Legged robots often use separate control policies that are highly engineered for traversing difficult terrain such as stairs, gaps, and steps, where switching between policies is only possible when the robot is in a region that is common to adjacent controllers. Deep Reinforcement Learning (DRL) is a promising alternative to hand-crafted control design, though typically requires the full set of test conditions to be known before training. DRL policies can result in complex (often unrealistic) behaviours that have few or no overlapping regions between adjacent policies, making it difficult to switch behaviours. In this work we develop multiple DRL policies with Curriculum Learning (CL), each that can traverse a single respective terrain condition, while ensuring an overlap between policies. We then train a network for each destination policy that estimates the likelihood of successfully switching from any other policy. We evaluate our switching method on a previously unseen combination of terrain artifacts and show that it performs better than heuristic methods. While our method is trained on individual terrain types, it performs comparably to a Deep Q Network trained on the full set of terrain conditions. This approach allows the development of separate policies in constrained conditions with embedded prior knowledge about each behaviour, that is scalable to any number of behaviours, and prepares DRL methods for applications in the real world

NN variants have been designed to increase performance in certain problem domains; the convolutional neural network (CNN) excels in the context of image-based tasks, and the recurrent neural network (RNN) in the space of natural language processing and time series analysis. NNs have also been leveraged as components in composite DL frameworks -- they have been used as trainable generators and discriminators in generative adversarial networks (GANs), and as encoders and decoders in transformers [46]. Although they seem unrelated, the images used as inputs in computer vision, and the sentences used as inputs in natural language processing can both be represented by a single, general data structure: the graph (see Figure 1). Formally, a graph is a set of distinct vertices (representing items or entities) that are joined optionally to each other by edges (representing relationships). The learning architecture that has been designed to process said graphs is the titular graph neural network (GNN). Uniquely, the graphs fed into a GNN (during training and evaluation) do not have strict structural requirements per se; the number of vertices and edges between input graphs can change. In this way, GNNs can handle unstructured, non-Euclidean data [4], a property which makes them valuable in certain problem domains where graph data is abundant. Conversely, NN-based algorithms are typically required to operate on structured inputs with strictly defined dimensions.

Language understanding research is held back by a failure to relate language to the physical world it describes and to the social interactions it facilitates. Despite the incredible effectiveness of language processing models to tackle tasks after being trained on text alone, successful linguistic communication relies on a shared experience of the world. It is this shared experience that makes utterances meaningful. Natural language processing is a diverse field, and progress throughout its development has come from new representational theories, modeling techniques, data collection paradigms, and tasks. We posit that the present success of representation learning approaches trained on large, text-only corpora requires the parallel tradition of research on the broader physical and social context of language to address the deeper questions of communication.

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At the outset of World War II, you'd have a better chance of finding a needle in a haystack with a camel stuck in its eye than you did shooting down an enemy aircraft in your first dozen or so shots. This is because anti-aircraft shells at the time used manual fuses that had to be dialed in for specific lengths of time to delay their explosion. The idea was that you'd estimate where the targeted plane would be in, say five seconds, based on its currently flight path, then time the shell for that length, fire the shell at the plane and hope that the timing and location were close enough that shrapnel from the exploding shell hits the plane. If your calculations were off by even a hair, the shell would miss by thousands of feet. And if shooting down piloted aircraft was this hard, intercepting Germany's terrifyingly fast V1 and V2 rockets required far more luck than skill. But that's exactly what the team at Section T set out to do.

Written by Camylle Lanteigne (@CamLante), who's currently pursuing a Master's in Public Policy at Concordia University and whose work on social robots and empathy has been featured on Vox. This explainer was written in response to colleagues' requests to know more about temporal bias in AI ethics. It begins with a refresher on cognitive biases, then dives into: how humans understand time, time preferences, present-day preference, confidence changes, planning fallacies, and hindsight bias. Bias is a really big topic, but I'll try to succinctly define a subsection of it--implicit cognitive bias--in a way that is useful for AI ethics, particularly. Humans have cognitive biases, which means every one of us, to varying degrees, holds beliefs and impressions that are not backed up by fleshed out reasoning or evidence, or that we never bothered questioning in the first place.¹

After the stock market closed today, Five9 announced the acquisition of intelligent virtual agent (IVA) company Inference Solutions. The purchase price is $172 million, $148 million in cash and $24 million when certain bookings targets are met. Inference brings 550 customers, among them several joint Five9 customers -- including Chick-fil-A and Wyndham Hotels. Inference was founded in 2005, spun out from Telstra Research Labs -- think of it as the Australian version of Bell Labs. Headquartered in San Francisco, the company has additional offices in Austin, TX and Melbourne, Australia.

In real-world applications, data do not reflect the ones commonly used for neural networks training, since they are usually few, unlabeled and can be available as a stream. Hence many existing deep learning solutions suffer from a limited range of applications, in particular in the case of online streaming data that evolve over time. To narrow this gap, in this work we introduce a novel and complex setting involving unsupervised meta-continual learning with unbalanced tasks. These tasks are built through a clustering procedure applied to a fitted embedding space. We exploit a meta-learning scheme that simultaneously alleviates catastrophic forgetting and favors the generalization to new tasks. Moreover, to encourage feature reuse during the meta-optimization, we exploit a single inner loop taking advantage of an aggregated representation achieved through the use of a self-attention mechanism. Experimental results on few-shot learning benchmarks show competitive performance even compared to the supervised case. Additionally, we empirically observe that in an unsupervised scenario, the small tasks and the variability in the clusters pooling play a crucial role in the generalization capability of the network. Further, on complex datasets, the exploitation of more clusters than the true number of classes leads to higher results, even compared to the ones obtained with full supervision, suggesting that a predefined partitioning into classes can miss relevant structural information.

Brisbane-based robotics company Lyro Robotics has successfully deployed its robots and world-leading picking and packing technology in commercial trials held at the avocado facility, Sunnyspot Packhouse, in Ravensbourne, Queensland. Summer harvest looms, and so does a widespread labour shortage for much of Australia's horticulture industry. The Australian Fresh Produce Alliance (AFPA) indicated in September the country's fruit and vegetable industry was facing a workforce shortage of up to 26,000 people throughout the peak summer season. This figure was reached by Ernst & Young. This is where robotics technology, such as Lyro Robotics, could provide solutions assisting farmers with seasonal and short-term farm work.