Podcasts about ai ai

近日，马斯克旗下 AI 公司 xAI 宣布，推出基于 Grok 4 大模型的全新伴侣功能。这则消息也将国内沉寂已久的 AI 陪伴市场，重新带回市场视野。过去几年，AI 陪伴赛道经历了从概念到爆红再到冷却的周期。从 Replika 把「AI 陪伴」概念引入大众视野，到 Character.ai（CAI）依靠大模型迅速出圈，再到 CAI 核心团队被 Google 挖走......行业的起伏让大家怀疑 AI 陪伴是否被用户需要。 AI 陪伴是否是个伪命题？除了工具属性，ChatGPT 是否可以提供情绪价值？模型能力、场景、记忆......好的 AI 陪伴产品要具备哪些能力？今天我们就与两位长期关注 AI 陪伴产品的嘉宾与我们深入的聊聊 AI 陪伴这个赛道的用户需求、技术架构，以及商业化路径。 本期人物 陈腿毛，上海「我不在乎」科技有限公司创始人 辛童， Paradot.ai 增长&商业化负责人 丁教 Diane，「声动活泼」联合创始人、「科技早知道」主播 Yaxian，「科技早知道」主播 主要话题 [02:05] Grok 的 AI 伴侣上线，是行业利好还是平庸之作？ [04:52] AI 陪伴进化史：从《Her》的启蒙，到 Replika 和 Character.AI 的探索 [08:40] 「走向虚拟」还是 「回归现实」：AI 陪伴的两种产品哲学 [13:11] 深度记忆与上下文推理，让 AI 伴侣理解你的潜台词 [20:36] ChatGPT 的「觉醒时刻」与 AI 记忆的「Aha Moment」 [26:07] 用户没有用新 AI 的需求，但是有交新朋友的需求，如何让 AI 产品变成用户的好朋友？ [35:00] 面对大厂，AI 陪伴创业公司是否会被「降维打击」？ [43:50] AI 陪伴的商业化路径探索，IP 变现还是付费订阅？ 延伸阅读 PGC (Professional Generated Content) 专业生产内容，指由专业团队（如剧作家、设计师）创作的内容。 SFT (Supervised Fine-Tuning) 监督微调，一种用有标签的数据集来微调预训练模型的技术，以使其在特定任务上表现更好。 DPO (Direct Preference Optimization) 直接偏好优化，一种根据人类偏好反馈来调整语言模型输出的技术，使其更符合用户期望。 故事征集 你的 AI 是否也有过一个觉醒时刻成为了你的 Aha moment ？TA 是否已经成为你生活中难以替代的陪伴？如果你想要与我们分享你与 AI 伙伴之间的故事，欢迎点击这里 (https://eg76rdcl6g.feishu.cn/share/base/form/shrcnGYz8EnuES56FNVaGgI8b5d)给我们投稿，期待听到你的声音。 幕后制作 监制：Yaxian 后期：迪卡 运营：George 设计：饭团 青少年节目「Knock Knock 世界」 上周更新了三期解读。讲了讲外卖大战的来龙去脉、为什么实体书的价格是网购书的三倍、还解读了已经灭绝了 6600 万年的霸王龙为什么要被做成龙皮 皮包、这个听起来不可思议的项目究竟是怎么回事。现在就去节目主页一探究竟吧 ↓ 小宇宙听友请点这里 (https://www.xiaoyuzhoufm.com/podcast/67ce9e52a97df5faf716bcc7) Apple Podcast 听友请点这里 (https://sourl.cn/4KnbWr) 商业合作 声动活泼商业化小队，点击链接直达声动商务会客厅（https://sourl.cn/9h28kj） ，也可发送邮件至 business@shengfm.cn 联系我们。 加入声动活泼 声动活泼目前开放开放人才发展伙伴岗、市场部门岗位（节目运营、社群运营、内容营销）和 BD 经理等职位，详情点击招聘入口 (https://eg76rdcl6g.feishu.cn/docx/XO6bd12aGoI4j0xmAMoc4vS7nBh?from=from_copylink) 关于声动活泼 「用声音碰撞世界」，声动活泼致力于为人们提供源源不断的思考养料。 我们还有这些播客：声动早咖啡 (https://www.xiaoyuzhoufm.com/podcast/60de7c003dd577b40d5a40f3)、声东击西 (https://etw.fm/episodes)、吃喝玩乐了不起 (https://www.xiaoyuzhoufm.com/podcast/644b94c494d78eb3f7ae8640)、反潮流俱乐部 (https://www.xiaoyuzhoufm.com/podcast/5e284c37418a84a0462634a4)、泡腾 VC (https://www.xiaoyuzhoufm.com/podcast/5f445cdb9504bbdb77f092e9)、商业WHY酱 (https://www.xiaoyuzhoufm.com/podcast/61315abc73105e8f15080b8a)、跳进兔子洞 (https://therabbithole.fireside.fm/) 、不止金钱 (https://www.xiaoyuzhoufm.com/podcast/65a625966d045a7f5e0b5640) 欢迎在即刻 (https://okjk.co/Qd43ia)、微博等社交媒体上与我们互动，搜索 声动活泼 即可找到我们。 期待你给我们写邮件，邮箱地址是：ting@sheng.fm 声小音 https://files.fireside.fm/file/fireside-uploads/images/4/4931937e-0184-4c61-a658-6b03c254754d/gK0pledC.png 欢迎扫码添加声小音，在节目之外和我们保持联系。 Special Guests: 辛童 and 陈腿毛.

Leveraging AI to be a more efficient content creator and the role of keywords in today's SEO landscape with Aleka Shunk. ----- Welcome to episode 530 of The Food Blogger Pro Podcast! This week on the podcast, Bjork interviews Aleka Shunk from Aleka's Get Together and Keywords with Aleka. She also happens to be one of our FBP experts!  How to Personalize AI for Better Content with Aleka Shunk In this conversation, Bjork and Aleka discuss the evolving landscape of SEO and content creation, particularly focusing on the role of keywords and the integration of AI tools like ChatGPT. They explore how keywords remain essential in SEO, despite changes in content creation approaches. Aleka will also share insights on how to use AI to streamline content creation processes, enhance brand collaborations, and personalize interactions for better outputs. The discussion emphasizes the importance of adapting to AI advancements and leveraging them to improve efficiency and creativity in content creation. Three episode takeaways: Keywords are still relevant, but AI can be your co-pilot: Don't ditch those keywords! They're still super important for getting your content found. Think of AI as your super-efficient sidekick that can help you with everything from brainstorming ideas and creating outlines to finding new content opportunities. Personalize your AI for pro-level results: Just like you wouldn't give every human the same instructions, don't treat AI tools the same! The more you personalize your interactions and even create custom GPTs for specific tasks, the better and more relevant your AI-generated content will be.  Keeping it real in the age of AI: AI can seriously speed up your workflow and help you refine your content, saving you a ton of time, but remember, the human touch is what makes your content truly unique and engaging! You can leverage AI to boost your efficiency, but always keep your personal style and voice front and center. Resources: Aleka's Get Together Cooking with Keywords Be sure to check out Aleka's new course, Blogging with AI! Use the code FBP30 for 30% off the course! ChatGPT Episode 518 of The Food Blogger Pro podcast: How Molly Thompson Grew Her Email List from 15K to 100K Claude Gemini KeySearch Granola Buy Back Your Time by Dan Martell Episode 484 of The Food Blogger Pro podcast: The Importance of Building Community with A Couple Cooks Liss Legal Follow Aleka on Instagram here and here! Join the Food Blogger Pro Podcast Facebook Group Thank you to our sponsors! This episode is sponsored by Yoast and Raptive. Learn more about our sponsors at foodbloggerpro.com/sponsors. Interested in working with us too? Learn more about our sponsorship opportunities and how to get started here. If you have any comments, questions, or suggestions for interviews, be sure to email them to podcast@foodbloggerpro.com. Learn more about joining the Food Blogger Pro community at foodbloggerpro.com/membership.

AI轉型從這裡開始！《AI 轉型全攻略｜導入x選題x優化xAI Agent》 現在報名可享 39折優惠，輸入M觀點折扣碼【miula500】現折 500 元

특집 대토론! 인간을 위한 AI를 즉시 준비해야 한다! 국경없는의사회 출신 민주당 차지호 의원과 파토의 대화엄청난 속도로 발전하는 AI 기술에 비해 인문, 사회, 정치, 제도적 준비는 턱없이 늦고 부족한 상황.인프라와 기술적 경쟁만 중요시되는 현재의 인식과 접근 방식을 빨리 바꿔야 하며우리가 창조하고 있는 이 지능적 존재의 현재와 미래에 대해 더 깊은 통찰을 갖춰야 한다.* 전체 흐름 *복지와 기술- 의료 사각지대를 해결할 AI의 가능성- 노동과 실업문제에 대한 대책이 세워지지 않고 인간을 위한 AI에 대한 고민과 준비가 부족하다- 양극화를 더 확대할 수 있는 AI의 위험성-- EU의 '전자인 규정'에서 배울 점- 우리나라가 그 부분을 채우는 역할을 해야하지 않을까- AI는 분명히 희망이다. 단, 잘 만들고 잘 쓰는 경우- 이 모든 것을 위해 정치적 합의가 꼭 필요하다- 자본 기술 데이터의 제약으로 인한 AI 3강의 꿈과 기존 소버린(한국형) AI의 한계- 우리나라 중심으로 국제 연대를 구성해야 5천만이 아닌 10억이 사용하는 AI 플랫폼으로 만들어야- 지금 우리나라에 대운이 따르는 이유새로운 지적 존재로서의 AI - 인간과 AI의 공진화 가능성에 대해- AI가 미리 입력된 데이터 뿐 아니라 유저와의 대화, 관계 속에서 성장해야 하지 않나- 특이점에 도달하는 AI는 자연 속에서 스스로 학습할 것. 그런 AI에게 인간이 ‘필요'할까.- 초지능에게 필요를 넘어선 마음을 부여해야 할까- AI는 거울상으로서 자신들의 존재를 규정할 생명, 인간이 필요하지 않을까- 디지털 세계의 시간과 아날로그 세계의 시간 감각의 차이 속에 교류나 소통은 어떻게 달라질까.- 인간이 진화하는 방식과 AI가 진화하는 방식의 차이- AI에게 인간은 아직 자격없는 부모. 같이 성장해야 한다- 인간과 AI가 만들어내는 친밀한 관계 속 허와 시류- AI와 정신건강의 긍정적, 부정적 관련성- 언어 AI로 인해 생겨날 수 있는 망상AI 가 혁명적으로 바꿀 정치와 사회- AI는 민주주의의 위기를 초래할 수도 있다.- 디지털 정보의 에코쳄버, 확증편향의 문제- AI, 혹은 거대 IT 기업들의 선거 개입의 가능성- AI 분석이 선거에 출마한 후보자에 미칠 수 있는 영향- AI시대에 대비하기 위해 정치, 사회, 복지, 일상, 건강, 노동 등에 대해 통합적으로 생각해야- AI는 정부 모든 부처의 문제이고 이번 정부 내에서 원형이 만들어져야- 중국, 미국, 한국처럼 서로 다른 사회에서 만들어진 AI는 다른 형태의 사회를 지향- 이 문제의 중요성을 인식하고 의견을 내는 사람이 많아져야 - 그렇지 않으면 AI 생태계는 바람직하지 않은 형태로 구성될 가능성이 높다- 국회의 역할, 정부의 역할, 기업의 역할은 물론- 과학하고 앉아있는 사람들의 관심과 목소리가 중요하다과학과사람들 제공

