Learning to Speak and Act in a Fantasy Text Adventure Game
- We introduce a large scale crowdsourced text adventure game as a research platform for studying grounded dialogue. In it, agents can perceive, emote, and act whilst conducting dialogue with other agents. Models and humans can both act as characters within the game. We describe the results of training state-of-the-art generative and retrieval models in this setting. We show that in addition to using past dialogue, these models are able to effectively use the state of the underlying world to condition their predictions. In particular, we show that grounding on the details of the local environment, including location descriptions, and the objects (and their affordances) and characters (and their previous actions) present within it allows better predictions of agent behavior and dialogue. We analyze the ingredients necessary for successful grounding in this setting, and how each of these factors relate to agents that can talk and act successfully.
New run in the dungeon. Exciting!
Finished my pass through Antonio’s paper
Zoe Keating (May 1) or Imogen Heap (May 3)?
Understanding China’s AI Strategy
- In my interactions with Chinese government officials, they demonstrated remarkably keen understanding of the issues surrounding AI and international security. It is clear that China’s government views AI as a high strategic priority and is devoting the required resources to cultivate AI expertise and strategic thinking among its national security community. This includes knowledge of U.S. AI policy discussions. I believe it is vital that the U.S. policymaking community similarly prioritize cultivating expertise and understanding of AI developments in China.
Russian Trolls Shift Strategy to Disrupt U.S. Election in 2020
- Russian internet trolls appear to be shifting strategy in their efforts to disrupt the 2020 U.S. elections, promoting politically divisive messages through phony social media accounts instead of creating propaganda themselves, cybersecurity experts say.
Work on SASO paper – started
Rachel’s dungeon run is tomorrow! Maybe cross 10,000 posts?
Look at using BERT and the full Word2Vec model for analyzing posts
The Promise of Hierarchical Reinforcement Learning
- To really understand the need for a hierarchical structure in the learning algorithm and in order to make the bridge between RL and HRL, we need to remember what we are trying to solve: MDPs. HRL methods learn a policy made up of multiple layers, each of which is responsible for control at a different level of temporal abstraction. Indeed, the key innovation of the HRL is to extend the set of available actions so that the agent can now choose to perform not only elementary actions, but also macro-actions, i.e. sequences of lower-level actions. Hence, with actions that are extended over time, we must take into account the time elapsed between decision-making moments. Luckily, MDP planning and learning algorithms can easily be extended to accommodate HRL.
Travelling to the Tensorflow developer’s conference
Started on my iConf talk
More BAA while flying
7:00 – very, very, late ASRC
- Tomorrow is March! I need to write a few paragraphs for Antonio this weekend
- YouTube stops recommending alt-right channels
- For the first two weeks of February, YouTube was recommending videos from at least one of these major alt-right channels on more than one in every thirteen randomly selected videos (7.8%). From February 15th, this number has dropped to less than one in two hundred and fifty (0.4%).
- Working on text splitting Group1 in the PHPBB database
- Updated the view so the same queries work
- Discovered that you can do this: …, “message” as type, …. That gives you a column of type filled with “message”. Via stackoverflow
- Mostly working, I’m missing the last bucket for some reason. But it’s good overlap with the Slack data.
- Was debugging on my office box, and was wondering where all the data after the troll was! Ooops, not loaded
- Changed the time tests to be > ts1 and <= ts2
- Working on the white paper. Deep into strategy, Cyberdefense, and the evolution towards automatic active response in cyber.
- Looooooooooooooooooooooooooong meeting of Shimei’s group. Interesting but difficult paper: Learning Dynamic Embeddings from Temporal Interaction Networks
- Emily’s run in the dungeon finishes tonight!
- Looks like I’m going to the TF Dev conference after all….