Blueprint Genetics has raised €14M to boost its genetic diagnostics platform, using artificial intelligence to interpret next-generation sequencing data. Based in Helsinki, Finland, Blueprint Genetics is a startup that addresses the main bottleneck of genetic testing through next-generation sequencing (NGS), the interpretation of massive volumes of data, with the aid of artificial intelligence (AI). Its diagnostics panels cover over 2,200 disorders in 14 medical areas that include cardiology, neurology and immunology. With a target market of 350 million people affected by severe inherited diseases, expected to grow to double-digit billions in coming years, the company has convinced new and existing investors to raise a €14M round to boost sales and improve the efficiency of its platform. The round was led by Creathor Venture and Medtech Innovation Partners, both new to the company.

Today, Toyota announced the launch of Toyota AI Ventures, a new venture capital subsidiary focused on startup tech companies working on artificial intelligence. The fund has received an initial $100 million from the Toyota Research Institute (TRI), an AI-, robotics- and autonomous car-focused initiative created in 2015. AI Ventures will direct its investments towards AI, robotics, autonomous vehicles and data and cloud technology. Along with funding, it will also offer companies it invests in both mentorship and support at its Silicon Valley headquarters. "One of the biggest challenges entrepreneurs face is knowing if they're building the right product for the right market. We can help them navigate that uncertainty, and we're committed to doing so in a founder-friendly way because their success is our success," said TRI VP Jim Adler in a statement.

Engineers widely use Gaussian process regression framework to construct surrogate models aimed to replace computationally expensive physical models while exploring design space. Thanks to Gaussian process properties we can use both samples generated by a high fidelity function (an expensive and accurate representation of a physical phenomenon) and a low fidelity function (a cheap and coarse approximation of the same physical phenomenon) while constructing a surrogate model. However, if samples sizes are more than few thousands of points, computational costs of the Gaussian process regression become prohibitive both in case of learning and in case of prediction calculation. We propose two approaches to circumvent this computational burden: one approach is based on the Nystr\"om approximation of sample covariance matrices and another is based on an intelligent usage of a blackbox that can evaluate a~low fidelity function on the fly at any point of a design space. We examine performance of the proposed approaches using a number of artificial and real problems, including engineering optimization of a rotating disk shape.

Subsurface applications including geothermal, geological carbon sequestration, oil and gas, etc., typically involve maximizing either the extraction of energy or the storage of fluids. Characterizing the subsurface is extremely complex due to heterogeneity and anisotropy. Due to this complexity, there are uncertainties in the subsurface parameters, which need to be estimated from multiple diverse as well as fragmented data streams. In this paper, we present a non-intrusive sequential inversion framework, for integrating data from geophysical and flow sources to constraint subsurface Discrete Fracture Networks (DFN). In this approach, we first estimate bounds on the statistics for the DFN fracture orientations using microseismic data. These bounds are estimated through a combination of a focal mechanism (physics-based approach) and clustering analysis (statistical approach) of seismic data. Then, the fracture lengths are constrained based on the flow data. The efficacy of this multi-physics based sequential inversion is demonstrated through a representative synthetic example.

The goal of this paper is to assess the utility of Reduced-Order Models (ROMs) developed from 3D physics-based models for predicting transient thermal power output for an enhanced geothermal reservoir while explicitly accounting for uncertainties in the subsurface system and site-specific details. Numerical simulations are performed based on Latin Hypercube Sampling (LHS) of model inputs drawn from uniform probability distributions. Key sensitive parameters are identified from these simulations, which are fracture zone permeability, well/skin factor, bottom hole pressure, and injection flow rate. The inputs for ROMs are based on these key sensitive parameters. The ROMs are then used to evaluate the influence of subsurface attributes on thermal power production curves. The resulting ROMs are compared with field-data and the detailed physics-based numerical simulations. We propose three different ROMs with different levels of model parsimony, each describing key and essential features of the power production curves. ROM-1 is able to accurately reproduce the power output of numerical simulations for low values of permeabilities and certain features of the field-scale data, and is relatively parsimonious. ROM-2 is a more complex model than ROM-1 but it accurately describes the field-data. At higher permeabilities, ROM-2 reproduces numerical results better than ROM-1, however, there is a considerable deviation at low fracture zone permeabilities. ROM-3 is developed by taking the best aspects of ROM-1 and ROM-2 and provides a middle ground for model parsimony. It is able to describe various features of numerical simulations and field-data. From the proposed workflow, we demonstrate that the proposed simple ROMs are able to capture various complex features of the power production curves of Fenton Hill HDR system. For typical EGS applications, ROM-2 and ROM-3 outperform ROM-1.

