这是一篇测试的内容 一、道路交通安全法律法规交通信号• 信号灯规则：红灯停、绿灯行、黄灯警示；箭头灯与圆盘灯区别（如右转箭头红灯需停车）。• 标志标线：o 禁令标志（禁止左转、停车等）、警告标志（注意行人、急弯）、指示标志（直行车道、人行横道）。o 标线分类：虚实线（虚线可跨越、实线禁止）、导流线（禁止压线或停车）。通行规则• 让行原则：右方来车先行（无信号灯路口）、转弯让直行、辅路让主路。• 车道规则：机动车道、非机动车道划分；超车需左侧超车。• 特殊路段：学校区域限速30km/h，人行横道前需减速或停车让行。违法行为与处罚• 扣分重点（2024新规）：o 12分：酒驾、超速50%以上、伪造号牌、高速公路倒车。o 9分：遮挡号牌、驾驶与准驾车型不符车辆。o 6分：占用应急车道、违反信号灯、超速20%~50%。• 罚款常见项：未系安全带（驾驶人罚50元）、开车打电话（罚200元扣3分）。二、上海地方性交通规定限行政策• 外地车牌限行：工作日上午7:00-10:00、下午15:00-20:00，内环内（含高架）禁止通行。• 沪牌限行：部分高架路段早晚高峰限行（如南北高架7:00-10:00）。• 新能源车政策：专用牌照申领条件、部分区域免费停车等。特殊标志与标线• 潮汐车道：根据时段调整行驶方向，需注意LED指示牌。• 左转待转区：直行绿灯亮时，左转车辆可进入待转区等候。• 公交专用道：工作日7:00-10:00、16:00-19:00禁止社会车辆占用。三、安全驾驶与应急处置安全行车常识• 跟车距离：车速≥100km/h时，保持100米以上距离。• 恶劣天气：雾天开雾灯+危险报警闪光灯，能见度<50米时最高车速20km/h。事故处理流程• 轻微事故：拍照取证后撤离现场，走“快处易赔”线上处理。• 人员伤亡事故：立即报警、保护现场、抢救伤员（移动前标记位置）。紧急情况处置• 爆胎：紧握方向盘，点刹减速，禁止急刹车。• 制动失效：抢挂低速挡+手刹辅助，利用避险车道或障碍物减速。四、机动车基础知识车辆装置与检查• 必备装置：灭火器、三角警示牌（普通道路摆放在车后50米，高速150米）。• 定期检验：小型车6年免检（每2年申领检验标志），逾期未检扣3分罚200元。仪表与指示灯• 关键指示灯：机油压力报警（红色）、ABS故障（黄色）、安全带未系提示。• 车速表与实际车速：通常表显车速比实际高5%-10%。五、考试技巧与注意事项答题策略• 排除法：先排除明显错误选项（如“直接加速通过”多为错误）。• 关键词定位：“减速”“观察”“让行”多为正确选项。易错题整理• 优先级混淆：无信号灯路口，让右方来车先行（非左方）。• 限速陷阱：城市道路无中心线最高30km/h，有中心线最高50km/h。 附：高频考点速记口诀• 左转大弯右转小弯，实线不压虚线可越。• 站30口50，停车远离公交站和路口。• 能见度200-100-60，车速60-40-20，车距100-50-离开。建议结合官方教材与本地题库查漏补缺，预祝考试顺利！分• 驾驶行为类o 饮酒后驾驶机动车。o 驾驶与准驾车型不符的机动车。o 造成交通事故后逃逸，尚不构成犯罪。o 驾驶机动车在高速公路上倒车、逆行、穿越中央分隔带掉头。• 证件使用类o 使用伪造、变造的机动车号牌、行驶证、驾驶证、校车标牌或者使用其他机动车号牌、行驶证。o 代替实际机动车驾驶人接受交通违法行为处罚和记分牟取经济利益。• 车速及载客类o 驾驶营运客车（不包括公共汽车）、校车载人超过核定人数 20％以上。o 驾驶中型以上载客载货汽车、校车、危险物品运输车辆在高速公路、城市快速路上行驶超过规定时速 20％以上或者在高速公路、城市快速路以外的道路上行驶超过规定时速 50％以上。分• 车型及车速类o 驾驶 7 座以上载客汽车载人超过核定人数 50% 以上未达到 100%。o 驾驶中型以上载客载货汽车、校车、危险物品运输车辆在高速公路、城市快速路以外的道路上行驶超过规定时速 50％以上。• 车辆及停车类o 驾驶机动车在高速公路或者城市快速路上违法停车。o 故意遮挡、污损、不按规定安装机动车号牌。• 证件及安全类o 未取得校车驾驶资格驾驶校车。o 连续驾驶中型以上载客汽车、危险物品运输车辆超过 4 小时未停车休息或者停车休息时间少于 20 分钟。分• 车速及载客类o 驾驶营运客车（不包括公共汽车）、校车载人超过核定人数未达 20% 的，或者驾驶其他载客汽车载人超过核定人数 20% 以上。o 驾驶中型以上载客载货汽车、校车、危险物品运输车辆在高速公路、城市快速路上行驶超过规定时速未达 20%。• 安全及应急类o 驾驶机动车运载超限的不可解体的物品，未按指定的时间、路线、速度行驶或者未悬挂明显标志。o 驾驶机动车载运爆炸物品、易燃易爆化学物品以及剧毒、放射性等危险物品，未按指定的时间、路线、速度行驶或者未悬挂警示标志并采取必要的安全措施。o 驾驶机动车在高速公路或者城市快速路上违法占用应急车道行驶。• 其他类o 驾驶机动车违反道路交通信号灯通行。o 机动车驾驶证被暂扣期间驾驶机动车。o 造成致人轻微伤或者财产损失的交通事故后逃逸，尚不构成犯罪。分• 车速及载客类o 驾驶中型以上载客载货汽车、危险物品运输车辆在高速公路、城市快速路以外的道路上行驶超过规定时速未达 20%。o 驾驶其他机动车行驶超过规定时速 20% 以上未达 50%。o 驾驶校车、公路客运汽车、旅游客运汽车、7 座以上载客汽车以外的其他载客汽车载人超过核定人数 20% 以上未达到 50%。• 安全及行驶类o 驾驶机动车在高速公路上行驶低于规定最低时速。o 驾驶机动车在高速公路或者城市快速路上不按规定车道行驶。o 驾驶机动车行经人行横道，不按规定减速、停车、避让行人。o 驾驶机动车不按规定超车、让行，或者在高速公路、城市快速路以外的道路上逆行。o 驾驶机动车违反禁令标志、禁止标线指示。• 其他类o 驾驶机动车不按规定安装机动车号牌。o 驾驶机动车有拨打、接听手持电话等妨碍安全驾驶的行为。o 驾驶机动车在道路上车辆发生故障、事故停车后，不按规定使用灯光和设置警告标志。分• 行驶行为类o 驾驶机动车行经交叉路口不按规定行车或者停车。o 驾驶机动车有拨打、接听手持电话等妨碍安全驾驶的行为。o 驾驶机动车在高速公路或者城市快速路上行驶时，驾驶人未按规定系安全带。• 安全及操作类o 驾驶机动车遇前方机动车停车排队或者缓慢行驶时，借道超车或者占用对面车道、穿插等候车辆。o 不按规定为校车配备安全设备，或者不按规定对校车进行安全维护。o 驾驶机动车时，违反道路交通信号灯通行以外的其他违反交通信号的行为。科目一考试中有关于扣 1 分的相关规定，具体如下7：校车、中型以上客货车、危险品运输车超速：驾驶校车、中型以上载客载货汽车、危险物品运输车辆在普通道路上超速 10%-20%。会车、倒车、掉头违规：驾驶机动车不按规定会车，或者在高速公路、城市快速路以外的道路上不按规定倒车、掉头。灯光使用不当：不按规定使用灯光。违反禁令标志、标线：驾驶机动车违反禁令标志、禁止标线指示。载货违规：载货长宽高超过规定尺寸；载货超重未达 30%。车辆未定期检验：驾驶未按规定定期进行安全技术检验的公路客运汽车、旅游客运汽车、危险物品运输车辆以外的机动车上道路行驶。擅自改造货车：擅自改造货车上路。未系安全带、未戴头盔：驾驶机动车在道路上行驶时，机动车驾驶人未按规定系安全带；驾驶摩托车不戴安全头盔。基本概念• ABS 全称：Anti-lock Braking System，即防抱死制动系统4。• 工作原理：利用电子控制器，通过传感器实时监测车轮速度，当某个车轮即将抱死时，ABS 系统会迅速通过减少或增加制动力来防止轮胎抱死，使车辆更加稳定2。功能作用• 防止车轮抱死：在紧急制动时，能避免车轮被完全锁死，确保车轮处于边滚边滑的状态，从而保持车辆的转向能力。比如在遇到突发情况急刹车时，如果没有 ABS 系统，车轮抱死，车辆就无法按照驾驶员的意愿转向14。• 提高制动稳定性：使车辆在制动过程中能保持直线行驶，减少侧滑、甩尾等危险情况的发生，尤其是在湿滑路面、冰雪路面等附着力较低的路况下，效果更为明显124。• 并非缩短制动距离：一般情况下，说 ABS 能缩短制动距离是不准确的，有时在不平整道路、有沙砾或积雪的道路上，ABS 的运行可能导致制动距离比没有安装 ABS 的车辆长一些4。使用方法及注意事项• 正确操作：驾驶装有 ABS 的车辆，紧急制动时应果断地一脚将制动踏板踩到底，不要松开，让 ABS 系统自动发挥作用，而不要像驾驶没有 ABS 的车辆那样，通过来回踩制动踏板来试图提高制动效果，这种操作会阻碍 ABS 系统正常工作4。• 工作反馈：ABS 系统工作时，会产生适当的噪音，制动踏板也会产生脉动而反复顶脚，这是正常现象，驾驶员不必惊慌，说明系统正在自动调节制动油压4。• 故障判断：如果仪表盘上的 ABS 故障警告灯点亮，说明 ABS 系统可能出现故障，此时应将车平稳驶到正规的维修店进行检修；若 ABS 和手刹灯同时点亮，表明车辆的制动系统可能有严重问题，应立即减速靠边，等待救援4。边滑的状态，从而保持车辆的转向能力。比如在遇到突发情况急刹车时，如果没有 ABS 系统，车轮抱死，车辆就无法按照驾驶员的意愿转向14。提高制动稳定性：使车辆在制动过程中能保持直线行驶，减少侧滑、甩尾等危险情况的发生，尤其是在湿滑路面、冰雪路面等附着力较低的路况下，效果更为明显124。并非缩短制动距离：一般情况下，说 ABS 能缩短制动距离是不准确的，有时在不平整道路、有沙砾或积雪的道路上，ABS 的运行可能导致制动距离比没有安装 ABS 的车辆长一些4。使用方法及注意事项正确操作：驾驶装有 ABS 的车辆，紧急制动时应果断地一脚将制动踏板踩到底，不要松开，让 ABS 系统自动发挥作用，而不要像驾驶没有 ABS 的车辆那样，通过来回踩制动踏板来试图提高制动效果，这种操作会阻碍 ABS 系统正常工作4。工作反馈：ABS 系统工作时，会产生适当的噪音，制动踏板也会产生脉动而反复顶脚，这是正常现象，驾驶员不必惊慌，说明系统正在自动调节制动油压4。故障判断：如果仪表盘上的 ABS 故障警告灯点亮，说明 ABS 系统可能出现故障，此时应将车平稳驶到正规的维修店进行检修；若 ABS 和手刹灯同时点亮，表明车辆的制动系统可能有严重问题，应立即减速靠边，等待救援4。

2-02 聊聊Sora – 视频生成里程碑是不是名副其实 最近Sora的热度还没有褪去，我们也来聊聊从使用者的角度，Sora在未来究竟能带给我们什么。在Sora发布的第一时间，国内的AI知名博主有欣喜，有惊叹，但是大部分我刷到的视频都在表达一个观点，就是焦虑，他们有谈到科技无国界，但是AI有国界的论点，这个观点，我在圈子的沟通里面也有提到过，国内很多开发大模型的公司，一直妄想的弯道超车的梦想被浇灭了。这些大佬们，在Sora发布之后，已经感受到了跨纬度的打击。我们今天不聊技术层面的事情，我们还是从使用者的角度出来，来看看，Sora的发布究竟意味着什么。先说说我自己的看法，Sora的发布，对于影视行业，内容创作领域都会带来巨大的变化，其中从当前发布的介绍来看，对于大部分的市面上的应用而言，几乎是颠覆性的创新。当然，我其实在第一时间，业找了很多渠道，没有发现具体的Sora应用，可能也是我渠道有限吧，有博学广闻的同学，欢迎大家留言找到的应用，可以方便大家快速应用。从内容创作和视频拍摄从业者的角度出发，我认为最重要的意义，是【效率】工作效率的提升： Sora的出现使得视频内容的创作过程更加高效。以前，视频制作需要花费大量的时间和精力，但现在，借助Sora等工具，用户可以在短时间内生成高质量的视频内容，大大提升了工作效率。 大家可能需要理解，短视频的平均时长是30秒，中长视频在5分钟左右。所以对于一分钟生成原创视频的能力，意味着可能需要几段提示词，配合文稿，就可以完成当前主流的短视频和中视频的制作了，这对于那些需要频繁发布内容或者追求内容更新速度的人来说是一个重要的创新。人员效率提升： 传统上，视频制作需要专业的技能和软件，而Sora的发布，伴随着对应应用的发布。无形中可以将人员的效能带来提升，例如你是一个文案人员，你本来需要为文案配置视频素材，可能有了Sora，这一块的人力成本和时间成本，都可以带来压缩，也许一个人就可以完成了。她没有专业的视频编辑和视频拍摄技能，也可以轻松地生成高质量的视频内容。创意表达的多样化： 一个人的创意总归是有瓶颈的，我们可以使用Sora来无限地提供创意，而对于使用者来说，只要有足够的审美和判断能力，即轻松地表达自己的创意并针对地选择素材。无论是想要制作宣传视频、教育视频还是个人Vlog，都可以通过Sora找到合适的模板和风格，实现创意的多样化表达。还有一个非常重要的事情，就是【版权】【版权】【版权】，这个问题的严重性，我就不过多描述了，内容创作者们应该都懂。而对于我们一般的用户而言，关键词就是【提升素养和降低门槛】 【降低门槛】，其实跟提高效率的理解比较相似，对于野生博主，我们平时会花大量的时间找视频素材，力求切合主题和兼具美观，使用Sora之类的软件，只是从创意的角度出发，就不再会存在合不合适的困扰了，只有好不好的问题了。而且调整起来也比较快，这样一来，可以顺利创作质量较高的视频作品了。【提升素养】这里其实是说明，我们不仅可以制作出高质量的视频内容，还能够通过学习视频编辑等技术，提升自己对数字媒体和人工智能的理解和应用能力，从而增强自己在人工智能时代的竞争力。我还是相信在这个时代，拥有良好的数字素养对个人发展至关重要。其实我现在就已经在想象，在Sora的应用能发布出来以后，我以后的创作会是什么样子的了。第一步、我使用ChatGPT，帮我整理一下大纲，调整一下文字描述，对于一些内容进行补充第二步、我们通过Sora去生成对应的视频。第三步、通过invideo，整合文案和视频，生成完整的视频去发布。这几步，预计只要半个小时，就已经完成了视频的创作。"Sora的问世，无疑将开启一个全新的数字内容创作时代。但是，我们对于未来的探索才刚刚开始。在人工智能创新的路上，Sora只是一个起点，未来的发展将带来更多的惊喜与挑战。让我们一起拭目以待，见证未来的发展，探索更广阔的创作天地！"在创造力与智慧的交融中，我们发现了Sora所带来的内容创新的真谛。Sora的发布不仅仅是一次产品上线，更是对数字创作时代的一次革命性颠覆。它以创意为灵感，以智慧为引擎，为用户们开启了一场创作的盛宴。从AI圈内的震撼到内容创作者的思考，Sora的影响力超越了产品本身。它为我们带来了工作效率的飞跃提升，让原本繁琐的创作过程变得高效而有趣；它为我们拓展了创意的表达空间，让每个人都能成为创意的发现者和实现者；同时，它也在提升我们的数字素养，引领我们走向数字创作的未来。正如爱因斯坦所说，创造力就是智慧的乐趣。Sora的发布，为我们打开了一扇通往创意无限的大门，让我们能够尽情享受创作的乐趣，展现出无限的创造力。让我们怀揣着饥渴的心态，继续探索、创新，在Sora的引领下，开启数字创作的新时代。感谢大家的观看，我们后续再发布一个视频，给大家一步步地演示一下，如何通过半个小时，创作一个原创视频，敬请期待。最后希望大家能够点赞，留言，关注，收藏，大家的支持是我们持续创作的动力。