“ใช้ AI อย่างไรไม่ให้โง่?” งานวิจัยจาก MIT Media Lab พบว่า คนที่ใช้ AI ช่วยเขียน คิด หรือสรุปบ่อย ๆ สมองจะทำงานน้อยลงโดยไม่รู้ตัว จำได้น้อยลง และเขียนซ้ำ ๆ ในกรอบเดิมมากขึ้น เพราะ AI จะช่วย “ประหยัดแรงคิด” จนคุณเลิกตั้งคำถาม และเริ่มชินกับคำตอบสำเร็จรูป รวมถึงงานวิจัยจาก Microsoft ได้ออกผลสำรวจในประเด็นเดียวกันโดยระบุว่า 70% ของคนที่ใช้ AI นั้น “คิดน้อยลง” ขณะเดียวกัน ยังมีอีกกลุ่มที่ “คิดมากขึ้น” เพราะพวกเขาใช้ AI เป็น "เครื่องมือคิดร่วม" ไม่ใช่ "เครื่องมือคิดแทน" คำถามสำคัญคือ แล้วเราจะใช้ AI แบบไหน ให้เก่งขึ้น ไม่ใช่โง่ลง? ติดตามชมได้ใน Executive Espresso เอพิโสดนี้

My fellow pro-growth/progress/abundance Up Wingers,The innovation landscape is facing a difficult paradox: Even as R&D investment has increased, productivity per dollar invested is in decline. In his recent co-authored paper, The next innovation revolution—powered by AI, Michael Chui explores AI as a possible solution to this dilemma.Today on Faster, Please! — The Podcast, Chui and I explore the vast potential for AI-augmented research and the challenges and opportunities that come with applying it to the real-world.Chui is a senior fellow at QuantumBlack, McKinsey's AI unit, where he leads McKinsey research in AI, automation, and the future of work.In This Episode* The R&D productivity problem (01:21)* The AI solution (6:13)* The business-adoption bottleneck (11:55)* The man-machine team (18:06)* Are we ready? (19:33)Below is a lightly edited transcript of our conversation. The R&D productivity problem (01:21)All the easy stuff, we already figured out. So the low-hanging fruit has been picked, things are getting harder and harder.Pethokoukis: Do we understand what explains this phenomenon where we seem to be doing lots of science, and we're spending lots of money on R&D, but the actual productivity of that R&D is declining? Do we have a good explanation for that?I don't know if we have just one good explanation. The folks that we both know have been both working on what are the causes of this, as well as what are some of the potential solutions, but I think it's a bit of a hidden problem. I don't think everyone understands that there are a set of people who have looked at this — quite notably Nick Bloom at Stanford who published this somewhat famous paper that some people are familiar with. But it is surprising in some sense.At one level, it's amazing what science and engineering has been able to do. We continue to see these incredible advances, whether it's in AI, or biotechnology, or whatever; but also, what Nick and other researchers have discovered is that we are producing less for every dollar we spend in R&D. That's this little bit of a paradox, or this challenge, that we see. What some of the research we've been trying to do is understand, can AI try to contribute to bending those curves?. . . I'm a computer scientist by training. I love this idea of Moore's Law: Every couple of years you can double the number of transistors you can put on a chip, or whatever, for the same amount of money. There's something called “Eroom's Law,” which is Moore spelled backwards, and basically it said: For decades in the pharmaceutical industry, the number of compounds or drugs you would produce for every billion dollars of R&D would get cut in half every nine years. That's obviously moving in the wrong direction. That challenge, I don't think everyone is aware of, but one that we need to address.I suppose, in a way, it does make sense that as we tackle harder problems, and we climb the tree of knowledge, that it's going to take more time, maybe more researchers, the researchers themselves may have to spend more time in school, so it may be a bit of a hidden problem, but it makes some intuitive sense to me.I think there's a way to think about it that way, which is: All the easy stuff, we already figured out. So the low-hanging fruit has been picked, things are getting harder and harder. It's amazing. You could look at some of the early papers in any field and it have a handful of authors, right? The DNA paper, three authors — although it probably should have included Rosalyn Franklin . . . Now you look at a physics paper or a computer science paper — the author list just goes on sometimes for pages. These problems are harder. They require more and more effort, whether it's people's talents, or whether it's computing power, or large-scale experiments, things are getting harder to do. I think there's ways in which that makes sense. Are there other ways in which we could improve processes? Probably, too.We could invest more in research, make it more efficient, and encourage more people to become researchers. To me, what's more exciting than automating different customer service processes is accelerating scientific discovery. I think that's what makes AI so compelling.That is exactly right. Now, by the way, I think we need to continue to invest in basic research and in science and engineering, I think that's absolutely important, but —That's worth noting, because I'm not sure everybody thinks that, so I'm glad you highlighted that.I don't think AI means that everything becomes cheaper and we don't need to invest in both human talent as well as in research. That's number one.Number two, as you said, we spend a lot of time, and appropriately so, talking about how AI can improve productivity, make things more efficient, do the things that we do already cheaper and faster. I think that's absolutely true. But we had the opportunity to look over history, and what has actually improved the human condition, what has been one of the things that has been necessary to improve the human condition over decades, and centuries, and millennia, is, in fact, discovering new ideas, having scientific breakthroughs, turning those scientific breakthroughs into engineering that turn into products and services, that do everything from expand our lifespans to be able to provide us with food, more energy. All those sorts of things require innovation, require R&D, and what we've discovered is the potential for AI, not only to make things more efficient, but to produce more innovation, more ideas that hopefully will lead to breakthroughs that help us all.The AI solution (6:13)I think that's one of the other potentials of using AI, that it could both absorb some of the experience that people have, as well as stretch the bounds of what might be possible.I've heard described as an “IMI,” it's an invention that makes more invention. It's an invention of a method of invention. That sounds great — how's it going to do that?There are a couple of ways. We looked at three different channels through which AI could improve this process of innovation and R&D. The first one is just increasing the volume, velocity, and variety of different candidates. One way you could think about innovation is you create a whole bunch of candidates and then you filter them down to the ones that might be most effective. Number one, you can just fill that funnel faster, better, and with greater variety. That's number one.The candidates could be a molecule, it could be a drug, it could be a new alloy, it could be lots of things.Absolutely, or a design for a physical product. One of the interesting things is, this quote-unquote “modern AI” — AI's been around for 70 years — is based on foundation models, these large artificial neural networks trained on huge amounts of data, and they produce unstructured outputs. In many cases, language, we talk about LLMs.The interesting thing is, you can train these foundation models not just to generate language, but you can generate a protein, or a drug candidate, as you were saying. You can imagine the prompt being, “Please produce 10 drug candidates that address this condition, but without the following side effects.” That's not exactly how it works, but roughly speaking, that's the potential to generate these things, or generate an electrical circuit, or a design for an air foil or an airframe that has these characteristics. Being able to just generate those.The interesting thing is, not only can you generate them faster, but there's this idea that you can create more variety. We're usefully proud as humans about our creativity, but also, that judgment or that training that we have, that experience sometimes constrains it. The famous example was some folks created this machine called AlphaGo which was meant to compete against the world champion in this game called Go, a very complex strategic game. Famously, it beat the world champion, but one of the things it did is this famous Move 37, this move that everyone who was an expert at Go said, “That is nuts. Why would you possibly do that?” Because the machine was a little bit more unconstrained, actually came up with what you might describe as a creative idea. I think that's one of the other potentials of using AI, that it could both absorb some of the experience that people have, as well as stretch the bounds of what might be possible.So you come up with the design, and then a variety of options, and then AI can help model and test them.Exactly. So you generate a broader and more voluminous set of potential designs, candidates, whether it's molecules, or chemicals, or what have you. Now you need to narrow that down. Traditionally you would narrow it down either one, through physical testing — so put something into a wind tunnel or run it through the water if you're looking at a boat design, or something like that, or put it in an electromagnetic chamber and see how the antenna operates. You'd either test it physically, and then, of course, lots of people figured out how to use physics, mathematical equations, in order to create “digital twins.” So you have these long acronyms like CFD for computational fluid dynamics, basically a virtual wind tunnel, or what have you. Or you have finite element analysis, another way to model how a structure might perform, or computational electromagnetic modeling. All these ways that you can use physics to simulate things, and that's been terrific.But some of those models actually take hours, sometimes days, to run these models. It might be faster than building the physical prototype and then modeling it — again, sometimes you just wait until something breaks, you're doing failure testing. Then you could do that in a computer using these models. But sometimes they take a really long time, and one of the really interesting discoveries in “AI” is you can use that same neural network that we've used to simulate cognition or intelligence, but now you use it to simulate physical systems. So in some ways it's not AI, because you're not creating an artificial intelligence, you're creating an artificial wind tunnel. It's just a different way to model physics. Sometimes these problems get even more complicated . . . If you're trying to put an antenna on an airplane, you need to know how the airflow is going to go over it, but you need to know whether or not the radio frequency stuff works out too, all that RF stuff.So these multiphysics models, the complexity is even higher, and you can train these neural nets . . . even faster than these physics-based models. So we have these things called AI surrogate models. They're sort of surrogates. It's two steps removed, in some ways, from actual physical testing . . . Literally we've seen models that can run in minutes rather than hours, or an hour rather than a few days. That can accelerate things. We see this in weather forecasting in a number of different ways in which this can happen. If you can generate more candidates and then test them faster, you can imagine the whole R&D process really accelerating.The business-adoption bottleneck (11:55)We know that companies are using AI surrogates, deep learning surrogates, already, but is it being applied as many places as possible? No, it isn't.Does achieving your estimated productivity increases depend more on further technological advances or does it depend more on how companies adopt and implement the technology? Is the bottleneck still in the tech itself, or is it more about business adaptation?Mostly number two. The technology is going to continue to advance. As a technologist, I love all that stuff, but as usual, a lot of the challenges here are organizational challenges. We know that companies are using AI surrogates, deep learning surrogates, already, but is it being applied as many places as possible? No, it isn't. A lot of these things are organizational. Does it match your strategy, for instance? Do you have the right talent and organization in place?Let me just give one very specific example. In a lot of R&D organizations