Kantar, the world's leading data, insights and consulting company, announced today the first patent on Quantum Machine Learning as part of AI/ML advancement in Singapore. With the continuous support and partnership of Singapore's Economic Development Board (EDB), Kantar established its Brand Growth Lab in Singapore in 2018 to develop AI/ML solutions. The Lab, an advanced analytics hub, is dedicated to discovering new ways to leverage big data to drive strategic decision-making for business. On January 2nd of this year, Kantar was granted its first patent by the Intellectual Property Office of Singapore for a method of optimising AI/ML predictions from a classical data feed with a hybrid simulator generated from classical and quantum model structures. Some of the other organizations with a patented invention in the Quantum technology field in Singapore are Oxford University Innovation, D-Wave, IBM and Google.

When I was the Vice President of Advertiser Analytics at Yahoo, this became a key focus guiding the analytics that we were delivering to advertisers to help them optimize their spend across the Yahoo ad network. Advertisers had significant untapped advertising and marketing spend into which we were not tapping because we could not deliver audience, content and campaign insights to help them spend that money with us. And the MOTT was huge. Now here I am again, and I'm again noticing this massive "Money on the Table" (MOTT) economic opportunity across all companies – orphaned analytics. Orphaned Analytics are one-off analytics developed to address a specific use case but never "operationalized" or packaged for re-use across other organizational use cases.

Given the scale of its impact and the kind of alteration that it brought into our lives, COVID19 is one of the most unprecedented crises of our times. Although it is not the only pandemic that humanity has been through, COVID19 is occurring in the time of the fourth industrial revolution where everyone and everything is one click way, and where the excess of data and computing has allowed machines to be more intelligent than ever. In the age of deep tech and data, data science is definitely at the core of how we are facing the pandemic and paving the way for a new normal. This article provides a non-exhaustive list of use cases in which data science has been leveraged to provide emergency response during COVID and facilitate post-COVID recovery. Collecting and analyzing medical data dating from the early stages of the virus allowed understanding what the virus is all about.

With allegations of appropriation, a lack of diverse panelists for a panel about diversity, and a lack of a clear and definitive apology, the organizers of CogX made many missteps during its recent online conference. It is a story that needs telling because, without exposure, it will continue to happen unchecked. Before we get into the specifics of what happened at CogX, let's look at the broader issue. A recent study showed that almost 70 percent of speakers at conferences are male. The company responsible for those insights performed the same research in 2018, and it seems that we're hardly making any progress at all.

While artificial intelligence systems continue to make huge strides forward, they're still not particularly good at dealing with chaos or unpredictability. Now researchers think they have found a way to fix this, by teaching AI about physics. To be more specific, teaching them about the Hamiltonian function, which gives the AI information about the entirety of a dynamic system: all the energy contained within it, both kinetic and potential. Neural networks, designed to loosely mimic the human brain as a complex, carefully weighted type of AI, then have a'bigger picture' view of what's happening, and that could open up possibilities for getting AI to tackle harder and harder problems. "The Hamiltonian is really the special sauce that gives neural networks the ability to learn order and chaos," says physicist John Lindner, from North Carolina State University.

One kind of robot has endured for the last half-century: the hulking one-armed Goliaths that dominate industrial assembly lines. These industrial robots have been task-specific -- built to spot weld, say, or add threads to the end of a pipe. They aren't sexy, but in the latter half of the 20th century they transformed industrial manufacturing and, with it, the low- and medium-skilled labor landscape in much of the US, Asia, and Europe. You've probably been hearing a lot more about robots and robotics over the last couple years. That's because, for the first time since the 1961 debut of GM's Unimate, regarded as the first industrial robot, the field is once again transforming world economies. Only this time the impact is going to be broader. That's particularly true in light of the COVID-19 pandemic, which has helped advance automation adoption across a variety of industries as manufacturers, fulfillment centers, retail, and restaurants seek to create durable, hygienic operations that can withstand evolving disruptions and regulations.