什么是人工智能 人工智能可谓是计算机科学的一个大范畴，旨在创造能够自主发挥智能功能的系统，与人类进行语言交流。咱们来看看其中的一些分支。语音识别领域首先，有语音识别领域，就是让机器能听懂人说话，也能够回应。想象一下你和手机的语音助手交流，就是这方面的应用。想象一下，你正准备出门，你对着手机说：“嘿，智能助手，明天的天气怎么样？” 这时，语音识别技术开始发挥作用。你的声音被传输到系统中，经过复杂的算法和模型分析，智能助手成功将你的口头语言转化为文字，并理解了你的请求。接下来，它通过与天气数据库的连接，找到了明天的天气预报，并以口头语言回应你：“明天将会是晴天，最高气温25摄氏度。” 这一切都是语音识别领域的体现。自然语言处理（NLP）领域然后是自然语言处理（NLP）领域，让机器能够理解和使用语言，就像我们平时书写和阅读文本一样。这使得聊天机器人能够理解你的问题并做出合理回答。当你正坐在电脑前，对着机器人输入文字：“今天有什么新鲜事？” 这一切都牵扯到了NLP的技术。你的文字输入被计算机理解，系统能够识别你的询问并作出恰当的回应，仿佛是与一个了解你言辞、语气的朋友对话一般。在社交媒体上，NLP技术也发挥着关键作用。当你在评论朋友的照片时，系统可以分析你的语言，了解你的情感，从而更好地理解你对照片的反馈。这使得社交媒体平台能够根据用户的兴趣和喜好为其个性化推荐内容。计算机视觉领域计算机视觉则关注机器如何通过处理图像信息来理解和模拟人类视觉。当你的手机相机能够自动识别人脸或者车辆时，就是计算机视觉在发挥作用。想象一下，你正在使用智能手机的相机拍摄一张照片。计算机视觉技术立即介入，分析这张照片中的各种元素。它可以识别图像中的人物、物体和背景，甚至可以自动调整相机设置以获得更佳的照片效果。这样的技术使得拍照变得更加智能和便捷。在安防领域，计算机视觉也起到了关键作用。想象一下，在一个商业区域的监控摄像头中，计算机视觉系统能够检测异常行为，如盗窃或破坏，然后及时发出警报。这种技术的应用使得安全监控更加智能化，提高了反应速度和准确性。图像处理领域图像处理领域涉及如何处理和编辑图像，虽然它与人工智能直接挂钩较小，但对于计算机视觉的完整性是必要的。当你拍摄了一张照片，但由于光线不足，整个画面显得较暗。这时，图像处理技术就能派上用场。通过一系列的算法，计算机可以智能地调整图像的亮度和对比度，使得照片更加清晰、生动，仿佛重新捕捉到了光影的美妙。在医学影像方面，图像处理也发挥着巨大的作用。想象一下，医生需要对一张CT扫描图进行分析，但由于图像质量问题，一些细节可能被掩盖。图像处理技术可以帮助突显重要的结构，使医生更容易检测到潜在的病变或异常。机器人行为学习机器人行为学习使机器能够理解周围环境，并以流畅的方式移动，有点像我们人类能够在复杂环境中自如行走的感觉。想象一下，你的家里有一台智能扫地机器人。刚开始，它可能并不知道房间的布局，但通过机器人行为学习的技术，它能够逐渐学习并记住家里的每一个角落。当机器人开始扫地时，它能够智能地避开家具和其他障碍物，优化清扫路径，从而提高清扫的效率。在仓储和物流领域，机器人行为学习也带来了革命性的变化。机器人可以通过学习仓库中物品的摆放和货物的移动规律，智能地规划路径，实现高效的仓储管理和货物运输。在服务业中，像餐厅和酒店等场景中的服务机器人也是机器人行为学习的典型应用。它们能够通过与顾客的互动学习，适应不同环境下的需求，提供更个性化和友好的服务体验。模式识别（特征识别）模式识别则涉及到机器如何辨别并理解数据中的模式，就像我们能够把相似的事物放在一起。在金融领域，模式学习技术被广泛用于检测欺诈行为。银行可以通过学习正常的交易模式，发现异常模式，从而迅速识别可能的欺诈活动。这种技术的应用使得金融机构能够更加迅速和准确地应对潜在的风险。而在市场营销领域，模式学习技术可以用于分析消费者行为。通过学习消费者的购物模式和偏好，商家能够更好地理解市场需求，提供个性化的推荐和服务，从而提升客户满意度和销售效益。神经网络领域神经网络则是一种模拟人脑结构和功能的方式，通过学习和适应，机器可以具备某种认知能力。而深度学习则是在神经网络的基础上更深入、更复杂的学习过程，这使得机器能够处理更加复杂的任务和数据。这一段的案例没有办法那么通俗易懂，想象一下，你正在训练一台神经网络来辨识猫和狗的照片。一开始，这个神经网络就像是一张白纸，一无所知。你向它展示了一堆猫和狗的图片，并告诉它哪些是猫，哪些是狗。通过这个过程，神经网络开始学习图片中的各种特征，比如猫耳朵的形状、狗的尾巴的长度等。机器学习领域在机器学习的范畴下，有监督学习，就是通过大量标记好的数据来教机器学习，比如训练机器识别朋友的名字。还有强化学习，这就像是给机器设定一个目标，然后机器通过试错来不断优化，就好比让机器人尝试翻墙直到成功的例子。说一个贴近生活的例子，假如你有一堆关于房价的真实数据，包括房屋的大小、位置、周围环境等信息，以及相应的房价。通过机器学习，你可以训练一台模型，使其能够根据这些数据来预测未来房价。这个模型会通过分析数据中的各种关系和趋势，逐渐学到房价与各个因素之间的复杂关系。还有强化学习，就像教狗狗做技能一样。你告诉模型在某种情况下应该采取什么行动，而模型通过反馈机制不断调整策略，逐渐学到在不同环境中如何做出最优的决策。最后，我们越深入了解，我们越能理解到人工智能不仅仅是一种技术，更是一系列使机器具备智能的方法和技巧的集合。

数字化转型新话题，如何全面拥抱AI ✦数字化转型需要重新构建组织，并且不能外包。00:04 这很困难，需要最高管理层来推动变革。 哈佛商学院的 Karim Lakhani 博士讨论了技术、创新和商业的交叉点，以及数字化、数字数据科学以及新商业和运营模式的设计的指数效应。✦技术作为一种赋能工具贯穿于整个组织。02:58 现代公司及其基本模型的历史视角。 从部门和职能结构过渡到基于生态系统的组织。✦数字化转型需要运营和组织的根本变革08:58 云服务的集成可实现可变成本和规模经济 人工智能和算法正在推动以数据为中心的组织模式的变化✦人工智能优先组织从人类瓶颈转向算法瓶颈11:40 人工智能优先的组织面临着算法和分析能力的瓶颈，从而实现了巨大的扩展机会。 对企业的影响包括改变商业模式、通过人工智能和数字化旅程增强价值创造和价值获取。✦数据科学和人工智能对商业领袖至关重要17:18 了解数据科学和人工智能对于商业领袖来说至关重要，而不仅仅是成为数据科学家。 拥抱更广泛的技术堆栈并将其视为一项持续的任务也至关重要。公司需要持续投资技术而不是将其外包。✦组织转型是数字化转型战略的重要组成部分。19:45 仅仅专注于购买数字化转型技术往往会导致失败，因为它忽视了内部组织变革。 与不同人工智能采用成熟度水平的供应商、合作伙伴和内部部门达成共识是一项挑战，需要强大的领导力和支持需求。✦通过人工智能功能进行个性化内容创建和营销。24:53 人工智能能够以零边际成本创建按需、个性化的内容。 人工智能驱动的营销可以大规模创建个性化广告和内容，从而改变营销供应链。✦人工智能驱动的实时销售辅导正在改变销售流程。27:34 由于大流行，销售转移到 Zoom，从而提高了虚拟销售对话的舒适度。 与 Zoom 集成的人工智能工具为销售人员提供实时指导，彻底改变销售流程。✦数字化转型战略需要自上而下的协调和项目排序。32:32 人工智能项目的优先顺序应与公司的战略保持一致，无论是关注成本领先还是客户满意度和新业务模式。 成功的数字化转型通常始于高层的支持，并涉及按顺序扩展项目以满足总体战略。✦公司需要将流程嵌入软件和技术中以实现数字化转型。35:09 人工智能和技术将增强人类流程，而不是取代它们。资源分配决策需要反映这一点。 解决人工智能中的偏见至关重要，因为有偏见的数据和算法可能会大规模放大伤害。数据科学知识和法律问题是需要关注的关键领域。✦人工智能增强而不是摧毁人类能力40:38 人工智能带来的是能力和容量的提高，而不是适当的规模和成本效率 企业领导者需要投资于自己和员工的学习，以适应人工智能✦系统地投资于学习和为您的团队构建框架。42:58 随着组织变得更加数字化，数据安全变得至关重要。 数据标签和管道的安全问题对于算法的准确性也很重要。It's a total rewiring of your organization. That's what's ahead for most people. Guess what. It's damn difficult. [Laughter] [Laughter] And it's something that the C-suite – this is CXOTalk – cannot outsource to other people. They have to become experts in what's going on and drive the change. That's Karim Lakhani from the Harvard Business School. My work is at the intersection of technology, innovation, and business, how digital technologies are transforming businesses and changing business models and operating models. On the research side of things, I run a lab called The Laboratory for Innovation Science where I'm the founder and co-director. We've done a lot of work on crowdsourcing: crowdsourcing for innovation, crowdsourcing for algorithms. That's what got me into this AI space more than a decade ago. We had partners like NASA, Harvard Medical School, The Broad Institute, and so forth. In light of that, I have (over the last year) launched a new institute at Harvard called The Digital, Data, and Design Institute (D^3) because we think that these three technologies (digitization and digital data science and also the design of new business and operating models) are having an exponential effect. We have launched with more than 30 faculty members at Harvard Business School (and some colleagues at the engineering school) with 12 different labs. We're trying to work closely with companies to solve problems and do great research as well. Your focus is on how companies can compete, so what is going on? What's unique about our present time with AI that caused you to need to look at this problem? The book title is Competing in the Age of AI. We're not even saying competing with AI but in the age of AI. In many ways, the consumer economy (with our mobile phones) has already put the vast majority of humanity in the age of AI. If you think about how you navigate your email, how you navigate your music selection, your viewing habits, your reading habits, your directions, all of that is already immersed through AI. Increasingly now, the tech giants have sort of brought that to us. But that whole world is now shifting into the rest of the economy as well. The book is really about we're not turning back with less data, less digital, less algorithms. We're going to be doing more and more of it. How does that shape what companies do, how companies compete above and beyond you being an AI native firm? The book really sort of starts with the fact that the technology is going to be an enabling tool and it's no longer a thing which sits on its own but is woven throughout the fabric of the organization.That means that your operating model and your business model are going to change. That's what the book really tries to go after. How is this different from business as we've known it historically? A modern corporation really is maybe about 120 years old. If you think about the history of humanity, most of the time we've been sort of agrarian, small, little shops, and so forth. The modern corporation basically got set up 120, 130 years ago. If there were sort of seminal views of what happened in America, you look at Alfred Sloan setting up General Motors as a multidivisional company, Thomas Edison setting up General Electric as a multidivisional company, as models by which we have always run our organizations. The idea here was that you focused. You went after one thing after the other. You had visions set up. You had functional silos set up, and you were able to go and serve your customer needs. That model has done tremendous things. Our built environment, our built organization has been set up this way. Starting with the advent of computation and computers (with IBM and Microsoft), that edifice started to change where we thought that basically now what matters is not just the ways in which we organized from the top-down but the ways in which information flows across an organization. This information flow view of the world really first started with the tech industry and the software industry and the emergence of, let's say, Windows as a computational tool that allowed lots of people that power to analyze data and do things, and the spreadsheet as the way in which you would get work done. But everything was still very much in the model of divisional structures and functional structures set up. Every time you had to share data, you would be sharing large files, and there would not be things coming together. But what we saw emerge (even in the Microsoft era) was a new type of company was emerging. This company was set up as an