Artificial intelligence and machine learning are proving to be very useful in just about every business function in the enterprise, and marketing is no exception. AI is already impacting marketing, and it's going to further shape the future of how business is done and how relationships are forged between companies and their clients. As I wrote recently in MarTechToday, most AI in marketing applications are focused on B2C use cases, many of which we're very familiar with as consumers ourselves. Most of us know that the ads that show up on Facebook, on banners or on Google are targeting individual users directly based on past behavior, demographic data, location information and more -- a process that couldn't be done at scale without the aid of AI. For companies that sell to businesses, communication between salespeople and marketing teams is critical. A day in the life of a salesperson is often chock-full of tasks that could be seen as marketing-related.

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.

Reviews for the Luc Besson-directed sci-fi flick "Valerian and the City of a Thousand Planets" filed in Tuesday, with most critics describing the film as a beautiful and expensive disaster. Adapted from the French comic "Valerian et Laureline," Dane Dehaan and Cara Delevinge star in the STX-EuraCorp production slated to premiere July 21. "Valerian" has earned a range of polarized reactions from "simply childish" to "mind-meltingly beautiful and strange" from top critics, making it hard to pin down as a viable contender for the summer box office. The sci-fi project currently holds 74 percent on Rotten Tomatoes' aggregator scale. The Hollywood Reporter claimed that "Valerian" is a potential Razzie contender, saying: "The Razzies don't need to wait until the end of the year to anoint a winner for 2017. The Golden Turkey Awards should be republished with a new cover."

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.

Gradient Descent, and its variants, are a popular method for solving empirical risk minimization problems in machine learning. However, if the size of the training set is large, a computational bottleneck is the computation of the gradient, and hence, it is common to distribute the training set among worker nodes. Doing this in a synchronous fashion faces yet another challenge of stragglers (i.e., slow or unavailable nodes) which might cause a considerable delay, and hence, schemes for mitigation of stragglers are essential. It was recently shown by Tandon et al. that stragglers can be avoided by carefully assigning redundant computations to the worker nodes and coding across partial gradients, and a randomized construction for the coding was given. In this paper we obtain a comparable deterministic scheme by employing cyclic MDS codes. In addition, we propose replacing the exact computation of the gradient with an approximate one; a technique which drastically increases the straggler tolerance, and stems from adjacency matrices of expander graphs.

Given samples from a distribution, how many new elements should we expect to find if we continue sampling this distribution? This is an important and actively studied problem, with many applications ranging from unseen species estimation to genomics. We generalize this extrapolation and related unseen estimation problems to the multiple population setting, where population $j$ has an unknown distribution $D_j$ from which we observe $n_j$ samples. We derive an optimal estimator for the total number of elements we expect to find among new samples across the populations. Surprisingly, we prove that our estimator's accuracy is independent of the number of populations. We also develop an efficient optimization algorithm to solve the more general problem of estimating multi-population frequency distributions. We validate our methods and theory through extensive experiments. Finally, on a real dataset of human genomes across multiple ancestries, we demonstrate how our approach for unseen estimation can enable cohort designs that can discover interesting mutations with greater efficiency.

Class imbalance is a challenging issue in practical classification problems for deep learning models as well as traditional models. Traditionally successful countermeasures such as synthetic over-sampling have had limited success with complex, structured data handled by deep learning models. In this paper, we propose Deep Over-sampling (DOS), a framework for extending the synthetic over-sampling method to exploit the deep feature space acquired by a convolutional neural network (CNN). Its key feature is an explicit, supervised representation learning, for which the training data presents each raw input sample with a synthetic embedding target in the deep feature space, which is sampled from the linear subspace of in-class neighbors. We implement an iterative process of training the CNN and updating the targets, which induces smaller in-class variance among the embeddings, to increase the discriminative power of the deep representation. We present an empirical study using public benchmarks, which shows that the DOS framework not only counteracts class imbalance better than the existing method, but also improves the performance of the CNN in the standard, balanced settings.

Audi has made a significant leap with the all-new Audi A8 premium luxury saloon. While the new Audi A8 brings with itself a host of styling and interior upgrades, the biggest headline here is the highly automated driving system, comprising of 41 individual driver assist systems. Alongside, it houses an all-new infotainment system with two touchscreen interfaces, an intelligent voice processor, self-learning navigation, and other elements like a dynamic steering wheel and active suspension adjustment. Pilot-driving on the Audi A8 The artificial intelligence-powered traffic jam pilot system in the Audi A8 incorporates radar, ultrasonic sensors, a laser sensor and surround cameras to compute an image of the surroundings and gauge proximity. It uses this data to automate starting/stopping, braking, acceleration and steering in traffic conditions at speeds of up to 60kmph.

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."

There are 139 Chinese poems in the new book "The Sunlight that Lost the Glass Window," and the fact they're all written by one artificially intelligent bot doesn't make local scholars too pleased. "It disgusted me with its slippery tone and rhythm," poet Yu Jian told local newspaper China Youth Daily, according to the South China Morning Post. "The sentences were aimless and superficial, lacking the inner logic for emotional expression." Others said computers couldn't create poetry because they weren't alive, and that the work could "kill our beloved art." The book's contentious author is Xiaoice, a natural-language chat bot developed by Microsoft in 2014.