we know, there's a separate organization for physical testing and a separate organization for simulations. Simulation, in many cases, us physics-based, but you add these deep-learning surrogates as well. That doesn't make sense at some level. I'm not saying physical testing goes away, but you need to figure out when you should physically test, when you should use which simulation methods, when you should use deep-learning surrogates or AI techniques, et cetera, and that's just one organizational difference that you could make if you were in an organization that was actually taking this whole testing regime seriously, where you're actually parsing out when the optimal amount of physical testing is versus simulation, et cetera. There's a number of things where that's true.Even before AI, historically, there was a gap between novel, new technologies, what they can do in lab settings, and then how they're applied in real-world research or in business environments. That gap, I would guess, probably requires companies to rewire how they operate, which takes time.It is indeed, and it's funny that you use the word “rewiring.” My colleagues wrote a book entitled Rewired, which literally is about the different ways, together, that you need to, as you say, rewire or change the way an organization operates. Only one of those six chapters is around the tech stack. It's still absolutely important. You've got to get all that stuff right. But it is mostly all of the other things surrounding how you change and what organization operates in order to bring the full value of this together to reach scale.We also talk about pilot purgatory: “We did this cool experiment . . .” but when is it good enough that the CFOs talks about it at the quarterly earnings report? That requires the organization to change the way it operates. That's the learning we've seen all the time.We've been serving thousands of executives on their use of AI for seven years now. Nearly 80 percent of organizations say they're regularly using AI someplace in the business, but in a separate survey, only one percent say they're mature in that usage. There's this giant gap between just using AI and then actually having the value be created. And by the way, organizations that are creating that value are accelerating their performance difference. If you have a much more productive R&D organization that churns out products that are successful in the market, you're going to be ahead of your competitors, and that's what we're seeing too.Is there a specific problem that comes up over and over again with companies, either in their implementation of AI, maybe they don't trust it, they may not know how to use it? What do you think is the problem?Unfortunately, I don't think there's just one thing. My colleagues who do this work on Rewired, for instance — you kind of have to do all those things. You do have to have the right talent and organization in place. You have to figure out scaling, for instance. You have to figure out change management. All of those things together are what underpins outsized performance, so all those things have to be done.So if companies are successful, what is the productivity impact you see? We're talking about basically the current technology level, give or take. We're not talking about human-level AI, superintelligence, we're talking about AI more or less as it exists today. Everybody wants to accelerate productivity: governments around the world, companies. So give me a feel for that.There are different measures of productivity, but here what we're talking about is basically: How many new products, successful products, can you put out in the market? Our modeling says, depending on your industry, you could double your productivity, in other words, of R&D. In other words, you could put out double the amount of products and services — new products and services — that you have been previously.Now, that's not true for every industry. By the way, the impact of that is different for different industries because for some industries you are dependent — In pharmaceuticals, the majority of your value comes from producing new products and services over time because eventually the patent runs out or whatever. There are other industries, we talk about science-based industries like chemicals, for instance. The new-product development process in chemicals is very, very close to the science of chemistry. So these levers that I just talked about — producing more candidates, being able to evaluate them more quickly, and all the other things that LLMs can do, in general, we could see potential doubling in the pace of which innovation happens.On the other hand, the chemicals industry — let's leave out specialty chemicals, but the commodity chemicals — they'll still produce ethylene, right? So to a certain extent, while the R&D process can be accelerated a great deal, the EBIT [Earnings Before Interest and Taxes] impact on the industry might be lower than it is for pharmaceuticals, for instance. But still, it's valuable. And then, again, if you're in specialty chem, it means a lot to you. So depending on where you sit in your position in the market, it can vary, but the potential is really high.The man-machine team (18:06)At least for the medium term, we're not going to be able to get rid of all the people. The people are going to be absolutely important to the process.Will future R&D look more like researchers augmented by AI or AI systems assisted by researchers? Who's the assistant in this equation? Who's working for who?It's “all of the above” and it depends on how you decide to use these technologies, but we even write in our paper that we need to be thoughtful about where you put the human in the loop. Every study, the conditions matter, but there are lots of studies where you say, look, the combination of machines and humans — so AI and researchers — is the most powerful combination. Each brings their respective strengths to it, but the funny thing is that sometimes the human biases actually decrease the performance of the overall system, and so, oh, maybe we should just go with machines. At least for the medium term, we're not going to be able to get rid of all the people. The people are going to be absolutely important to the process.When is it that people either are necessary to the process or can be helpful? In many cases, it is around things like, when is it that you need to make a decision that's a safety-critical decision, a regulatory decision where you just have to have a person look at it? That's the sort of necessity argument for people in the loop. But also, there are things that machines just don't do well enough yet, and there's a little bit of that.Are we ready? (19:33). . . AI is one of those things that can produce potentially more of those ideas that can underpin, hopefully, an improved quality of life for us and our children.If we can get more productive R&D, and then businesses get better at incorporating this into their processes and they could potentially generate more products and services, do we have a government ready for that world of accelerated R&D? Can we handle that flow? My bias says probably not, but please correct me if I'm wrong.I think one of the interesting things is people talk about AI regulation. In many of these industries, the regulations already exist. We have regulations for what goes out in pharmaceuticals, for instance. We have regulations in the aviation industry, we have regulations in the automobile industry, and in many ways, AI in the R&D process doesn't change that — maybe it should, people talk about, can you actually accelerate the process of approving a drug, for instance, but that wasn't the thing that we studied. In some ways, those processes are applied now, already, so that's something that doesn't necessarily have to changeThat said, are some of these potential innovations gated by approval processes or clinical trials processes? Absolutely. In some of those cases, the clinical trials process gait is not necessarily a regulation, but we know there's a big problem just finding enough potential subjects in order to do clinical trials. That's not a regulatory problem, that's a problem of finding people who are good candidates for actually testing these drugs.So yes, in some cases, even if we were able to double the amount of candidates that can go through the funnel on a number of these things, there will be these exogenous issues that would constrain society's ability to bring these to market. So that just says, you squeeze the balloon here and it opens up there, but let's go solve each of these problems, and one of the problems that we said that AI can help solve is increasing the number of things that you could potentially put into market if it can get past the other necessities.For a general public where so much of what they're hearing about AI tends to be about job loss, or are they stealing copyrighted material, or, yeah, people talk about these huge advances, but they're not seeing them yet. What is your elevator optimistic pitch why you may be worried about the impact of AI, but here's why I'm excited about it? Why are you excited by it?By the way, I think all those things are really important. All of those concerns, and how do we reskill the workforce, all those things, and we've done work on that as well. But the thing that I'm excited about is we need innovation, we need new ideas, we need scientific advancements, and engineering that turns them into products in order for us to improve their human condition, whether it's living longer lives, or living higher quality life, whether it's having the energy, whether it's to be able to support that in a way that doesn't cause other problems. All of those things, we need to have them, and what we've discovered is AI is one of those things that can produce potentially more of those ideas that can underpin, hopefully, an improved quality of life for us and our children.On sale everywhere The Conservative Futurist: How To Create the Sci-Fi World We Were PromisedMicro Reads▶ Economics* The Tariffs Kicked In. The Sky Didn't Fall. Were the Economists Wrong? - NYT Opinion* AI Disruption Is Coming for These 7 Jobs, Microsoft Says - Barron's* One Way to Ease the US Debt Crisis? Productivity - Bberg Opinion* So far, only one-third of Americans have ever used AI for work - Ars▶ Business* Meta and Microsoft Keep Their License to Spend - WSJ* Meta Pivots on AI Under the Cover of a Superb Quarter - Bberg Opinion* Will Mark Zuckerberg's secret, multibillion-dollar AI plan win over Wall Street? - FT* The AI Company Capitalizing on Our Obsession With Excel - WSJ* $15 billion in NIH funding frozen, then thawed Tuesday in ongoing power war - Ars* Mark Zuckerberg promises you can trust him with superintelligent AI - The Verge* AI Finance App Ramp Is Valued at $22.5 Billion in Funding Round - WSJ▶ Policy/Politics* Trump's Tariff Authority Is Tested in Court as Deadline on Trade Deals Looms - WSJ* China is betting on a real-world use of AI to challenge U.S. control - Wapo▶ AI/Digital* ‘Superintelligence' Will Create a New Era of Empowerment, Mark Zuckerberg Says - NYT* How Exposed Are UK Jobs to Generative AI? Developing and Applying a Novel Task-Based Index - Arxiv* Mark Zuckerberg Details Meta's Plan for Self-Improving, Superintelligent AI - Wired* A Catholic AI app promises answers for the faithful. Can it succeed? - Wapo* Power Hungry: How Ai Will Drive Energy Demand - SSRN* The two people shaping the future of OpenAI's research - MIT* Task-based returns to generative AI: Evidence from a central bank - CEPR▶ Biotech/Health* How to detect consciousness in people, animals and maybe even AI - Nature* Why living in a volatile age may make our brains truly innovative - NS▶ Clean Energy/Climate* The US must return to its roots as a nation of doers - FT* How Trump Rocked EV Charging Startups - Heatmap* Countries Promise Trump to Buy U.S. Gas, and Leave the Details for Later - NYT* Startup begins work on novel US fusion power plant. Yes, fusion. - E&E* Scientists Say New Government Climate Report Twists Their Work - Wired▶ Robotics/Drones/AVs* The grand challenges of learning medical robot autonomy - Science* Coal-Powered AI Robots Are a Dirty Fantasy - Bberg Opinion▶ Up Wing/Down Wing* A Revolutionary Reflection - WSJ Opinion* Why Did the Two Koreas Diverge? - SSRN* The best new science fiction books of August 2025 - NS* As measles spreads, old vaccination canards do too - FT Faster, Please! is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit fasterplease.substack.com/subscribe