ecosystem. Microsoft won the PC battle because they figured out how to build an ecosystem where they had lots of complementors and lots of consumers, and they were in the middle of it. This emergence of this ecosystem in the software industry then basically spread the tech industry, and more and more companies in the tech industry got organized this way. But an interesting thing happened along the way. As these ecosystems got built – and you can think about the mobile ecosystem with iOS and Google, and then, of course, Facebook dominating from that as well, and then, of course, Slack and Salesforce, and so forth, coming on its heels – what people saw was that the ways in which you would run an ecosystem platform-based company was very different than the way in which General Motors ran or General Electric ran. This meant, oh, all of a sudden we need data to cut across our entire enterprise. This meant that we had a better view of customer journeys and could personalize and create better offerings for our clients regardless of if it was a B2B setting or B2C setting or B2B2C setting, for example. The typical silos that we had in our enterprise were no longer the ways for us to organize. I think this is what's new is that what we're seeing is this pressure to de-silofy our traditional ways of organizing and to take advantage of the fact that we now have digital footprints and data across both our company operations and our interactions with customers and our suppliers. How can we put it to use more effectively and more efficiently? What we see really is, in many ways, sort of two models emerge. There is the traditional model in which all organizations, including Harvard Business School, has been in for about 100 years, which basically scales very fast and then reaches a plateau in terms of our ability to serve more and more customers and drive more and more value. You could imagine basically a concave curve of the number of users, your scale, and the value you're creating.Then we have these digital, AI-first native companies emerging which are growing, in many ways, in exponential rates. It takes a while for them to achieve scale. But once they achieve scale, they can keep growing exponentially. And so, there's a convex curve that shows up instead. These convex organizations, these exponential organizations, at their core are set up with data cutting across the entire enterprise. At the core, drive automation in their processes. At the core, are set up to basically use algorithms to make decisions and make predictions and drive pattern recognition. That shift, we think, is fundamental. We worked our way into it through these tech giants but, increasingly, more and more industries are facing that as well. In essence, what you're saying is the rise of ecosystems and then the rise of data becomes the underlying driver that forces organizations to change in some pretty fundamental ways. One hundred percent. Of course, we'd add in cloud computing and then the advances in algorithms in the last 20 years. Cloud, in many ways, made technology a variable cost instead of a fixed cost. You could then drive massive economies of scale, the cloud company provider, to then be able to take advantage of what you needed. All that have been these trends going in lockstep. But that has meant that the way in which you run a company and the way in which you organize production, operations, are fundamentally different in the ways you might have done things before. Subscribe to our YouTube channel and hit the subscribe button at the top of our website so we can send you our newsletter and you can stay up to date on these amazing live shows. Now overlay algorithms and AI on top of this ecosystem and data-centric model for how we must exist as organizations. Overlay the AI aspect on top. It's no surprise that the leaders in AI actually— Of course, universities were at the source of some of the breakthroughs in neural nets and so forth. But the adopters and the drivers of the changes in AI have come from industry. Why is that? Well, if you think about a company like Alphabet or Google, they face significant challenges in their infrastructure. I remember, in 2005 or '04, talking to people at Google. They said, "Oh, yeah. We built our own server farm with, like, 40,000 servers, and we have 5 people managing it." I was just like, "What?!" [Laughter] At that time. [Laughter] And they said, "Well, we had to make advances in algorithms to be able to make this all self-managed. We didn't want to hire thousands of people running our server farms." Remember, in 2004 or '05, this is novel, right? And so, what happened is that as these ecosystems grew, as they got embedded within the lifestyles of us – we search all the time for information – they were generating a ton of data. This data was laying fallow, and they were like, "Oh, okay. Well, we need to analyze this data, so let's use artificial intelligence and drive the advancement of these algorithms so we can better understand the data." But then they were set up in a very different way. If you think about it, there's no auctioneer at the backend of Google running auctions. It's all machine-driven. The human element is acquire the customer. Acquire the customer, then the algorithms take over. They work with you on your keywords. They work with you on your SEO optimization and the auctions. Then every step in your Google journey is mediated through algorithms. They felt the need to advance the algorithms themselves as a way to drive their own usage and their own growth. Then those spilled over into the rest of the economy. To your question, what's interesting is that the bottleneck in most traditional organizations are humans. Like, "Mike, answer my damn email, please. I sent you the spreadsheet. Can you analyze this for me," or "Can you please FedEx me the hard drive so I can go look at this data?" which is what happens in most organizations or stuck in some Slack conversation and so forth. In many of these AI-first organizations, the bottlenecks are not humans but algorithms and our capacity to actually analyze that. That then opens up scaling opportunities that are quite significant. What should people in business then be doing? Okay, you're running an organization and you're surrounded by this change. What are the implications of this for you? Let's be systematic about our analysis because I really think that the advantage is really technology folks now becoming business folks and also, by the way, HR folks becoming technology folks and technology folks becoming HR folks in thinking about what this change is. The first is, in the book with Marco Iansiti and I, we talk about business models are changing. When we talk about business models, we say you need to be clear about what a business model is. It's both the ways in which you create value, why do customers want to interact with you, and the ways in which you capture value, the ways in which your company makes money. Those need to be separate sets of analyses that you need to do. Now, you can create more value with algorithms and with AI and with digital. You can be more personalized. You can scale better. You can offer your