The MyFood Family Smart Greenhouse got a lot of traction at CES 2020, so much that the unit exhibited at the FrenchTech pavilion was sold during the show! Given that the price tag for such a large model (Family model, 22 m² / 242 ft²) varies between 8,600 to 22,000 euros (VAT incl.), that sale was a great validation of the product and the technology powering it. Mickaël Gandecki, co-founder, CTO and Managing Partner, MyFood, told me that the Chef of the Tao restaurant at the Venitian Hotel was so impressed that he wanted his team to visit the smart solar aquaponic greenhouse during CES. Founded in 2015 by Mickaël Gandecki, Matthieu Urban and Johan Nazaraly, MyFood aims to fight the damages caused by industrial agriculture by bringing food production back home, off-grid, using 90% less water, and without pesticide. The company's mission statement reads: "Our ambition: to make it possible to produce at home a healthy, diverse and ultra-fresh diet all-year-round. Reconnect with nature and enjoy a sense of well-being."

We present a new hybrid physics-based machine-learning approach to reservoir modeling. The methodology relies on a series of deep adversarial neural network architecture with physics-based regularization. The network is used to simulate the dynamic behavior of physical quantities (i.e. saturation) subject to a set of governing laws (e.g. mass conservation) and corresponding boundary and initial conditions. A residual equation is formed from the governing partial-differential equation and used as part of the training. Derivatives of the estimated physical quantities are computed using automatic differentiation algorithms. This allows the model to avoid overfitting, by reducing the variance and permits extrapolation beyond the range of the training data including uncertainty implicitely derived from the distribution output of the generative adversarial networks. The approach is used to simulate a 2 phase immiscible transport problem (Buckley Leverett). From a very limited dataset, the model learns the parameters of the governing equation and is able to provide an accurate physical solution, both in terms of shock and rarefaction. We demonstrate how this method can be applied in the context of a forward simulation for continuous problems. The use of these models for the inverse problem is also presented, where the model simultaneously learns the physical laws and determines key uncertainty subsurface parameters. The proposed methodology is a simple and elegant way to instill physical knowledge to machine-learning algorithms. This alleviates the two most significant shortcomings of machine-learning algorithms: the requirement for large datasets and the reliability of extrapolation. The principles presented in this paper can be generalized in innumerable ways in the future and should lead to a new class of algorithms to solve both forward and inverse physical problems.

In high-energy physics (HEP) experiments, a thorough understanding of the properties of known physics forms the basis of any searches that look for new effects. This can only be achieved by an accurate simulation, which in many cases boils down to performing an integral and sampling from it. Often high-dimensional phase space integrals with nontrivial correlations between dimensions are required in important theory calculations. Monte-Carlo (MC) methods still remain as the most important techniques for solving high-dimensional problems across many fields, including for instance: biology [1, 2], chemistry [3], astronomy [4], medical physics [5], finance [6] and image rendering [7]. In high-energy physics, all analyses at the Large Hadron Collider (LHC) rely strongly on multipurpose Monte Carlo event generators [8, 9] for signal or background prediction.

TechRepublic's Teena Maddox talked to Jamie Garcia, senior manager, Algorithms, Applications and Theory Team at IBM Research, at CES 2020 about about the quantum news that IBM has released this week and what's to come. The following is an edited transcript of their conversation. Jamie Garcia: We just released news that we have achieved a 32-quantum volume, which is in line with us doubling our quantum volume every single year. Another one that just came out is that we hit a 100 partners in our IBM Q network. Teena Maddox: With the race for quantum computing, what does that mean when you've increased the volume?

The distinction between actual AI and fake or algorithmic alternatives is important, but marketers are blurring the lines in ways that everyone may regret.

Evolve Energy uses AI and machine learning to optimize energy usage, providing customers with the best wholesale energy prices on fluctuating renewable resources. "We want to help our customers save a significant amount of money on electricity costs and help them decarbonize the grid," CEO Michael Lee tells InnovationMap."There's been a serious of emerging events that enable us to do both at the same time, it's no longer a choice." Evolve Energy, founded in 2018, sells wholesale electricity at cost to residential customers in Texas, charging a $10 monthly subscription fee plus the cost of wholesale electricity. Using their AI technology, they predict when price surges are likely and determine how much energy the customer needs to hit the parameters set on the app by the customer and usage history. The customer does not need to do anything but pair Evolve with their smart thermostat.