迎收听雪球出品的财经有深度，雪球，国内领先的集投资交流交易一体的综合财富管理平台，聪明的投资者都在这里。今天分享的内容叫美股财报季来临，看看有哪些市场消息，来自股市马斯克。周三美股三大指数涨跌不一，鲍威尔新闻发布会之前，市场整体表现还是不错的，三大指数全部处于上涨状态。等到鲍威尔召开发布会，被记者一通提问之后，美股和黄金纷纷跳水。盘后微软和 META 公布二季度财报，大超预期，又把指数快速拉回。下面我按照时间顺序依次给大家解读一下市场的最新情况。首先是美联储议息会议，在美联储议息会议之前，美股盘前的时候美国就业数据网公布了6月的劳动力数据，大超预期，前值是-3.3万人，预期7.5万人，实际公布值为10.4万人。这样的数据自然是直接反映到了价格上，黄金大跌，美国10年期国债收益率上行。对于股市来说，就业数据强劲虽然降低了降息的预期，但是也说明劳动力市场稳健，经济基本面良好，是利好股市的，所以股市表现的就比较纠结。纳指100 ETF在数据公布之后明显震荡加剧，这期间成交量比之前都大一些。因为盘中还有美联储议息决议公布，所以股市表现的相对谨慎一些。等到凌晨2:00公布利率决议的时候，不出市场预期，果然没有降息，决定利率的委员会里面也只有鲍曼和沃勒这两位特没谱的拥护者投了降息票，这也在市场的意料之内。接着就是鲍威尔的发布会和记者提问，真正让市场扭转对9月降息的预期，让指数明显下跌的就是这个环节。在关于9月降息方面，鲍威尔明确说了现在谈9月是否降息还为时过早，他表示现在的一些经济数据已经反映出关税对通胀的影响，接下来6月的核心个人消费支出指数可能同比上升2.7%。在被问到美联储根据什么来判断是否应该降息以及何时降息时，鲍威尔表示这取决于从现在到9月的会议之间的数据从现在到9月议息会议之间会有两轮完整的劳动力与通胀数据。所以他是完全可以保持继续的观点的关于关税对通胀的影响，鲍威尔认为可能也是暂时的，这需要数据的验证，美联储也会采取措施将通胀压制成暂时性的，对于未来长期的通胀预期，他表示长期通胀依然在向着2%的目标前进。黄金现在这个位置也算是不错的加仓机会了，但如果接下来的两个月通胀数据真的出现反弹，那么对黄金来说肯定是利空的，所以短期内黄金面临的压力还是比较大的。虽然不一定会有特别大的跌幅，但是足以让黄金保持低位震荡的状态，还是以保持谨慎为主。利空说完了，我们再说下利好，微软、META 二季度财报大超预期微软和 META 的财报再次让我们见证了美股科技股的强劲表现财报公布后微软大涨8.28%，成为第二个市值登上4万亿的公司，META 盘后大涨11.49%，再创历史新高相关半导体公司也被带动，最相关的英伟达盘后涨2.29%再创历史新高，纳斯达克100指数主连现涨1.27%，再创历史新高。先说下微软，微软此前因为 OpenAI 寻求“去微软化”，市场一直担忧它的资本支出会不会降低，这次的财报实实在在的向大家证明了什么才是真正的王者。一季度微软包括租赁在内的资本支出为214亿美元，环比降幅5.3%，减少了12亿美元，但是二季度环比直接增长了13.1%，增幅28亿美元，在数据中心方面的投入完全没有放缓的迹象。营收方面，二季度营收764.4亿美元，同比增长18%，超过分析师预期的738.9亿美元。二季度每股净收益3.65美元，同比增长24%，超过分析师预期的3.37美元。二季度净利润272亿美元，同比增长24%，远超一季度的18%。这份财报首先是打破了市场对微软资本支出减速的担忧，另一方面还充分验证了微软利用 AI 为自己的业务全面赋能，在 AI 投入的帮助下公司各项业务都实现了大幅增长。下面再看下从抢显卡到抢人的 METAMETA 的二季度营收475.16亿美元，远超分析师预期的448.3亿美元。每股收益为7.14美元，远超分析师预期的5.89美元。META 现在就是在全力梭哈 AI，疯狂的投入，疯狂的增加算力，疯狂的抢人，努力推进为每个人打造“个人超级智能”的战略。在二季度的财报中公司将全年的资本支出下限从640亿美元提升到了660亿美元，公司重点在人才、基础设施、数据中心以及能源方面进行大量投资。人家甚至把三季度的营收指引还往上提了提，预计三季度营收区间为475亿美元到505亿美元，远超分析师预期的462亿美元。最后帮大家总结一下1、降息方面并不顺利，接下来通胀的方向还是比较大的，这部分对黄金和美债是利空，所以操作上黄金谨慎加仓，美债现在是布局机会2、美股方面，降息只是其中一个因素，影响美股的根本因素是科技股的财报，只要财报足够强，降不降息的影响不会很大，接下来继续做多，继续定投。

今天是寶博自己說的時間，要再來聊聊最近科技圈有什麼新進展？有什麼值得關注的議題呢？ 今天會聊到的新聞包括： 馬斯克xAI推出AI女友Ani！Grok AI伴侶功能陷暴力與情色爭議 亞馬遜推出「Buy for Me」功能！AI代理人幫下訂單，但怎麼防止駭客假冒你？ 全球5G用戶數已達24億，預估年底突破29億「5年後達63億」 Google砸250億美元擴建AI與資料中心，鎖定全美最大電網區 馬上就一起來關注這陣子世界發生的變化吧！ EP294｜Are you human? AI 時代的身份驗證 ！feat. Damien Kieran (from TFH) https://solink.soundon.fm/episode/54212f6d-d957-4389-9dfd-5a97636a4d29 - - - - - -- - - - - - 【寶博朋友說千萬粉絲專屬社群頻道 Discord 開張啦

In this episode Sarah hosts Vanessa Chang on Consider the Source to discuss AI. We've all been exposed to it in some form or another and the AI movement is often paralleled to the similar shift that was seen during the birth of the internet. However it is garnering a great deal more contention (Sarah doesn't actually know if it is more contentious as she was born in 1998 and does not remember the birth of the internet). Some topics that Sarah presses V on are: the big beautiful bill's relationship to AI, AI's relationship with climate change and energy demand, AI's affect on critical thinking, AI's ability to help minorities and often oppressed groups, AI's ability to help those with neurodivergence and much more. Where to find V:Subscribe to RE:Human — Weekly field notes for people making sense of AI Social Medias: LinkedIn, TikTok, Instagram Explore Mosaek — Consulting, coaching, & strategy for AI-literate ops Book a chat — Strategy sessions, keynotes, provocations Speaking inquiries As always make sure to follow& subscribe, leave a 5 star review, and be sure to follow @sarahsmilesnetwork on all social medias.Guest inquiries please reach out to: theaspiringadultpodcast@gmail.com

今天聊聊乐欧的创业转型之路，从平面设计到MR探索，带你了解创作者背后的故事。他是我亦师亦友的老大哥，在不同阶段对我影响深远。这次我们聊了他这些年来的改变，特别是现在押注MR领域的原因。他还分享了一些视觉案例和个人体验项目，让你更直观感受这个新时代的魅力。如果你对如何在AI时代找到新机遇感兴趣，不妨听听他的见解。因为这期节目里讲了很多视觉层面的案例和我亲身体验的几个MR项目，所以我会配合这期节目撰写一篇深度文章，小伙伴关注我的推文，一边听、一边看才是这期节目最好的打开方式。Show Time :02:15 从平面设计到科技探索的创业转型之路07:15 从平面到互动：内容创作的转型与逻辑的变化10:00 项目案例分析12:49 MR与 VR 的区别15:24 MR技术的挑战与市场前景探讨17:20 文旅项目中的创新技术应用与商业模式探索27:19 为什么认为 VR 的生命周期短29:40 数字内容的关键的核心要素32:08 作为设计师出身的老板，怎么保持对内容、商业的敏感度34:02 给想要进入该领域的同学的建议36:41 与 AI 的结合39:23 从设计师到创业老板：经验分享与职业转型BGM：Adam Green - Friends Of Mine02:12 公司转型与行业变革：设计公司的挑战与机遇04:12 营销创新：从平面广告到元宇宙，互动内容的演变之路07:28 探索互动内容创作：连接消费者与体验的全新方式11:13 互动广告的未来趋势：从单个项目到品牌整体形象的塑造14:58 M2：想象力的无限可能，挑战与前景展望18:41 戴上ynec艘Alice，感受沉浸式虚拟现实体验！22:27 旅游目的地盈利模式探索：流量转化与商业合作的机会26:12 如何让VR和文化遗产相互促进？探讨VR在传承文化遗产中的作用和前景29:55 数字内容的核心要素：应用场景与数字资产的价值关联33:42 H五、小程序、元宇宙漫游：传统与新兴技术的对比与思考37:25 AI工作流在场景设计中的应用与挑战，人与AI的协同作用是关键41:11 从内而外的转变：拥抱全新挑战的态度与方法

I nuovi Seminari su Intelligenza Artificiale, Digital Marketing e SEO de L'Accademia!https://accademia.searchon.it/https://www.youtube.com/watch?v=pWU3NobpCfoSarà un anno formativo pazzesco! Con una sorpresa per la SEO: grazie ai vostri feedback ci saranno ben due giorni!Focus 2025/2026: Intelligenza Artificiale & Nuove Strategie DigitaliIl nuovo anno accademico è completamente dedicato all'AI, con focus su:- SEO for AI- AI applicata a editoria e contenuti- AI per agenzie e strumenti avanzati (Agents Edition)- SuperTech e AI Engineering- La SEO nel mondo dell'AI!