customers much more variety and scope and so forth. Think about how your customer journeys and the value creation journeys that your company does can be enhanced through AI and digital, digital journeys. That's the first bit. You lay that all out. Then separately say, "Now that I'm creating all of this value, how might I capture all of this value as well?" The typical model, I would say, if I create value from you, I charge value, some portion of that value from you. When people come to Harvard Business School, we create a ton of learning value for them. Then we charge them tuition for our value capture. What's happened is that, now with AI, you could automate value capture. You can scale value capture. You can actually even be more creative in value capture, like for example, again, the tech industry has been based on the fact that they create value for us as users and they capture value from advertisers. There are just many more ways to capture value. Thinking systematically about how algorithms, AI, and digital can help you capture value is a separate conversation that opens up. That's just on the business model side. Then we can go, "Okay, now let's bring it to the operating model," which is what actually delivers the value, what happens inside the company. There, we think about three things to scale. How do you serve more and more customers through digital operations and so forth? Here again, what you can imagine is that you want to reduce the marginal cost of acquiring more and more customers through digital. You can impact scale this way. Scope, which you offer them. If you think about your experience now with tech industries, you do more and more things with these tech businesses. How can you improve the scope of things that you do? Then learning, how do you learn better, how do you innovate better as well through machines and the data being infused throughout your organization? We see the transformation tasks for business leaders is to systematically think about applying this technology to your business model and your operating model. Which then completely begs the question how to do it because it's very easy to describe this but the execution and practice is massively difficult because the implications, the tentacles extend through every part of the company. It's a total rewiring of your organization, and that's what's ahead for most people. Guess what. It's damn difficult. [Laughter] [Laughter] And it's something that the C-suite – this is CXOTalk – cannot outsource to other people. They have to become experts in what's going on and drive the change. I would say there are three things. One is the burden on our current leaders of organizations is to learn this new stuff and not be afraid of it. This is a new body of knowledge that you need to acquire not so that you're going to become a data scientist or machine learning engineer or a cloud specialist. The joke I make at HPS is people come to HPS and we have a required curriculum for the MBA program, for sure, and we teach them accounting. If we made accounting an optional course, nobody would take it – or very few people would take it. My dean was the chair of the accounting unit. It was like, no offense to my dean, but accounting, we make it required because we feel like this is important. We feel – we know that in order for you to run a modern business, you need to understand accounting. Our sense is now today, in November of 2022, that data science and algorithms is as essential as accounting for people in business to know. Here's why. We don't want you to become an accountant when you come to HBS, but we want you to be a good business leader. Similarly, when we teach you data science and we teach you algorithms, it's not so that you're going to become a data scientist. We want you to become a good business leader. That becomes the essential bit because if data science and AI is going to be infused throughout your organization, you better understand the ways this works and, in many ways, the downfall of not doing this properly as well. That's the first thing. The second thing is this embrace of the broader technology stack. What I mean by this is that too often technology has been viewed as edifice-building, like we'll go do this technology project like we're building a factory, and then we'll forget about it. I'm sure – in all of your more than 700 programs you have run – you know that this is an ongoing task. Nothing happens in companies without software, without technology, today. We might have it done really poorly but, in fact, that's what we need to do. We know, company after company, the tech companies, Amazon has written the systems 3 times in their last 20 years of existence. Many companies have to keep rewriting their systems over and over again. Leaders need to say that the technology build is an ever-going thing and we can't sort of have that be outsourced and put away. We need to own it and think about it and be thinking about this as an ongoing set of investments we'd be making. The last bit, which I think is the most critical bit. If you think about the first two bits, the data science and the technology stack, I will say that's like 30%. The 70% is the change management you need to do and the change in the organization you need to do. That is the hardest, hardest part. What I tell technology executives that I encounter here at Harvard Business School is that I'm like, "Guess what. You better become an HR specialist as well. You better become a change management leader as well. You can't outsource this to anybody else. This is a change process that you need to embody and lead as much as other business leaders need to as well." That for me is the 70% part of what lies ahead. I think too many people, too many boards, too many CXOs index on the 30% and not the 70%. I am convinced the 70% is necessary but not sufficient. You have to do that stuff, and you have to become good at it. But you as a leader now have to drive the organizational transformation as well with this. Well, of course, it's much easier to focus on buying technology. Let's buy a digital transformation. There's a great vendor. We pay our money, and they just do it, and it's done. But it fails because it never took inside the company. [Laughter] [Laughter] Right. Exactly. So, as I have interviewed so many business leaders, without a doubt the common theme is just as you've said that the hard part about any kind of transformation (whether it's digital transformation or the kind of next evolution of digital transformation that you're describing), it's always the people. But we have a really interesting question from Twitter. This is from Arsalan Khan. He says, "AI needs business process optimization along with integration of data (inside and outside the organization)." Here's his question. It's a great question. He says, "How do you reach consensus with vendors, partners, even internal departments who are not at the same maturity when it comes to AI adoption? How do you make this happen?" An interesting case study that we should think about doing later is the transformation at Disney. If you think about Disney and Disney+ and how they are actually now beating Netflix at their game is an amazing technology and business model transformation story as well. I had a chance to interview Bob Iger. Last year, I led an effort here at HBS to drive our own digital transformation, and I had a chance to interview Bob Iger, the former CEO and chairman of Disney. He said you don't just ask for buy-in. You demand buy-in. [Laughter] This is Bob Iger, the icon of the entertainment industry and so forth. But he said, "Look. Leaders have to demand buy-in. You can't just say, 'Oh, I need your buy-in.' No, no. 