Meta-learning is a promising strategy for learning to efficiently learn using data gathered from a distribution of tasks. However, the meta-learning literature thus far has focused on the task segmented setting, where at train-time, offline data is assumed to be split according to the underlying task, and at test-time, the algorithms are optimized to learn in a single task. In this work, we enable the application of generic meta-learning algorithms to settings where this task segmentation is unavailable, such as continual online learning with unsegmented time series data.

We propose a heterogeneous meta-learning method that trains a model on tasks with various attribute spaces, such that it can solve unseen tasks whose attribute spaces are different from the training tasks given a few labeled instances. Although many meta-learning methods have been proposed, they assume that all training and target tasks share the same attribute space, and they are inapplicable when attribute sizes are different across tasks. Our model infers latent representations of each attribute and each response from a few labeled instances using an inference network. Then, responses of unlabeled instances are predicted with the inferred representations using a prediction network. The attribute and response representations enable us to make predictions based on the task-specific properties of attributes and responses even when attribute and response sizes are different across tasks. In our experiments with synthetic datasets and 59 datasets in OpenML, we demonstrate that our proposed method can predict the responses given a few labeled instances in new tasks after being trained with tasks with heterogeneous attribute spaces.

Enterprises are quickly shifting their IT help desk strategies away from one where every employee's issue or request requires human intervention to one that leverages artificial intelligence (AI)/natural language processing (NLP) for automated resolution. One area that the enterprise service management (ESM) market is now focusing on is the automation of tasks (e.g., fulfill a service request, create a new mailing list, schedule PTO, reserve guest desk). Once this problem was tackled, customers looked to automate virtually anything an employee could ask for -- essentially becoming a system of engagement for issues and requests. One of the most complex parts of creating automation with virtual support agents is to connect the automation to the human language. As the trend toward intelligent automation is moving well beyond IT to include HR and more, it's important that virtual support agent platforms enable organizations to accomplish tasks such as creating simple or complex integrations with virtually any REST API-enabled system.

New developments in automation, hardware, model development, and more that will shape AI in 2020. Roger Magoulas, VP of Radar at O'Reilly takes a look at the new developments in automation, hardware, tools, model development, and more that will shape (or accelerate) AI in 2020. We see the AI space poised for an acceleration in adoption, driven by more sophisticated AI models being put in production, specialised hardware that increases AI's capacity to provide quicker results based on larger datasets, simplified tools that democratise access to the entire AI stack, small tools that enables AI on nearly any device, and cloud access to AI tools that allow access to AI resources from anywhere. Integrating data from many sources, complex business and logic challenges, and competitive incentives to make data more useful all combine to elevate AI and automation technologies from optional to required. And AI processes have unique capabilities that can address an increasingly diverse array of automation tasks--tasks that defy what traditional procedural logic and programming can handle, for example, image recognition, summarisation, labeling, complex monitoring, and response.

Delane Parnell is the cofounder and CEO of PlayVS. If there's ever a constant in the flourishing world of esports, it's that enthusiasm often outpaces the necessary infrastructure to match it. In particular, high school students and teachers who hope to participate in competitive gaming must self-organize without the structure of an official body. Delane Parnell's high school science teacher was someone who took it upon themselves to organize a gaming club for students. He provided the equipment, he kept track of stats and even awarded trophies for the myriad of games they played.

Digital computers have transformed work in almost every sector of the economy over the past several decades (1). We are now at the beginning of an even larger and more rapid transformation due to recent advances in machine learning (ML), which is capable of accelerating the pace of automation itself. However, although it is clear that ML is a "general purpose technology," like the steam engine and electricity, which spawns a plethora of additional innovations and capabilities (2), there is no widely shared agreement on the tasks where ML systems excel, and thus little agreement on the specific expected impacts on the workforce and on the economy more broadly. We discuss what we see to be key implications for the workforce, drawing on our rubric of what the current generation of ML systems can and cannot do [see the supplementary materials (SM)]. Although parts of many jobs may be "suitable for ML" (SML), other tasks within these same jobs do not fit the criteria for ML well; hence, effects on employment are more complex than the simple replacement and substitution story emphasized by some.

Tech's biggest players have fully embraced the AI revolution. Apple, Qualcomm and Huawei have made mobile chipsets that are designed to better tackle machine learning tasks, each with a slightly different approach. Huawei launched its Kirin 970 at IFA this year, calling it the first chipset with a dedicated neural processing unit (NPU). Then, Apple unveiled the A11 Bionic chip, which powers the iPhone 8, 8 Plus and X. The A11 Bionic features a neural engine that the company says is "purpose-built for machine learning," among other things.

It has been argued by Hartley and Sleeman.

This paper reports the results obtained with a group of 24 14-year-old pupils when presented with sets of algebra tasks by the Leeds Modelling System.