AI and analytics, are the two driving forces of every digital transformation. Organizations across industries are embracing these disruptive technologies and utilizing their applications to make a difference in their business processes. As a tech enthusiast or a business owner, it is important to be updated with the latest developments and applications of modern technology as it helps in having a competitive edge in this cut-throat business world. Analytics Insight has a playlist with interesting tech videos and out of these, these are the 10 best videos about artificial intelligence and analytics. Antarctica is an icy-deserted continent with no human settlements. Monitoring Antarctica is crucial for scientists as a lot of information about our planetary shift, environment, etc can be collected for further research.

Killer whales – also known as orcas – have complex social structures including close'friendships', a new study reveals. Scientists at the University of Exeter used drones to film the animals – one of the world's most powerful predators – in the Pacific Ocean. The team found killer whales (Orcinus orca) spend more time interacting with certain individuals in their pod, and tend to favour those of the same sex and similar age. Results from the new study are based on 651 minutes of video filmed over 10 days. Orcas are the largest member of the dolphin family.

Tens of thousands of times a year, a technician places a drop of blood on a slide and peers at it under a microscope, searching for malaria parasites. Making a definitive diagnosis requires the technician to look at up to 300 different fields of view over roughly half an hour. This process is repeated over and over, day after day, on every continent except Antarctica. It's tedious work, but it saves lives. Malaria parasites infect over 200 million people and kill 400,000 every year, mostly children in Africa. Trained and experienced malaria microscopists are rare, however.

Climate models are critical tools for developing strategies to manage the risks posed by sea-level rise to coastal communities. While these models are necessary for understanding climate risks, there is a level of uncertainty inherent in each parameter in the models. This model parametric uncertainty leads to uncertainty in future climate risks. Consequently, there is a need to understand how those parameter uncertainties impact our assessment of future climate risks and the efficacy of strategies to manage them. Here, we use random forests to examine the parametric drivers of future climate risk and how the relative importances of those drivers change over time. We find that the equilibrium climate sensitivity and a factor that scales the effect of aerosols on radiative forcing are consistently the most important climate model parametric uncertainties throughout the 2020 to 2150 interval for both low and high radiative forcing scenarios. The near-term hazards of high-end sea-level rise are driven primarily by thermal expansion, while the longer-term hazards are associated with mass loss from the Antarctic and Greenland ice sheets. Our results highlight the practical importance of considering time-evolving parametric uncertainties when developing strategies to manage future climate risks.

Drones aren't just flying through the air -- they're also sailing the Pacific Ocean as the newest scientific weapon to combat climate change. The hope is that by mapping the ocean floor, collecting weather and ocean data, and counting fish and wildlife populations, the autonomous Saildrones will measure the changes happening right now on our planet. Climate change is reshaping planet Earth, causing sea levels to rise, melting Arctic ice and raising global temperatures. According to NASA, the global average sea level has risen seven inches over the past 100 years. Arctic summer sea ice has shrunk to its lowest levels on record, and the average global temperature has gone up 2.1 degrees Fahrenheit since 2000, posing a threat to life as we know it.

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To understand these manipulations of the quantum world, we need to understand the... Harvey pulled Einstein's brain out of the skull. Then put it in a formalin jar.... To understand these manipulations of the quantum world, we need to understand the... What is the most coveted and prestigious award in the world? Scientists recently developed this new process in the lab. Let's take a look at what Bill Gates was saying about the epidemic that we have... The whole world is now pointing to the Chinese wildlife market as a possible source... Today we are going to discuss Marie Tharp and Marie Tharp's historical map.

During the first weeks of February, we asked recipients of our Data and AI Newsletters to participate in a survey on AI adoption in the enterprise. We were interested in answering two questions. First, we wanted to understand how the use of AI grew in the past year. We were also interested in the practice of AI: how developers work, what techniques and tools they use, what their concerns are, and what development practices are in place. The most striking result is the sheer number of respondents. In our 2020 survey, which reached the same audience, we had 1,239 responses. This year, we had a total of 5,154. After eliminating 1,580 respondents who didn't complete the survey, we're left with 3,574 responses--almost three times as many as last year.

Causality analysis is an important problem lying at the heart of science, and is of particular importance in data science and machine learning. An endeavor during the past 16 years viewing causality as real physical notion so as to formulate it from first principles, however, seems to go unnoticed. This study introduces to the community this line of work, with a long-due generalization of the information flow-based bivariate time series causal inference to multivariate series, based on the recent advance in theoretical development. The resulting formula is transparent, and can be implemented as a computationally very efficient algorithm for application. It can be normalized, and tested for statistical significance. Different from the previous work along this line where only information flows are estimated, here an algorithm is also implemented to quantify the influence of a unit to itself. While this forms a challenge in some causal inferences, here it comes naturally, and hence the identification of self-loops in a causal graph is fulfilled automatically as the causalities along edges are inferred. To demonstrate the power of the approach, presented here are two applications in extreme situations. The first is a network of multivariate processes buried in heavy noises (with the noise-to-signal ratio exceeding 100), and the second a network with nearly synchronized chaotic oscillators. In both graphs, confounding processes exist. While it seems to be a huge challenge to reconstruct from given series these causal graphs, an easy application of the algorithm immediately reveals the desideratum. Particularly, the confounding processes have been accurately differentiated. Considering the surge of interest in the community, this study is very timely.

Ice thickness estimation is an important aspect of ice sheet studies. In this work, we use convolutional neural networks with multiple output nodes to regress and learn the thickness of internal ice layers in Snow Radar images collected in northwest Greenland. We experiment with some state-of-the-art networks and find that with the residual connections of ResNet50, we could achieve a mean absolute error of 1.251 pixels over the test set. Such regression-based networks can further be improved by embedding domain knowledge and radar information in the neural network in order to reduce the requirement of manual annotations.