Variational inference approximates the posterior distribution of a probabilistic model with a parameterized density by maximizing a lower bound for the model evidence. Modern solutions fit a flexible approximation with stochastic gradient descent, using Monte Carlo approximation for the gradients. This enables variational inference for arbitrary differentiable probabilistic models, and consequently makes variational inference feasible for probabilistic programming languages. In this work we develop more efficient inference algorithms for the task by considering importance sampling estimates for the gradients. We show how the gradient with respect to the approximation parameters can often be evaluated efficiently without needing to re-compute gradients of the model itself, and then proceed to derive practical algorithms that use importance sampled estimates to speed up computation. We present importance sampled stochastic gradient descent that outperforms standard stochastic gradient descent by a clear margin for a range of models, and provide a justifiable variant of stochastic average gradients for variational inference.

Fire ants use their bodies to build tall Eiffel Tower-like structures when they run into an obstacle in a fascinating show of teamwork and strength. New research using high-speed cameras has shown for the first time how the ants rapidly build these towers through trial and error. The finding could help scientists to develop modular robots that connect together to quickly build towers and bridges. Fire ants use their bodies to build tall, tower-like structures when they run into an obstacle in a fascinating show of teamwork and strength. Fire ants march as a group while searching for food or escaping a new area until they come to something blocking their way.

To ride the rising wave of AI, financial services companies will have to navigate evolving standards, regulations and risk dynamics--particularly regarding data rights, algorithmic accountability and cybersecurity. The success of artificial intelligence (AI) algorithms hinges on the ability to gain easy access to the right kind of data in sufficient volume. Put more simply, AI depends on good data. Even Google--which is famous for the pioneering work in AI that underpins its standard-setting search-based advertising business--makes no bones about the critical role of data in AI. Peter Norvig, Google's director of research, has said: "We don't have better algorithms, we just have more data." Companies increasingly realize that data is critical to their success--and they are paying striking sums to acquire it. Microsoft's US$26 billion purchase of the enterprise social network LinkedIn is a prime example. But other technology companies are also seeking to acquire data-related assets, typically to acquire more than just identity-linked information from social media sources by focusing instead on vast troves of anonymized consumer data. Think, for example, of Oracle pursuing an M&A-led strategy for its Oracle Data Cloud data aggregation service, or IBM buying, within the past two years, both The Weather Company and Truven Health Analytics.

The VRTO Virtual & Augmented Reality World Conference & Expo just wrapped up Monday night in Toronto. The two day conference was packed with simultaneous, back-to-back presentations and workshops from industry leaders such as Microsoft, Google, AMD, The VOID, IMAX, Two Bit Circus, Secret Location, Globacore, Quantum Capture, The Canadian Film Centre, and many more. By the end of the conference, 5 themes on what will guild the VR industry forward, became glaringly clear. This is the budding technology that innovators, dreamers, and creators see as the mechanism for changing lives. Rikard Steiber, President of Viveport and SVP Virtual Reality at HTC, is constantly sharing his belief that VR "will change the world."

This "Weekend Think" article is adapted from Brett King's latest book, Augmented: Life in the Smart Lane, published last year. Since 2005 I've been predicting the fundamental decline of branch banking. For almost 10 years I fought bankers who decried my assessment that branches would cease to be the most important channel in banking, to be replaced by far more efficient mechanisms for revenue generation and relationship. Today the discussion is increasingly resorting to a sort of desperate plea: "But branches aren't going to die completely, are they?" No one who is watching these trends today is still saying branches will grow.