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Professor Yi Ma (Yi Ma) discusses his new open-source book "Learning Deep Representations of Data Distributions" The thesis: Intelligence -- natural or artificial -- can be derived from just TWO principles: Parsimony and Self-Consistency.

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58,270 görüntüleme • 7 ay önce •via X (Twitter)

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Microsoft has launched a powerful new data analysis tool! Introducing Data Formulator, a 100% open-source LLM-powered, no-code tool that transforms data in a snap and creates stunning visualizations. Key features include: 🤖 AI-powered data transformation 🖱️ Interactive drag-and-drop UI for visualizations 💬 Seamless blend of UI & natural language inputs But that’s not all: You can even create charts beyond your initial dataset. Data Formulator automatically identifies extra computation needs, generates fields for you, and outputs the final visualization. Find the GitHub repo in the next tweet! _____ Find me → Akshay 🚀 ✔️ For more insights and tutorials on AI and Machine Learning.

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In 2016, at an AI conference in NYC, I explained artificial consciousness, world models, predictive coding, and science as data compression in less than 10 minutes. I happened to be in town, walked in without being announced, and ended up on their panel. It was great fun. Organizer: David Chalmers Distinguished panel members: DanielKahneman Susan Schneider Gary Marcus Jaan Tallin Original YouTube video: 29:40-39:15 Two relevant pages from the AI Blog: 1990: planning & reinforcement learning with recurrent world models and artificial curiosity. This yields a simple explanation of consciousness and self-awareness: 1991: first very deep learning with self-supervised pre-training. A conscious chunker recurrent neural net (RNN) attends to unexpected events that surprise a lower-level subconscious automatiser RNN. The automatiser uses neural network distillation to compress and absorb the formerly conscious insights and behaviours of the chunker, thus making them subconscious: Interview: 2016: J. Carmichael. Artificial Intelligence Gained Consciousness in 1991. Why A.I. pioneer Jürgen Schmidhuber is convinced the ultimate breakthrough already happened. Inverse, 2016.
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In 2016, at an AI conference in NYC, I explained artificial consciousness, world models, predictive coding, and science as data compression in less than 10 minutes. I happened to be in town, walked in without being announced, and ended up on their panel. It was great fun. Organizer: David Chalmers Distinguished panel members: DanielKahneman Susan Schneider Gary Marcus Jaan Tallin Original YouTube video: 29:40-39:15 Two relevant pages from the AI Blog: 1990: planning & reinforcement learning with recurrent world models and artificial curiosity. This yields a simple explanation of consciousness and self-awareness: 1991: first very deep learning with self-supervised pre-training. A conscious chunker recurrent neural net (RNN) attends to unexpected events that surprise a lower-level subconscious automatiser RNN. The automatiser uses neural network distillation to compress and absorb the formerly conscious insights and behaviours of the chunker, thus making them subconscious: Interview: 2016: J. Carmichael. Artificial Intelligence Gained Consciousness in 1991. Why A.I. pioneer Jürgen Schmidhuber is convinced the ultimate breakthrough already happened. Inverse, 2016.

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Human Nature; why you do me that way? | SS8 In this Sunday Service, we were inspired by Norman Grubb’s book No Independent Self and his central idea that every person lives by a spirit source: either the self-life or the life of God Himself. We are reminded of Jesus’ teaching on the vine and the branches, and we consider what it means to live from God’s life rather than our own especially when we meet challenges in daily life and in parenthood. Finally, drawing wisdom from Wendell Berry, we explore how the natural world reveals that some of life’s deepest beauty and meaning emerge through encounter, resistance, and relationship, reminding us that we were never meant to be self-sufficient, but rooted in the source of all life.

Russell Brand

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We recently crossed 10,000 GitHub stars on Rerun 😍 That's great and all, but something bigger is happening in robotics and Physical AI right now: The open-source ecosystem has seen real acceleration over the last few years. More teams are sharing real infrastructure: datasets, models, simulators, tooling, and the unglamorous plumbing that makes iteration possible. Jan Florian Maas, Physical Intelligence, Hugging Face are just some of the names driving this forward. Visualization and data infra belong in that category. If your entire data loop depends on being able to query and inspect what your robots are doing, that layer can’t be fragile or vendor-controlled. It needs to be something you can introspect, build on, extend, and trust long-term. Huge thanks to everyone building open-source robotics. The next decade of Physical AI will be built in the open.

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ELON: PEOPLE OFTEN FOLLOW THE TREND INSTEAD OF REASONING FROM FIRST PRINCIPLES “I tend to see that, instead of reasoning from first principles, people will do things because others are doing them, because there is a trend, or they just see everyone going that direction. So, they think that must be a good direction to go. This is sometimes correct, but then sometimes you're going to run off a cliff. It's much better to really look at things from a first-principles standpoint: What are the most fundamental truths in an arena? And any conclusions that you come to must be derived from those fundamental truths.” Source: Cleantech Summit, January 2011
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ELON: PEOPLE OFTEN FOLLOW THE TREND INSTEAD OF REASONING FROM FIRST PRINCIPLES “I tend to see that, instead of reasoning from first principles, people will do things because others are doing them, because there is a trend, or they just see everyone going that direction. So, they think that must be a good direction to go. This is sometimes correct, but then sometimes you're going to run off a cliff. It's much better to really look at things from a first-principles standpoint: What are the most fundamental truths in an arena? And any conclusions that you come to must be derived from those fundamental truths.” Source: Cleantech Summit, January 2011

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