In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
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Question: Recurrent Neural Networks architectures excel at processing sequences by
 modelling dependencies over different timescales. The recently introduced
 Recurrent Weighted Average (RWA) unit captures long term dependencies
 far better than an LSTM on several challenging tasks. The RWA achieves
 this by applying attention to each input and computing a weighted average
 over the full history of its computations. Unfortunately, the RWA cannot
 change the attention it has assigned to previous timesteps, and so struggles
 with carrying out consecutive tasks or tasks with changing requirements.
 We present the Recurrent Discounted Attention (RDA) unit that builds on
 the RWA by additionally allowing the discounting of the past.
 We empirically compare our model to RWA, LSTM and GRU units on
 several challenging tasks. On tasks with a single output the RWA, RDA and
 GRU units learn much quicker than the LSTM and with better performance.
 On the multiple sequence copy task our RDA unit learns the task three
 times as quickly as the LSTM or GRU units while the RWA fails to learn at
 all. On the Wikipedia character prediction task the LSTM performs best
 but it followed closely by our RDA unit. Overall our RDA unit performs
 well and is sample efficient on a large variety of sequence tasks.

Answer: We introduce the Recurrent Discounted Unit which applies attention to any length sequence in linear time


Question: In recent years we have made significant progress identifying computational principles that underlie neural function. While not yet complete, we have sufficient evidence that a synthesis of these ideas could result in an understanding of how neural computation emerges from a combination of innate dynamics and plasticity, and which could potentially be used to construct new AI technologies with unique capabilities. I discuss the relevant principles, the advantages they have for computation, and how they can benefit AI. Limitations of current AI are generally recognized, but fewer people are aware that we understand enough about the brain to immediately offer novel AI formulations.


Answer: Limitations of current AI are generally recognized, but fewer people are aware that we understand enough about the brain to immediately offer novel AI formulations.


Question: Reinforcement learning provides a powerful and general framework for decision
 making and control, but its application in practice is often hindered by the need
 for extensive feature and reward engineering. Deep reinforcement learning methods
 can remove the need for explicit engineering of policy or value features, but
 still require a manually specified reward function. Inverse reinforcement learning
 holds the promise of automatic reward acquisition, but has proven exceptionally
 difficult to apply to large, high-dimensional problems with unknown dynamics. In
 this work, we propose AIRL, a practical and scalable inverse reinforcement learning
 algorithm based on an adversarial reward learning formulation that is competitive
 with direct imitation learning algorithms. Additionally, we show that AIRL is
 able to recover portable reward functions that are robust to changes in dynamics,
 enabling us to learn policies even under significant variation in the environment
 seen during training.

Answer:
We propose an adversarial inverse reinforcement learning algorithm capable of learning reward functions which can transfer to new, unseen environments.