Company Overview - Prepared is an AI emergency response platform transforming public safety, starting with 911 call centers [1] - The company originated as an idea in a Yale dorm room and has rapidly grown into a nationwide platform [1] - Prepared aims to augment emergency response through AI [1] AI in Emergency Services - Prepared is using AI to solve macro problems for 911 call centers [1] - The company integrates voice AI into sensitive environments [1] - AI agents in emergency services require a "human-in-the-loop" approach during crisis moments [1] Customer & Market - Customer reactions to using AI in 911 call centers have been positive [1] - Building trust with government agencies is crucial for adoption [1] - AI's future in government and public services is just beginning [1] Future Trajectory - Prepared is focused on scaling its operations [1] - The company has goals set for 2025 [1]

AI在教育领域的应用现状 - 教师是AI教育工具的主要使用者，他们利用AI来提高工作效率，尤其是在行政管理方面，如评分、反馈和课程设计[14][16] - K12教育领域对AI仍持谨慎态度，但约80%的学区已成立生成式AI团队，积极寻求和评估新技术[8] - 高等教育在AI应用方面领先，许多大学与OpenAI等公司合作，试点AI教育平台，并将其视为学生未来工作和生活必备工具[9] - 尽管AI在教育领域的应用迅速发展，但目前主要集中在辅助工具层面，如生成工作表和作业，尚未深入到核心教学环节[23][35] AI对学习效果的影响 - 评估AI在教育中效果的关键在于长期跟踪学生的学习成果，但目前AI主要在教育边缘，难以评估其独立影响[23][24] - 投资者关注用户留存率和参与度，每周使用天数是衡量产品好坏的指标，如果学生仅为完成作业而使用，参与度会下降[20][21] - Alpha School等私立学校通过全面应用AI，学生成绩显著提高，但其高成本和特殊性使其经验难以直接推广到公立学校[26][30] 未来发展趋势 - 未来一年，高等教育领域有望在AI应用方面取得显著进展，探讨大型模型公司与教育机构的合作模式[66] - 行业期待出现由AI驱动的、具有高度个性化的虚拟教师，能够根据学生的学习进度和风格进行实时调整[68][70] - 脑洞大开的短视频内容正在成为一种新的学习模式，通过优化内容和呈现方式，提高学习的趣味性和参与度[41][53] - 教材公司在AI教育的未来发展中扮演关键角色，他们需要积极创新，与AI公司合作，将AI技术融入现有内容，避免业务被蚕食[56][57]

Bot Traffic Landscape - 50% of internet traffic is bot traffic, indicating a significant portion of online activity is automated [1][52] - AI-driven agents are poised to cause an explosion in automated traffic, necessitating a shift in how websites manage and filter traffic [1][53] - Simply blocking AI traffic is not the right approach; a nuanced understanding of the purpose, behavior, and origin of bots is crucial [1][53] Challenges in Bot Management - Traditional methods of blocking bots based on IP addresses or user agents are becoming increasingly imprecise and can lead to blocking legitimate traffic [6][7] - Distinguishing between good and bad bots is a key challenge, especially with AI bots acting on behalf of humans [4] - Legacy providers' network-level blocking is insufficient for modern applications, as it lacks application context [12][13] Granular Control and Application Context - Application context is crucial for making nuanced decisions about allowing or denying traffic, especially for e-commerce operations where blocking transactions can result in lost revenue [8][9] - Site owners need to understand what kind of automated traffic they want to allow and what they are getting in return [5] - Developers, site owners, and security teams need to make nuanced decisions to understand whether traffic should be allowed or not [9] Techniques for Bot Detection and Management - Building layers of protection, starting with robots.txt, managing IPs, and understanding traffic origins is essential [34] - Reputation databases around IP addresses, considering factors like country of origin and network, can aid in decision-making [34][35] - Fingerprinting techniques, such as J3 and J4 hashes, analyze session metrics to identify and block malicious clients [40][41][42][43] The Future of Bot Management and AI - AI is driving significant revenue to companies, and blocking AI traffic indiscriminately can harm business [14] - The industry is moving towards verified, well-behaved AI crawlers that follow rules, making it easier to detect bots with criminal intent [58][59] - Emerging technologies like Privacy Pass and Cloudflare's automated request fingerprinting aim to identify and authenticate automated clients [47][48]

AI Video Advancements - AI video is rapidly dominating social media, with V3 being a pivotal moment similar to ChatGPT for AI video [1][4][5] - V3, Google DeepMind's video model, generates both audio and video from text prompts, enabling full talking-head videos [7][8] - V3 is limited to 8-second generations and only generates audio from text prompts, leading to creative workarounds for longer videos [9][10] - "Faceless channels" are emerging, allowing AI-generated characters to tell stories without the need for a human face [15][16] Accessibility and Pricing - V3 was initially exclusive to Google AI Ultra plan at $250 per month, causing hype and FOMO [12] - V3 is now accessible via API through platforms like Hedra and Crea at around $10 per month, or through pay-per-video platforms like Fall or Replicate at approximately 75 cents per second [13][14] Future Expectations - Industry anticipates Google to develop larger models capable of generating longer videos, while also addressing coherence and pricing challenges [17] - The market expects more condensed, optimized models that can perform similarly at a lower cost [17] Voice AI Updates - ChatGPT's advanced voice mode has been updated to be more human-like, enabling real-time consumer voice conversations [18][19]

AI 视频工具概览 - A16Z 的 Justine 分享了她用于创作 AI 视频的工具栈，主要面向消费者创作者 [1][2][3] - 强调了在众多 AI 模型中选择合适工具的重要性，不同的模型有不同的优势 [2][3] 文本生成视频 - V3 被认为是目前最佳的文本生成视频模型，可通过 Google Labs 中的 Flow 工具访问 (labs.google/fx/tools/flow) [3][4] - 使用 V3 需要 Google Ultra AI 订阅 [4] - V3 的文本生成视频功能支持原生生成音频，而帧到视频和成分到视频功能则不支持 [4][5] - 建议每次提示生成两个输出，并确保模型设置为 V3 以避免被切换到 V2 [5][6] - 建议使用简洁的提示，并通过迭代来优化结果 [7] - 如果文本内容不足以填充 8 秒的音频，模型可能会生成奇怪的填充词 [9] 图像生成视频 - Cling 2.1% 是从图像生成视频的首选模型，用于动画化图像，使人物或背景移动 [13] - Cling 2.1% 目前仅支持起始帧，但未来可能会增加更多帧 [14] - 用户可以上传图像或从历史记录中选择，并使用灵感和预设来控制相机移动 [14][15] 角色口型同步 - Hedra 是使角色说话的首选工具，需要起始帧（角色图像）、音频脚本和文本提示 [18][19] - Hedra 允许用户生成语音、录制音频或上传音频，并支持克隆用户自己的声音 [20][21] 视觉特效 - Higsfield 是一个视觉特效平台，用户可以浏览和运行其他用户创建的效果 [27] 开放源代码模型测试 - Korea 是一个多模态生成和编辑平台，允许用户在不同的模型上运行相同的提示和起始图像 [30][32] - Korea 提供了多种模型，并允许用户使用 Topaz 或 Korea 自己的模型来增强 AI 输出 [34]

So robot body process automation is literally if human had to do something you basically open some browser or whatever take some data put into some other system click some button and all that stuff. So it records that human clicks on that desktop and tries to keep repeating it. So you kind of like get that automated and the hard part that they had is you can't do robotic process for unstructured data because it's not fixed they change it.So anything will be very very brutal. The bet that we are taking is th ...