'Hey, you're in or out.'" There's a hard answer, for sure, which is like, you've got to drive buy-in. The second thing I would say is, look, I think, in many ways, we as the people driving the transformation have to become good teachers. We have to make sure that people come along with us. And the way to do that is to take on a teaching role, to take on a learning role for them. That's our job. They won't be able to do it themselves. You have to be taking on the responsibility to say, "How do I show you that A) this is approachable and B) that this is doable?" My great colleague Tsedal Neeley, she's at Harvard Business School too and a professor, she has this great thing called the hearts and minds matrix. You've got to change the hearts, but you've got to change the minds. The minds are changed by training, by learning, by making people see that, yes, I am doable. The minds you do through motivation and by showing the relevance that this has. You have to attack both sides simultaneously. Change the hearts and change the minds, and invest in both of them. Again, that's part of the transformation journey that many companies get stuck at because they don't think about the hearts and minds collectively together. Karim, if we think about the kind of changes that AI and algorithms drive across a company, can you maybe give us some examples? For example, you mentioned the business model. You mentioned relationships with customers. There's talent. There's sales. AI changes relationships across all these different processes. There's been a massive explosion in these diffusion models and large language models. Some analysis shows that the rate of improvement is 10x Moore's Law, 10x Moore's Law in these large language models and in these image-generating diffusion models, and so forth. Somebody showed me this Twitter thing which sort of blew my mind, which was like you can now autogenerate videos saying— Mike, let me ask you. Are you a dog person or a cat person? My wife loves cats, and so the right answer is I'm a cat person – and that's for sure. And is there a particular breed of cat that your wife likes that you have? Oh, we love all cats – and I hope you're listening. We love all cats. All right. Great. Now that we know this about you, we can custom create on-the-fly content for you and your wife that always has cats in our promotional videos at zero marginal cost. Now I'll say, okay, we're going to sell Mike some microphones, but we should have little cats floating by because his wife will see it and say, "Oh, definitely those microphones are more fine than the other ones without the cats." Right? That level of personalization is kind of incredible. The fact that I can now generate, on demand, at zero marginal cost, these videos and fine-tune it to you is kind of mind-blowing. But that capability is here today. That capability is here today. What OpenAI is doing, what Google is doing, what Facebook is doing with these kinds of technologies is mind-blowing. Just think that I can now generate personalized ads for each person, tweaking based on their preferences, changes marketing. How would I run a marketing department now when I can create personalized content at scale for each individual? Think about the marketing supply chain from how ideas get generated, how campaigns get created, to how they get launched, to how they get observed and they get monetized. That whole function with these large language models, both in terms of text creation and in terms of content creation, blown away, blown away and rethought through. One example. I spent a bunch of time with Flagship Pioneering to think about how AI and biology are merging together. The same diffusion models that we see for ad creation can also be applied to creating proteins. The same capability for proteins. Now just think how the R&D process changes because now I can generate any protein I want. In fact, one of the companies that we have in our portfolio that I've been advising is Generate Biomedicines, and their view is that they're creating a platform that can generate any protein, proteins that have actually not even existed in the world before, based on these types of technologies. Just think about the R&D function changing. Now I've looked at two very distinctive settings: the R&D function, which we've always thought requires this creativity and geniuses, massively augmented by AI. But then the marketing supply chain being completely turned upside down and fully automated this way. Now companies that will have access to data about you and your wife and can have permission from you and your wife to use that data to do that kind of marketing will be very differently organized. Companies that have an ad agency, creative department, they take six months to create a new ad. That ad is put on TV or even runs on YouTube but is nondifferentiated and so forth – examples. A cool thing I recently saw on this was in sales. Apparently, now lots of sales, because of the pandemic, a lot of sales moved to Zoom and people are now comfortable with having initial sales conversations on Zoom and so forth. Well, there are toolings that you can add onto Zoom that becomes, like an earpiece here, an earpiece for the salesperson to say, "You're talking too much. Slow down." Live, while you're in the conversation. "Pause for more questions. Ask a question this way. Your tonality seems to be more aggressive. Be softer." Realtime coaching for salespeople as to how to respond to a customer, and that's all AI-driven. Imagine how your sales, your face-to-face sales, process is now changing because you have this technology available. It's really augmenting capabilities that we just have not thought through properly before. That I think is the amazing thing that's ahead of us. For CXOs, then the question becomes, well, where do you begin? Do I start in marketing? Do I start in R&D? Do I start in sales? Do I start in operations? Where do I begin? That's why these guys that you bring on your show get paid the big bucks. That's part of the judgment that they need to have to say, "What are the high-value opportunities for me to start to do this? Then as I begin the transformation, how do I bring everybody else along in this way?" Of course, there are innumerable software companies now who are selling products and each one promises that it will be easier than the next. Yes. "And we all have incredible capabilities because of the data," and blah-blah-blah. We've all heard these sales pitches endlessly. Lisbeth Shaw asks a question on Twitter that is directly related to this. She asks, "How can established companies become AI companies