You can be forgiven if your first reaction to hearing the term "smart cities" is an eye roll. Sure, we have smart diapers, smart toothbrushes and smart faucets, but cities? How is that even possible? While the word is a bit amorphous, there's no question that smart cities are a thing and an important one at that. Cities are the locus of much of the world's population and economic activity. By 2050, a full 66 percent of the world is expected to reside in one, according to the United Nations.

In Copenhagen, dispatchers now have help from AI. If you call for an ambulance, an artificially intelligent assistant called Corti will be on the line, using speech recognition software to transcribe the conversation, and using machine learning to analyze the words and other clues in the background that point to a heart attack diagnosis. The dispatcher gets alerts from the bot in real time. It's a situation where dispatchers typically have to rely only on their own knowledge. "If you and I have a problem, we end up Googling or asking people," says Andreas Cleve, CEO of the startup that created the technology. "These people are handling more or less the worst days of our lives but they have no tools to do it."

Another week has passed, and another piece of research has emerged about the potential of artificial intelligence (AI), but the human cost is starting to become a very big elephant, reports Telecoms.com The research itself is from SAS and highlights businesses are becoming normalised to the idea of AI. The concept of trusting a machine might have been nightmarish in months gone, but now it seems business leaders are interested in the efficiency gains which can be realised. "Whether we realise it or not, AI has already arrived," says Peter Pugh-Jones, head of technology at SAS UK & Ireland. "From financial services to retail, AI has become more commonplace and we are seeing its use progress from solely back-office support to increasingly front-end, customer-focused roles.

Hyperscalers have billions of users who get access to their services for free, but the funny thing is that these users act like they are paying for it and expect for these services to be always available, no excuses. Organizations and consumers also rely on Facebook, Google, Microsoft, Amazon, Alibaba, Baidu, and Tencent for services that they pay for, too, and they reasonably expect that their data will always be immediately accessible and secure, the services always available, their search returns always popping up milliseconds after their queries are entered, and the recommendations that come to them personalized for them. These hyperscalers have built networks of massive datacenters, spanning the globe, to ensure the data and services are close to their customers and that latency doesn't become a problem. Given all this, disaster recovery becomes a critical part of the business. Hyperscale companies need to make sure business can continue as usual even if a datacenter goes down.

Machine learning is increasingly enabling companies to gain strategic advantage by turning big data into insightful and actionable information. But how do they access this new world of artificial intelligence? Offering easy access to machine learning, predictive APIs [application programming interfaces] are emerging as a key driving factor for machine learning (ML) and the overall world of artificial intelligence (AI). Predictive APIs offer the flexibility of deciding to host machine learning in the cloud, in-house or both, giving developers the freedom to work in the language and tools they want. "A predictive API exposes machine learning capability so it can either be an API that gives access to a predictive model, and it can also expose access to an ability to learn new models and to create new models from data," said Louis Dorard, who has a PhD in machine learning and is the author of Bootstrapping Machine Learning and is founder of the series of predictive API conferences, including PAPIs.io "There's machine learning APIs where you can send a data set -- for example, one column for the tweet and another column for positive or negative or neutral -- and then they send it back [through] the API and then you can reuse the model for data," Dorard said.

Here's a cute video about how UK-based Rusty Squid designs robots. Rusty Squid is a studio for experimental robotic engineering and design, working within the contemporary arts. David McGoran, Creative Director says "We explore the design space before committing to sensors and autonomous behaviour. During the design process, we created our own bespoke tools to effectively communicate with engineers, artists and designers. One of the bespoke tools featured in How We Build a Robot is called the Story Machine; we use it for, what we call, 'Relationship Design'."

We consider the task of weakly supervised one-shot detection. In this task, we attempt to perform a detection task over a set of unseen classes, when training only using weak binary labels that indicate the existence of a class instance in a given example. The model is conditioned on a single exemplar of an unseen class and a target example that may or may not contain an instance of the same class as the exemplar. A similarity map is computed by using a Siamese neural network to map the exemplar and regions of the target example to a latent representation space and then computing cosine similarity scores between representations. An attention mechanism weights different regions in the target example, and enables learning of the one-shot detection task using the weaker labels alone. The model can be applied to detection tasks from different domains, including computer vision object detection. We evaluate our attention Siamese networks on a one-shot detection task from the audio domain, where it detects audio keywords in spoken utterances. Our model considerably outperforms a baseline approach and yields a 42.6% average precision for detection across 10 unseen classes. Moreover, architectural developments from computer vision object detection models such as a region proposal network can be incorporated into the model architecture, and results show that performance is expected to improve by doing so.

Stochastic variational inference (SVI) employs stochastic optimization to scale up Bayesian computation to massive data. Since SVI is at its core a stochastic gradient-based algorithm, horizontal parallelism can be harnessed to allow larger scale inference. We propose a lock-free parallel implementation for SVI which allows distributed computations over multiple slaves in an asynchronous style. We show that our implementation leads to linear speed-up while guaranteeing an asymptotic ergodic convergence rate $O(1/\sqrt(T)$ ) given that the number of slaves is bounded by $\sqrt(T)$ ($T$ is the total number of iterations). The implementation is done in a high-performance computing (HPC) environment using message passing interface (MPI) for python (MPI4py). The extensive empirical evaluation shows that our parallel SVI is lossless, performing comparably well to its counterpart serial SVI with linear speed-up.

The Internet has enabled the emergence of massive online collaborative projects. As the number of contributors to these projects grows, it becomes increasingly important to understand and predict whether the edits that users make will eventually impact the project positively. Existing solutions either rely on a user reputation system or consist of a highly-specialized predictor tailored to a specific peer-production system. In this work, we explore a different point in the solution space, which does not involve any content-based feature of the edits. To this end, we formulate a statistical model of edit outcomes. We view each edit as a game between the editor and the component of the project. We posit that the probability of a positive outcome is a function of the editor's skill, of the difficulty of editing the component and of a user-component interaction term. Our model is broadly applicable, as it only requires observing data about who makes an edit, what the edit affects and whether the edit survives or not. Then, we consider Wikipedia and the Linux kernel, two examples of large-scale collaborative projects, and we seek to understand whether this simple model can effectively predict edit survival: in both cases, we provide a positive answer. Our approach significantly outperforms those based solely on user reputation and bridges the gap with specialized predictors that use content-based features. Furthermore, inspecting the model parameters enables us to discover interesting structure in the data. Our method is simple to implement, computationally inexpensive, and it produces interpretable results; as such, we believe that it is a valuable tool to analyze collaborative systems.

We explore the use of random forest and gradient boosting, two powerful tree-based machine learning algorithms, for the detection of cosmic strings in maps of the cosmic microwave background (CMB), through their unique Gott-Kaiser-Stebbins effect on the temperature anisotropies.The information in the maps is compressed into feature vectors before being passed to the learning units. The feature vectors contain various statistical measures of processed CMB maps that boost the cosmic string detectability. Our proposed classifiers, after training, give results improved over or similar to the claimed detectability levels of the existing methods for string tension, $G\mu$. They can make $3\sigma$ detection of strings with $G\mu \gtrsim 2.1\times 10^{-10}$ for noise-free, $0.9'$-resolution CMB observations. The minimum detectable tension increases to $G\mu \gtrsim 3.0\times 10^{-8}$ for a more realistic, CMB S4-like (II) strategy, still a significant improvement over the previous results.