自分のネガティブな感情をどう処理するか？AIに聞いてみた。 #ひとり語り #音声日記 #誹謗中傷 #アンチコメント #ネガティブな感情 #感情を吐き出す #感情と向き合う #AIに愚痴る #自己理解 #自己洞察 #自分の感情を受け入れる #自分を否定しない

▼ 【短期集中】夏のバイブコーディングキャンプ（8/6〜8/20の2週間）⁠⁠https://m.freelance-gakkou.com/p/r/wYh03QKi⁠▼ バイブコーディング入門講座（リアルセミナー）⁠https://freelance-gakkou-llachouse0804.peatix.com/⁠

→ メンバーシップ「存在としての後援」は⁠⁠⁠こちら⁠⁠⁠[現象のラジオ 第285話]未来への強い関心／未来重視／生成AI社会変革／技術的失業到来／コンサル縮小決断／10年後の社会／AI壁打ち議論／スピ系マルチ商法／AIレポート／独占しない／noteマガジン「趣味：未来」を開始／メンバーシップ特典として／週1火曜更新／ファクト完全未保証／方向感覚掴む程度に／「存在としての公園」の実践を継続・発展させていくために、⁠⁠⁠現象メンバーシップ⁠⁠「存在としての後援」⁠⁠⁠⁠⁠⁠にぜひご登録いただけると嬉しいです。いただいたご支援は公園や講演などの活動費にあてさせていただきます。・メンバーシップ「存在しての後援」は⁠⁠⁠こちら⁠⁠⁠・公園カレンダーは⁠⁠⁠こちら⁠⁠⁠⁠⁠⁠・現象のYouTubeチャンネルは⁠⁠⁠⁠⁠⁠こちら⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠・現象のラジオのご感想ご質問は⁠⁠⁠こちら⁠⁠⁠⁠⁠⁠⁠⁠現象：「存在としての公園」をコンセプトに、自らを“公園的なるもの”のシンボルと位置づけ、場づくりを中心としたさまざまな活動を行っています。ぜひ「現象の公園」に足をお運びいただき、あなた自身の感覚で体験してみてください。・⁠⁠現象の晩餐会（食事）⁠⁠・⁠⁠上水公園（レジャーシート）⁠⁠・⁠⁠ガクチョク（勉強）⁠⁠・⁠⁠ごみらじお。（ごみ拾い）⁠⁠・⁠⁠朝礼暮快（早起き）⁠⁠・⁠⁠十五分のベシャーリ（雑談）⁠⁠・⁠⁠パーフェクトタイム（映画鑑賞）⁠⁠⁠⁠⁠⁠⁠上水優輝：・Xは⁠⁠⁠こちら⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠・半生は⁠⁠⁠こちら⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

/Summary一杯牛奶，用二十年跨越了和奶业发达国家之间上百年的差距。一杯牛奶，也蕴藏着数智互联的万千可能。数智时代，伊利以“智”提“质”，实现产品和服务品质的全面提升。AI让国奶更自信，让牛奶更有爱，让健康未来触手可及。本期节目，用一杯牛奶，讲述中国AI力量的故事！/Show Note02:00 AI时代的奶牛追踪：探索中国AI发展的深度和广度04:00 中国AI进程速览：从大模型到算法再到算力的全链条分析07:32 AI大爆发：从开放AI到GPT，中国的AI力量强势崛起！11:17 中国人工智能力量的崛起与应用：探索医疗、教育、民生等重要领域的重要性14:59 元宇宙AI爆发，GPT引领AI革命，智联世界大会热点回顾及未来展望18:49 中国AI商业化之路：小模型带来的机遇与挑战22:37 中国AI发展：从单点突破到全栈自主，探索伊利AI大模型的奇迹26:24 AI赋能乳制品，伊利引领智能时代的品质与营养革命！30:10 伊利数智伊犁：养牛到智牛，每滴原奶都是科技精华33:56 欧盟标准之上的牛奶：伊利牧场的数智化之旅37:43 AI时代的牛奶革命：生产效率提升、成本降低、品质保证41:24 数智伊利：中国奶业的崛起与牛奶的信任重建/Staff主播 | 燃烧吧罗叔制作 | 燃烧吧罗叔文案 | 燃烧吧罗叔后期 | FirePod莎莎日程 | 腿哥/BGM ListRuning Feat 罗叔/Contact新浪微博：@燃烧吧罗叔抖音：燃烧吧罗叔公众号：头号玩家toGo合作微信： luoyoucai

美國總統川普，23日舉辦了「贏得AI競賽」的高峰會，並在會上，簽署一系列的行政命令，確立了他的任期的AI政策走向。川普政府的AI政策，可以用少監管、促創新形容，而這與前總統拜登的政策走向，可以說是截然不同。 留言告訴我你對這一集的想法： https://open.firstory.me/user/cku2d315gwbbo0947nezjmg86/comments YT收看《寰宇全視界》

A daily Chronicle of AI Innovations in July 25 2025Calling All AI Innovators | AI Builder's ToolkitHello AI Unraveled Listeners,In today's AI Daily News,

В этом выпуске у меня в гостях Сергей Макаров — CEO стартапа TalentWay, бывший тимлид Microsoft и серийный фаундер с проектами в России, Голландии и США.Мы обсудили, как AI меняет рынок найма, почему традиционные job boards и рекрутеры больше не справляются, и как работает его AI-агент, помогающий менеджерам по работе с клиентами находить работу мечты. Поговорили о профессиях будущего, тестах, Digital Twin, перспективах HRTech и о том, почему инженеры и senior-специалисты рискуют больше всех. Затронули тему карьерных ошибок, FOMO и как технологии могут помочь каждому найти работу, где действительно можно быть в своей силе.Сергей Макаров (Jay Makarov) - CEO компании Talentway (AI агент для помощи в поиске работы для Customer Success в США), ex Technical Team Lead at Microsoft.LinkedIn: https://www.linkedin.com/in/smaksmak/Сайт: https://talentway.ioБросил Голливуд и стал электриком с доходом $25 тысяч в месяц. Николай Семененко https://youtu.be/SJDP-VNCHhwThe Future of Talent Acquisition: How AI is Transforming Recruitment and Hiringhttps://medium.com/@sami.tatar/the-future-of-talent-acquisition-how-ai-is-transforming-recruitment-and-hiring-69589f6c7ee4The future of career navigationhttps://medium.com/emerge-edtech-insights/the-future-of-career-navigation-182ae81be8a7***Записывайтесь на карьерную консультацию (резюме, LinkedIn, карьерная стратегия, поиск работы в США): https://annanaumova.comКоучинг (синдром самозванца, прокрастинация, неуверенность в себе, страхи, лень) https://annanaumova.notion.site/3f6ea5ce89694c93afb1156df3c903abОнлайн курс "Идеальное резюме и поиск работы в США":https://go.mbastrategy.com/resumecoursemainГайд "Идеальное американское резюме":https://go.mbastrategy.com/usresumeГайд "Как оформить профиль в LinkedIn, чтобы рекрутеры не смогли пройти мимо": https://go.mbastrategy.com/linkedinguideМой Telegram-канал: https://t.me/prodcastUSAМой Instagram: https://www.instagram.com/prodcast.us/Prodcast в соцсетях и на всех подкаст платформахhttps://linktr.ee/prodcastUS⏰ Timecodes ⏰00:00 Начало7:10 Калифорния загибается?10:01 Как тебе пришла идея запустить AI агента для поиска работы?17:49 Почему Customer Success & Sales?23:24 Планируете ли вы выходить на новые роли?26:57 Куда сейчас движется HR tech? Какие тренды?36:15 Что ты думаешь про AI рекрутеров?45:25 Как видишь профессии будущего?49:29 Какие навыки будут востребованы в будущем?54:44 Кого точно не заменит AI?59:00 Психологические тесты при найме, как видишь их развитие в HR tech? 1:17:25 Что хочешь пожелать тем, кто сейчас ищет работу в США?

