Import AI: 110: Training smarter robots with NavigationNet; DIY drone surveillance; and working out how to assess Neural Architecture Search
by Jack Clark
US hospital trials delivering medical equipment via drone:
…Pilot between WakeMed, Matternet, and NC Department of Transportation…
A US healthcare organization, WakeMed Health & Hospitals, is conducting experiments at transporting medical deliveries around its sprawling healthcare campus (which includes a hospital). The project is a partnership between WakeMed and drone delivery company Matternet. The flights are being conducted as part of the federal government’s UAS Integration Pilot Program.
Why it matters: Drones are going to make entirely new types of logistics and supply chain infrastructures possible. As happened with the mobile phone, emerging countries across Africa and developing economies like China and India are adopting drone technology faster than traditional developed economies. With pilots like this, there is some indication that might change, potentially bringing benefits of the technology to US citizens more rapidly.
Read more: Medical Drone Deliveries Tested at North Carolina Hospital (Unmanned Aerial).
Does your robot keep crashing into walls? Does it have trouble navigating between rooms? Then consider training it on NavigationNet:
…Training future systems to navigate the world via datasets with implicit and explicit structure and topology…
NavigationNet consists of hundreds of thousands of images distributed across 15 distinct scenes – collections of images from the same indoor space. Each scene contains approximately one to three rooms (spaces separated from eachother by doors), and each room has at least 50m^2 in area; each room contains thousands of positions, which are views of the room separated by approximately 20cm. In essence, this makes NavigationNet a large, navigable dataset, where the images within it comprise a very particular set of spatial relationships and hierarchies.
Navigation within NavigationNet: Agents tested on the corpus can perform the following movement actions: move forward, backward, left, right; and turn left and turn right. Note that this ignores the third dimension.
Dataset collection: To gather the data within NavigationNet the team built a data-collection mobile robot codenamed ‘GoodCar’ equipped with an Arduino Mega2560 and a Raspberry Pi 3. They stuck the robot on a motorized base and stuck eight cameras at a height of around 1.4 meters to capture the images.
Testing: The researchers imagine that this sort of data can be used to develop the brains of AI agents trained via deep reinforcement learning to navigate unfamiliar spaces for purposes like traversing rooms, automatically mapping rooms, and so on.
The push for connected spaces: NavigationNet isn’t unusual, instead it’s part of a new trend for dataset creation for navigation tasks: researchers are now seeking to gather real (and sometimes simulated) data which can be stitched together into a specific topological set of relationships, then they are using these datasets to train agents with reinforcement learning to navigate the spaces described by their contents. Eventually the thinking goes, datasets like this will give us the tools we need to develop some bits of the visual-processing and planning capabilities demanded by future robots and drones.
Why it matters: Data has been one of the main inputs to innovation in the domain of supervised learning (and increasingly in reinforcement learning). Systems like NavigationNet give researchers access to potentially useful sources of data for training real world systems. However, it’s unclear right now if simulated data can be as good a substitute given the increasing maturity of sim2real transfer techniques – I look forward to seeing benchmarks of systems trained in NavigationNet against systems trained via other datasets.
Read more: NavigationNet: A Large-scale Interactive Indoor Navigation Dataset (Arxiv).
Google rewards its developers with ‘Dopamine’ RL development system:
…Free RL framework designed to speed up research; ships with DQN, C51, Rainbow, and IQN implementations…
Google has released Dopamine, a research framework for the rapid prototyping of reinforcement learning algorithms. The software is designed to make it easy for people to run experiments, try out research ideas, compare and contract existing algorithms, and increase the reproducability of results.
Free algorithms: Dopamine today ships with implementations of the DQN, C51, Rainbow, and IQN algorithms.
Warning: Frameworks like this tend to appear and disappear according to the ever-shifting habits and affiliations of the people that have committed code into the project. In that light, the note in the readme that “this is not an official Google product” may inspire some caution.
Read more: Dopamine (Google Github).
UN tries to figure out regulation around killer robots:
…Interview with CCW chair highlights the peculiar challenges of getting the world to agree on some rules of (autonomous) war…
What’s more challenging than dealing with a Lethal Autonomous Weapon? Getting 125 member states to state their opinions about LAWS and find some consensus – that’s the picture that emerges from an interview in The Verge with Amandeep Gill, chair of the UN”s Convention on Conventional Weapons (CCW) meetings which are happening this week. Gill has the unenviable job of playing referee in a debate whose stakeholders range from countries, to major private sector entities, to NGOs, and so on.
AI and Dual-Use: In the interview Gill is asked about his opinion of the challenge of regulating AI given the speed with which the technology has proliferated and the fact most of the dangerous capabilities are embodied in software. “AI is perhaps not so different from these earlier examples. What is perhaps different is the speed and scale of change, and the difficulty in understanding the direction of deployment. That is why we need to have a conversation that is open to all stakeholders,” he says.
