• 陶哲轩, 全球最伟大的数学家, AI人工智能水平尚未接近他的高度 - TJKCB - ♀   (7990 bytes) (2524 reads) 10/07/2024  13:26:32 (1)

• 帮他瞎吹。AI人工智能的数学高度，不如任何一位数学博士。Terence赶时髦，正儿八经的数学，他是不打算再做了。 - 蒋闻铭 - ♂   (0 bytes) (7 reads) 10/07/2024  14:54:10 (1)

• 还数学的莫扎特，真敢说。就讲对数学的贡献影响，他跟丘成桐，就没法比。一个好的Problem Solver而已。 - 蒋闻铭 - ♂   (0 bytes) (4 reads) 10/07/2024  14:57:31 (1)

• AI Math is his future! He got that right! - TJKCB - ♀   (0 bytes) (1 reads) 10/07/2024  16:54:30

• Maybe is his future, but it is unlikely the future of math - 蒋闻铭 - ♂   (23 bytes) (7 reads) 10/07/2024  17:37:36

• research. My humble opinion. ：） - 蒋闻铭 - ♂   (0 bytes) (1 reads) 10/07/2024  17:38:20

• 老（小）T a o 又不是毛主席，他能有方向，我们不应该为他高兴吗：）你希望他指引数学方向？ - JSL2023 - ♂  (0 bytes) (1 reads) 10/07/2024  18:04:34

• “industrial-scale mathematics” has never been possible! - TJKCB - ♀   (275 bytes) (14 reads) 10/07/2024  18:45:17

• 好像给我也推送了，可惜没认真看：）谢介绍。 - JSL2023 - ♂  (0 bytes) (1 reads) 10/07/2024  20:09:14 (1)

• 陶对deep learning的理解很肤浅。 - 衡山老道 - ♂   (0 bytes) (2 reads) 10/08/2024  04:30:31

• Tao 在这个事情上，花那么大的功夫，他的理解，肯定有独到的地方。我只是觉得这样吹他，吹他做的这个事，太夸张了。 - 蒋闻铭 - ♂   (0 bytes) (4 reads) 10/08/2024  06:04:02 (1)

• 这些数学家做什么，在美国，who cares.但是在中文媒体上，就大不相同。您没注意到，在美国再正常不过。 - 蒋闻铭 - ♂   (0 bytes) (2 reads) 10/08/2024  07:31:27 (1)

• deep learning的本质和统计学的regression类似，只不过是用神经网络，也就是一组单向依赖的线性方程组 - 衡山老道 - ♂   (0 bytes) (6 reads) 10/08/2024  12:46:07 (1)

• 来代表一组数据（样本），使得整体误差最小。这种以统计为基础的学习方法，需要和逻辑系统有机结合起来，不然就不可靠，不完整。 - 衡山老道 - ♂   (0 bytes) (4 reads) 10/08/2024  12:48:41 (1)

• 人的创新能力，有很多基础，如抽象，归纳，推广等，不可能用统计规律表达。 - 衡山老道 - ♂   (0 bytes) (1 reads) 10/08/2024  12:56:48 (1)

• It's impact, not methods, for Joe/Jane;ChatGPT can do sth - TJKCB - ♀   (137 bytes) (0 reads) 10/08/2024  13:59:53

• not clear How can he make “industrial-scale mathematics” ? - TJKCB - ♀   (545 bytes) (0 reads) 10/08/2024  14:04:03

• 能模型化，过程化的东西，都是相对简单的东西。世界的本质，超出了人脑结构所能认知的范围。 - 衡山老道 - ♂   (0 bytes) (1 reads) 10/08/2024  14:25:34 (1)

• That's why elder Einstein n Newton kept asking what God tink - TJKCB - ♀   (77 bytes) (0 reads) 10/08/2024  16:10:01

• not clear How can he make “industrial-scale mathematics” ? - TJKCB - ♀   (545 bytes) (2 reads) 10/08/2024  14:04:03

• 是炒作和噱头，数学既然是基础学科， 他也不懂什么是Industrial , 这就像盖房子， 数学是地基， industrial 涉及到到很多工业科技， 这些都是凌驾于地基之上的楼层， 做纯数学的底层是不了解的。

 - eciel567 - ♀  (145 bytes) (3 reads) 10/08/2024  14:59:51 (1)

• 纯理科（数理化） 是单一的基础学科， 数学 比物理和化学 要狭窄，生物专业更糟糕， - eciel567 - ♀  (206 bytes) (6 reads) 10/08/2024  14:58:48 (1)

• 生物专业狭窄? Not really, AI-neurolink came from biology - TJKCB - ♀   (0 bytes) (0 reads) 10/08/2024  16:12:26

• 纯理科（数理化） 是单一的基础学科， 数学 比物理和化学 要狭窄，生物专业更糟糕， - eciel567 - ♀  (206 bytes) (7 reads) 10/08/2024  14:58:48 (1)