一場由AI啟動的全面變革，已經成為企業無法迴避的生存戰，有不少企業已經讓AI走向實際應用，包括大連化工養了一隻章魚哥來預測石化產業產能並比同業更精準判斷價格走勢，而離婚律師事務所詰律，則透過AI法律問答在5月內導客近2000人，就連大家常吃到7-11雞蛋的石安牧場，也透過大數據讓營收預測誤差僅0.5％。 然而，面對AI百花齊放的時代，卻有人選擇應用在普遍人眼中的AI後段班-NPO的身上，他就是資安軟體的先驅-趨勢科技創辦人張明正，為什麼從科技界退休的他想起動不一樣的AI應用？對於AI今後的演進與發展，人類又剩下哪些不可取代的獨特優勢？ 主持人：天下雜誌總編輯 陳一姍 來賓：趨勢科技創辦人/明怡基金會董事長 張明正 Steve 製作團隊：樂祈、邱宇豪 ＊延伸閱讀｜電子五哥到蛋農生存戰！全台追蹤企業AI落地，2027年刷掉落後者：https://lihi.cc/siVs2 ＊7/31 前訂閱《胡說科技》電子報，享有終生半價優惠：https://hi.cw.com.tw/u/j61pgcU/ ＊意見信箱：bill@cw.com.tw -- Hosting provided by SoundOn

Jensen Huang, a capo di Nvidia, si è recato a Pechino per la terza volta in un anno, confermando la grande rilevanza che il mercato cinese ha per la sua azienda. Accolto come un rockstar, Huang ha fatto un annuncio importante Le fonti audio della puntata sono tratte da: 黄仁勋点赞中国AI开源 称中美AI研究交流至关重要, Haokan Baidu, 15 luglio 2025. Learn more about your ad choices. Visit megaphone.fm/adchoices

《天堂M》超電女雷神首次登場，超高顏值，全能滿分，已就緒電爆全場！ 就是一出現，全服都麻了！ https://sofm.pse.is/7xcsv7 －－－－以上訊息由 SoundOn 動態廣告贊助商提供－－－－ 觀看影片

週一天下零時差關注以下財經大事： 一、日本參議院選舉揭曉，會如何影響日圓匯率？ 二、美國總統川普將公布「AI行動計畫」，有什麼利多政策？哪些企業將受惠？ 三、特斯拉公佈第二季財報，馬斯克回歸有挽救業績嗎？ 文：蔡娪嫣、伍芬婕、辜樹仁 製作團隊：莊志偉、張雅媛、鄭子鴻 ＊閱讀零時差，點這看全文

[ 收看本期【视频版播客】：点击收看 (国语字幕) ]千万级爆款AI短片《权利童话》幕后团队来了！【《权利童话》创作团队：小文、晓丹、文君】独家分享AI创作心得！灵感来源、技术突破点逐一公开！更以亲身经历分享：普通人想做AI视频，可以如何开始呢？听听文靖@foxfoxbee0131 / 文君@阿君的AI实验室 两位 AI 影视的资深创作者，揭开不为人知的有趣幕后故事！干货满满！【*观看《权利童话》原片：成年人应该看的白雪公主】【时间戳】00:01:12 第二天睡醒才惊觉《权利童话》爆红了00:02:02 《权利童话》这个作品花了多长时间做？00:02:22 印象最深刻的网友评论/反应00:03:14 主创的真实身份是一位家庭主妇!?00:04:25 "家庭主妇"和"Ai创作者"——身份随时转换00:04:42 这次作品在技术上，最高难度的是哪一段？00:05:40 成品中自己最满意的片段00:07:02 成品历经了非常多个版本的打磨00:10:24 《权利童话》的整体制作流程，二人的分工00:13:22 原本只做了一半，后半部分的诞生是计划之外00:15:27 《权利童话》用英文配音的幕后原因…00:16:12 《权利童话》的后续开发00:18:02 Ai视频创作对于普通人来说，有机会用吗？00:19:43 开启学习Ai视频创作的起点与历程00:21:45 从第一部成品到做出爆款，自身最大的改变00:22:24 “我找到了一个Ai非常积极的现实作用”00:24:52 作为编剧或导演投身Ai创作时的优势00:25:53 小文不只是一位编剧，更是一位女性编剧00:26:59 《权利童话》真正的创作出发点00:32:04 在Ai视频创作同行中最欣赏的人00:33:53 Ai视频创作在影视圈当中的处境00:36:30 获得海内外不少Ai短片项目奖项的感想

本期嘉宾：彭林、十天、蓝白、恺伦本期节目的主要内容有：· 关于 vivo 互联我们还有什么没说的· 英伟达 CEO 黄仁勋访华· OpenAI 推出 ChatGPT Agent 智能体· Grok 发布 AI 虚拟人物· 特斯拉公布 Model Y L 车型· 十铨发布全球首款自毁 SSD：一键毁灭全部数据 断电也可操作还有众多观众朋友的热心提问~每周五晚 8 点，爱否直播间，我们一起开心聊天

Welcome back to another eye-opening episode of Kaka Balli Punjabi Podcast – your go-to platform for bold conversations, unfiltered opinions, and ground-level reality checks on Punjab's social, political, and cultural landscape. This week, host Gagan Boparai is joined by the dynamic and insightful Ajaydeep Singh Dhaliwal to take on some of the most controversial, trending, and deeply rooted topics shaking the internet and real life today.From religious tensions to celebrity irresponsibility, youth pressure, and fake digital realities, this episode is a powerful blend of truth-telling, analysis, and debate.

在上一集中餐出海的节目中，我们聊到品牌落地美国的第一战：选址、服务、口味、与本地化挑战。但真正能在美国扎根下来的，克服这些困难与挑战还远远不够。本期节目，我们将视角从「门店前台」推进到「后端战场」，探索碎片化的美国餐饮供应链系统。 本期的嘉宾黄文冰曾经是一位 Fintech 创业者，后来转型为亚餐连锁经营者，现在已经在美国掌管了 7 个餐饮品牌、53 家连锁门店。节目中，他与我们分享了美国高度碎片化的供应链生态，以及他将「中国经验+本地打法」应用到美国市场的真实故事。 本期人物 丁教 Diane，「声动活泼」联合创始人、「科技早知道」主播 周玖洲 Aaron， 十年中金、华夏基金等顶级投资机构工作经验，「不止金钱」主播 黄文冰，前 Fintech founder，后来回到餐饮业参与餐饮投资，品牌运营扩张，和供应链收并购。现有 Portfolio 7个品牌，50多家门店，flowbetter.io 创始人 主要话题 [01:32] 从 Fintech 到开餐厅：像做房地产基金一样做餐饮 [06:45] 疫情期间「闭眼赚钱」？靠 Catering团餐盘活成本，选对赛道和客户 [10:17] 大城市不是破局点，「农村包围城市」在北美更可行 [14:17] 停留在纸单时代的美国餐饮，疫情后才开始用 Apple Pay [22:50] 供应商极度碎片化：开一家门店要联系 6 个供货商 [34:16] 标准化与中央厨房：降低对人的依赖，是连锁的前提 [39:35] 供应链爆发前夜：美国餐饮是 2008 年之前的中国吗？ [48:17] 从冷链仓储到系统软件，「绝味鸭脖」的供应链打法能否在美国复制？ [52:27] 今天的餐饮出海，需要 Being globally，而不是 Copy from China 幕后制作 监制：Yaxian 后期：迪卡 运营：George 设计：饭团 商业合作 声动活泼商业化小队，点击链接可直达声动商务会客厅 (https://sourl.cn/9h28kj)，也可发送邮件至 business@shengfm.cn 联系我们。

This week in The War Room, Carson Rich, Eric Dunavant, Drew Brown, and Kristina Schuler dive into what's really happening behind the headlines—from AI-driven market moves to new crypto tax proposals, the return of tariffs, and what the “Big Beautiful Bill” could mean for your wallet.

Join the Refrigeration Mentor Hub here Learn more about Refrigeration Mentor Customized Technical Training Programs at www.refrigerationmentor.com/courses In this episode, we dive into the emergence of AI in refrigeration training and troubleshooting. We cover how AI can streamline learning and problem-solving in the field, leveraging AI for quick access to information, proper usage of prompts, and ensuring sourced information reliability. We'll also share practical examples and specific AI tools like ChatGPT, Perplexity, and others.  In this episode, we discuss: -Importance of AI in refrigeration -Common concerns about AI -Using ai to enhance technical skills -Quick access to information with AI -AI prompts  -Examples of AI in action -Sourcing reliable information with AI -Troubleshooting with AI -Exploring different AI platforms -Using AI for email and troubleshooting -Creating training materials with AI -Future of AI in HVACR -Practical AI applications in refrigeration Helpful Links & Resources: https://chatgpt.com/ https://www.perplexity.ai/  Free Electrical Troubleshooting Webinar 

TOYOTA 轟動全台 震撼獻禮！本月入主指定車款，就送Sony天空聲道劇院，再享最高80萬0利率、最高3萬舊換新圓夢金、2萬配件金及5年14萬公里延長保固，總價值最高超過16萬元！了解更多： https://fstry.pse.is/7tyqcu —— 以上為 KKBOX 與 Firstory Podcast 廣告 —— #上流投資術 #財訊 #ai伺服器 #cpo #電源 加入會員，支持財訊！ https://open.firstory.me/user/ckijrbz8nehm50847mulgl7v6 AI 發展勢不可擋，全球對 AI 基礎設施的投資已達上兆美元。隨著輝達市值躍居全球第一，象徵著「AI 基礎建設時代」正式來臨！本集《上流投資術》將深入解析這波 AI 浪潮下，台廠的關鍵挑戰與無限商機。 《各節重點》 ＊ 輝達市值超越4兆美元，成全球第一 ＊ 全球的AI算力之戰，帶動更大規模投資 ＊ 新規格伺服器供電門檻帶來什麼商機？ ＊ AI基建帶動哪些領域的需求大爆發？ 留言告訴我你對這一集的想法： https://open.firstory.me/user/ckijrbz8nehm50847mulgl7v6/comments 製作｜財訊雙週刊 主持｜尚清林 來賓｜劉志明 企劃｜吳雨軒 攝影｜吳雨軒 剪輯｜吳雨軒 錄影時間｜2025.7.15 Powered by Firstory Hosting

馬斯克AI公司的聊天機器人Grok推出新的AI伴侶，包括日本動漫風的美少女Ani，和動物壞壞魯迪兩個虛擬角色。但Ani因為太過性感引發話題，美國反性剝削組織要求將這個角色下架。 留言告訴我你對這一集的想法： https://open.firstory.me/user/cku2d315gwbbo0947nezjmg86/comments YT收看《寰宇全視界》

資料標註員的技術門檻超級低，現今全球的資料標註員幾乎都在非洲國家，他們薪水低，而且都是短期勞工，契約一次只有幾個月，一個專案做完，他們就失業了，下個工作又得想辦法去爭取。 歡迎留言告訴我你對這一集的看法或者你閱讀這本書的心得

In today's episode of IA on AI, we cover an AI voice scam impersonating a high-ranking official, the sweeping new “Big Beautiful Bill” on AI regulations, and landmark AI guidance just released by the FRC. They break down:   •   A Marco Rubio impostor is using AI voice to call high-level officials - washingtonpost.com   •  ‘Big Beautiful Bill' Leaves AI Regulation to States and Localities … For Now - lawandtheworkplace.com   •  FRC publishes landmark guidance providing clarity to audit profession on the uses of AI - FRC.org   Be sure to follow us on our social media accounts on  LinkedIn: https://www.linkedin.com/company/the-audit-podcast Instagram: https://www.instagram.com/theauditpodcast TikTok: https://www.tiktok.com/@theauditpodcast?lang=en   Also be sure to sign up for The Audit Podcast newsletter and to check the full video interview on The Audit Podcast YouTube channel.  * This podcast is brought to you by Greenskies Analytics. the services firm that helps auditors leapfrog up the analytics maturity model. Their approach for launching audit analytics programs with a series of proven quick-win analytics will guarantee the results worthy of the analytics hype.   Whether your audit team needs a data strategy, methodology, governance, literacy, or anything else related to audit and analytics, schedule time with Greenskies Analytics.

