机器之心海外团队作者

Synced Global AI Weekly | 2018.8.4—8.10

Interesting Uses of AI That You May Never Think ofAI Spots 40,000 Prominent Scientists Overlooked by Wikipedia

AI is often criticized for its tendency to perpetuate society’s biases, but it’s equally capable of fighting them. Machine learning is currently being used to scan scientific studies and news stories to identify prominent scientists who aren’t featured on Wikipedia.

(The Verge)


This Robot Uses AI to Find Waldo, thereby ruining Where's Waldo

Built by creative agency Redpepper, There’s Waldo zeroes in and finds Waldo with a sniper-like accuracy. The metal robotic arm is a Raspberry Pi-controlled uArm Swift Pro which is equipped with a Vision Camera Kit that allows for facial recognition. 

(The Verge)


Indian Techie Claims to Have Created First Hindi-Speaking Humanoid

The Indian equivalent of Sophia, the world’s first humanoid, Rashmi uses linguistic interpretation and artificial intelligence to pick up the feeling of a conversation and articulate appropriate responses.

(Sputnik News)


Technology

Object that Sounds

In look, Listen, and Learn and Objects that Sound (to appear at ECCV 2018), we explore this observation by asking: what can be learnt by looking at and listening to a large number of unlabelled videos?

(DeepMindAI)


CycleGAN Bikini Fix for Nudes

Adult content recognition with deep neural networks (ACORDE) for example can distinguish between sensitive and non-sensitive content, providing a binary classification result to completely block content containing nudity. However, such a solution can compromise user experience with digital media. An alternative approach is censoring only sensitive regions of an image.

(Synced)


When Recurrent Models Don't Need to be Recurrent

"In this post, we explore the trade-offs between recurrent and feed-forward models. Feed-forward models can offer improvements in training stability and speed, while recurrent models are strictly more expressive. "

(Berkeley AI Research)


You May Also Like

Remaking Human Skin: Neurobotics at Gordon Cheng's ICS Lab in Germany

The Technical University of Munich (TUM) had lured Cheng from a Japanese research institute. At TUM he founded the Institute of Cognitive Systems (ICS). With eight employees in a central office on Karlstraße 45, Cheng set to work on his arduous task: recreating the complexities of human skin and wiring it all to a brain.

(Synced)


Harvard & University of Toronto Researchers Apply Deep Generative Models to Inverse Molecular Design

Benjamin Sanchez-Lengeling from Harvard University and Alán Aspuru-Guzik from the University of Toronto have successfully applied machine learning models to speed up the materials discovery process. 

(Synced)


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NewsLetter
相关数据
机器学习技术

机器学习是人工智能的一个分支,是一门多领域交叉学科,涉及概率论、统计学、逼近论、凸分析、计算复杂性理论等多门学科。机器学习理论主要是设计和分析一些让计算机可以自动“学习”的算法。因为学习算法中涉及了大量的统计学理论,机器学习与推断统计学联系尤为密切,也被称为统计学习理论。算法设计方面,机器学习理论关注可以实现的,行之有效的学习算法。

计算机视觉技术

计算机视觉(CV)是指机器感知环境的能力。这一技术类别中的经典任务有图像形成、图像处理、图像提取和图像的三维推理。目标识别和面部识别也是很重要的研究领域。

机器人技术技术

机器人学(Robotics)研究的是「机器人的设计、制造、运作和应用,以及控制它们的计算机系统、传感反馈和信息处理」 [25] 。 机器人可以分成两大类:固定机器人和移动机器人。固定机器人通常被用于工业生产(比如用于装配线)。常见的移动机器人应用有货运机器人、空中机器人和自动载具。机器人需要不同部件和系统的协作才能实现最优的作业。其中在硬件上包含传感器、反应器和控制器;另外还有能够实现感知能力的软件,比如定位、地图测绘和目标识别。之前章节中提及的技术都可以在机器人上得到应用和集成,这也是人工智能领域最早的终极目标之一。

生成模型技术

在概率统计理论中, 生成模型是指能够随机生成观测数据的模型,尤其是在给定某些隐含参数的条件下。 它给观测值和标注数据序列指定一个联合概率分布。 在机器学习中,生成模型可以用来直接对数据建模(例如根据某个变量的概率密度函数进行数据采样),也可以用来建立变量间的条件概率分布。

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