while they run their existing business, because you don't want to go out of business while you're transforming your company?" A thousand percent. Lisbeth has it right, which is that's the biggest challenge. We don't have the luxury to be greenfield. We actually have to transform ourselves. What we've seen is there is a joint top-down and bottom-up approach. Declarations by the C-suite to say, "This is the journey we see ahead for us, and this is the way we need to go towards." You need the C-suite, the CXO buy-in, and belief, and a painting of a vision of what that means. Then what I would say is – that's the first thing – in that vision is, how will my customer value get enhanced; how will my clients be better off if I imagine this world to be? This is part of the top-down strategy around this. Then it's a question of saying, "Okay. Which are the problems that we should go after?" What I would say is it's easy for you to say, "I've got to rebuild everything," and it's like you're never going to rebuild everything. You don't want to be in this world of, like, I'm going to pause for five years and rebuild everything. You want to say, "All right. There are two things I need to do. I need to deliver value but also build capability so that I can do this more and more often and do it along the way." You then look around, either on your business model side or on your operating model side. Again, on value creation and value capture or on scale, scope, and learning. Say, "Where are some high-value problems that if I solve and I demonstrate that these get solved that I can then take that and then scale it across my enterprise?" I start with a prototype. I start with a POC. But the POC doesn't sit by itself. The POC is designed to scale. You say to the folks the green light to the POC that if this works, what is our plan to scale, and you have the plan to scale agreed upon before even the POC starts. What we've seen over and over again is that the POCs actually work. I've seen amazing hit rates for POCs working. But then they all are dead zombie projects in many organizations because there's been no commitment to scale. The commitment to scale then means, "Oh, I've got to change my operating mode, the ways in which I do that," but you need buy-in. It's the bottom-up identification of use cases, bottom-up identification of POCs, top-down agreement that we're going to do this and that, as these POCs start to scale, you prioritize. We will then make them go across the enterprise. The thing I learned from some colleagues – you know I was just spending a bunch of time at Boston Consulting Group (before I became academic), and I've reacquainted with them since I wrote the book – they had some very interesting perspective that oftentimes people get into this prioritization game, like, "Oh, which projects am I going to prioritize?" The reality is, in a top-down transformation, you'll need to do everything. And so, the question is one of sequencing. The sequencing of the projects and the scaling actually has to be based very cleverly on your strategy. Is the strategy to blow away your competition and be the low-cost provider? Then the projects you would do for AI are very different than saying, "I'm going to be number one in customer satisfaction and new business model creation." That's a very different set of perspectives. The use cases get identified at the bottoms-up level. You need top-level agreement that this is the journey they want to go on. But then top-level agreement to say that as these POCs get developed, we're going to sequence them and scale them to meet our strategy. That's the way that these transformations will work. If you think about Disney as an example, where did they start? They first started by buying Pixar. Then they boat anchored Disney Animation Studios to create digital animation. That was 15 years ago, 17 years ago. Then in that journey, they've gone step-by-step to build their own digital capabilities and start to build a platform where then Disney+ launches just before the pandemic and can take advantage of people's home viewing, but then keep going that way by being able to actually beat out Netflix at their own game. It's interesting. Just as you were describing Disney on Twitter, Michelle Batt came in to point out that Disney's success is also related to leadership pushing from the top down. This is Bob Iger saying, you know, demanding buy-in. Lag [Laughter] It reminds me of the great leader of our time Elon Musk going to Twitter and saying, "You will now work 24/7. And, by the way, we're firing half of you today." You may disagree with his personality and his politics and his incessant use of Twitter, but his ability to change the space industry, the auto industry, the electric industry, you know, electrification with SolarCity, you can't— He's done things that we'd be lucky to do in one lifetime. He's done three of them already, and we'll see what he does with Twitter. I'm not a big fan of his management style but, guess what. One of the most important questions he asked at Twitter was, "How many people are writing code that ships versus managing?" It's like, "Oh." I think the ratio was 5:1. He goes, "Okay, that has to change," because, in the end, most of our companies are going to be embedding our processes in software and technology. That's the key thing that I think CXOs have to get their head around that everything we do is going to be embedded through software, through technology, through AI. That's where you have to then make resource allocation decisions and so forth. The technology and the AI is going to augment our humans. It's not going to replace them. It's going to augment them, but the processes you have would have to be very different. We have another really important point from Arsalan Khan. He comes back, and he asks about the bias question. He said, "With data and algorithms—" I'm paraphrasing his question but, essentially, he wants to know. He phrased it really well. He says, "How do we reduce bias in AI when the ultimate goal is increasing profit and not necessarily AI's impact? For example, changes on the workforce or in society." How do we balance these? It's a really important issue. Let's unpack this. One is, why is there a bias problem with AI? Well, because bias can exist because our data that we are using to train the algorithms is not representative. We just have one class of citizens generating the data instead of another class. Our labeling operations may not be representative as well. For example, lots of tech companies have problems identifying blacks in their image processing systems because the labelers weren't able to identify them properly or distinguish the features that way as well. One is a story of data and data operations. This is why data science is a critical skill for all executives because you have to understand the data generation processes and all of the faults that would happen. I think that's the first thing. To take it to the limit, just as I can scale the benefits of AI exponentially, I can also scale the harms of AI exponentially. Bias is one of those things. The second thing is that there is a real legal issue, which has been