AI Thoughts | Original
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Satya Nadella mentioned Jevons paradox. It is worth to learn.
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Yin Wang: There is no “intelligence” in artificial intelligence, no “neural” in neural network, no “learning” in machine learning, and no “depth” in deep learning. There is no “depth” in deep learning. What really works in this field is called “calculus”. So I prefer to call this field “differentiable computing”, and the process of building models is called “differentiable programming”.
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Yin Wang: Machine learning is really useful, one might even say beautiful theory, because it is simply calculus after a makeover! It is the old and great theory of Newton, Leibniz, in a simpler, elegant and powerful form. Machine learning is basically the use of calculus to derive and fit some functions, and deep learning is the fitting of more complex functions.
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Currently, large language models can’t filter by file language like YAML or Python. However, a significant portion of information in the real world is organized this way. This means that we could train large language models using files.
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For training large language models, we could develop a system that finds exact matches. Perhaps we can combine the KMP (Knuth-Morris-Pratt) search algorithm with transformer architecture to enhance search capabilities.
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There are no technological secrets. Open source will reveal all the secrets that are closely guarded.
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AI will affect many tools, including indirect ones. People say they won’t need Figma to draw prototypes, they will directly go to code. I think Postman will be similar; people will directly use Python or other scripts to call or test APIs.
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One reason we don’t use Postman or Figma in the AI era is that their functionalities can’t be generated through text. They also lack a command + K shortcut to trigger component replacement.
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User interfaces are becoming a barrier in the AI era. Why upgrade Postman to be AI-powered for testing applications when we can directly use Python’s requests library or other programming languages to test code, as the latter will be powered by AI?
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Why upgrade Figma to be AI-powered for UI creation when code-based UI generation, enhanced by AI, offers a more direct and potentially powerful approach?
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LLMs will change text-related applications first, like Google, Search Engines, Text Editors and Writing Tools, Quizlet, Zendesk, DeepL, Medium, WordPress, Trello, Asana, Gmail, GitHub, Goodreads, Duolingo, and Feedly.
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Conversely, LLMs are unlikely to revolutionize technologies like Git, Linux, ffmpeg, mobile phones, hardware, browsers, operating systems, or voice and video calls. These technologies are code-centric, and their code is not easily generated by AI, unlike API testing tools such as Postman.
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Technologies with more code are hard to be revolutionized by AI, like OpenOffice, MySQL, Mozilla Firefox, Chromium, VLC Media Player, Qt Framework, LLVM/Clang, and GNOME. If AI could help make these technologies, they wouldn’t be replaced. AI should be helping to make better technologies, and to do that, AI will need more computing power to generate the same magnitude of code.
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There are two ways LLMs can bring change: first, by altering the content or data within a platform or software, such as content translation in apps like TikTok; second, by directly replacing certain software or platforms, like Postman or Google Search, including Google Translate.
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There are two ways AI audio tools can bring change: first, by altering the content or data within a platform or software, such as generating audio books for Audiable; second, by directly replacing certain software or platforms, for example, the Sing songs app, as AI can now perform the same tasks humans do, making it easier for people to sing songs as a hobby.
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There are several ways to measure how AI impacts current software or platforms. One way is to measure how much data or content can be generated or improved by AI, either partly or completely. Another way is to measure how much code can be written or improved by AI, either partly or completely. This means we use what AI generates to improve current platforms. Additionally, AI can help invent new software and platforms.
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There are three types of products, Generative AI products, the products that use APIs of Generative AI products, other products.
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One product idea is to use AI to accumulate real-time information, news, or updates from social platforms such as Reddit, GitHub Trending, Twitter Trending, Quora Trending, and Zhihu Trending. Users can use prompts to customize the feed or even add specific social accounts.
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There are five important types of data: text, image, audio, video, and code.
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Other important types of data include numerical, geospatial, biometric, sensor, transactional, metadata, time-series, structured, unstructured, semi-structured, health, environmental, log, network, and behavioral data.
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Google is still better for website indexing, especially if you want to download software or a document from a specific site. It works like a domain search. You’re not using it to find information, but rather to navigate to other sites to perform tasks. An LLM may not have the latest download links.
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Google works like a domain search; if you want to go to a Maven repository site to check the latest version, you can use it.
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Google remains useful for image search, whereas LLMs excel at text generation. Still, people often prefer real images to verify hardware details, dimensions, object shapes, or a person’s appearance.
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AI chatbots are popular because text is harder to process than images. People prefer real images over AI-generated ones since images are easier to understand at a glance. However, AI image generation has untapped potential - users could ask AI to show different angles, zoom in on faces, or magnify circuit board details. Since people primarily work with text rather than images, there’s significant room for growth in AI image tools.
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AI excels at explaining concepts and facilitating understanding. Furthermore, users can pose questions regarding any specific detail. This is likely the most significant utility of AI tools.
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I used AI to learn about Large Language Models. The moment it helped me understand K, Q, and V was wonderful.
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The reason I prefer to use Ubuntu since the release of LLM is that the rich and colorful apps in macOS are less appealing to me. I prefer writing my programs and doing everything through the terminal and text.
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AI can be evaluated by how well it can update a pom.xml or requirements.txt file to the latest version, update libraries, and perform checks. This process can involve a significant amount of work and can sometimes be complex.
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In the AI era, programming languages that have better performance and robustness are more important and will be more popular, while syntax is less important. This is because LLM will help generate code, making it less of a hassle as long as the program executes well.
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People tend to read everything from AI chatbots because it is easy to learn, they can ask questions about any aspect, the format is consistent, and the quality is often among the best found on the Internet.