Bengaluru: While nearly 60 per cent business leaders in India are planning to use Artificial Intelligence (AI) to automate tasks to a large extent, 20 per cent believe their workforce has the skills needed to work with advanced technologies such as AI, according to a survey by Accenture on Thursday. More than half of Indian business leaders recognise skills shortages as a critical hindrance to future growth, but few plan to increase their training investments over the next three years, according to the research titled "Future Workforce Study". Four in five (80 per cent) also recognise that their workforce is underprepared to adopt advanced technologies. "The commitment to using advanced technologies for growth needs to be supported by an equal commitment to transforming the organization for the future," said Sunit Sinha, Managing Director at Accenture. The study is based on a survey of 1,100 workers across skill levels in India and a survey of 100 senior executives in India.

In AI, the race is between America and China, and China may win. For the same reason, Germany is likely to lose. There is only one thing worse than being talked about, as Oscar Wilde noted, and that is not being talked about. That should give Germans pause. In the global conversation about a crucial technology and industry of the future -- Artificial Intelligence (AI) -- Germany is not being talked about.

To gauge by the news headlines, it would be easy to believe that artificial intelligence (AI) is about to take over the world. Kai-Fu Lee, a Chinese venture capitalist, says that AI will soon create tens of trillions of dollars of wealth and claims China and the U.S. are the two AI superpowers. There is no doubt that AI has incredible potential. But the technology is still in its infancy; there are no AI superpowers. The race to implement AI has hardly begun, particularly in business.

Artificial Intelligence (AI) has the peculiar ability to simultaneously amaze, enthrall, leave us gasping and intimidate. The possibilities of AI are innumerable and they easily surpass our most artistically fecund imaginations. What all we read in science fiction novels or saw in movies like'The Matrix' could someday materialize into reality. Bill Gates, the founder of Microsoft, recently said that'AI can be our friend' and is good for the society. From decision-making to computing to robotics to vehicles and even cosmetics, AI has left its mark everywhere and it will usher in the grandest social engineering experiment in the history of the world.

What is the AI scene like in China compared to the U.S.? originally appeared on Quora: the place to gain and share knowledge, empowering people to learn from others and better understand the world. A major difference between US AI and China AI is that China AI is all about implementation. In research, US has about 60% of the world's top 1000 top researchers, and China less than 10%. The top US researchers are both academia and industry, while the top Chinese researchers are generally in the industry, while academia lags behind the US substantially. Chinese research papers have increased in quality rapidly over the years, but it will take a long time to catch up with the US.

Text analytics based on supervised machine learning classifiers has shown great promise in a multitude of domains, but has yet to be applied to Seismology. We test various standard models (Naive Bayes, k-Nearest Neighbors, Support Vector Machines, and Random Forests) on a seismological corpus of 100 articles related to the topic of precursory accelerating seismicity, spanning from 1988 to 2010. This corpus was labelled in Mignan (2011) with the precursor whether explained by critical processes (i.e., cascade triggering) or by other processes (such as signature of main fault loading). We investigate rather the classification process can be automatized to help analyze larger corpora in order to better understand trends in earthquake predictability research. We find that the Naive Bayes model performs best, in agreement with the machine learning literature for the case of small datasets, with cross-validation accuracies of 86% for binary classification. For a refined multiclass classification ('non-critical process' < 'agnostic' < 'critical process assumed' < 'critical process demonstrated'), we obtain up to 78% accuracy. Prediction on a dozen of articles published since 2011 shows however a weak generalization with a F1-score of 60%, only slightly better than a random classifier, which can be explained by a change of authorship and use of different terminologies. Yet, the model shows F1-scores greater than 80% for the two multiclass extremes ('non-critical process' versus 'critical process demonstrated') while it falls to random classifier results (around 25%) for papers labelled 'agnostic' or 'critical process assumed'. Those results are encouraging in view of the small size of the corpus and of the high degree of abstraction of the labelling. Domain knowledge engineering remains essential but can be made transparent by an investigation of Naive Bayes keyword posterior probabilities.

