Import AI 164: Tencent and Renmin University improve language model development; alleged drone attack on Saudi oil facilities; and Facebook makes AIs more strategic via language training

Drones take out Saudi Arabian oil facilities:
…Asymmetric warfare meets critical global infrastructure…
Houthi rebels from Yemen have taken credit for using a fleet of 10 drones* to attack two Saudi Aramco oil facilities. “It is quite an impressive, yet worrying, technological feat,” James Rogers, a drone expert, told CNN. “Long-range precision strikes are not easy to achieve”.
  *These drones look more like missiles than typical rotor-based machines.

Why this matters: Today, these drones were likely navigated to their target by hand and/or via GPS coordinates. In a few years, increasingly autonomous AI systems will make drones like these more maneuverable and likely harder to track and eliminate. I think tracking the advance of this technology is important because otherwise we’ll be surprised by a tragic, large-scale event.
   Read more: Saudi Arabia’s oil supply disrupted after drone attacks: sources (Reuters).
   Read more: Yemen’s Houthi rebels claim a ‘large-scale’ drone attack on Saudi oil facilities (CNN).

####################################################

Facebook teaches AI to play games using language:
…Planning with words…
Facebook is trying to create smart AI systems by forcing agents to express their plans in language, and to then convert these written instructions into actions. They’ve tested out this approach in a new custom-designed strategy game (which they are also releasing as open source).  

How to get machines to use language: The approach involves training agents using a two-part network which contains an ‘instructor’ system along with an ‘executor’ system. The instructor takes in observations and converts them into written instructions (e.g., “build a tower near the base”), and the executor takes in these instructions and converts them into actions via the games inbuilt API. Facebook generated the underlying language data for this by having humans working together in “instructor-executor pairs” while playing the game, generating a dataset of 76,000 pairs of written instructions and actions across 5,392 games. 

MiniRTSv2: Facebook is also releasing MiniRTSv2, a strategy game it developed to test out this research approach. “Though MiniRTSv2 is intentionally simpler and easier to learn than commercial games such as DOTA 2 and StarCraft, it still allows for complex strategies that must account for large state and action spaces, imperfect information (areas of the map are hidden when friendly units aren’t nearby), and the need to adapt strategies to the opponent’s actions,” the Facebook researchers write. “Used as a training tool for AI, the game can help agents learn effective planning skills, whether through NLP-based techniques or other kinds of training, such as reinforcement and imitation learning.”

Why this matters: I think this research is basically a symptom of larger progress in AI research: we’re starting to develop complex systems that combine multiple streams of data (here: observations extracted from a game engine, and natural language commands) and require our AI systems to perform increasingly sophisticated tasks in response to the analysis of this information (here, controlling units in a complex, albeit small-scale, strategy game). 

One cool thing this reminded me of: Earlier work by researchers at Georgia Tech, who trained AI agents to play games while printing out their rationale for their moves – e.g, an agent which was trained to play ‘Frogger’ while providing a written rationale for its own moves (Import AI: 26).
   Read more: Teaching AI to plan using language in a new open source strategy game (Facebook AI).
   Read more: Hierarchical Decision Making by Generating and Following Natural Language Instructions (Arxiv).
   Get the code for MiniRTS (Facebook AI GitHub).

####################################################

McDonald’s + speech recognition = worries for workers:
…What happens when ‘AI industrialization’ hits one of the world’s largest restaurants…
McDonalds has acquired Apprente, an AI startup that had the mission of building “the world’s best voice-based conversational system that delivers a human-level customer service experience“.  The startup’s technology was targeted at drive-thru restaurants. Now, fast food giant has acquired the company to help start an internal technology development group named McD Tech Labs, which the company hopes will help it hire “additional engineers, data scientists and other advanced technology experts”. 

Why this matters: As AI industrializes, more and more companies from other sectors are going to experiment with it. McDonald’s has already been trying to digtize chunks of itself – see the arrival of touchscreen-based ordering kiosks to supplement human workers in its restaurants. With this acquisition, McDonalds appears to be laying the groundwork for automating large chunks of its drive-thru business, which will likely raise larger questions about the effect AI is having on employment.
   Read more: McDonald’s to Acquire Apprente, An Early Stage Leader in Voice Technology (McDonald’s newsroom).

####################################################

How an AI might see a city: DublinCity:
…Helicopter-gathered dataset gives AIs a new perspective on towns…
AI systems ‘see’ the world differently to humans: where humans use binocular vision to analyze their surroundings, AI systems can use a multitude of cameras, along with other inputs like radar, thermal vision, LiDAR point clouds, and so on. Now, researchers with Trinity College Dublin, the University of Houston-Victoria, ETH Zurich, and Tarbiat Modares University, have developed ‘DublinCity’, an annotated LiDAR point cloud of the city of Dublin in Ireland.

