"AI is the new Electricity" – Andrew Ng* Since the advent of 20th century electricity became the main source of invention in every major industry ranging from transportation, manufacturing to healthcare, communications and many more. Today Artificial Intelligence (AI) is bringing the same big transformation across all the major industries. The part of AI that is rapidly growing and which is driving most of these transformations is Deep Learning. Today, Deep Learning has become one of the most sought after skills in the technology world. Agriculture is one industry where Deep Learning scientists and researchers are working with farmers to help them with their produce.

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John Deere, established in 1837 to manufacture hand tools, announced it had acquired Blue River Technology, founded in 2011, late Wednesday. John Stone, an executive in the company's intelligent-solutions group, says Blue River's computer-vision technology will help Deere's equipment view and understand the crops it is working with. Stone says that Blue River's technology can make a larger impact on productivity because it makes decisions up close, on the ground. That system can target weeds with squirts of herbicide no larger than a postage stamp.

EARTH AI are helping mineral explorers identify promising areas. They do this by analysing data from multiple sources and using a machine learning algorithm to identify areas where minerals are likely to be found.

– Any one working within industries like the mobility, fintech, mobile money, payments, banking or InsureTech with little knowledge of data science is actually sitting on gold mine to explore and show what Data Science / AI can do for that company. Today every company on this planet collect vast quantities of data on a daily basis or even per second. For example credit card issuers with every credit card swipe and completed transaction capture critical customer information, In case of mobile payments/money the same thing happen or even in banks same scenarios. However, the raw data alone does not generate the insights needed to drive business decisions or simply not good enough at all. It's the proper analysis of this data that unlocks its true value.

Chemical multisensor devices need calibration algorithms to estimate gas concentrations. Their possible adoption as indicative air quality measurements devices poses new challenges due to the need to operate in continuous monitoring modes in uncontrolled environments. Several issues, including slow dynamics, continue to affect their real world performances. At the same time, the need for estimating pollutant concentrations on board the devices, espe- cially for wearables and IoT deployments, is becoming highly desirable. In this framework, several calibration approaches have been proposed and tested on a variety of proprietary devices and datasets; still, no thorough comparison is available to researchers. This work attempts a benchmarking of the most promising calibration algorithms according to recent literature with a focus on machine learning approaches. We test the techniques against absolute and dynamic performances, generalization capabilities and computational/storage needs using three different datasets sharing continuous monitoring operation methodology. Our results can guide researchers and engineers in the choice of optimal strategy. They show that non-linear multivariate techniques yield reproducible results, outperforming lin- ear approaches. Specifically, the Support Vector Regression method consistently shows good performances in all the considered scenarios. We highlight the enhanced suitability of shallow neural networks in a trade-off between performance and computational/storage needs. We confirm, on a much wider basis, the advantages of dynamic approaches with respect to static ones that only rely on instantaneous sensor array response. The latter have been shown to be best choice whenever prompt and precise response is needed.

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The word'cyborg' may bring to mind the terrifying robot from the Terminator film. But in a new study, scientists have created a less scary, and much more useful cyborg, by adapting bacteria. The cyborg bacteria are covered in tiny crystals that act as highly efficient solar panels, producing a range of useful compounds, with zero waste. The cyborg bacteria are covered in tiny crystals that act as highly efficient solar panels, producing a range of useful compounds (artist's impression pictured) The team used a type of bacteria called Moorella thermoacetica, which naturally produces acetic acid from carbon dioxide. Acetic acid is a versatile chemical that can be readily converted to a number of fuels, polymers, pharmaceuticals and chemicals.

During reactive transport modeling, the computational cost associated with chemical reaction calculations is often 10-100 times higher than that of transport calculations. Most of these costs results from chemical equilibrium calculations that are performed at least once in every mesh cell and at every time step of the simulation. Calculating chemical equilibrium is an iterative process, where each iteration is in general so computationally expensive that even if every calculation converged in a single iteration, the resulting speedup would not be significant. Thus, rather than proposing a fast-converging numerical method for solving chemical equilibrium equations, we present a machine learning method that enables new equilibrium states to be quickly and accurately estimated, whenever a previous equilibrium calculation with similar input conditions has been performed. We demonstrate the use of this smart chemical equilibrium method in a reactive transport modeling example and show that, even at early simulation times, the majority of all equilibrium calculations are quickly predicted and, after some time steps, the machine-learning-accelerated chemical solver has been fully trained to rapidly perform all subsequent equilibrium calculations, resulting in speedups of almost two orders of magnitude. We remark that our new on-demand machine learning method can be applied to any case in which a massive number of sequential/parallel evaluations of a computationally expensive function $f$ needs to be done, $y=f(x)$. We remark, that, in contrast to traditional machine learning algorithms, our on-demand training approach does not require a statistics-based training phase before the actual simulation of interest commences. The introduced on-demand training scheme requires, however, the first-order derivatives $\partial f/\partial x$ for later smart predictions.

Significant advances in artificial intelligence over the past few years have broadened AI's reach into industries such as healthcare, finance and even retail. Businesses and consumers alike are benefiting from the rise of big data and the growth of AI techniques like deep learning and natural language processing. But we're still only scratching the surface of what is possible with AI, and the full impact of the technology may be years away. In the near-future, however, AI advances will give rise to increasingly powerful applications like personal assistants with more robust utility in the workplace and in our personal lives. These assistants could provide personalized information, help us make more informed decisions, and perhaps even provide physical assistance.