The reparameterization trick is widely used in variational inference as it yields more accurate estimates of the gradient of the variational objective than alternative approaches such as the score function method. Although there is overwhelming empirical evidence in the literature showing its success, there is relatively little research exploring why the reparameterization trick is so effective. We explore this under the idealized assumptions that the variational approximation is a mean-field Gaussian density and that the log of the joint density of the model parameters and the data is a quadratic function that depends on the variational mean. From this, we show that the marginal variances of the reparameterization gradient estimator are smaller than those of the score function gradient estimator. We apply the result of our idealized analysis to real-world examples.

Deep artificial neural networks (DNNs) are powerful tools for recognition and classification as they learn sophisticated mapping rules between the inputs and the outputs. However, the rules that learned by the majority of current DNNs used for pattern recognition are largely fixed and do not vary with different conditions. This limits the network's ability to work in more complex and dynamical situations in which the mapping rules themselves are not fixed but constantly change according to contexts, such as different environments and goals. Inspired by the role of the prefrontal cortex (PFC) in mediating context-dependent processing in the primate brain, here we propose a novel approach, involving a learning algorithm named orthogonal weights modification (OWM) with the addition of a PFC-like module, that enables networks to continually learn different mapping rules in a context-dependent way. We demonstrate that with OWM to protect previously acquired knowledge, the networks could sequentially learn up to thousands of different mapping rules without interference, and needing as few as $\sim$10 samples to learn each, reaching a human level ability in online, continual learning. In addition, by using a PFC-like module to enable contextual information to modulate the representation of sensory features, a network could sequentially learn different, context-specific mappings for identical stimuli. Taken together, these approaches allow us to teach a single network numerous context-dependent mapping rules in an online, continual manner. This would enable highly compact systems to gradually learn myriad of regularities of the real world and eventually behave appropriately within it.

Memetic Computing is a subject in computer science which considers complex structures as the combination of simple agents, memes, whose evolutionary interactions lead to intelligent structures capable of problem-solving. This paper focuses on Memetic Computing optimization algorithms and proposes a counter-tendency approach for algorithmic design. Research in the field tends to go in the direction of improving existing algorithms by combining different methods or through the formulation of more complicated structures. Contrary to this trend, we instead focus on simplicity, proposing a structurally simple algorithm with emphasis on processing only one solution at a time. The proposed algorithm, namely Three Stage Optimal Memetic Exploration, is composed of three memes; the first stochastic and with a long search radius, the second stochastic and with a moderate search radius and the third deterministic and with a short search radius. This is suggestive of the fact that complexity in algorithmic structures can be unnecessary, if not detrimental, and that simple bottom-up approaches are likely to be competitive is here invoked as an extension to Memetic Computing basing on the philosophical concept of Ockham's Razor. An extensive experimental setup on various test problems and one digital signal processing application is presented. Numerical results show that the proposed approach, despite its simplicity and low computational cost displays a very good performance on several problems, and is competitive with sophisticated algorithms representing the-state-of-the-art in computational intelligence optimization. Key words: Memetic Computing, Evolutionary Algorithms, Memetic Algorithms, Computational intelligence Optimization 1. Introduction Emerging technologies in computer science and engineering, as well as the demands of the market and the society, often impose the solution, in the every day life, of complex optimization problems. The complexity of today's problems is due to various reasons such as high non-linearities, high multi-modality, large scale, noisy fitness landscape, computationally expensive fitness functions, real-time demands, and limited hardware available(e.g. when the computational device is portable and cheap). In these cases, the use of exact methods is unsuitable because, in general, there is not sufficient prior knowledge (hypotheses) on the optimization problem; thus, computational intelligence approaches become not only advisable but often the only alternative to face the optimization. Scientific research in computational intelligence optimization can be classified into two general categories.

The critics say the Creech-based operators of the remote-controlled, unmanned aircraft in Afghanistan can't tell if they're targeting terrorists or civilians. They say thousands of civilians have been killed there, including women and children.