Where you BIM all my life? Comet Labs ❤ Autonomous Construction

The Comet — A digest of robotics & machine learning news and investments, published every other week

As some of you may know, the next Comet Labs Intelligent Machines Summit is focusing on the topic of autonomous construction. We’re very excited to be hosting this event with Caterpillar Ventures, Prologis, Brick & Mortar Ventures and Rhumbix. We’re also working on a top secret report on urban agriculture at the moment. So keep your eyes peeled for lots of interesting insights into construction, urban planning and smart cities in the next couple of weeks :)

It seems like we’re not the only ones focused on making cities “smarter” with CB insights tallying 56 startups already in the smart city space. Adaptive artificial intelligence is now a must for smart cities, and it is obvious that the transformation to “self-aware” high connectivity city has already begun.

In the United States, Alphabet Sidewalk Labs is introducing Flow, a transportation coordination platform that uses analytics to help cities work with citizens to increase the efficiency of road networks, parking, and transit use. There is also an app that uses AI that analyzes posts on social media as well as police radio chatter and feeds of the local airspace to detect unrest and potential rumbling and alert the public.

In Europe, Switzerland is trial testing autonomous buses and self driving robots will start making deliveries: drive to collect cargo, store it in their shopping bags and then complete delivery. These bots will be controlled remotely at first, but as time goes on, they build a map of the delivery areas and start delivering autonomously.

Similar robotics delivery services are also taking off in China, but via air through drones. Baidu is also planning driverless car tourist services with local tourism agencies in ten cities. In Japan, Fujitsu just started to field trial an AI-based train delay prediction functionthat solves their huge transportation route-planning problem. Many more functions are being rolled out all over the world that will shape how we live in cities for decades.

At a conference in India on Saturday, Mark S. Fox, professor at the University of Toronto, : “Today over 50% of the world population lives in cities. If society has to sustain, cities have to be smart.”

One possible reason everyone is starting to “freak out” about this:

By 2050, 70% of the world’s population is expected to live in urban environments.

Also, as promised ;)

During the week of June 26th to July 1st 2016, the 29th IEEE Conference on Computer Vision and Pattern Recognition was held in Las Vegas. CVPR, comprising of the main conference and several co-located workshops and short courses, has been hosted annually since 1985 by the IEEE Computer Society and Computer Vision Foundation (CVF).

In case you couldn’t attend, here are three summaries of award-winning CVPR papers:

Best paper

Deep Residual Learning for Image Recognition by Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun of Microsoft Research:

This paper presents a novel way of training very deep neural networks. Having deeper neural networks allows for much better image recognition and computer vision, but increasing network depth beyond a ‘suitable deepness’ can lead to rapidly decreasing accuracy (this is known as the degradation problem). Residual learning is one way to alleviate the degradation problem. A residual network has additional layers fit a residual mapping, with the end goal being to have each layer learn something new about the network. The Microsoft team used the residual network architecture described in this paper to win first place in the 2015 ILSVRC and COCO competitions in the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.

Best paper honorable mention

Sublabel-Accurate Relaxation of Nonconvex Energies by Thomas Möllenhoff, Emanuel Laude, Michael Moeller, Jan Lellmann, Daniel Cremers:

In this paper the researchers propose a spatially continuous framework for convex relaxations based on functional lifting. The framework uses far fewer labels that conventional functional lifting methods and has better memory usage and runtime. This primal-dual algorithm shows significant improvements in the computer vision problems of stereo matching, phase unwrapping, and depth from focus.

Best student paper

Structural-RNN: Deep Learning on Spatio-Temporal Graphs, by Ashesh Jain, Amir R. Zamir, Silvio Savarese, Ashutosh Saxena:

Deep Recurrent Neural Networks are effective for sequence modeling but lack intuitive spatio-temporal structures. Many applications of computer vision and robotics are inherently spatio-temporal in nature, such as coordinating multiple objects for a cooking automaton. In this paper the researchers develop a generic tool to transform spatio-temporal graphs into a feedforward mixture of RNNs called structural RNNs (S-RNNs). As a result, RNNs can be used to solve spatio-temporal problems.

BIM? What’s BIM? (Thanks Autodesk!)

Comet Labs Portfolio & Community Updates

Announcing Iron OX

We’re very excited to welcome Iron Ox to our growing list of portfolio companies! Iron Ox makes fresh, sustainable produce cheaper in their robotic greenhouses.

Tissue Analyzer 3Scan Builds Out Machine Learning With $14M Series B

Investors include Lux Capital, Data Collective and Comet Labs.

IAM Robotics Takes on Automated Warehouse Picking

“IAM Robotics is one of the first companies to take on the picking problem on a commercial level.”

Xconomy Panel

Comet Labs’s Co-Founder and Director of Partnerships Melissa Pancoast discussed robotics design on the Xconomy Panel.

Chinese Capital Betting On Moonshots Overseas

“[Comet Labs] has managed to secure a combined 40 million U.S. dollars in follow-up funding rounds that have also drawn investors including Google Ventures, Andreessen Horowitz and Y Combinator.”

Other Intelligent Machines Headlines

MIT and DARPA Pack Lidar Sensor Onto Single C

“[Their] lidar chips are produced on 300-millimeter wafers, making their potential production cost on the order of $10 each at production volumes of millions of units per year … Today, commercially available high-end lidar systems can range from $1,000 to upwards of $70,000.”

Amazon’s Latest Robot Champion Uses Deep Learning to Stock Shelves

The latest champion of Amazon’s robotic picking challenge can pick up 100 items in an hour, which is much slower than what humans can do but much faster than last year’s champion, who picked up 30 items in the same time span.

EU Data Deal Will Help U.S. Cloud Companies If It Holds Up

A vote that took place in Brussels cleared the way for E.U.-U.S. Privacy Shield, allowing American companies transfer data about customers and employees in and out of Europe without stepping on legal landmines.

Meet Facebook’s Stratospheric Internet Drone

Aquila, the V-shaped carbon fiber drone with the size of a Boeing 737 airliner is built to bring Internet connectivity to hundreds of millions of people beyond the reach of today’s telecommunications infrastructure.

Autonomous Cars Will Get New Federal Guidelines

In wake of a fatal crash in May involving a Tesla Model S, the Transportation Secretary announced changes in upcoming federal guidelines.

Artificial Intelligence And Data Driven Medicine

“Data driven medicine has the ability to not only improve the speed and accuracy of diagnosis for genetic diseases but also unlock the possibility of personalized medical treatments.”

50 Smartest Companies in 2016

50 Companies chosen by MIT Technology Review editors that best demonstrate the combination of innovative technology and an effective business model.

Why Pokemon GO is Taking Off

Possible reasons behind why this game has become an international phenomenon.

Intelligent Machines Funding, June 28 — August 11, 2016

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