Artificial Intelligence or simply AI is the science of designing intelligent computer programs or machines. AI will change the world as we know it by making everyday tasks easier and more efficient. AI is already created by major developers like IBM but has not nearly reached its full potential. Regardless of the benefits of AI there are many concerns with what the creation of AI can lead to, some as drastic as humanity creating their own uncontrollable superiors to even a third World War. Artificial Intelligence has been an enduring concept since the fifties when Arthur Samuel created the first computer program that taught itself how to play checkers in 1952.

We present an approach based on machine learning (ML) to distinguish eruption and precursory signals of Chimay\'{o} geyser (New Mexico, USA) under noisy environments. This geyser can be considered as a natural analog of $\mathrm{CO}_2$ intrusion into shallow water aquifers. By studying this geyser, we can understand upwelling of $\mathrm{CO}_2$-rich fluids from depth, which has relevance to leak monitoring in a $\mathrm{CO}_2$ sequestration project. ML methods such as Random Forests (RF) are known to be robust multi-class classifiers and perform well under unfavorable noisy conditions. However, the extent of the RF method's accuracy is poorly understood for this $\mathrm{CO}_2$-driven geysering application. The current study aims to quantify the performance of RF-classifiers to discern the geyser state. Towards this goal, we first present the data collected from the seismometer that is installed near the Chimay\'{o} geyser. The seismic signals collected at this site contain different types of noises such as daily temperature variations, seasonal trends, animal movement near the geyser, and human activity. First, we filter the signals from these noises by combining the Butterworth-Highpass filter and an Autoregressive method in a multi-level fashion. We show that by combining these filtering techniques, in a hierarchical fashion, leads to reduction in the noise in the seismic data without removing the precursors and eruption event signals. We then use RF on the filtered data to classify the state of geyser into three classes -- remnant noise, precursor, and eruption states. We show that the classification accuracy using RF on the filtered data is greater than 90\%.These aspects make the proposed ML framework attractive for event discrimination and signal enhancement under noisy conditions, with strong potential for application to monitoring leaks in $\mathrm{CO}_2$ sequestration.

The world's most patient mom may be a deep-sea octopus that tends her eggs for nearly 4.5 years. But now, there may be a new contender for her throne. Scientists have caught a rare glimpse of another deep-sea dweller that may also spend four or more years nursing its eggs, and it does it in an even more unusual place: on hydrothermal vents, where hot water spews from the ocean floor. It's called the Pacific white skate (Bathyraja spinosissima), a bone-white, bug-eyed relative of sharks that can live almost two miles (2,900 meters) underwater. Deep-sea skates, which are shark relatives that resemble rays, lay large eggs that can take years to hatch in cold water.

For decades, futurists have predicted that commercial miners would one day tap the unimaginable mineral wealth of the world's ocean floor. Soon, that subsea gold rush could finally begin: The world's first deep-sea mining robots are poised to rip into rich deposits of copper, gold, and silver 1,600 meters down at the bottom of the Bismarck Sea, near Papua New Guinea. The massive machines, which are to be tested sometime in 2016, are part of a high-stakes gamble for the Toronto-based mining company Nautilus Minerals. Nautilus's machines have been ready to go since 2012, when a dispute between the firm and the Papua New Guinean government stalled the project. What broke the impasse was the company's offer, in 2014, to provide Papua New Guinea with certain intellectual property from the mining project.