Open-domain conversation systems integrate multiple conversation skills into a single system through a modular approach. One of the limitations of the system, however, is the absence of management capability for external memory. In this paper, we propose a simple method to improve BlenderBot3 by integrating memory management ability into it. Since no training data exists for this purpose, we propose an automating dataset creation for memory management. Our method 1) requires little cost for data construction, 2) does not affect performance in other tasks, and 3) reduces external memory. We show that our proposed model BlenderBot3-M^3, which is multi-task trained with memory management, outperforms BlenderBot3 with a relative 4% performance gain in terms of F1 score.

We have a vision of a Network Compute Fabric where the lines between networking and computing disappear. On the journey there, edge cloud computing provides a critical stepping-stone where computing is pushed very close to where it is needed. This distribution of computing capabilities in the network creates new challenges for its management and operation. We argue that a data-centric approach that extensively uses artificial intelligence (AI) and machine learning (ML) technologies to realize specific management functions is a good candidate to tackle these challenges. As can be seen in Figure 1, edge computing services can be provided through compute/storage resources at different locations in a network, such as on-premises at a customer/enterprise site (industrial control, for example) or at access and local/regional sites (telco operators, for example).

Back in September, we announced Azure CLI 2.0 Preview. These commands provide a rich interface for a large array of use cases, from disk and extension management to container cluster creation. Today's announcement means that customers can now use these commands in production, with full support by Microsoft both through our Azure support channels or GitHub. We don't expect breaking changes for these commands in new releases of Azure CLI 2.0. This new version of Azure CLI should feel much more native to developers who are familiar with command line experiences in the bash enviornment for Linux and macOS with simple commands that have smart defaults for most common operations and that support tab completion and pipe-able outputs for interacting with other text-parsing tools like grep, cut, jq and the popular JMESpath query syntax .