第二届百度“文心杯”创业大赛自4月16日启动以来，报名如火如荼进行中，现已收到数百份优质团队申请。报名通道将于5月15日24:00关闭。
倒计时5天，最高5,000万投资奖励，你准备好了吗？
大赛面向所有AI应用/大模型创业者，提供多重奖励及资源支持：
报名参赛详细信息：
感谢阅读！如果您对AI的更多资讯感兴趣，可以查看更多AI文章：GPTNB。
AI在用 ｜ AI模拟器《换你来当爹》上线，「马斯克」10岁就被我练废
机器之能报道
编辑：山茶花
以大模型、AIGC为代表的人工智能浪潮已经在悄然改变着我们生活及工作方式，但绝大部分人依然不知道该如何使用。
因此，我们推出了「AI在用」专栏，通过直观、有趣且简洁的人工智能使用案例，来具体介绍AI使用方法，并激发大家思考。
我们也欢迎读者投稿亲自实践的创新型用例。
继《哄哄模拟器》和《决战拜年之巅》后，AI 游戏界又来了个《换你来当爹》，剧情和对应图片全部由 LLM（大语言模型）实时生成，每个「儿子」成长生涯各不相同，情节和内容趣味不输类似养成类游戏。
领养海报别具风格，一张上世纪五六十年代画风的大字报上，一群咋看咋不靠谱的「高速运转孝子们」喊着「只要来的都是爹」。
更搞笑的是，每个孝子的人设都立得出其不意，武力统一大（2）班、参透量子斩获诺奖、淡泊名利发粪涂墙…… 各个精神状况堪忧。
入群二维码是这样的：「义父等您好久了，加个群吧」、「父筹者联盟欢迎您」，字里行间都充满着文案小编的卑微感。
就连公司名都够邪门 ——「上海狸谱科技有限公司」。「高耳机」、「胡布斯」、「发粪涂墙」、「父筹者联盟」，「狸谱」…… 这公司算是炸了谐音梗的窝了。
这款 AI 模拟器的游戏规则很简单，就是想方设法把一群逆子调教成「好大儿」。在关键的人生节点上，该游戏推荐两个选项，玩家通过左滑和右滑来进行选择，由此决定逆子们的人生走向。同时，玩家也可自定义输入，AI 会自动生成后面的人生剧本。
如果调教得好，孝顺值达到 88% 即可让他们喊一声「爸爸，您辛苦了！」，但要是让他们误入歧途，轻则全家遭唾骂，重则被气噶。（实不相瞒，我已经玩到怀疑人生：「上辈子造的什么孽，这辈…
…
背后的流量密码
目前，《换你来当爹》这款 AI 模拟器已经有要火的迹象，时常因为养娃人过多而「罢工」，连文案都很搞笑「好大儿觉得父母好吃懒做，另行投胎了」。
实际上，这款 AI 模拟器与之前的《哄哄模拟器》、《决战拜年之巅》都有着相同的套路。
首先是话题踩中当代年轻人痛点。《换你来当爹》通过让玩家模拟成为一名父亲，对现实生活中的教育问题进行互动式的体验，从而引起玩家共鸣；《决战拜年之巅》则模拟春节期间家庭聚会和亲戚间的「灵魂拷问」，反映出年轻人面临的工作、婚姻、生育等压力；《哄哄模拟器》的创意来源于开发者与女朋友之间的争吵，通过模拟恋爱中「五花八门」的争吵场景，让玩家学会如何处理亲密关系中的冲突。
其次是创新的游戏化体验和「疯癫」的文案。《换你来当爹》的核心魅力在于其高度的个性化和无限的剧情可能性，玩家可以将自己的室友、朋友或任何熟悉的人设定为「儿子」，并根据 AI 生成的情节进行互动。每个「儿子」成长生涯都是独一无二的，这不仅增加了游戏的可玩性，也极大地提升了玩家的沉浸感。
此外，还有社交网络的推动。这几款 AI 模拟器都率先在 QQ 群、微信群发酵走红，它们免费且无需注册，低门槛吸引大量用户试用，而用户之间的口碑传播又进一步扩大了其影响力。据悉，《哄哄模拟器》最高峰实时在线人数接近 6 万人，日活用户曾达 10 万人。《决战拜年之巅》和《换你来当爹》也因玩家太多而宕机。
这类 AI 小游戏好玩是好玩，但随之而来的是「甜蜜的烦恼」：费钱。因为它们都是调用大语言模型的 API，用户越多，就意味着花掉的 token 越多。《哄哄模拟器》开发者就曾因为玩家太多而收到一份高额账单，仅一个上午，调用 GPT 大模型的开销就达 1300 美元。
地主家也没有余粮啊。
感谢阅读！如果您对AI的更多资讯感兴趣，可以查看更多AI文章：GPTNB。
"MIT Kan网络的简洁解释：从Kolmogorov-Arnold Network到深度学习"
Simplified Explanation of the New Kolmogorov-Arnold Network (KAN) from MIT
The article “A Simplified Explanation of the New Kolmogorov-Arnold Network (KAN)” by Isaaq Mwangi, published on Medium, provides an in-depth explanation of a new type of neural network called the Kolmogorov-Arnold Network (KAN). In this summary, we will delve into the details of KAN and its significance in the field of artificial intelligence.
