From the Russian President Putin to the Facebook CEO Mark Zuckerberg, everybody is talking about artificial intelligence. Yes, AI is promising a gamut of benefits to forward-thinking businesses. This has been grabbing the attention of everyone who is in the business world. If I tell the definition of artificial intelligence in a single line, "AI is a method of building human intelligence in machines". So machines can think like human, work like human (faster than human), without requiring human intervention.

One of the biggest experiments being conducted in the medical field is the possibility of deploying nanobots to treat diseases. In a major breakthrough, a team of researchers from the University of Manchester and the Chinese University of Hong Kong has created biodegradable nanobots, whose degradation can be controlled by doctors, making it possible to use them in medical applications involving non-invasive surgery. "Creating robotic systems which can be propelled and guided in the body has been and still is a holy-grail in the field of delivery system engineering. Our work takes advantage of some elements offered by nature such as fluorescence, degradability, shape. But we add engineered features such as magnetization and biological activity to come up with a proof-of-concept behind our bio-hybrid, magnetically propelled microrobots," professor Kostas Kostarelos, one of the researchers on the project, said in a press release.

But against this opinion some arguments are in the left side of Artificial intelligence. Elon Musk, an Expert of AI industry came out with another artificial intelligence warning on Monday. Competition for AI superiority at national level most likely cause of WW3 imo" The SpaceX and Tesla CEO suggested that AI could be the cause of Third World War. Replying to this Statement, indrasen smiled and says that "In the war situation, Artificial Intelligence could replace Human with Robots in the Army, hence can reduced the loss of Lives to some extent.

The government of Karnataka has decided to partner US tech giant Microsoft to use artificial intelligence (AI) for digital agriculture. The collaboration intends to empower smallholder farmers with technology-oriented solutions that will help them increase income using ground-breaking, cloud-based technologies, machine learning and advanced analytics. The collaboration will experiment with the Karnataka Agricultural Price Commission (KAPC), department of agriculture to help improve price forecasting practices to benefit farmers. Microsoft, with guidance from KAPC, is attempting to develop a multi-variant agricultural commodity price forecasting model considering the following datasets--historical sowing area, production, yield, weather datasets and other related datasets as relevant. For this season, Tur crop has been identified for this prediction model.

The Hong Kong and Beijing-based deep learning company founded by Tang Xiaoou, a professor at the Chinese University of Hong Kong, is a leader among Chinese AI start-ups that are enjoying fast growth thanks to demand from the government and private sector for their facial recognition technology.[nL4N1N72PS]

Robotics surgeries may currently be an expensive proposition for hospitals, but robots and artificial intelligence will certainly play a major role in the healthcare sector in the future. Several startups such as DiFacto Robotics, SigTuple and Aindra are working to bring new technologies to reality in India. A group of top executives from hospital chains, investment firms and startups discussed the future of healthcare at the News Corp VCCircle Healthcare Investment Summit, held in Mumbai recently. Sign up for our daily newsletter to get our top reports & videos.

Chinese artificial intelligence start-up SenseTime Group is planning an initial public offering (IPO) and aims to open a research and development (R&D) center in the United States as early as next year, its founder told Reuters in an interview. The Hong Kong and Beijing-based deep learning company founded by Tang Xiaoou, a professor at the Chinese University of Hong Kong, is a leader among Chinese AI start-ups that are enjoying fast growth thanks to demand from the government and private sector for their facial recognition technology.[nL4N1N72PS]

To address three important issues involved in latent variable models (LVMs), including capturing infrequent patterns, achieving small-sized but expressive models and alleviating overfitting, several studies have been devoted to "diversifying" LVMs, which aim at encouraging the components in LVMs to be diverse. Most existing studies fall into a frequentist-style regularization framework, where the components are learned via point estimation. In this paper, we investigate how to "diversify" LVMs in the paradigm of Bayesian learning. We propose two approaches that have complementary advantages. One is to define a diversity-promoting mutual angular prior which assigns larger density to components with larger mutual angles and use this prior to affect the posterior via Bayes' rule. We develop two efficient approximate posterior inference algorithms based on variational inference and MCMC sampling. The other approach is to impose diversity-promoting regularization directly over the post-data distribution of components. We also extend our approach to "diversify" Bayesian nonparametric models where the number of components is infinite. A sampling algorithm based on slice sampling and Hamiltonian Monte Carlo is developed. We apply these methods to "diversify" Bayesian mixture of experts model and infinite latent feature model. Experiments on various datasets demonstrate the effectiveness and efficiency of our methods.

The maximum correntropy criterion (MCC) has recently been successfully applied in robust regression, classification and adaptive filtering, where the correntropy is maximized instead of minimizing the well-known mean square error (MSE) to improve the robustness with respect to outliers (or impulsive noises). Considerable efforts have been devoted to develop various robust adaptive algorithms under MCC, but so far little insight has been gained as to how the optimal solution will be affected by outliers. In this work, we study this problem in the context of parameter estimation for a simple linear errors-in-variables (EIV) model where all variables are scalar. Under certain conditions, we derive an upper bound on the absolute value of the estimation error and show that the optimal solution under MCC can be very close to the true value of the unknown parameter even with outliers (whose values can be arbitrarily large) in both input and output variables. Illustrative examples are presented to verify and clarify the theory.

Nintendo is riding high off the runaway success of its new, hybrid Nintendo Switch game console. The Japanese gaming giant dominated industry sales charts for both hardware and software in October thanks to high demand for the Switch and the release of the hotly anticipated Super Mario Odyssey, a flagship game for the system. Nintendo's brands are so hot right now that the best you can hope for on Black Friday is the Switch being in stock at all. The company is also using this success as an opportunity to break one of its longstanding traditions of not doing much outside the realm of video games. Back in June, Nintendo announced a Super Nintendo World theme park addition to Universal Studios Japan, set to open in 2020.