In this paper, we present a technique for generating artificial datasets that retain statistical properties of the real data while providing differential privacy guarantees with respect to this data. We include a Gaussian noise layer in the discriminator of a generative adversarial network to make the output and the gradients differentially private with respect to the training data, and then use the generator component to synthesise privacy-preserving artificial dataset. Our experiments show that under a reasonably small privacy budget we are able to generate data of high quality and successfully train machine learning models on this artificial data.

SentRNA: Improving computational RNA design by incorporating a prior of human design strategies Jade Shi, EteRNA players, Rhiju Das, and Vijay S. Pande Abstract: Designing RNA sequences that fold into specific structures and perform desired biological functions is an emerging field in bioengineering with broad applications from intracellular chemical catalysis to cancer therapy via selective gene silencing. Effective RNA design requires first solving the inverse folding problem: given a target structure, propose a sequence that folds into that structure. Although significant progress has been made in developing computational algorithms for this purpose, current approaches are ineffective at designing sequences for complex targets, limiting their utility in real-world applications. However, an alternative that has shown significantly higher performance are human players of the online RNA design game EteRNA. Through many rounds of gameplay, these players have developed a collective library of "human" rules and strategies for RNA design that have proven to be more effective than current computational approaches, especially for complex targets. Here, we present an RNA design agent, SentRNA, which consists of a fully-connected neural network trained using the eternasolves dataset, a set of 1.8 x 10 The agent first predicts an initial sequence for a target using the trained network, and then refines that solution if necessary using a short adaptive walk utilizing a canon of standard design moves. Through this approach, we observe SentRNA can learn and apply humanlike design strategies to solve several complex targets previously unsolvable by any computational approach. We thus demonstrate that incorporating a prior of human design strategies into a computational agent can significantly boost its performance, and suggests a new paradigm for machine-based RNA design. Introduction: Solving the inverse folding problem for RNA is a critical prerequisite to effective RNA design, an emerging field of modern bioengineering research. A RNA molecule's function is highly dependent on the structure into which it folds, which in turn is determined by the sequence of nucleotides that comprise it. Therefore, designing RNA molecules to perform specific functions requires designing sequences that fold into specific structures. As such, significant efforts have been made over the past several decades in developing computational algorithms to reliably predict RNA sequences that fold into a given target. Existing computational methods for inverse RNA folding can be roughly separated into two types. The first type generates an initial guess of a sequence and then refines the sequence using some form of stochastic search.

Quantifying similarity between data objects is an important part of modern data science. Deciding what similarity measure to use is very application dependent. In this paper, we combine insights from systems theory and machine learning, and investigate the weighted cepstral distance, which was previously defined for signals coming from ARMA models. We provide an extension of this distance to invertible deterministic linear time invariant single input single output models, and assess its applicability. We show that it can always be interpreted in terms of the poles and zeros of the underlying model, and that, in the case of stable, minimum-phase, or unstable, maximum-phase models, a geometrical interpretation in terms of subspace angles can be given. We then devise a method to assess stability and phase-type of the generating models, using only input/output signal information. In this way, we prove a connection between the extended weighted cepstral distance and a weighted cepstral model norm. In this way, we provide a purely data-driven way to assess different underlying dynamics of input/output signal pairs, without the need for any system identification step. This can be useful in machine learning tasks such as time series clustering. An iPython tutorial is published complementary to this paper, containing implementations of the various methods and algorithms presented here, as well as some numerical illustrations of the equivalences proven here.

