This post discusses highlights of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19).

I attended AAAI 2019 in Honolulu, Hawaii last week. Overall, I was particularly surprised by the interest in natural language processing at the conference. There were 15 sessions on NLP (most standing-room only) with ≈10 papers each (oral and spotlight presentations), so around 150 NLP papers (out of 1,150 accepted papers overall). I also really enjoyed the diversity of invited speakers who discussed topics from AI for social good, to adversarial learning and imperfect-information games (videos of all invited talks are available here). Another cool thing was the Oxford style debate, which required debaters to take controversial positions. This was a nice change of pace from panel discussions, which tend to converge to a uniform opinion.

Table of contents:

Dialogue

In his talk at the Reasoning and Learning for Human-Machine Dialogues workshop, Phil Cohen argued that chatbots are an attempt to avoid solving the hard problems of dialogue. They provide the illusion of having a dialogue but in fact do not have a clue what we are saying or meaning. What we should rather do is recognize intents via semantic parsing. We should then reason about the speech acts, infer a user's plan, and help them to succeed. You can find more information about his views in this position paper.

During the panel discussion, Imed Zitouni highlighted that the limitations of current dialogue models affect user behaviour. 75-80% of the time users only employ 4 skills: "play music", "set a timer", "set a reminder", and "what is the weather". Phil argued that we should not have to learn how to talk, how to make an offer, etc. all over again for each domain. We can often build simple dialogue agents for new domains "overnight".

Reproducibility

At the Workshop on Reproducible AI, Joel Grus argued that Jupyter notebooks are bad for reproducibility. As an alternative, he recommended to adopt higher-level abstractions and declarative configurations. Another good resource for reproducibility is the ML reproducibility checklist by Joelle Pineau, which provides a list of items for algorithms, theory, and empirical results to enforce reproducibility.

Unit tests for AI experiments recommended by Joel Grus

A team from Facebook reported on their experiments reproducing AlphaZero in their ELF framework, training a model using 2,000 GPUs in 2 weeks. Reproducing an on-policy, distributed RL system such as AlphaZero is particularly challenging as it does not have a fixed dataset and optimization is dependent on the distributed environment. Training smaller versions and scaling up is key. For reproducibility, the random seed, the git commit number, and the logs should be stored.

During the panel discussion, Odd Eric Gunderson argued that reproducibility should be defined as the ability of an independent research team to produce the same results using the same AI method based on the documentation by the original authors. Degrees of reproducibility can be measured based on the availability of different types of documentation, such as the method description, data, and code.

Pascal van Hentenryck argued that reproducibility could be made part of the peer review process, such as in the Mathematical Programming Computation journal where each submission requires an executable file (which does not need to be public). He also pointed out that—empirically—papers with supplementary materials are more likely to be accepted.

Question answering

At the Reasoning and Complex QA Workshop, Ken Forbus discussed an analogical training method for QA that adapts a general-purpose semantic parser to a new domain with few examples. At the end of his talk, Ken argued that the train/test method in ML is holding us back. Our learning systems should use rich relational representations, gather their own data, and evaluate progress.

Ashish Sabharwal discussed the OpenBookQA dataset presented at EMNLP 2018 during his talk. The open book setting is situated between reading comprehension and open-ended QA on the textual QA spectrum (see below).

The textual QA spectrum

It is designed to probe a deeper understanding rather than memorization skills and requires applying core principles to new situations. He also argued that while entailment is recognized as a core NLP task with many applications, it is still lacking a convincing application to an end-task. This is mainly due to multi-sentence entailment being a lot harder, as irrelevant sentences often have significant textual overlap.

Furthermore, he discussed the design of leaderboards, which have to make tradeoffs along multiple competing axes with respect to the host, the submitters, and the community. A particular deficit of current leaderboards is that they make it difficult to share and build upon successful techniques.

The first part of the final panel discussion focused on important outstanding technical challenges for question answering. Michael Witbrock emphasized techniques to create datasets that cannot easily be exploited by neural networks, such as the adversarial filtering in SWAG. Ken argued that models should come up with answers and explanations rather than performing multiple choice question answering, while Ashish noted that such explanations need to be automatically validated.

