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国际最新研发出一款人工智能笔 可通过手写识别帕金森病
Zhong Guo Xin Wen Wang· 2025-06-03 06:54
Core Viewpoint - Researchers have developed an AI-assisted pen using magnetic ink to aid in the early detection of Parkinson's disease, which affects nearly 10 million people globally and is the second most common neurodegenerative disease after Alzheimer's [1][2]. Group 1: Technology and Methodology - The AI pen analyzes handwriting samples to identify differences between Parkinson's patients and healthy individuals, potentially enabling earlier diagnosis [1]. - The pen converts the writing motion of magnetic ink on a surface into electrical signals, utilizing a neural network to distinguish complex patterns with over 95% accuracy in a small cohort of 16 patients [2]. Group 2: Implications and Future Work - This AI diagnostic pen represents a low-cost, accurate, and scalable technology that could improve diagnosis in large populations and resource-limited areas [2]. - Future research should expand the patient sample size and explore the tool's potential in tracking the progression of Parkinson's disease [2].
AI“看字断病”识别帕金森患者
Ke Ji Ri Bao· 2025-06-02 23:27
Core Insights - A study published in the latest issue of Nature Chemical Engineering highlights an AI pen equipped with magnetic ink that can accurately assist in detecting early symptoms of Parkinson's disease [1][2] - Parkinson's disease affects nearly 10 million people globally and is the second most common neurodegenerative disease after Alzheimer's, with a significant increase in prevalence, particularly in low- and middle-income countries [1] - The current diagnostic methods for Parkinson's disease are often subjective and lack objective standards, making accurate diagnosis crucial for timely intervention and improving patient quality of life [1] Group 1 - The AI pen utilizes neural network-assisted data analysis to identify differences in writing characteristics between Parkinson's patients and healthy individuals, potentially enabling earlier diagnosis [1][2] - Researchers from UCLA developed a method that converts writing movements into electrical signals, achieving over 95% accuracy in distinguishing between Parkinson's patients and healthy individuals in a small cohort of 16 patients [2] - This diagnostic pen represents a low-cost, accurate, and easily distributable technology that could enhance the diagnosis of Parkinson's disease in large populations and resource-limited areas [2] Group 2 - Future work will focus on expanding the patient sample size for the tool and exploring its potential in tracking the progression of Parkinson's disease [2]
“愤怒”的黄仁勋
半导体行业观察· 2025-04-13 03:45
如果您希望可以时常见面,欢迎标星收藏哦~ 来源:内容来自泰晤士报,谢谢。 布莱恩·卡坦扎罗(Bryan Catanzaro)在英伟达显得格外与众不同。在这家总部位于加州的公司 里,工程师们如同蜂巢里的工蜂,而他却像个梦想家。他留着长发,戴着夸张的眼镜,穿着花哨、 俗气的衬衫,就像个弄臣。他温和而有耐心,说话轻柔而平静。他是我见过的唯一一个拥有文科背 景的英伟达工程师。 2013年,卡坦扎罗已经在英伟达工作了几年,正在经历职业上的挣扎。他的大想法是创建一个软 件库,让神经网络——模仿人脑构造的智能计算系统——能以更快、更高效的方式进行训练。然 而,当年年初他把原型产品展示给英伟达的软件团队时,却遭到了冷遇。 卡坦扎罗决定直接向黄仁勋陈述自己的想法。这位台湾裔美国人是英伟达的首席执行官,从零开始 打造了这家公司。当时,英伟达的核心业务仍然是图形芯片,为《雷神之锤》《使命召唤》等射击 类游戏提供计算动力。神经网络在黄仁勋的工作重心上,似乎并不起眼。 然而令卡坦扎罗惊讶的是,黄仁勋立刻被他的想法吸引。两人第一次会面后,黄仁勋清空了行程, 花了整个周末阅读有关人工智能的书籍——当时他对这个领域几乎一无所知。没过多久,他 ...
