陶哲軒, 全球最偉大的數學家, AI人工智能水平尚未接近他的高度

• 陶哲軒, 全球最偉大的數學家, 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)

• 幫他瞎吹。AI人工智能的數學高度，不如任何一位數學博士。Terence趕時髦，正兒八經的數學，他是不打算再做了。 - 蔣聞銘 - ♂   (0 bytes) (7 reads) 10/07/2024  14:54:10 (1)

• 還數學的莫紮特，真敢說。就講對數學的貢獻影響，他跟丘成桐，就沒法比。一個好的Problem Solver而已。 - 蔣聞銘 - ♂   (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 - 蔣聞銘 - ♂   (23 bytes) (7 reads) 10/07/2024  17:37:36

• research. My humble opinion. ：） - 蔣聞銘 - ♂   (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，我們不應該為他高興嗎：）你希望他指引數學方向？ - 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

• 好像給我也推送了，可惜沒認真看：）謝介紹。 - JSL2023 - ♂  (0 bytes) (1 reads) 10/07/2024  20:09:14 (1)

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

• Tao 在這個事情上，花那麽大的功夫，他的理解，肯定有獨到的地方。我隻是覺得這樣吹他，吹他做的這個事，太誇張了。 - 蔣聞銘 - ♂   (0 bytes) (4 reads) 10/08/2024  06:04:02 (1)

• 這些數學家做什麽，在美國，who cares.但是在中文媒體上，就大不相同。您沒注意到，在美國再正常不過。 - 蔣聞銘 - ♂   (0 bytes) (2 reads) 10/08/2024  07:31:27 (1)

• deep learning的本質和統計學的regression類似，隻不過是用神經網絡，也就是一組單向依賴的線性方程組 - 衡山老道 - ♂   (0 bytes) (6 reads) 10/08/2024  12:46:07 (1)

• 來代表一組數據（樣本），使得整體誤差最小。這種以統計為基礎的學習方法，需要和邏輯係統有機結合起來，不然就不可靠，不完整。 - 衡山老道 - ♂   (0 bytes) (4 reads) 10/08/2024  12:48:41 (1)

• 人的創新能力，有很多基礎，如抽象，歸納，推廣等，不可能用統計規律表達。 - 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，過程化的東西，都是相對簡單的東西。世界的本質，超出了人腦結構所能認知的範圍。 - 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

• 是炒作和噱頭，數學既然是基礎學科， 他也不懂什麽是Industrial , 這就像蓋房子， 數學是地基， industrial 涉及到到很多工業科技， 這些都是淩駕於地基之上的樓層， 做純數學的底層是不了解的。

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

• 純理科（數理化） 是單一的基礎學科， 數學 比物理和化學 要狹窄，生物專業更糟糕， - eciel567 - ♀  (206 bytes) (6 reads) 10/08/2024  14:58:48 (1)

• 生物專業狹窄? Not really, AI-neurolink came from biology - TJKCB - ♀   (0 bytes) (0 reads) 10/08/2024  16:12:26

• 純理科（數理化） 是單一的基礎學科， 數學 比物理和化學 要狹窄，生物專業更糟糕， - eciel567 - ♀  (206 bytes) (7 reads) 10/08/2024  14:58:48 (1)

• 生物專業狹窄? 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: 純理科（數理化） 是單一的基礎學科， 數學 比物理和化學 要狹窄，生物專業更糟糕， 由 eciel567 於 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.

*** 

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

26 September 2018

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…

Technology

We’re Entering Uncharted Territory for Math

 

 

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

By Matteo Wong

Illustration by The Atlantic. Source: Steve Jennings / Getty.Terence Tao, a mathematics professor at UCLA, is a real-life superintelligence. The “Mozart of Math,” as he is sometimes called, is widely considered the world’s greatest living mathematician. He has won numerous awards, including the equivalent of a Nobel Prize for mathematics, for his advances and proofs. Right now, AI is nowhere close to his level.

 

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.

October 4, 2024

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Matteo Wong is a staff writer at The Atlantic.

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關於數學，科技正在進入未知的領域

**泰瑞斯·陶，全球最偉大的數學家，對AI有著獨到的見解。**

被譽為“數學莫紮特”的陶，作為加州大學洛杉磯分校的數學教授，已斬獲諸多榮譽，包括數學界的最高獎項。他被認為是當今世界上最頂尖的數學家。然而，當前的人工智能水平尚未接近他的高度。

盡管如此，科技公司正在努力縮短這一差距。目前大多數備受矚目的AI係統，諸如ChatGPT等，主要側重於語言處理，而非數學推理。早期的AI無法進行複雜的數學運算，而隻是基於詞語序列的可能性提供答案。然而，OpenAI的新一代“推理模型”（o1係列）正是專為解決複雜數學問題和科學任務而設計的。如果這些模型成功，將為像陶這樣的數學家提供前所未有的幫助。

陶設想了一種由AI支持的“工業級數學”形式，雖然AI本身不會成為獨立的創造性合作者，但可以作為數學家假設和推理過程中的輔助工具。這種新型數學，雖然前景廣闊，但核心仍然是以人為本，強調人類與機器的互補性，而非競爭。

（2024年10月4日）