This paper proposes a formal langauge to describe the search space of architecture search problem. This langauge is a domain specific language embedded in python. Users can write modular, composable, and reusable search space by using this langauge. Originality: The contribution is new. This is the first work that tries to provide a formal langauge for the space definition.

Artificial neural network (NN) architecture design is a nontrivial and time-consuming task that often requires a high level of human expertise. Neural architecture search (NAS) serves to automate the design of NN architectures and has proven to be successful in automatically finding NN architectures that outperform those manually designed by human experts. NN architecture performance can be quantified based on multiple objectives, which include model accuracy and some NN architecture complexity objectives, among others. The majority of modern NAS methods that consider multiple objectives for NN architecture performance evaluation are concerned with automated feed forward NN architecture design, which leaves multi-objective automated recurrent neural network (RNN) architecture design unexplored. RNNs are important for modeling sequential datasets, and prominent within the natural language processing domain. It is often the case in real world implementations of machine learning and NNs that a reasonable trade-off is accepted for marginally reduced model accuracy in favour of lower computational resources demanded by the model. This paper proposes a multi-objective evolutionary algorithm-based RNN architecture search method. The proposed method relies on approximate network morphisms for RNN architecture complexity optimisation during evolution. The results show that the proposed method is capable of finding novel RNN architectures with comparable performance to state-of-the-art manually designed RNN architectures, but with reduced computational demand.

Artificial Intelligence (AI), machine learning and the internet of things (IoT) lead emerging technology conversations across the world. Companies recognise that these technologies are ready to be used to drive real business benefits. The Asia Pacific and Japan (APJ) region is set to pick up the pace on these two fronts. According to a recent cloud survey by MIT Technology Review Custom and VMware, more than 70% of non-users of AI in APJ said their organisations will adopt the technology within five years. IDC forecasted global IoT spending to surpass $1 trillion USD in 2020, with APJ leading the way.

I begin with an assumption that you are somewhat familiar with "whitebox" term as it relates to networking gears and cognizant of its trend. The term "whitebox" and "open networking" to some extent synonymous, both innately suggest networking gears that are "open" meaning adheres to "open" standards and ecosystems. The former is a byproduct of open networking concept and refers to the disaggregation model in which hardware and software are separated. This decoupling of hardware and software created opportunities for a software ecosystem to flourish: well, almost! As with any technologies advents, early days are little bumpy.