Driverless cars could make our roads safer and reduce congestion. But the algorithms driving them will also have to make life-or-death decisions. At some stage in the future, a fully autonomous car may determine who lives and who dies on our roads. These machines are being tested right now and Australian politicians are looking overseas for leadership, emboldened by the promise of fewer fatalities and less congestion. At the moment, there must be a human behind the wheel of these cars at all times, but government agencies are already working on a legal framework for when machines are totally in control.

It was 2011; a year that may be considered the beginning of IT/OT convergence. LTE was the new technology on the block; touting fast ethernet speeds from almost anywhere. Machine to Machine (M2M) communications between industrial control system (ICS) devices was becoming a cost effective way to improve the reliability, redundancy, and operational timeliness of the nations' critical infrastructure. To pave the way for this radical change, the way devices responsible for controlling electric, gas, and water commodities communicate, an even more radical change of the status quo was required; the merging of the traditionally separate information technology and operational technology paradigms. Traditionally, operational technology was physically air gapped from the internet.

The UK has a genuine opportunity to take a lead on the ethics of artificial intelligence, says Nigel Shadbolt, principal of Jesus College, Oxford and co-founder of the Open Data Institute (ODI). You forgot to provide an Email Address. This email address doesn't appear to be valid. This email address is already registered. You have exceeded the maximum character limit.

Aug 20, 2018 (Heraldkeeper via COMTEX) -- A new research document is added in HTF MI database of 90 pages, titled as'Global Deep Learning Market (2018-2023)' with detailed analysis, Competitive landscape, forecast and strategies. The study covers geographic analysis that includes regions like North America, Europe, Asia-Pacific, Latin America, and the Middle East and Africa and important players/vendors such as Google, Microsoft, IBM, Intel etc. The report will help you gain market insights, future trends and growth prospects for forecast period of 2018-2023. In enterprise computing, deep learning is evolving into one of the most advanced technologies. Deep learning is a subset of machine learning in Artificial Intelligence (AI) that has networks capable of learning unsupervised, from data that is unstructured or unlabeled.

We characterize the statistical properties of network Lasso for semi-supervised regression problems involving network- structured data. This characterization is based on the con- nectivity properties of the empirical graph which encodes the similarities between individual data points. Loosely speaking, network Lasso is accurate if the available label informa- tion is well connected with the boundaries between clusters of the network-structure datasets. We make this property precise using the notion of network flows. In particular, the existence of a sufficiently large network flow over the empirical graph implies a network compatibility condition which, in turn, en- sures accuracy of network Lasso.

State-of-the-art distributed machine learning suffers from significant delays due to frequent communication and synchronizing between worker nodes. Emerging communication-efficient SGD algorithms that limit synchronization between locally trained models have been shown to be effective in speeding-up distributed SGD. However, a rigorous convergence analysis and comparative study of different communication-reduction strategies remains a largely open problem. This paper presents a new framework called Coooperative SGD that subsumes existing communication-efficient SGD algorithms such as federated-averaging, elastic-averaging and decentralized SGD. By analyzing Cooperative SGD, we provide novel convergence guarantees for existing algorithms. Moreover this framework enables us to design new communication-efficient SGD algorithms that strike the best balance between reducing communication overhead and achieving fast error convergence.

Topology optimization is computationally demanding that requires the assembly and solution to a finite element problem for each material distribution hypothesis. As a complementary alternative to the traditional physics-based topology optimization, we explore a data-driven approach that can quickly generate accurate solutions. To this end, we propose a deep learning approach based on a 3D encoder-decoder Convolutional Neural Network architecture for accelerating 3D topology optimization and to determine the optimal computational strategy for its deployment. Analysis of iteration-wise progress of the Solid Isotropic Material with Penalization process is used as a guideline to study how the earlier steps of the conventional topology optimization can be used as input for our approach to predict the final optimized output structure directly from this input. We conduct a comparative study between multiple strategies for training the neural network and assess the effect of using various input combinations for the CNN to finalize the strategy with the highest accuracy in predictions for practical deployment. For the best performing network, we achieved about 40% reduction in overall computation time while also attaining structural accuracies in the order of 96%.

Generative adversarial networks (GANs) often suffer from unpredictable mode-collapsing during training. We study the issue of mode collapse of Boundary Equilibrium Generative Adversarial Network (BEGAN), which is one of the state-of-the-art generative models. Despite its potential of generating high-quality images, we find that BEGAN tends to collapse at some modes after a period of training. We propose a new model, called \emph{BEGAN with a Constrained Space} (BEGAN-CS), which includes a latent-space constraint in the loss function. We show that BEGAN-CS can significantly improve training stability and suppress mode collapse without either increasing the model complexity or degrading the image quality. Further, we visualize the distribution of latent vectors to elucidate the effect of latent-space constraint. The experimental results show that our method has additional advantages of being able to train on small datasets and to generate images similar to a given real image yet with variations of designated attributes on-the-fly.

Mini-batch stochastic gradient methods are the current state of the art for large-scale distributed training of neural networks and other machine learning models. However, they fail to adapt to a changing communication vs computation trade-off in a system, such as when scaling to a large number of workers or devices. More so, the fixed requirement of communication bandwidth for gradient exchange severely limits the scalability to multi-node training e.g. in datacenters, and even more so for training on decentralized networks such as mobile devices. We argue that variants of local SGD, which perform several update steps on a local model before communicating to other nodes, offer significantly improved overall performance and communication efficiency, as well as adaptivity to the underlying system resources. Furthermore, we present a new hierarchical extension of local SGD, and demonstrate that it can efficiently adapt to several levels of computation costs in a heterogeneous distributed system.

In treatment allocation problems the individuals to be treated often arrive sequentially. We study a problem in which the policy maker is not only interested in the expected cumulative welfare but is also concerned about the uncertainty/risk of the treatment outcomes. At the outset, the total number of treatment assignments to be made may even be unknown. A sequential treatment policy which attains the minimax optimal regret is proposed. We also demonstrate that the expected number of suboptimal treatments only grows slowly in the number of treatments. Finally, we study a setting where outcomes are only observed with delay.