机器之心海外团队作者

Synced Global AI Weekly | 2018.8.25—8.31

Europe Welcomes Its First Self Driving Taxi

Yandex Just Launched Europe’s First Autonomous Car Ride Hailing Service

The self-driving car revolution is making waves in Russia. On Tuesday, the country’s largest tech firm Yandex announced Europe’s first autonomous taxi service. The service, which will see two vehicles ferry passengers to fixed destinations for free, is a big step for the technology outside of the confines of Silicon Valley.

(Inverse)


AutoX is using self-driving vehicles to deliver groceries

Autonomous vehicle startup AutoX  has launched a grocery delivery and mobile store pilot in a partnership with GrubMarket.com and local high-end grocery store DeMartini Orchard. The pilot will initially be limited to an area of about 400 homes in north San Jose.

(TechCrunch)


Toyota Investing $500 Million in Uber in Driveless-Car Pact

Toyota Motor Corp. is investing about $500 million in Uber Technologies Inc. as part of an agreement by the companies to work jointly on autonomous vehicles aimed at improving safety and lowering transportation costs.

(WSJ)


Waymo's Big Ambitions Slowed by Tech Trouble

It has said it would launch a driverless robo-taxi service to suburban Phoenix residents this year. Yet its self-driving minivan prototypes have trouble crossing the T-intersection closest to the company’s Phoenix-area headquarters here.

(The Information)


Technology

Semantic Cache for AI-Enabled Image Analysis

"Edge computing, as this is known, not only reduces the strain on bandwidth but also reduceslatency of obtaining intelligence from raw data. However, availability of resources at the edge is limited due to the lack of economies of scale that make cloud infrastructure cost-effective to manage and offer."

(IBM Research)


Observation of topological phenomena in a programmable lattice of 1,800 qubits

"Here we demonstrate a large-scale quantum simulation of this phenomenon in a network of 1,800 in situ programmable superconducting niobium flux qubits whose pairwise couplings are arranged in a fully frustrated square-octagonal lattice. "

(Nature)


Introducing a New Framework for Flexible and Reproducible Reinforcement Learning Research

Today we’re introducing a new Tensorflow-based framework that aims to provide flexibility, stability, and reproducibility for new and experienced RL researchers alike. 

(Google AI)


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Silicon Never Fever: Berkeley AI Gets Down

Electrifying an entire dance club is easy if you have killer moves like John Travolta in Saturday Night Fever. But for the rest of us, not so much. We may shake our butts and swing our arms, but let’s face it: some people just can’t dance. But now there’s hope, thanks to AI.

(Synced)


Brown University Paper Shows Research Robot Vulnerability

Google Brain Research Scientist Ian Goodfellow has tweeted an alarm about IoT hacking of a particularly nightmarish type, after Brown University security researchers were able to remotely access and control a robot in a university research lab. The research also showed that many robotic labs worldwide may be vulnerable to such a takeover technique.

(Synced)


Global AI Events

2–6 SepInterspeech 2018Hyderabad, India
3–6 SepBMVCNewcastle, UK
4–5 SepO’Reilly Artificial Intelligence Conference.San Francisco, USA
6–7 SepIntelligent Systems Conference (IntelliSys).London, UK
8–14 SepECCVMunich, Germany
10–11 SepRobots and Deep LearningSingapore


NewsLetter
相关数据
Ian Goodfellow人物

Ian Goodfellow 是机器学习领域备受关注的年轻学者之一,他在本科与硕士就读于斯坦福大学,师从吴恩达,博士阶段则跟随蒙特利尔大学的著名学者Yoshua Bengio研究机器学习。Goodfellow 最引人注目的成就是在2014年6月提出了生成对抗网络(GAN)。这一技术近年来已成为机器学习界最火热的讨论话题,特别是在最近几个月里,与GAN有关的论文不断涌现。GAN已成为众多学者的研究方向。

自动驾驶技术技术

从 20 世纪 80 年代首次成功演示以来(Dickmanns & Mysliwetz (1992); Dickmanns & Graefe (1988); Thorpe et al. (1988)),自动驾驶汽车领域已经取得了巨大进展。尽管有了这些进展,但在任意复杂环境中实现完全自动驾驶导航仍被认为还需要数十年的发展。原因有两个:首先,在复杂的动态环境中运行的自动驾驶系统需要人工智能归纳不可预测的情境,从而进行实时推论。第二,信息性决策需要准确的感知,目前大部分已有的计算机视觉系统有一定的错误率,这是自动驾驶导航所无法接受的。

张量技术

张量是一个可用来表示在一些矢量、标量和其他张量之间的线性关系的多线性函数,这些线性关系的基本例子有内积、外积、线性映射以及笛卡儿积。其坐标在 维空间内,有 个分量的一种量,其中每个分量都是坐标的函数,而在坐标变换时,这些分量也依照某些规则作线性变换。称为该张量的秩或阶(与矩阵的秩和阶均无关系)。 在数学里,张量是一种几何实体,或者说广义上的“数量”。张量概念包括标量、矢量和线性算子。张量可以用坐标系统来表达,记作标量的数组,但它是定义为“不依赖于参照系的选择的”。张量在物理和工程学中很重要。例如在扩散张量成像中,表达器官对于水的在各个方向的微分透性的张量可以用来产生大脑的扫描图。工程上最重要的例子可能就是应力张量和应变张量了,它们都是二阶张量,对于一般线性材料他们之间的关系由一个四阶弹性张量来决定。

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