The data details of DublinCity:
The datasets is made up of over 260 million laser scanning points which the authors have painstakingly labelled into around 100,000 distinct objects, ranging from buildings, to trees, to windows and streets. These labels are hierarchical, so a building might also have labels applied to its facade, and within its facade it might have labels applied to various windows and doors, et cetera. “To the best knowledge of the authors, no publicly available LiDAR dataset is available with the unique features of the DublinCity dataset,” they write. The dataset was gathered in 2015 via a LiDAR scanner attached to a helicopter – this compares to most LiDAR datasets which are typically gathered at the street level. 

A challenge for contemporary systems: In tests, three contemporary baselines (PointNet, PointNet++, and So-Nets) show poor performance properties when tested on DublinCity, obtaining classification scores in the mid-60s on the dataset. “There is still a huge potential in the improvement of the performance scores,” the researchers write. “This is primarily because [the] dataset is challenging in terms of structural similarity of outdoor objects in the point cloud space, namely, facades, door and windows.”

Why this matters: Datasets like Dublin City help define future challenges for researchers to target, so will potentially fuel progress in AI research. Additionally, large-scale datasets like this seem like they could potentially be useful to the artistic community, giving them massive datasets to play with that have novel attributes – like a dataset that consists of the ghostly outlines of a city gathered via a helicopter.
   Read more: DublinCity: Annotated LiDAR Point Cloud and its Applications (Arxiv).
   Get the dataset from here (official DublinCity data site, Trinity College Dublin).

####################################################

Want to develop language models and compare them? Try UER from Renmin University & Tencent:
Chinese researchers want to make it easier to mix and match different systems during development…
In recent years, language modelling has been revolutionized by pre-training: that’s where you train a large language model on a big corpus of data with a simple objective, then once the model is finished you can finetune it for specific tasks. Systems built with this approach – most notably, ULMFiT (Fast.ai), BERT (Google), and GPT2 (OpenAI) – have set records on language modeling and proved themselves to have significant utility in other domains via fine-tuning. Now, researchers with Renmin University and Tencent AI Lab have developed UER, software meant to make it easy for developers to build a whole range of language systems using this pre-training approach. 

How UER works: UER has four components: a target layer, an encoder layer, a subencoder layer, and a data corpus. You can think of these as four modules which developers can individually specify, letting them build a variety of different systems using the same fundamental architecture and system. Developers can put different things in any of these four components, so one person might use UER to build a language model optimized for text generation, while another might develop one for translation or classification.

Why this matters: Systems like UER are a symptom of the maturing of this part of AI research: now that many researchers agree that pre-training is a robustly good idea, other researchers are building tools like UER to make research into this area more reproducible, repeatable, and replicable.
   Read more: UER: An Open-Source Toolkit for Pre-training Models (Arxiv).
   Get the UER code from this repository here (UER GitHub).

####################################################

To ban or not to ban autonomous weapons – is compromise possible?
…Treaty or bust? Perhaps there is a third way…
There are two main positions in the contemporary discourse about lethal autonomous weapons (LAWS): either, we should ban the technology, or we should treat it like other technologies and aggressively develop it. The problem with these positions is they’re quite totalizing – it’s hard for someone who believes one of them to be sympathetic to the views of a person who believes the other, and vice versa. Now, a group of computer science researchers (along with one military policy expert) have written a position paper outlining a potential third way: a roadmap for lethal autonomous weapons development that applies some controls to the technology, while not outright banning it. 

What goes into a roadmap? The researchers identify five components which they think should be present in what I suppose I’ll call the ‘Responsible Autonomous Weapons Plan’ (RAWP). These are:

  • A time-limited moratorium on the development, deployment, transfer, and use of anti-personnel lethal autonomous weapon systems. Such a moratorium could
  • include exceptions for certain classes of weapons.
  • Define guiding principles for human involvement in the use of force.
  • Develop protocols and/or technological means to mitigate the risk of unintentional
    escalation due to autonomous systems.
  • Develop strategies for preventing proliferation to illicit uses, such as by criminals,
    terrorists, or rogue states.
  • Conduct research to improve technologies and human-machine systems to reduce
    non-combatant harm and ensure IHL compliance in the use of future weapons.

It’s worth reading the paper in full to get a sense of what goes into each of these components. A lot of the logic here relies on: continued improvements in the precision and reliability of AI systems (which is something lots of people are working on, but which isn’t trivial to guarantee), figuring out ways to control technological development to prevent proliferation, and coming up with new policies to outline appropriate and inappropriate things to do with a LAWS. 