• 生物专业狭窄? Not really, AI-neurolink came from biology - TJKCB - ♀   (0 bytes) (2 reads) 10/08/2024  16:12:26

• biology is anything but narrow. It is an inherently multidis - TJKCB - ♀   (3090 bytes) (0 reads) 10/08/2024  17:07:11

回答: 纯理科（数理化） 是单一的基础学科， 数学 比物理和化学 要狭窄，生物专业更糟糕， 由 eciel567 于 2024-10-08 14:58:48

Biology is anything but narrow. It is an inherently multidisciplinary science that not only integrates fundamental knowledge from other fields but also drives innovation across these domains. This expansive nature allows biology to explore the vast complexities of life and tackle modern challenges like personalized medicine, environmental conservation, and bioengineering, which can be detailed as below. 

1. Mathematics in Biology: Modern biology relies heavily on mathematical models to explain complex biological systems. Fields like population genetics, evolutionary biology, and systems biology use statistical models and mathematical theories to understand the dynamics of ecosystems, genetic variation, and regulatory networks within cells. Mathematical algorithms are essential for bioinformatics and genomics, helping to analyze vast amounts of genetic data.

2. Physics in Biology: Biophysics is a prominent interdisciplinary area where the principles of physics are applied to biological phenomena. The study of molecular motors, the mechanics of cells, and the physical forces that shape organisms (e.g., biomechanics) are all rooted in physics. Techniques like X-ray crystallography, nuclear magnetic resonance (NMR), and electron microscopy, which stem from physics, are indispensable for understanding biological structures at the molecular level.

3. Chemistry in Biology: Chemistry forms the backbone of molecular biology and biochemistry. The processes of life, such as DNA replication, protein synthesis, metabolism, and enzyme catalysis, are fundamentally chemical reactions. Understanding how biomolecules interact and how energy is transferred within cells requires a deep knowledge of chemistry.

4. Computer Science and AI in Biology: With the advent of big data, bioinformatics, and computational biology have become crucial for processing and analyzing biological data. Machine learning and AI are being used to predict protein structures, understand gene expression patterns, and even develop personalized medicine approaches. AI algorithms are also pivotal in drug discovery and the interpretation of genomic and proteomic data.

5. Interdisciplinary Nature: Unlike traditional fields that might seem more siloed, biology’s vastness and complexity force it to draw upon and integrate multiple disciplines. Advances in one area, such as AI, can lead to breakthroughs in biological research. Synthetic biology, for example, fuses biology, chemistry, and engineering to design new biological systems and organisms.

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Technology

Mind-reading devices can now access your thoughts and dreams using AI

We can now decode dreams and recreate images of faces people have seen, and everyone from Facebook to Elon Musk wants a piece of this mind reading reality

By Timothy Revell

For decades, neuroscientists have been trying to decipher what people are thinking from their brain activity. Now, thanks to an explosion in artificial intelligence, we can decipher patterns in brain scans that once just looked like meaningless squiggles.

“Nobody dreamed that you could get to the content of thought like we’ve been able to in the past 10 years. It was considered science fiction,” says Marcel Just at Carnegie Mellon University in Pennsylvania. Researchers have already peered into the brain to recreate films people have watched and decoded dreams.

Now the world’s biggest players in AI are racing to develop their own mind-reading capabilities. Last year, Facebook announced plans for a device to allow people to type using their thoughts. Microsoft, the US Defense Advanced Research Projects Agency and Tesla’s Elon Musk all have their own projects under way. This is no longer just a case of seeing parts of the brain light up on a screen, it is the first step towards the ultimate superpower. I had to give it a…

We’re Entering Uncharted Territory for Math

 

Terence Tao, the world’s greatest living mathematician, has a vision for AI.

By Matteo Wong

 

But technology companies are trying to get it there. Recent, attention-grabbing generations of AI—even the almighty ChatGPT—were not built to handle mathematical reasoning. They were instead focused on language: When you asked such a program to answer a basic question, it did not understand and execute an equation or formulate a proof, but instead presented an answer based on which words were likely to appear in sequence. For instance, the original ChatGPT can’t add or multiply, but has seen enough examples of algebra to solve x + 2 = 4: “To solve the equation x + 2 = 4, subtract 2 from both sides …” Now, however, OpenAI is explicitly marketing a new line of “reasoning models,” known collectively as the o1 series, for their ability to problem-solve “much like a person” and work through complex mathematical and scientific tasks and queries. If these models are successful, they could represent a sea change for the slow, lonely work that Tao and his peers do.

 he described a kind of AI-enabled, “industrial-scale mathematics” that has never been possible before: one in which AI, at least in the near future, is not a creative collaborator in its own right so much as a lubricant for mathematicians’ hypotheses and approaches. This new sort of math, which could unlock terra incognitae of knowledge, will remain human at its core, embracing how people and machines have very different strengths that should be thought of as complementary rather than competing.

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