Foraminifera are single-celled marine organisms, which may have a planktic or benthic lifestyle. During their life cycle they construct shells consisting of one or more chambers, and these shells remain as fossils in marine sediments. Classifying and counting these fossils have become an important tool in e.g. oceanography and climatology. Currently the process of identifying and counting microfossils is performed manually using a microscope and is very time consuming. Developing methods to automate this process is therefore considered important across a range of research fields. The first steps towards developing a deep learning model that can detect and classify microscopic foraminifera are proposed. The proposed model is based on a VGG16 model that has been pretrained on the ImageNet dataset, and adapted to the foraminifera task using transfer learning. Additionally, a novel image dataset consisting of microscopic foraminifera and sediments from the Barents Sea region is introduced.

In many real-world machine learning applications, unlabeled data can be easily obtained, but it is very time-consuming and/or expensive to label them. So, it is desirable to be able to select the optimal samples to label, so that a good machine learning model can be trained from a minimum amount of labeled data. Active learning (AL) has been widely used for this purpose. However, most existing AL approaches are supervised: they train an initial model from a small amount of labeled samples, query new samples based on the model, and then update the model iteratively. Few of them have considered the completely unsupervised AL problem, i.e., starting from zero, how to optimally select the very first few samples to label, without knowing any label information at all. This problem is very challenging, as no label information can be utilized. This paper studies unsupervised pool-based AL for linear regression problems. We propose a novel AL approach that considers simultaneously the informativeness, representativeness, and diversity, three essential criteria in AL. Extensive experiments on 14 datasets from various application domains, using three different linear regression models (ridge regression, LASSO, and linear support vector regression), demonstrated the effectiveness of our proposed approach.

A BSTRACT The success of deep learning has brought forth a wave of interest in computer hardware design to better meet the high demands of neural network inference. In particular, analog computing hardware has been heavily motivated specifically for accelerating neural networks, based on either electronic, optical or photonic devices, which may well achieve lower power consumption than conventional digital electronics. However, these proposed analog accelerators suffer from the intrinsic noise generated by their physical components, which makes it challenging to achieve high accuracy on deep neural networks. Hence, for successful deployment on analog accelerators, it is essential to be able to train deep neural networks to be robust to random continuous noise in the network weights, which is a somewhat new challenge in machine learning. In this paper, we advance the understanding of noisy neural networks. We outline how a noisy neural network has reduced learning capacity as a result of loss of mutual information between its input and output. To combat this, we propose using knowledge distillation combined with noise injection during training to achieve more noise robust networks, which is demonstrated experimentally across different networks and datasets, including ImageNet. Our method achieves models with as much as 2 greater noise tolerance compared with the previous best attempts, which is a significant step towards making analog hardware practical for deep learning. However, DNN inference is typically very demanding in terms of compute and memory resources Li et al. (2019). Consequently, larger models are often not well suited for large-scale deployment on edge devices, which typically have meagre performance and power budgets, especially battery powered mobile and IoT devices. To address these issues, the design of specialized hardware for DNN inference has drawn great interest, and is an extremely active area of research (Whatmough et al., 2019). To date, a plethora of techniques have been proposed for designing efficient neural network hardware (Sze et al., 2017; Whatmough et al., 2019).

Given the lack of evidence for new particle discoveries at the Large Hadron Collider (LHC), it is critical to broaden the search program. A variety of model-independent searches have been proposed, adding sensitivity to unexpected signals. There are generally two types of such searches: those that rely heavily on simulations and those that are entirely based on (unlabeled) data. This paper introduces a hybrid method that makes the best of both approaches. For potential signals that are resonant in one known feature, this new method first learns a parameterized reweighting function to morph a given simulation to match the data in sidebands. This function is then interpolated into the signal region and then the reweighted background-only simulation can be used for supervised learning as well as for background estimation. The background estimation from the reweighted simulation allows for non-trivial correlations between features used for classification and the resonant feature. A dijet search with jet substructure is used to illustrate the new method. Future applications of Simulation Assisted Likelihood-free Anomaly Detection (SALAD) include a variety of final states and potential combinations with other model-independent approaches.