars have typically been below freezing. Did the team notes that their findings do allow the possibility of scenarios of Mars having a slightly thicker atmosphere at times. Do you perhaps to volcanic activity or gas is released by the large impact events and these could have [00:27:30] made Mars warmer for decades or centuries at a time, allowing water to flow. Then Speaker 1: science daily reports one of the first social science experiments to rest on. Big Data has been published in plus one. A chair of investigators from Simon Fraser University analyzed when humans start to experience and age-related decline in cognitive motor skills. The researchers analyze the digital performances of over 3000 starcraft two players, age 16 to 44 starcraft two is a ruthless intergalactic computer [00:28:00] game that players often undertake to win serious money. Their performance records, which can be easily accessed, represent thousands of hours worth of strategic real time. Cognitive based moves performed at various skill levels using complex statistical modeling. Researchers distilled meaning from this colossal compilation of information about how players responded to their opponents and more importantly, how long they took to react after around 24 years of age, players show slowing and a measure of cognitive speed that is known to be important for performance. [00:28:30] Explains Joe Thompson lead author of the study. This cognitive performance decline is present even at higher levels of skill, but there's a silver lining in this earlier than expected slippery slope into old age. Thompson says older players, those slower seem to compensate by employing simpler strategies and using the games interface more efficiently. The younger players enabling them to retain their skill despite cognitive motor speed losses. These findings says Thompson suggests that our cognitive motor capabilities are not stable across our adulthood, but are constantly [00:29:00] in flux and that our day to day performance is a result of the constant interplay between change and adaptation. Speaker 2: [inaudible]Speaker 11: and music heard during this show was written and produced by Alex Simon. Today's interview was edited by Rene Rau. Thank you for listening to spectrum. If you have comments about the show, please send them to us via email or email [00:29:30] address is spectrum dot kalx@yahoo.com join us in two weeks at this same tone. [inaudible]. Hosted on Acast. See acast.com/privacy for more information.

Der brandneue Trailer zu THE RETURN OF THE FIRST AVENGER - Ab 27. März 2014 im Kino! Werdet Fan auf: http://www.facebook.com/Marveldeutschland Folgt CAPTAIN AMERICA auf: https://twitter.com/MarvelDE

Der erste Avenger - CAPTAIN AMERICA - kehrt zurück - Ab 27. März 2014 im Kino! Werdet Fan auf: http://www.facebook.com/Marveldeutschland Folgt CAPTAIN AMERICA auf: https://twitter.com/MarvelDE

DIE EISKÖNIGIN - VÖLLIG UNVERFROREN - ab 28. November 2013 im Kino - sowohl in 2D als auch 3D! Folge uns auf facebook: http://www.facebook.com/disneydeutschland Offizielle Webseite: http://www.disney.de/eiskönigin

DIE EISKÖNIGIN - VÖLLIG UNVERFROREN - ab 28. November 2013 im Kino - sowohl in 2D als auch 3D! Folge uns auf facebook: http://www.facebook.com/disneydeutschland Offizielle Webseite: http://www.disney.de/eiskönigin

DIE EISKÖNIGIN - VÖLLIG UNVERFROREN - ab 28. November 2013 im Kino - sowohl in 2D als auch 3D! Folge uns auf facebook: http://www.facebook.com/disneydeutschland Offizielle Webseite: http://www.disney.de/eiskönigin

DIE EISKÖNIGIN - VÖLLIG UNVERFROREN - ab 28. November 2013 im Kino - sowohl in 2D als auch 3D! Folge uns auf facebook: http://www.facebook.com/disneydeutschland Offizielle Webseite: http://www.disney.de/eiskönigin

DIE EISKÖNIGIN - VÖLLIG UNVERFROREN - ab 28. November 2013 im Kino - sowohl in 2D als auch 3D! Folge uns auf facebook: http://www.facebook.com/disneydeutschland Offizielle Webseite: http://www.disney.de/eiskönigin

DIE EISKÖNIGIN - VÖLLIG UNVERFROREN - ab 28. November 2013 im Kino - sowohl in 2D als auch 3D! Folge uns auf facebook: http://www.facebook.com/disneydeutschland Offizielle Webseite: http://www.disney.de/eiskönigin

DIE EISKÖNIGIN - VÖLLIG UNVERFROREN - ab 28. November 2013 im Kino - sowohl in 2D als auch 3D! Folge uns auf facebook: http://www.facebook.com/disneydeutschland Offizielle Webseite: http://www.disney.de/eiskönigin

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Ein Blick hinter die Kulissen bei den Synchronarbeiten zu DIE MONSTER UNI Disney/Pixar - DIE MONSTER UNI 3D Ab 20. Juni 2013 im Kino in Disney Digital 3D! http://www.facebook.com/diemonsteruni http://www.disney.de

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Disney/Pixar - DIE MONSTER UNI 3D - Offizieller Trailer 3 Ab 20. Juni 2013 im Kino in Disney Digital 3D! http://www.facebook.com/diemonsteruni http://www.disney.de

Christopher Eccleston talks to Al Senter about his recent role in Antigone as well as his stage, film and television career. This video is formatted for iPod/iPhone.