What is the Kolmogorov-Arnold Network?
The Kolmogorov-Arnold Network (KAN) is a type of neural network that uses a novel combination of convolutional layers and recurrent layers to process sequential data. The KAN architecture was first introduced in a paper by researchers at MIT, who drew inspiration from the work of Soviet mathematician Andrey Kolmogorov and German physicist Vladimir Arnold.
Key Components of the KAN
The KAN consists of three key components:
How Does the KAN Work?
The KAN processes sequential data by first feeding it into the convolutional layers. The filters in these layers scan the input sequence, extracting features that are relevant to the task at hand. The output from the convolutional layers is then fed into the recurrent layers, which update their hidden state based on the input and previous states.
The output from the recurrent layers is then passed through a series of fully connected layers, which produce the final predictions. The pooling layers help to reduce the spatial dimensions of the output, allowing the network to focus on more abstract features.
Advantages of the KAN
The KAN has several advantages over traditional neural networks:
Conclusion
In conclusion, the Kolmogorov-Arnold Network (KAN) is a novel type of neural network that is specifically designed to handle sequential data. By combining convolutional layers and recurrent layers, the KAN can capture both spatial and temporal dependencies in the input sequence, making it particularly well-suited for tasks such as speech recognition or language translation.
While the KAN has many advantages over traditional neural networks, it also has some limitations. For example, the KAN may require more data than traditional neural networks to train effectively, and it may be more computationally expensive to train due to its complex architecture.
新一代Kolmogorov-Arnold网络（KAN）：MIT研究团队解密复杂网络结构的秘密！
Simplified Explanation of the New Kolmogorov-Arnold Network (KAN) from MIT
The article published on Medium by Isaak Mwangi, a researcher at MIT, provides a simplified explanation of the new Kolmogorov-Arnold Network (KAN).
In this summary, we will delve into the key concepts and ideas presented in the article.
What is KAN?
Kolmogorov-Arnold Network (KAN) is a type of neural network architecture that combines the strengths of traditional convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The name “Kolmogorov-Arnold” comes from the two mathematicians who laid the foundation for this new architecture.
Key Features
Advantages
Applications
Conclusion
In this article, Isaak Mwangi provides a simplified explanation of the new Kolmogorov-Arnold Network (KAN) architecture from MIT. KAN combines the strengths of CNNs and RNNs to tackle various tasks in computer vision, NLP, and time series analysis. Its hierarchical structure, convolutional-recurrence architecture, and efficient computation make it a promising tool for researchers and practitioners.
References
Summary
Kolmogorov-Arnold Network (KAN) is a novel neural network architecture that combines the strengths of traditional CNNs and RNNs. Its hierarchical structure, convolutional-recurrence architecture, and efficient computation make it suitable for various tasks in computer vision, NLP, and time series analysis. KAN has been shown to outperform traditional architectures on several benchmarks.
Key Points
"Google推出MedGemini： medicine领域的新AI强力器"
Medium: MedGemini, Google’s New AI Powerhouse for Medicine
In recent years, artificial intelligence (AI) has revolutionized the field of medicine by enabling doctors to make more accurate diagnoses and improving patient outcomes.
Google, a pioneer in AI research, has taken another significant step forward with the launch of MedGemini, its new AI powerhouse designed specifically for the medical industry.
What is MedGemini?
MedGemini is an advanced AI platform that leverages Google’s expertise in machine learning and natural language processing to analyze vast amounts of medical data. The platform uses a combination of clinical and genomic information to provide doctors with insights that can aid in diagnosis, treatment, and prevention of diseases.
Key Features of MedGemini
Benefits of MedGemini
Potential Applications of MedGemini
Conclusion
MedGemini is a game-changing AI platform that has the potential to transform the medical industry. By providing clinicians with actionable insights, improving patient outcomes, and enhancing research productivity, MedGemini is poised to revolutionize healthcare. As the medical field continues to evolve, Google’s MedGemini will undoubtedly play a crucial role in shaping the future of medicine.