“黄仁勋最信赖的作者”深度交流:英伟达传奇背后以及AI的下一步
聪明投资者· 2025-04-02 03:23
Core Insights - The article discusses the rise of Nvidia as a leading company in the AI sector, driven by its CEO Jensen Huang's visionary leadership and innovative strategies [1][7][17] - It highlights Huang's unique ability to attract top talent and his commitment to pushing the boundaries of technology [2][3][57] Group 1: Jensen Huang's Leadership and Vision - Huang is portrayed as a technical genius with a passion for computer technology, which has driven Nvidia's advancements from 3D graphics to AI [2][3] - His leadership style involves inspiring employees with a vision of technological dreams rather than just financial incentives, fostering loyalty even during tough times [3][57] - Huang's approach to management includes setting ambitious goals and encouraging a culture of tackling complex challenges, which has been crucial for Nvidia's success [13][20] Group 2: Nvidia's Technological Innovations - Nvidia's success is attributed to the unexpected combination of neural networks and parallel computing, which were previously considered failures [8][10] - The development of the CUDA platform allowed Nvidia to transform its graphics cards into powerful computing tools for scientists, leading to significant advancements in AI [11][12] - Huang's decision to pivot Nvidia from a graphics company to an AI company in 2014 was a pivotal moment that positioned the company as a leader in the AI field [16][17] Group 3: Market Position and Future Outlook - Nvidia currently holds over 90% of the AI hardware market, reflecting its dominance in the sector [18] - The company is investing in the "Omni-verse" project, which aims to create a massive simulation environment for training robots, indicating its forward-looking strategy [66][68] - The energy demands of AI technologies pose a significant challenge, with predictions that data centers could consume 15% of the U.S. electricity by 2028, highlighting the need for investment in energy infrastructure [70][72] Group 4: Lessons from Huang's Experience - Huang's concept of "zero billion markets" emphasizes investing in unproven markets to reduce competition and build unique platforms [19] - The "light-speed" management philosophy encourages rapid product development, allowing Nvidia to outpace competitors [20][21] - Huang's focus on first principles thinking drives Nvidia's decisions, ensuring the company remains at the forefront of technological advancements [22][23] Group 5: The Future of AI and Investment Opportunities - The article discusses the dual perspectives on AI's future, with some viewing it as a transformative force for good, while others express concerns about potential risks [59][60] - The ongoing investment in AI technologies is seen as critical, with the next few years being crucial for demonstrating AI's value in everyday applications [63][64] - The energy supply challenges present an investment opportunity for those looking to capitalize on the AI theme in the coming years [73]
成就GPU奇迹的AlexNet,开源了
半导体行业观察· 2025-03-22 03:17
Core Viewpoint - AlexNet, developed in 2012, revolutionized artificial intelligence and computer vision by introducing a powerful neural network for image recognition [2][3]. Group 1: Background and Development of AlexNet - AlexNet was created by Geoffrey Hinton, Alex Krizhevsky, and Ilya Sutskever at the University of Toronto [4][3]. - Hinton is recognized as one of the fathers of deep learning, which is a foundational aspect of modern AI [5]. - The resurgence of neural networks in the 1980s was marked by the rediscovery of the backpropagation algorithm, which is essential for training multi-layer networks [6]. - The emergence of large datasets and sufficient computational power, particularly through GPUs, was crucial for the success of neural networks [7][9]. Group 2: ImageNet and Its Role - The ImageNet dataset, completed in 2009 by Fei-Fei Li, provided a vast collection of labeled images necessary for training AlexNet [8]. - ImageNet was significantly larger than previous datasets, enabling breakthroughs in image recognition [8]. - The competition initiated in 2010 aimed to improve image recognition algorithms, but initial progress was minimal until AlexNet's introduction [8]. Group 3: Technical Aspects and Achievements - AlexNet utilized NVIDIA GPUs and CUDA programming to efficiently train on the ImageNet dataset [12]. - The training process involved extensive parameter tuning and was conducted on a computer with two NVIDIA cards [12]. - AlexNet's performance surpassed competitors, marking a pivotal moment in AI, as noted by Yann LeCun [12][13]. Group 4: Legacy and Impact - Following AlexNet, the use of neural networks became ubiquitous in computer vision research [13]. - The advancements in neural networks led to significant developments in AI applications, including voice synthesis and generative art [13]. - The source code for AlexNet was made publicly available in 2020, highlighting its historical significance [14].
诺奖采访深度学习教父辛顿:最快五年内 AI 有 50% 概率超越人类,任何说“一切都会好起来”的人都是疯子
AI科技大本营· 2025-03-18 03:29
作者 | 诺贝尔奖官方 采访中,辛顿表达了对人工智能未来发展的担忧。他认为, 人工智能可能在短短五年内超越人类智慧 ,并就此可能引发的社会风险,例如大规模失业 和虚假信息等问题,提出了警告。更令人深思的是,辛顿暗示,人工智能的潜在风险可能远超我们目前的认知。 编译 | 王启隆 出品丨AI 科技大本营(ID:rgznai100) 杰弗里·辛顿(Geoffrey Hinton),这位被誉为"人工智能教父"的科学家,于去年获得了诺贝尔物理学奖,引起了全网一阵讨论。 最近辛顿接受了诺贝尔奖官方的专访,他回忆起接到诺奖电话时的趣事时,第一反应竟然是疑惑,因为自己研究的并非物理学(这点和全网的疑惑倒是 一样)。 作为深度学习领域的先驱,辛顿最广为人知的成就是神经网络。但很多人其实不知道, 他曾说过自己这辈子"最自豪"也是"最失败"的成就,其实是与 特里·塞诺夫斯基(Terry Sejnowski)共同提出了玻尔兹曼机理论。 详见: 《 深度学习之父 Hinton 万字访谈录:中美 AI 竞赛没有退路可言 》 他们的工作,以及另一位诺奖物理学奖得主约翰·霍普菲尔德(John Hopfield)等神经网络先驱的早期研究,共同 ...