Give us your feedback Thank you for your feedback. It is not often that a new profession springs up almost overnight. It is also unusual for many of the people who find their way into this new field to do it without the formal training provided by the normal institutions of higher education. Machine learning, as well as the allied field of data science, is developing in a way that looks unlike most other professional career paths that preceded it. It represents both one of the most promising employment opportunities of the next few years and a model for how people entering the workforce today adapt to changes in employment demands in future.

While China has invested an enormous amount in artificial intelligence in recent years, most video analysis systems are currently used in national security, including defence and anti-terrorism activities.

DERA ISMAIL KHAN, Pakistan – Pakistani intelligence officials say a suspected U.S. drone strike has hit a militant compound near the Afghan border, killing three militants. Two officials say the unmanned drone fired two missiles at the Ghazni compound of the militant Haqqani network's commander Abdur Rasheed early in the morning on Thursday. The network is affiliated with the Taliban. They said it's unclear if Rasheed was at the compound located on the Pesho Ghar mountain in the Kurram tribal region's Ghuzgari area. The officials spoke on condition of anonymity because they are not authorized to speak to the media.

DERA ISMAIL KHAN, Pakistan (Reuters) - A suspected U.S. drone strike on Thursday targeted a hideout of the Haqqani militant network along Pakistan's mountainous border with Afghanistan, killing four people, officials said.

Artificial intelligence (AI) was a key topic at both MEDICA and the RSNA conference this year. But what are its applications in healthcare in general and radiology in particular? And what are the barriers? Dr. Michael Forsting, director of the Institute of Diagnostic and Interventional Radiology and Neuroradiology at Essen University Hospital in Germany talked to MedicalExpo e-magazine about his experiences with AI. MedicalExpo e-magazine: What are the major challenges facing AI in healthcare?

To fans of science fiction, artificial intelligence may remind them of robots like C-3PO, the loquacious but harmless golden droid in Star Wars, or Skynet in the Terminator movies, a calculating sentient computer that subjugated mankind. But AI is more than just a machine with human-level intelligence scientists hope they could one day create. It is a set of algorithms and technologies that is already powering many tasks in everyday life. Get our free ebook on how the Soviet Union became Putin's Russia. Chatbots that converse with you in Yahoo, Facebook and other sites use AI.

Warner Bros. vision of what the interior of a self-driving car with augmented reality could look like if configured to be like the Batmobile (Photo: Warner Bros./Intel) LOS ANGELES -- One of the nation's most prominent technology companies announced a deal Wednesday with a major Hollywood studio to try to jointly figure out how to keep people occupied entertainment as they are driven. Intel, a company that has become a major player in self-driving technology through the acquisition of automotive sensor maker Mobileye earlier this year, will partner with Warner Bros. to convert a self-driving car into one that becomes an experimental entertainment pod. The announcement was made at the Los Angeles Auto Show here. What do Audi, Volvo, Tesla and GM have in common? Yes, they all make cars. But they're also all customers of Israel's Mobileye, which had an initial public offering this time last year.

With movie theaters full of films about rogue robots taking over the world, many fear the impact artificial intelligence (AI) might have on human life. In light of this year's Queen's Lecture at the Technical University (TU) Berlin, DW has caught up with AI and engineering expert Zoubin Ghahramani. We asked him about human and artificial intelligence, machine learning and what he thinks our future with AI might look like. DW: Professor Ghahramani, before we speak about artificial intelligence and machine learning, could you define human intelligence for us? Zoubin Ghahramani: When they hear the word'intelligence' people often think about the differences between individual humans, but actually the more interesting question is'how are we different from other animals, plants and computers?'

We propose a framework that learns a representation transferable across different domains and tasks in a label efficient manner. Our approach battles domain shift with a domain adversarial loss, and generalizes the embedding to novel task using a metric learning-based approach. Our model is simultaneously optimized on labeled source data and unlabeled or sparsely labeled data in the target domain. Our method shows compelling results on novel classes within a new domain even when only a few labeled examples per class are available, outperforming the prevalent fine-tuning approach. In addition, we demonstrate the effectiveness of our framework on the transfer learning task from image object recognition to video action recognition.

In this paper, we show that the recent integration of statistical models with deep recurrent neural networks provides a new way of formulating volatility (the degree of variation of time series) models that have been widely used in time series analysis and prediction in finance. The model comprises a pair of complementary stochastic recurrent neural networks: the generative network models the joint distribution of the stochastic volatility process; the inference network approximates the conditional distribution of the latent variables given the observables. Our focus here is on the formulation of temporal dynamics of volatility over time under a stochastic recurrent neural network framework. Experiments on real-world stock price datasets demonstrate that the proposed model generates a better volatility estimation and prediction that outperforms stronge baseline methods, including the deterministic models, such as GARCH and its variants, and the stochastic MCMC-based models, and the Gaussian-process-based, on the average negative log-likelihood measure.