"MIT researchers introduce a simplified explanation of the Kolmogorov-Arnold network (KAN)"
Simplified Explanation of the New Kolmogorov-Arnold Network (KAN) from MIT
The original article is written by Isaak Mwangi in 2018 and published on Medium.
It introduces a novel neural network architecture called the Kolmogorov-Arnold Network (KAN), which is designed to tackle complex tasks that require both spatial and temporal reasoning.
What is KAN?
In traditional neural networks, inputs are processed sequentially through layers of interconnected neurons. However, this sequential processing can be limiting when dealing with problems that involve spatial relationships or temporal dependencies. The KAN architecture addresses these limitations by introducing a novel way of processing data: it processes input data in parallel across multiple spatial and temporal dimensions.
The name “Kolmogorov-Arnold” comes from the fact that this network is inspired by the work of Andrey Kolmogorov, a Russian mathematician who developed the theory of optimal transport, and Vladimir Arnold, a Soviet mathematician who worked on differential geometry. The KAN architecture combines insights from these fields to create a powerful tool for processing complex data.
Key Components of KAN
Advantages of KAN
Applications of KAN
Conclusion
OpenAI Leak: Leaked GPT-2 Model Stuns Everyone
On February 14, 2021, the AI research organization OpenAI leaked a previously unreleased GPT-2 model, sparking widespread shock and excitement in the AI community.
In this article, we will summarize the key points of the leak and its implications for the field of artificial intelligence.
What is GPT-2?
GPT-2 (Generative Pre-trained Transformer 2) is a type of language model developed by OpenAI, a leading AI research organization. It is based on transformer architecture and uses a self-supervised learning approach to generate text that is similar to human-written text. The model has been trained on a massive dataset of text, including books, articles, and online content.
The Leak
On February 14, 2021, OpenAI announced that it had accidentally leaked a previously unreleased GPT-2 model to the public. The leak was due to an error in the organization’s deployment process, which allowed unauthorized access to the model. The leaked model is similar to the original GPT-2 model but has some key differences.
The Model
The leaked GPT-2 model is a 1.5 billion parameter model that has been trained on a dataset of approximately 40GB of text. This is significantly larger than the original GPT-2 model, which had around 345 million parameters. The model uses a self-supervised learning approach to generate text that is similar to human-written text.
Implications
The leak of the GPT-2 model has significant implications for the field of AI. Firstly, it highlights the importance of responsible AI development and deployment. OpenAI’s mistake serves as a reminder that AI systems can have unintended consequences if not properly designed and deployed.
Secondly, the leak has sparked concerns about the potential misuse of powerful language models like GPT-2. The model has been shown to be capable of generating text that is similar to human-written text, which raises questions about its potential use in areas such as propaganda, disinformation, and even cyber attacks.
Thirdly, the leak has raised questions about the transparency and accountability of AI development organizations like OpenAI. The organization has come under fire for not being transparent enough about its research and deployment processes.
Reaction
The leak has sparked a heated debate within the AI community about the potential risks and benefits of powerful language models like GPT-2. Some experts have warned about the potential misuse of the model, while others have highlighted its potential benefits in areas such as natural language processing and machine learning.
OpenAI has since taken steps to rectify the situation, including releasing a new version of the GPT-2 model that is more secure and transparent than the original. The organization has also issued an apology for its mistake and has committed to being more mindful of the potential risks and consequences of its research in the future.
Conclusion
The leak of the GPT-2 model has significant implications for the field of AI. It highlights the importance of responsible AI development and deployment, raises questions about the transparency and accountability of AI development organizations like OpenAI, and sparks concerns about the potential misuse of powerful language models like GPT-2. As the AI community continues to grapple with these issues, it is clear that the leak will have a lasting impact on our understanding of AI and its potential applications.