Read more: Inside the United Nations’ Effort to Regulate Autonomous Killer Robots (The Verge).
IBM proposes AI validation documents to speed corporate adoption:
…You know AI has got real when the bureaucratic cover-your-ass systems arrive…
IBM researchers have proposed the adoption of ‘supplier’s declaration of conformity’ (SDoC) documents for AI services. These SDoCs are essentially a set of statements about the content, provenance, and vulnerabilities, of a given AI service. Each SDoC is designed to accompany a given AI service or product, and is meant to answer questions for the end-user like: when were the models most recently updated? What kinds of data were the models trained on? Has this service been checked for robustness against adversarial attacks? Etc. “We also envision the automation of nearly the entire SDoC as part of the build and runtime environments of AI services. Moreover, it is not difficult to imagine SDoCs being automatically posted to distributed, immutable ledgers such as those enabled by blockchain technologies”
Inspiration: The inspiration for SDoCs is that we’ve used similar labeling schemes to improve products in areas like food (where we have ingredient and nutrition-labeling standards), medicine, and so on.
Drawback: One potential drawback of the SDoC approach is that IBM is designing it to be voluntary, which means that it will only become useful if broadly adopted.
Read more: Increasing Trust in AI Services through Supplier’s Declarations of Conformity (Arxiv).
Smile, you’re on DRONE CAMERA:
…Training drones to be good cinematographers, by combing AI with traditional control techniques…
Researchers with Carnegie Mellon University and Yamaha Motors have taught some drones how to create steady, uninterrupted shots when filming. Their approach involves coming up with specific costs for obstacle avoidance and smooth movement. They use AI-based detection techniques to spot people and feed that information to a PD controller onboard the drone to keep the person centered.
Drone platform: The researchers use a DJI M210 model drone along with an NVIDIA TX2 computer. The person being tracked by the drone wears a Pixhawk PX4 module on a hat to send the pose to the onboard computer.
Results: The resulting system can circle round people, fly alongside them, follow vehicles and more. The onboard trajectory planning is robust enough to maintain smooth flight while keeping the targets for the camera in the center of the field of view.
Why it matters: Research like this is another step towards drones with broad autonomous capabilities for select purposes, like autonomously filming and analyzing a crowd of people. It’s interesting to observe how drone technologies frequently involve the mushing together of traditional engineering approaches (hand-tuned costs for smoothness and actor centering) as well as AI techniques (testing out a YOLOv3 object detector to acquire the person without need of a GPS signal).
Read more: Autonomous drone cinematographer: Using artistic principles to create smooth, safe, occlusion-free trajectories for aerial filming (Arxiv).
In search of the ultimate Neural Architecture Search measuring methodology:
…Researchers do the work of analyzing optimization across multiple frontiers so you don’t have to…
Neural architecture search techniques are moving from having a single objective to having multiple ones, which lets people tune these systems for specific constraints, like the size of the network, or the classification accuracy. But this modifiability is raising new questions about how we can assess the performance and tradeoffs of these systems, since they’re no longer all being optimized against a single objective. In a research paper, researchers with National Tsing-Hua University in Taiwan and Google Research review recent NAS techniques and then rigorously benchmark two recent multi-objective approaches: MONAS and DPP-Net.
Benchmarking: In tests the researchers find the results one typically expects when evaluating NAS systems: NAS performance tends to be better than systems designed by humans alone, and having tuneable objectives for multiple areas can lead to better performance when systems are appropriately tuned and trained. The performance of DPP-Net is particularly notable, as the researchers think this “is the first device-aware NAS outperforming state-of-the-art handcrafted mobile CNNs”.
Why it matters: Neural Architecture Search (NAS) approaches are becoming increasingly popular (especially among researchers with access to vast amounts of cheap computation, like those that work at Google), so developing a better understanding of the performance strengths and tradeoffs of these systems will help researchers assess them relative to traditional techniques.
Read more: Searching Toward Pareto-Optimal Device-Aware Neural Architectures (Arxiv).
Context: Intercepted transmissions from Generative Propaganda Bots (GPBs), found on a small atoll within the [REDACTED] disputed zone in [REDACTED]. GPBs are designed to observe their immediate environment and use it as inspiration for the creation of ‘context-relevant propaganda’. As these GPBs were deployed on an un-populated island they have created a full suite of propaganda oriented around the island’s populace – birds.
Intercepted Propaganda Follows:
Proud beak, proud mind. Join the winged strike battalion today!
Is your neighbor STEALING your EGGS? Protect your nest, maintain awareness at all times.
Birds of a feather stick together! Who is not in your flock?
Eyes of an angle? Prove it by finding the ENEMY!
Things that inspired this story: Generative text, cheap sensors, long-lived computers, birds.