Latent feature modeling allows capturing the latent structure responsible for generating the observed properties of a set of objects. It is often used to make predictions either for new values of interest or missing information in the original data, as well as to perform data exploratory analysis. However, although there is an extensive literature on latent feature models for homogeneous datasets, where all the attributes that describe each object are of the same (continuous or discrete) nature, there is a lack of work on latent feature modeling for heterogeneous databases. In this paper, we introduce a general Bayesian nonparametric latent feature model suitable for heterogeneous datasets, where the attributes describing each object can be either discrete, continuous or mixed variables. The proposed model presents several important properties. First, it accounts for heterogeneous data while keeping the properties of conjugate models, which allow us to infer the model in linear time with respect to the number of objects and attributes. Second, its Bayesian nonparametric nature allows us to automatically infer the model complexity from the data, i.e., the number of features necessary to capture the latent structure in the data. Third, the latent features in the model are binary-valued variables, easing the interpretability of the obtained latent features in data exploratory analysis. We show the flexibility of the proposed model by solving both prediction and data analysis tasks on several real-world datasets. Moreover, a software package of the GLFM is publicly available for other researcher to use and improve it.

Google is allowing UK users to make phone calls to mobile numbers and landlines on its Home smart speakers, free of charge. Itw Home and Home Mini smart speakers will be able to place hand-free calls to ordinary UK numbers using Wi-Fi. The latest update will be rolled out across the UK this week. Users in the US have been able to make phone calls using the device since August last year. Google Home users will soon be able to make phone calls to mobile numbers and landlines, free of charge.

Nearly one in five U.S. adults today have access to a smart speaker, according to new research out this week from Voicebot.ai. That means adoption of these voice-powered devices has grown to 47.3 million U.S. adults in two years – or 20 percent of U.S. adult population. To clarify, "access to a smart speaker" means the adults have one in their home, but they may not be a primary user. So, a spouse, a roommate, or a live-in partner would also qualify as a smart speaker user, according to this study. That's a difference worth pointing out, especially if making a comparison to other technology devices, like smartphones or wearables, which tend to have only one owner.

In 2018, worldwide IT spending is predicted to hit $3.7 trillion, an increase of 4.5% from 2017 according to a recent report by Gartner. The increase in spending will be driven by emerging technologies; IoT, Blockchain and AI are projected to be the key growth areas. Many feel the game-changer for emerging technologies lies in the convergence of IoT, Blockchain and AI. Recently IoT Tech Expo speaker, Cisco's Maciej Kranz* stated that 2018 will be the time when the leading technologies of today; AI, IoT and blockchain will converge to power new solutions. And we can already see an example of this from Porsche which is currently testing IoT, AI and blockchain technology solutions for smart cars.

The artificial intelligence market is expected to reach USD 190.61 billion by 2025 from USD 21.46 billion in 2018, at a CAGR of 36.62%. The market growth can be attributed to factors such as growing big data, the increasing adoption of cloud-based applications and services, and increasing demand for intelligent virtual assistants. The limited number of AI technology experts is restraining the market growth to a certain extent Software to hold largest market share during forecast period Software is expected to hold the largest share of the artificial intelligence market. Continuous developments have been witnessed in AI software and related software development kits. Also, artificial intelligence software is used in various applications, such as virtual assistants, marketing, search advertising, identity access management, intruder detection, and cybersecurity Market for computer vision technology to grow at highest CAGR during forecast period The artificial intelligence market for computer vision technology is expected to grow at the highest CAGR during the forecast period.

The vegetables you eat with your Sunday roast may soon be picked by a robot. Farmers in Cornwall are testing a machine invented using European funding that picks cauliflowers from the field without bruising them. It works in a similar way to the human hand by squeezing each cauliflower before deciding whether it is ready to be harvested. The GummiArm robot is believed to be a answer to any migrant staff shortages that may arise when the UK leaves the EU. A cauliflower picking robot has been developed which can tell when the vegetable is ready for harvest and pull it out of the ground without damaging it.

Cities of the near future could be home to driverless taxis that you order from an app on your smartphone or from designated pickup points, according to a French car-maker. Renault has unveiled an electric concept vehicle, called EZ-GO, that aims to blur the lines between private and public transportation. The six-seater vehicle, which could hit the streets by 2022, features a rooftop opening that allows passengers to enter by a ramp for easy access. Cities of the near future could be home to driverless taxis that you order from an app on your smartphone or from designated pickup points, according to a French car-maker Renault. Renault launched its futuristic car at a press preview, held as part of the Geneva International Motor Show taking place in Switzerland between March 8 and 18.