Eduard Hovy suggested that one way towards a system that can perform more complex QA could consist of the following steps:

  1. Build a symbolic numerical reasoner that leverages relations from an existing KB, such as Geobase, which contains geography facts.
  2. Look at the subset of questions in existing natural language datasets, which require reasoning that is possible with the reasoner.
  3. Annotate these questions with semantic parses and train a semantic parsing model to convert the questions to logical forms. These can then be provided to the reasoner to produce an answer.
  4. Augment the reasoner with another reasoning component and repeat steps 2-3.

The panel members noted that such reasoners exist, but lack a common API.

Finally, here are a few papers on question answering that I enjoyed:

AI for social good

During his invited talk, Milind Tambe looked back on 10 years of research in AI and multiagent systems for social good (video available here; slides available here). Milind discussed his research on using game theory to optimize security resources such as patrols at airports, air marshal assignments on flights, coast guard patrols, and ranger patrols in African national parks to protect against poachers. Overall, his talk was a striking reminder of the positive effects AI can have if it is employed for social good.

An overview of an ML approach for predicting poacher behaviour in an African national park

Debate

The Oxford style debate focused on the proposition “The AI community today should continue to focus mostly on ML methods” (video available here). It pitted Jennifer Neville and Peter Stone on the 'pro' side against Michael Littman and Oren Etzioni on the 'against' side, with Kevin Leyton-Brown as moderator. Overall, the debate was entertaining and engaging to watch.

The debater panel (from left to right): Peter Stone, Jennifer Neville, Kevin Leyton-Brown (moderator), Michael Littman, Oren Etzioni

Here are some representative remarks from each of the debaters that stuck with me:

"The unique strength of the AI community is that we focus on the problems that need to be solved." – Jennifer Neville
"We are in the middle of one of the most amazing paradigm shifts in all of science, certainly computer science." – Oren Etzioni
"If you want to have an impact, don’t follow the bandwagon. Keep alive other areas." – Peter Stone
"Scientists in the natural sciences are actually very excited about ML as much of their research relies on expensive computations, which can be approximated with neural networks." – Michael Littman

There were some important observations and ultimately a general consensus that ML alone is not enough and we need to integrate other methods with ML. Yonatan Belinkov also live tweeted, while I tweeted some remarks that elicited laughs.

Adversarial learning

During his invited talk (video available here), Ian Goodfellow discussed a multiplicity of areas to which adversarial learning has been applied. Among many advances, Ian mentioned that he was impressed by the performance and flexibility of attention masks for GANs, particularly that they are not restricted to circular masks.

He discussed adversarial examples, which are a consequence of moving away from i.i.d. data: attackers are able to confuse the model by showing unusual data from a different distribution such as graffiti on stop signs. He also argued—contrary to the prevalent opinion—that deep models that are more robust are more interpretable than linear models. The main reason is that the latent space of a linear model is totally unintuitive, while a more robust model is more inspectable (as can be seen below).

Traversing the latent space of a linear model (left) vs. a deep, more robust model (right) between different MNIST labels starting from "9"

Semi-supervised learning with GANs can allow models to be more sample-efficient. What is interesting about such applications is that they focus on the discriminator (which is normally discarded) rather than the generator where the discriminator is extended to classify n+1 classes. Regarding leveraging GANs for NLP, Ian conceded that we currently have not found a good way to deal with the large action space required to generate sentences with RL.

Imperfect-information games

In his invited talk (video available here), Tuomas Sandholm—whose AI Libratus was the first AI to beat top Heads-Up No-Limit Texas Hold'em professionals in January 2017—discussed new results for solving imperfect-information games. He stressed that only game-theoretically sound techniques yield strategies that are robust against all opponents in imperfect-information games. Other advantages of a game-theoretic approach are a) that even if humans have access to the entire history of plays of the AI, they still can't find holes in its strategy; and b) it requires no data, just the rules of the game.

Most real-world applications are imperfect-information games

For solving such games, the quality of the solution depends on the quality of the abstraction. Developing better abstractions is thus important, which also applies to modelling such games. In imperfect-information games, planning is important. In real-time planning, we must consider how the opponent can adapt to changes in the policy. In contrast to perfect-information games, states do not have well-defined values.

Inductive biases

There were several papers that incorporated different inductive biases into existing models:

Transfer learning

Papers on transfer learning ranged from multi-task learning and semi-supervised learning to sequential and zero-shot transfer:

Word embeddings

Naturally there were also a number of papers that provided new methods for learning word embeddings:

Miscellaneous

Finally, here are some papers that I enjoyed that do not fit into any of the above categories:

Cover image: AAAI-19 Opening Reception