the thing that has been so interesting for me. Statistically, computer scientists and statisticians, when they look at the algorithms, say, "Is this algorithm fair?" Oftentimes, when we think about fairness in statistics and in computer science, we think about on average. Is this algorithm treating people fairly, on average. But the law doesn't say average. The law says each and every individual has to be treated fairly. There's a lot of risk that companies are facing today because their algorithms are, on average, fair but to the individual they're not fair. They're open to a lot of liability questions. How do we make sure that those things are addressed upfront instead of addressed after the fact? This is where I think is the new frontier for many organizations, which is the conversation about bias and fairness and transparency in expanding the algorithms should not be a computer science or an AI task. This is a cross-functional task that resides with business, with technology, and with legal and policy. This has to be done collectively. Importantly, we can't do this ex-post, after the algorithms have launched. We have to do them pre- in the design phase. I think Satya Nadella has done the most thinking about this because, remember, Microsoft had a crazy amount of cybersecurity issues in the 2000s. What they had to do was retrain their software developers to build quality software and security into their processes instead of doing it ex-post. I think the same thing is going to happen with algorithms and bias and AI is that we have to build in the awareness about bias in our processes upfront instead of ex-post when the algorithms are released into the wild. The example I use is that when you go to Toyota (as a manufacturing company), there's no quality department at Toyota. Why? Because they feel like if your processes aren't creating quality then a quality department is never going to fix it. They make quality the responsibility of all employees, and they've built processes to ensure quality is built into the systems instead of doing it at the end. I think the same thing is going to happen around AI and bias as well. This is from LinkedIn. Cezar Babes comes back, and he responds to you the following way. I'll ask you to just keep your answer pretty brief. He says the following: "Should AI use be more tightly regulated? Every now and then there's a new technology that becomes the catalyst for—" and I'm readying his quote "—profit-driven goose chases. This results in loss of jobs and resentment towards that technological advancement." He says, "It would be great if AI would be a driver for human growth and result in increased capability and capacity rather than right-sizing and cost-efficiencies." Is there going to be displacement because of AI? Absolutely. Do we need to retrain people? 100%. But my belief is that, in the end, AI augments human capability instead of destroying human capability. Just as prior technologies have been enhancing us, the same thing is going to happen with AI as well. Is there a displacement period and are certain occupations going to be displaced? 1000%. That's where governments and so forth have to come together. But regulation, who is going to regulate AI and in what way? It just doesn't seem tenable to us because it is so widespread. What advice do you have for business leaders who are listening to this and saying, "All of this is fine, Professor Lakhani, but my business is successful. We don't have to deal with this stuff. We're pretty much happy as clams, so this doesn't affect us"? Go talk to your customers, and not about your products but other things that they're doing. I tell you; you will be shocked with how they're thinking about the world and how much technology is driving their decisions. When you ask them about your own products or your competitors' products, you will never hear the right answer. Ask them about other things that they're doing in their businesses, and you'll be shocked. What advice do you have for business leaders who are listening to this, nodding their heads, and saying, "I know this is true, everything you're saying. We feel the pain. I feel the pain, and I don't know what to do. It's too big and complex and hard." There is a learning mandate for this for all organizations, which is, we have a generation of leaders that came in the old model. They don't understand the technology, don't understand data science, don't understand statistics, don't understand algorithms, don't understand cloud, and feel like that's for the IT guys. I think there's a learning mandate for these leaders not just for them to become better at this but then to also get their whole organization to change as well. And so, I would start with learning. Invest in the learning for yourselves and your folks. There's so much stuff available. What you have done through this amazing series, what we offer for paid, there's lots of stuff. There's no excuse for not learning. There are lots of books. Invest systematically in learning, yourself, and building a framework for your whole team. Then cascading that down so that everybody has the same reference point. That's the first step. I see too many people shirk on learning and say, "This doesn't apply to me," when they don't know what's going to hit them over the head with this stuff. Jose Kurian just wants to point out that security is the most important of AI and machine learning services, and so can you just say something about the security dimension of all of this? Security, overall, as we get into more digitally intensive organizations, which all of us are becoming, data security, information security is going to be key-key-key for all of us. Secondly, there are actually a bunch of very important issues about data security and our data pipelines being secure and not being tampered with. Just think about labeling operations that many companies have. Many times, those labeling operations are outsourced. They could be subject to attack where even just the slight bit of mislabeling could give you flawed algorithms. As we start thinking about this stuff becoming infused throughout our enterprises, the security side around data itself is going to be massively important. I 100% agree, 1000% agree. With that, we are out of time and over time. I want to say thank you so much to Professor Karim Lakhani from the Harvard Business School. Thank you so much. I'm so grateful for your taking the time to be here with us today. It was so much fun, Mike, and it was great for me to be on this side instead of just listening. Thank you for the invitation again. Well, I hope you'll come back again. Absolutely. A huge thank you to everybody in the audience who watched and especially to the folks who ask such amazing questions. You guys are such a great audience. I have undying respect for you. Everybody, thank you so much. Check out CXOTalk.com. We have amazing shows coming up. Before you go, subscribe to our YouTube channel and hit the subscribe button at the top of our website so we can send you our newsletter and you can stay up to date on these amazing live shows. Thanks so much, everybody. I hope you have a great day. See you soon.