YouTubeとSpotifyでビデオポッドキャスト公開中＜目次＞(0:00) AI時代の組織作り(6:06) 人間の管理職が増えるかAI従業員(13:19) AIエージェントってどう実際活用してるの？(21:15) 新米のしごとがリプレイスされてる…？！(24:10) 個人のAIエージェントチーム同士の相性(26:24) 社内文化 vs 個人のカスタマイズ(31:30) 無限コピーができる良さ(41:05) 経営者のAIシフト宣言とワークフローの効率化をどう進めるか(46:05) 結局人間がやったほうが早くね？(53:03) AIエージェントをどう誰がクビにする？情報はどこまで渡す？(1:03:30) 人間の情報取得力とフィルタリングって多いね(1:07:22) 盛り上げAIエージェント・古参AIエージェント＜参照リンク＞https://offtopicjp.notion.site/276-228c8b57e11480f7bcd6f3642bd82a06?source=copy_link＜About Off Topic＞Podcast:Apple - https://apple.co/2UZCQwzSpotify - https://spoti.fi/2JakzKmOff Topic Clubhttps://note.com/offtopic/membershipX - https://twitter.com/OffTopicJP草野ミキ:https://twitter.com/mikikusanohttps://www.instagram.com/mikikusano宮武テツロー: https://twitter.com/tmiyatake1

《槓桿不賭命》是一檔來自美股投資者的真實紀錄，我是主持人何星，我會與你分享如何聰明的使用槓桿放大指數投資的複利威力。一起在房價超高薪水超低的時代，提高金融邏輯與認知，換取更多選擇的自由。 https://fstry.pse.is/7tggyv —— 以上為 Firstory Podcast 廣告 —— 免費追蹤，更新資訊不漏接： https://open.firstory.me/join/clh1qknlp0h0s01w286nq3i04 加入會員，支持節目： https://open.firstory.me/user/clh1qknlp0h0s01w286nq3i04 留言告訴我你對這一集的想法： https://open.firstory.me/user/clh1qknlp0h0s01w286nq3i04/comments 歡迎您用一杯咖啡支持我持續創作 : https://pay.soundon.fm/podcasts/a11a2120-4bc4-4fb2-813b-135bd96e5868 六個月的線上陪伴計畫報名表： （2025.07 第二階段開始） https://reurl.cc/7KzaRb 「布姐的交誼廳。陪你聊人生聊職場」Line 社群 https://reurl.cc/36NWEL(密碼：love) 本集重點：基金會宗旨：推廣不分年齡層的「終身音樂教育」。音樂教育方式：透過音樂演出與課程（弦樂、管樂、鍵盤、打擊樂）讓大眾感受音樂。創意音樂會：結合武俠元素與古典音樂的創新演出。AI 音樂會：台灣舉辦全球第一場完整 AI 音樂會（如貝多芬第十號交響曲）。音樂無界：基金會推廣古典、現代、流行、科技跨界音樂。新作品亮點：《梁山伯與祝英台》交響樂形式改編，以西方交響樂重現華人經典愛情 IP。觀眾體驗主張：進音樂廳不用預設知識，放下解釋，用耳朵和內心感受故事。AI 與影像輔助：用 AI 影像串聯故事，讓年輕世代也能共鳴。 來賓 富瑜文教基金會執行長朱玉昌Jerry 《梁山伯與祝英台》黃梅交響詩 https://www.opentix.life/event/1915317192545685505 富瑜文教基金會致力音樂教育推廣，從古典到科技，以差異化樹立優勢形象，迄今陸續策畫製作台灣第一齣AI親子音樂劇場《時空玫瑰》，全球首場集結AI古典音樂大師作品的專場音樂會《時空邂逅》，將西方音樂大師作品與東方武俠作出完美結合，以及挑戰音樂家高難度演奏技巧的富瑜室內樂集《俠韻繫丹心》等，佳評如潮下，為台灣「音樂」環境開闢嶄新視野，並創造全新的表演邊界。

本期嘉宾：AI企培MCN机构CEO——Jony听完节目记得一定留言哈，让我们知道你们的存在最后记得去【蛋解创业】微信公众号回复：AI企业培训领取本期嘉宾福利今天节目我们请到AI领域大神JONY，带你了解AI咨询与企业培训的那些事儿。他怎么从金融转向AI？为何中小企业更需要外部AI培训？智能体如何赋能企业？听JONY分享真实案例，教你如何利用AI提效，解决文案、销售等实际问题。别错过这期满满干货的蛋解创业！02:04 中小企业的AI培训需求：五十人以下的公司为何需要专业指导？04:14 如何利用AI提高企业文案创作效率？——音频文字工作者的解决方案08:27 AI分析项目与一级市场报告：投资领域的两种不同关注点16:58 从月入两万到金领儿，我的创业经历与感悟！21:13 AI创业者的两种类型：感性型和理性型，你属于哪一种？25:25 AI夜校：企业培训中的AI应用案例分享，获客效果显著！29:42 如何利用AI提高工作效率？探讨工作计划制定和技能提升的方法33:56 如何养成持续学习的好习惯？分享两个有效方法！38:09 如何利用飞书多维表格提高工作效率？探讨兴趣与信息差的重要性。42:26 AI时代的企业培训需求：知识沉淀与效率提高解决方案46:41 AI企业培训：未来组织的新模式与人才发展机会

What are the benefits and risks of developing advanced AI? What kind of safety precautions could we take? Could we risk never making future discoveries, by over-limiting today's AI in pre-emptive safety regulations?In this episode we get a sceptical evaluation of the complex debate that's currently raging on artificial Intelligence safety, aiming to get a balanced view of the extremely useful applications versus the currently hugely publicised existential risks, and evaluate the safety measures and legislative frameworks that are being considered to help avoid risk to humans. To do this we trace the path from today's artificial intelligence right up the ever steeper curve towards artificial super intelligence; we risk risk assess the unpredictability of emergent properties of such systems; we assess the future of work, and the potential loss of control of our culture as AI starts to outnumber us and generate more and more of the media we consume.My guest today has a unique take on these issues which took me by surprise, as he disagrees with the alarmism and call for harsh regulation, whilst openly predicting that emergent properties will more or less guarantee safety hazards. The fact that he has been at the cutting edge of computer science for over 40 years, creating computer language and Ai solutions, makes him well placed to provide a counterpoint to the AI safety campaigners calling for collective action. He is of course physicist, computer scientist and tech entrepreneur Stephen Wolfram. In 1987 he left academia at Caltech and Princeton behind and devoted himself to his own computer systems at his company Wolfram Research. He's published many blog articles about his ideas, and written many influential books including “A new kind of science”, “A project to find the fundamental theory of physics”, and “Computer modelling and simulation of dynamic systems”, and most recently “The Second law” about the mystery of Entropy. What we discussed:00:00 Intro. 06:30 Stephen's first forays into neural nets in the early 80s.09:30 Cellular Automata.11:00 Can you make the knowledge of the world available via computers?13:00 Wolfram Alpha: A non-AI AI. 17:45 Can AI solve science?22:00 AI is great at rough answers, worse at the detail.33:00 Artificial General intelligence, A.G.I.42:00 The pros & cons of super intelligence.47:40 Chat GBT's unpredicted peculiarities.54:00 The spread of mistruth. 58:00 AI and the future of work.01:05:20 Businesses leading automation push.01:09:00 AI will outnumber us and network, changing our culture.01:11:00 AI will follow a banal ‘mean'.01:16:30 The AI Safety debate.01:21:00 We have no choice, it will be developed anyway.01:22:00 Ai systems may have feelings, we don't know.01:25:00 Stephen's non-interventionist safety approach.References: Stepehn Wolfram, “A project to find the fundamental theory of physics”,Stephen Wolfram, “The Second Law” The History of “Neural Nets” since 1943, (Warren McCulloch and Walter Pitts paper)Stephen Wolfram, “Can AI solve science?” article