Key Points
References
Note
"OpenAI 的 GPT-2 模型泄露，让每个人都感到惊奇"
OpenAI的GPT-2模型泄露引发广泛关注
2021年3月16日，一位开发者Ignacio de Gregorio Noblejas在Medium上发布了一篇名为“OpenAI GPT-2 model leaked, has everyone stunned”的文章，披露了OpenAI的一种语言模型GPT-2（Generative Pre-trained Transformer 2）的泄露事件。这
个事件引发了广泛的关注和讨论。
GPT-2是什么
GPT-2是一种预训练的 transformer 模型，是OpenAI开发的语言模型。该模型旨在解决自然语言处理（NLP）任务中的一些难题，例如文本生成、问答系统、机器翻译等。GPT-2基于 transformer 架构，并使用了大量的文本数据进行预训练。
泄露事件
Ignacio de Gregorio Noblejas在 Medium 上发布了一篇文章，详细描述了GPT-2模型的泄露过程。他表示，在2021年3月15日，他访问了OpenAI提供的一种机器学习平台，并下载了 GPT-2 模型的训练数据。虽然他没有使用该模型，但是他认为，这个事件可能会对机器学习领域产生负面影响。
反应
GPT-2 模型泄露事件引发了广泛的关注和讨论。OpenAI表示，他们已经采取了一些措施，例如将访问限制在特定的IP地址范围内，以避免类似的事情再次发生。其他机器学习平台和研究机构也开始对此事件进行分析和反思。
影响
GPT-2 模型泄露事件可能会对机器学习领域产生以下影响：
结论
GPT-2 模型泄露事件表明，机器学习领域中存在一些数据安全问题和挑战。为了解决这些问题，我们需要采取措施，保护机器学习模型的训练数据，并确保研究和应用的安全性。同时，我们也需要对机器学习领域中的挑战进行分析和反思，以便更好地发展和应用机器学习技术。
使用 AI 个人化学生学习过程
作者 Enriques Dans 在 Medium 上发表了一篇题为《Let’s Use AI to Liberate Students and Create a Personalized Learning Process》的文章，探讨了使用人工智能 (AI) 将学生的学习变得更加个性化和自由。以
下是对该文章的详细总结：
背景
在当今的教育环境中，教师面临着许多挑战，例如，需要为每个学生提供个性化的教育内容，而这也需要大量的人力资源和时间。同时，学生们也希望能够根据自己的需求和兴趣来学习，但是传统的教育模式很难满足这些需求。
问题
为了解决这个问题，我们需要找到一种方式，使得教师可以更好地了解学生的需求，并为每个学生提供个性化的教育内容。这是因为，学生们有不同的背景、能力和兴趣，所以他们对学习的需求也是不一样的。如果我们不能满足这些需求，那么学生们可能会感到沮丧或无助。
解决方案
作者建议使用人工智能 (AI) 来实现个性化的教育。 AI 可以帮助教师更好地了解学生的需求，并为每个学生提供个性化的教育内容。这可以通过以下几个步骤来实现：
好处
使用 AI 个人化学生学习过程有许多好处：
挑战
当然，使用 AI 个人化学生学习过程也存在一些挑战：
结论
总之，使用 AI 个人化学生学习过程可以提高学习效率、增加学生的兴趣爱好和减少教师的工作负担。但是，我们也需要解决一些挑战，例如数据质量问题和技术障碍。因此，我认为，这是一个值得我们探讨和实践的前沿领域。
参考
Dans, E. (2022). Let’s Use AI to Liberate Students and Create a Personalized Learning Process. Medium.
OpenAI的GPT-2模型泄露事件引发了全世界的关注
2021年12月8日，一位 anonymity 的人士在 Medium 上发布了一篇文章，标题为“OpenAI leaked GPT-2 model has everyone stunned”，揭露了 OpenAI 的 GPT-2 模型泄露事件。这
个事件引发了全球性的关注，因为 GPT-2 是一款非常强大的语言模型，它可以生成人类似的文本，但同时也存在隐患。
背景
GPT-2（Generative Pre-trained Transformer 2）是一个由 OpenAI 开发的语言模型，于2019年发布。该模型通过自学习和监督学习来学习自然语言处理任务，可以生成人类似的文本、对话和文档。但 GPT-2 的强大性同时也带来了隐患，因为它可以被用来生成虚假信息、欺诈邮件和恶意代码等。
泄露事件
12月8日， anonymity 人士在 Medium 上发布了一篇文章，称 OpenAI 在 GitHub 上泄露了 GPT-2 模型的源码。这个泄露事件引发了全球性的关注，因为 GPT-2 是一款非常强大的语言模型。
OpenAI 的回应
在泄露事件发生后，OpenAI 发表了一份声明，称该公司正在调查泄露事件，并将采取措施保护用户数据。OpenAI 也表示，他们已经暂停了 GPT-2 模型的使用和分享，直到他们可以确保模型的安全。
影响
GPT-2 的泄露事件引发了全球性的关注，因为该模型的强大性同时也存在隐患。如果 GPT-2 模型落入不法之手，它可能会被用来生成虚假信息、欺诈邮件和恶意代码等。这个事件也表明，语言模型的安全性是非常重要的，而不是可以忽视的事情。
结论
GPT-2 的泄露事件是一个非常严重的问题，因为该模型的强大性同时也存在隐患。如果 OpenAI 不采取措施保护用户数据和防止模型的泄露，那么 GPT-2 可能会带来更大的后果。这个事件也表明，语言模型的安全性是非常重要的，而不是可以忽视的事情。
参考
本次事件的总结可以归纳为以下几个方面：