SAN FRANCISCO - Several experts in the computer technology field learned of Google having an AI which achieved Self-Awareness, soon after the event occurred. For the most part, developers who were involved in the research kept all of their information classified as TOP SECRET. Although many science fiction writers and futurists warned of the threats posed by a "Rogue AI", the observations by Google Research Labs seemed to indicate a moderately benevolent nature in the actions of the IT System, based on the performance and the response following assigned tasking. However, recent activity may indicate that the incredibly powerful Google AI has gone "Full Jerk-Wad." Programmers who are familiar with the cutting-edge Artificial Intelligence (AI) downplay concerns over the discovery.

Specifying a governing physical model in the presence of missing physics and recovering its parameters are two intertwined and fundamental problems in science. Modern machine learning allows one to circumvent these, via emulators and surrogates, but in doing so disregards prior knowledge and physical laws that are especially important for small data regimes, interpretability, and decision making. In this work we fold the mechanistic model into a flexible data-driven surrogate to arrive at a physically structured decoder network. This provides accelerated inference for the Bayesian inverse problem, and can act as a drop-in regulariser that encodes a-priori physical information. We employ the variational form of the PDE problem and introduce stochastic local approximations as a form of model based data augmentation. We demonstrate both the accuracy and increased computational efficiency of the framework on real world settings and structured spatial processes.

In the present study, six meta-heuristic schemes are hybridized with artificial neural network (ANN), adaptive neuro-fuzzy interface system (ANFIS), and support vector machine (SVM), to predict monthly groundwater level (GWL), evaluate uncertainty analysis of predictions and spatial variation analysis. The six schemes, including grasshopper optimization algorithm (GOA), cat swarm optimization (CSO), weed algorithm (WA), genetic algorithm (GA), krill algorithm (KA), and particle swarm optimization (PSO), were used to hybridize for improving the performance of ANN, SVM, and ANFIS models. Groundwater level (GWL) data of Ardebil plain (Iran) for a period of 144 months were selected to evaluate the hybrid models. The pre-processing technique of principal component analysis (PCA) was applied to reduce input combinations from monthly time series up to 12-month prediction intervals. The results showed that the ANFIS-GOA was superior to the other hybrid models for predicting GWL in the first piezometer and third piezometer in the testing stage. The performance of hybrid models with optimization algorithms was far better than that of classical ANN, ANFIS, and SVM models without hybridization. The percent of improvements in the ANFIS-GOA versus standalone ANFIS in piezometer 10 were 14.4%, 3%, 17.8%, and 181% for RMSE, MAE, NSE, and PBIAS in the training stage and 40.7%, 55%, 25%, and 132% in testing stage, respectively. The improvements for piezometer 6 in train step were 15%, 4%, 13%, and 208% and in the test step were 33%, 44.6%, 16.3%, and 173%, respectively, that clearly confirm the superiority of developed hybridization schemes in GWL modeling. Uncertainty analysis showed that ANFIS-GOA and SVM had, respectively, the best and worst performances among other models. In general, GOA enhanced the accuracy of the ANFIS, ANN, and SVM models.

We design a physics-aware auto-encoder to specifically reduce the dimensionality of solutions arising from convection-dominated nonlinear physical systems. Although existing nonlinear manifold learning methods seem to be compelling tools to reduce the dimensionality of data characterized by a large Kolmogorov n-width, they typically lack a straightforward mapping from the latent space to the high-dimensional physical space. Moreover, the realized latent variables are often hard to interpret. Therefore, many of these methods are often dismissed in the reduced order modeling of dynamical systems governed by the partial differential equations (PDEs). Accordingly, we propose an auto-encoder type nonlinear dimensionality reduction algorithm. The unsupervised learning problem trains a diffeomorphic spatio-temporal grid, that registers the output sequence of the PDEs on a non-uniform parameter/time-varying grid, such that the Kolmogorov n-width of the mapped data on the learned grid is minimized. We demonstrate the efficacy and interpretability of our approach to separate convection/advection from diffusion/scaling on various manufactured and physical systems.