【综合稿件-参考】数字化转型的未来，将是一个指数级的变化 https://www.youtube.com/watch?v=ystdF6jN7hc本篇大纲：1. 创新是唯一的出路 一切照旧已经不够了 在一切都已经很优秀的世界里，创新是进步的唯一途径 2. 技术正在迅速改变我们的工作和生活方式 自动驾驶汽车和计算机学习。 需要新的技能来跟上变化。 3. 在转型时期发现新机遇 颠覆已成为新常态 变化正在呈指数级发生 4. 我们的世界通过先进技术变得更加互联 量子计算、大数据、物联网和人工智能等新兴技术正在推动这种互联性。 这些技术相互放大，创造了一场完美的变革风暴。 5. 不可数字化的技术在未来将狠有价值 创造力、想象力和情感会更重要 机器人和软件只会做可以自动化的工作，人类可以专注于不可自动化的任务 6. 通过拥抱未来和超越技术来拥抱变革 为了取得成功，想象一下通过参与可能性而不仅仅是现在来实现变革。 让自己沉浸在不久的将来，了解其影响。 成功的关键在于人类的洞察力和智慧，而不仅仅是技术和数据。 未来需要一个专注于“为什么”的整体商业模式。 7. 学习必须及时进行，生态系统才是前进的方向 需要全面转型，而不仅仅是改进单个系统 人类通过变革性的经历创造真正的价值 8. 拥抱技术，超越技术 未来是科技 更大的未来在于超越 ——核心词：not good enough is dead从前，一切照旧往往就足够了 现在，我们的未来海生一切照旧的话，就死定了 在一个万物相连的世界里 一切都同样出色 性能达到完美 只有一个空间可以创新 你 现在，你就是 汹涌的变革龙卷风的中心点 由数字化推动 移动化 强化 非中介化 自动化 科幻小说正在变成科学事实 想想看 自动驾驶汽车或能够学习和思考的计算机 和思考的计算机 我们的工作方式将一去不复返 我们需要的技能将大不相同 现在，胜负的发生速度比以往任何时候都快 你将如何应对？ 您将如何发现新 机遇？ 变革时代 人类历史上 你是在推动变革，还是被变革所推动 颠覆已成为变革的新常态 它总是逐渐地突然发生 事情真的不再是逐渐发生的了 这种变化是指数级的 一切 过去笨拙和断线的东西现在都连上了线 和智能化 汽车 城市 港口 农场 甚至人体也将装上传感器 并相互对话 这些游戏变化也是组合性的 它们相互放大 形成一场完美的变革风暴 量子计算助推大数据 物联网 促进人工智能和深度学习 机器人技术 然而，任何无法数字化或自动化的东西 都将变得极其宝贵 人类独有的特质，如创造力 想象力、直觉 情感和道德 在未来将变得更加重要 因为机器擅长模拟 但不擅长成为 是的，机器人和软件将完成我们的部分工作 但这将使我们能够专注于 无法自动化的事情 想象变革 你必须开始更多地参与可能发生的事情 而不仅仅是现在 沉浸于眼前的未来 从今天开始的 5 到 7 年后 我们需要超越技术和数据 达到人类的洞察力 和智慧 技术代表着变革的方式 但人类代表的是为什么 未来关乎整体业务模式 机会在于及时学习的流动性 而不是以防万一 不是单一的改进，而是全面的变革 不是单个系统，而是新的生态系统 人性是创造真正和持久价值的源泉 我们将以事物为载体 因为它们提供的体验 因为它们具有变革的力量 未来不只是发生 未来已经发生 新的工作方式是拥抱技术 但不是成为技术 未来就在技术之中 但更大的未来在于超越科技 让我们从这里开始生活和领导英文原文 once upon a time business as usual was often good enough no more where we are going good enough is dead in a world where everything is connected where everything is equally excellent where performance is reaching perfection there's only one space left to innovate in you right now you are a central point of the raging tornado of change fueled by digitization mobilization augmentation disintermediation automation well the list goes on science fiction is becoming science fact think about self driving cars or computers that can learn and think the way we work will never be the same the skills we need will be dramatically different winning or losing are now happening faster than ever before so what's your response how will you discover new opportunities in one of the most transformational times in human history are you driving change or are you being driven by it disruption has become the new normal with change it's always gradually done suddenly well things really have stopped happening gradually this change is exponential everything that used to be dumb and disconnected is now wired and intelligent cars cities ports farms even how bodies will be wired with sensors and will talk to each other these game changes are also combinatorial they amplify each other creating a perfect storm of change quantum computing fuels big data the internet of things fuels artificial intelligence and deep learning which fields robotics however anything that cannot be digitized or automated will become extremely valuable human only traits such as creativity imagination intuition emotion and ethics will be even more important in the future because machines are very good at simulating but not at being yes robots and software will do some of our work but this will allow us to focus on things that cannot be automated to imagine change you've got to start engaging more with what might be not just with what is immerse yourself in the immediate future 5 to 7 years out from today we need to go beyond technology and data to reach human insights and wisdom technology represents the how of change but humans represent the why the future is about holistic business mode the opportunity is to be liquid to learn just in time not just in case not single improvements but complete transformations not individual systems but new eco systems humanity is where true and lasting value is created we will engage laden by things because of the experiences they provide because of their transformative power the future doesn't just happen the future get happened the new way to work is to embrace technology but not to become it the future is in technology yet the bigger future lies in transcending it let's live and lead from here——GPT整理文字—— Once upon a time, business as usual was often good enough. No more. Where we are going, good enough is dead. In a world where everything is connected, where everything is equally excellent, where performance is reaching perfection, there's only one space left to innovate in – you. Right now, you are a central point of the raging tornado of change, fueled by digitization, mobilization, augmentation, disintermediation, automation – well, the list goes on. Science fiction is becoming science fact. Think about self-driving cars or computers that can learn and think. The way we work will never be the same. The skills we need will be dramatically different. Winning or losing are now happening faster than ever before. So, what's your response? How will you discover new opportunities in one of the most transformational times in human history? Are you driving change, or are you being driven by it?Disruption has become the new normal. With change, it's always gradually done suddenly. Well, things really have stopped happening gradually; this change is exponential. Everything that used to be dumb and disconnected is now wired and intelligent – cars, cities, ports, farms, even how bodies will be wired with sensors and will talk to each other. These game changes are also combinatorial; they amplify each other, creating a perfect storm of change. Quantum computing fuels big data; the Internet of Things fuels artificial intelligence and deep learning, which fields robotics. However, anything that cannot be digitized or automated will become extremely valuable. Human-only traits such as creativity, imagination, intuition, emotion, and ethics will be even more important in the future because machines are very good at simulating but not at being.Yes, robots and software will do some of our work, but this will allow us to focus on things that cannot be automated. To imagine change, you've got to start engaging more with what might be, not just with what is. Immerse yourself in the immediate future, 5 to 7 years out from today. We need to go beyond technology and data to reach human insights and wisdom. Technology represents the how of change, but humans represent the why. The future is about a holistic business mode; the opportunity is to be liquid, to learn just in time, not just in case. Not single improvements but complete transformations, not individual systems but new ecosystems. Humanity is where true and lasting value is created. We will engage laden by things because of the experiences they provide, because of their transformative power.The future doesn't just happen; the future gets happened. The new way to work is to embrace technology but not to become it. The future is in technology, yet the bigger future lies in transcending it. Let's live and lead from here.—————不错的评论————— Indeed time to focus on Creativity, Imagination, Intuition, Emotion, Ethics 是时候专注于创造力、想象力、直觉、情感、道德