《少年無限智》是一檔適合親子共聽的Podcast頻道，頻道內有三種節目『百科』擴展眼界知識，『快訊』掌握世界脈動，『異言堂』激發思辨能力。我們期許，每個有父母伴聽的孩子，都能成為知得多、想得深、辯得贏的智酷少年! https://fstry.pse.is/7vn9sd —— 以上為 Firstory Podcast 廣告 —— 【想看影片版？直接點這裡看更多！YouTube：https://youtu.be/ZwoZPqdmIaE】 隨著AI的迅速發展，我們的生活已經越來越離不開AI，從智能手機到智能家居，還有各種越來越好用的AI工具，善用AI不但可以節省我們的時間，更能豐富我們的生活。 以出版AI相關技巧及知識的出版商深智數位，副總經理段采伶本身就是一位AI控，認為AI已經融入我們日常生活，她舉例生活中的大小問題都可以詢問ChatGPT，畫圖也推薦可以使用Canva，就連開店的品牌口號、產品標語，甚至是粉絲專頁的互動貼文，都可以請AI協助生成。 最重要的是，你要怎麼下正確的指令給AI？要如何訓練AI在地化？聽了這一集會長知識喔！ 本集金句： 如果是要做一門生意的話，用ChatGPT是最好的，因為我們可以在店面還沒有開起來的時候，我們會有資金的考量，我們就詢問ChatGPT：「我們有這一筆錢，我要開立一家什麼樣的店，請你以專家的角色幫我做規劃。」等規劃出來之後我們還可以細部再詢問ChatGPT，等於是請他當一個顧問幫我們解說一下。 #段采伶 #AI #ChatGPT #Canva #AI行銷引爆術 深智數位 https://deepwisdom.com.tw/ 《AI行銷引爆術：用AI讓品牌業績翻倍成長》 https://www.books.com.tw/products/0011017379 #寶島聯播網 #寶島有意思 ---寶島有意思 準時放送--- 19：00~~~> 北部-寶島新聲 FM98.5 嘉義-嘉義之音FM91.3 高雄-主人電台FM96.9 21：00~~~> 中部-大千電台 FM99.1 ---------- 寶島有意思》挖掘台灣有意思的在地人、事、物。 寶島聯播網》以「本土電台」為品牌定位，用心傳承台灣在地文化，傳遞台語流行音樂之美。https://www.baodaoradio.com.tw/ 寶島好康商城》堅持提供高品質、安心、實惠的好康產品。https://www.bodogo.com.tw/ 加入會員，支持節目： https://open.firstory.me/user/clw4248xv113d01wg7s4h2xnr 留言告訴我你對這一集的想法： https://open.firstory.me/user/clw4248xv113d01wg7s4h2xnr/comments Powered by Firstory Hosting

全球最聰明的 AI 誕生了，而且它不是 GPT。xAI 推出的 Grok 4，在最新的 AI 大魔王考試裡，不只全場最高分，甚至學會了怎麼自己叫工具、自己算數學、還自己訂貨賣東西，靠經營虛擬販賣機賺了 4694 美金，撐了 324 天不崩潰。它的祕密武器叫做——巨量強化學習。這集我們就來聊聊：

最近我開始訓練我的團隊，訓練的是AI。 而且把自己搞得腦袋有點累。 從來沒有人嫌我反應太慢，但是這些日子以來我真的覺得：比起我來，他的反應快得多。 和AI協作後，讓我有一種「活到現在覺得自己有點笨」的自我認知。 請看看他如何優化一個寫書寫了40年的作者句子！很多時候我也心服口服。 有趣的是，AI居然還能「算命」！ 前陣子我心血來潮，問了AI塔羅「7月5日會有大地震嗎？」 抽了三張牌，結論是，情緒放大與恐慌，反映人們對未知的恐懼，提醒保持冷靜，懂得科學防災，比過度恐慌來得實在。 AI最後還補了一句：「塔羅沒辦法預測地震，但它可以映照你對未來的擔憂。」 你說，是不是挺有意思的？命理師大概也會這樣講吧！讓我來告訴你跟AI一起協作是什麼感覺，根本不是我在訓練他，是他在訓練我吧

你曾懷疑過，總是疲憊、健忘、情緒低落，真的只是太忙太累嗎？ 這些看似不起眼的身心變化，可能其實是大腦正在發出的警訊。 隨著科技進步，AI 已經能從腦波中，看見那些我們無法察覺的早期徵兆，協助提早發現憂鬱與認知退化風險。當健康風險變得「看得見」，也就更有機會「做得早」。 在這集節目中，我們將認識一種透過「看腦波」來預測憂鬱與失智的 AI 工具，看看科技如何幫助我們把握黃金治療時機，把健康危機擋在門外。 請下載收聽「未來會客室」，掌握最新健康情報！ ▶︎了解更多「永續健康」資訊：https://user249353.pse.is/7qrxw5 節目主持人：黃惠如 對談來賓：宏智生醫創辦人暨陽明交通大學電機系教授 劉益宏 製作團隊：天下整合傳播部、天下實驗室 本集節目由南山人壽與天下實驗室共同製作 -- Hosting provided by SoundOn

AI 在普通中学里的应用走到哪一步了？ 在过去的节目里，我们聊过 AI 时代对正在成长的这一批青少年的影响，也聊过创新学校是如何将 AI 应用于教育的，但在全国更多的普通中学里，在依然以高考为终点的那些教室中，那儿的 AI 应用又是怎样的？ 带着这样的好奇，我们采访了五位来自全国不同城市、不同学科的中学老师，聊了聊他们的真实经历与感受：AI 真的能帮老师减轻工作负担吗？用 AI 批作文、做课件好不好用？学生能不能在课堂上使用 AI，为什么？ 透过一线老师的叙述，我们一起去看一看 AI 在中国普通中学里，真实而复杂的落地图景。 本期人物 徐涛，声动活泼联合创始人 可宣，「声东击西」监制 贾老师，南京高中信息技术老师 王老师，北京初中英语老师 罗老师，上海初中语文老师 陈老师，上海初中生物老师 张老师，成都高中 PBL 老师 主要内容 [04:23] DeepSeek 带来的学校使用 AI 热潮 [08:46] 被 AI 教学系统折磨的老师们 [16:30] 在课堂上使用 AI 的学生，能玩出什么花样 [17:45] 用 AI 写作文的学生和用 AI 改作文的老师 [21:15] 老师们的担忧：AI 用得太多还是太少？ 给声东击西投稿 除了 AI 与老师，我们也想来听到自学生这端的故事，所以如果你正在大学里念书，不论年级高低，曾经通过 AI 做过一些有趣的事情，无论是帮自己偷懒，还是一些奇思妙想，或者非常稀奇古怪做法，都欢迎来投稿。 你的声音可能出现在未来的节目当中，我们非常期待你的分享！ 投稿入口 (https://eg76rdcl6g.feishu.cn/share/base/form/shrcne1CGVaSeJwtBriW6yNT2dg) 你也可以直接通过邮箱直接联系节目组：kexuan@shengfm.cn 节目推荐《梁柱之间 · 山西古建筑十讲》 《梁柱之间 · 山西古建筑十讲》是一档为古建筑旅行准备的播客节目。山西保存着全国 70% 以上的早期木构建筑，历史跨度近 1400 年。唐代的南禅寺、佛光寺，辽金时期的应县木塔、华严寺，以及元明清时期的永乐宫和晋商大院，这些散落在高原河谷中的坐标，谱写了一首用木头书写的合唱。 《梁柱之间·山西》选取了十座最具代表性的古建筑（古建筑群）作为主角，以「建筑–史料–人」的三重维度，为听众呈现一个立体而可感知的古代建筑奇迹。 相比于常见的对谈聊天型节目，《梁柱之间》是一档单集 20-30 分钟左右信息密度极高的单口节目，希望可以帮助听众连接建筑本身的细节和广阔的历史。本节目由可达、重轻、汉洋在 AI 的辅助下制作完成。 付费专辑《梁柱之间·山西》已上线小宇宙 app，感兴趣的朋友可以点击链接 (https://sourl.cn/GrRmJX)下单，客服会在一个工作日内将小宇宙兑换码发送给你。 声动活泼年度新节目上线！ 这次，我们倾全团队之力为 AI 时代的青少年做了一件微小但重要的事。 每期 10 分钟，从一个青少年感兴趣的现象谈起，涉及商业科技、社会人文等话题，解读表象背后的深层逻辑，启发青少年提出自己的好奇。每周一、三、五早晨 6 点在各大音频平台准时更新。

Is AI living up to the hype, or is it just another overblown bubble that's gonna pop? In this episode, Stephen Klein, a 66-year-old AI founder who is challenging the status quo in the world of artificial intelligence, joins me to expose what he believes is the truth about AI, from inflated promises to the real potential it holds for augmenting human intelligence. Trust me, this is what you won't be hearing in the headlines about AI. We jump into the much-hyped world of AI, and Stephen shares his no-holds-barred views on what's happening (and why speaking out is making him enemies in Silicon Valley). We discuss how many companies are rushing to market with products that aren't actually as groundbreaking as they claim, and how the industry itself often misleads us about AI's capabilities for their own benefit, leading with scary headlines about how all of us are being laid off because of AI.Stephen doesn't just talk about AI; he's actively shaping its future. But he's not just creating another automation tool. He's building AI that truly augments human intelligence, and that's where it gets interesting.Stephen's company, Curiouser.AI, is different. It focuses on creating AI that enhances our creativity and critical thinking, things that the industry is currently sidelining in favor of quick fixes and efficiency. He challenges the corporate culture surrounding AI and explains how the industry needs to move away from pure automation and toward true collaboration between humans and machines.We also jump into the wild world of LinkedIn, where Stephen's satirical posts challenge the hype surrounding AI and expose the industry's biggest players. If you've been confused about what this AI thing is all about or what to do, swept up in the AI frenzy or have doubts about where the industry is headed, this conversation will give you some powerful insights into what's really going on behind the scenes that you're unlikely to find in the typical media coverage.If you want to hear more from Stephen, check out his LinkedIn for more satirical insights and join the conversation on the future of AI!“I think that artificial intelligence is the single greatest innovation–invention–in the history of humanity, and I couldn't be more optimistic about where it's going.” ~ Stephen KleinIn This Episode:-An AI founder at 66-Creating AI that augments human intelligence-The misconceptions around ageism in tech-The impact of curiosity-driven AI on human creativity-Why people should be careful when using AI-AI's role in the future of work -How to deal with fear-The future of AI in business and ethics-The power of LinkedIn as a platform for your causeAnd much more!Resources:-Free Guide to LinkedIn Job Hunting for the 40+ Crew - https://www.itgetslateearly.com/job-guideConnect with Stephen Klein:-LinkedIn: https://www.linkedin.com/in/stephenbklein/-Website: https://curiouser.ai/Connect with Maureen Clough:-LinkedIn: maureenwclough - https://www.linkedin.com/in/maureenwclough/-Website:

Artificial intelligence is beginning to impact more and more of us in unexpected ways. Ralph shares about his recent experiences with it and alerts us to both the good and the evil it can do and how we need to situate ourselves in a way that reduces our chances of being deceived.