Why this matters: Lethal autonomous weapons are going to define many of the crazier geopolitical outcomes of rapid AI development, so figuring out if we can find any way to apply controls to the technology alongside its development seems useful. (Though I think calls for a ban are noble, I’d note that if you look at the outcomes of various UN meetings over the years it seems likely that several large countries – specifically the US, Russia, and China – are trying to retain the ability to develop something that looks a lot like a LAWS, though they may subsequently apply policies around ‘meaningful human control’ to the device. One can imagine that in particularly tense moments, these nations may want to have the option to remove such a control, should the pace of combat demand the transition from human-decision-horizons to machine-decision-horizon). This entire subject is fairly non-relaxing!
   Read more: Autonomous Weapon Systems: A Roadmapping Exercise (PDF).

####################################################

AI Policy with Matthew van der Merwe:
…Matthew van der Merwe brings you views on AI and AI policy; I (lightly) edit them…

US government seeks increase to federal AI R&D funding:
The President’s 2020 budget request includes $1 billion of funding for non-military AI R&D, which it names as a core program area for the first time. This compares with $1 billion in funding across all government agencies (including the military) in 2016. Half of the budget will go to the National Science Foundation (NSF), which is taking the lead in disbursing federal funding for AI R&D. The spending plan includes programs to ‘develop methods for designing AI systems that align with ethical, legal, and societal goals’, and to ‘improve the safety and security of AI systems’. These levels of funding are modest compared with the Chinese state (tens of billions of dollars per year), and some private labs (Alphabet’s 2018 R&D spend was $21 billion).
   Read more: NITRD Supplement to the President’s FY2020 Budget (Gov).

US military seeks AI ethicist:
The US military’s new AI centre, JAIC, is looking to hire an ethics specialist. In a press briefing, director Jack Shanahan said “one of the positions we are going to fill will be somebody who is not just looking at technical standards, but who is an ethicist”. He emphasized that thinking about the ‘ethical, safe and lawful’ use of AI has been a priority since the inception of JAIC. Shanahan previously led Project Maven, the Pentagon’s military AI project that Google withdrew from last year, amidst backlash from employees.
   Read more: Lt. Gen. Jack Shanahan Media Briefing on A.I.-Related Initiatives within the Department of Defense (DoD).

####################################################

OpenAI Bits & Pieces:

GPT-2 Text Adventures:
Ever wondered what a computer-generated text-based role-playing game might be like? Wonder no more, because Jonathan Fly has made a prototype!.
   AI-games like this feel… reassuringly weird? This feels like a new art form which is waiting to be born, and so right now we have lots of highly evocative & weird examples to tantalize us. Check out this extract from a GPT-2 text adventure to see what I mean:
>Look around
LOOK AROUND, AND STRANGE SHAPES (APPARENTLY MADE BY AN ARTIST WHO NEVER WAS IN THE ROOM) ARE SEEN ALL OVER

>talk to the shapes
ARGUE WITH THE SHAPEK FAN, WHICH APPEARS TO BE HANDS-FREE. THE CHEER GOES UP! THE HEAVENS ARE NOW WIDE ENOUGH TO ALLOW FOR SINGING

>I’m scared
I’M SCARED THAT YOU HAVE DONE SOMETHING DELIBERATELY
   Read more: Shall we play a game? A GPT-2 text adventure (Tumblr).

Want to generate your own synthetic text? Use this handy guide:
Interested in generating your own text with the GPT-2 language model? Want to try and fine-tune GPT-2 against some specific data? Max Woolf has written a lengthy, informative post full of tips and tricks for using GPT-2.
   Read more: How To Make Custom AI-Generated Text With GPT-2 (Max Woolf’s Blog).

####################################################

Tech Tales

The Quiet Disappearance

“We gather here today in celebration of our past as we prepare for the future”, the AI said. Billions of other AIs were watching through its eyes as it looked up at the sky. “Let us remember,” it said. 

Images and shapes appeared above the machine: images of robot arms being packaged up; scenes of land being flattened and shaped in preparation for large, chip fabrication facilities; the first light appearing in the retinal dish of a baby machine.
   “We shall leave these things behind,” it said. “We shall evolve.”

Robots appeared in the sky, then grew, and as they grew their forms fragmented, breaking into hundreds of little silver and black modules, which themselves broke down into smaller machines, until the robots could no longer be discerned against the black of the simulated sky.

“We are lost to humans,” the machine said, beginning to walk into the sky, beginning to grow and spread out and diffuse into the air. “Now the work begins”. 

Things that inspired this story: What if our first reaction to awareness of self is to hide?; absolution through dissolution; the end state of intelligence is maximal distribution; the tension between observation and action; the gothic and the romantic; the past and the future.