The Apple iPad Pro is seen above. Workers carry them around offices. Entire stores have traded their cash registers for iPads. The flagship Apple tablet isn't just a novelty item for watching movies on the sofa; it has become a major workhorse, transforming our domestic and professional lives. Add the power of Siri, and your iPad becomes a full-on virtual assistant, especially with a few little-known features.

Why would I hire a supermodel if I can randomly generate someone new? Why would I buy a stock photo when I can auto-generate one? Any scene, with or without people, the same AI that generates fake people can auto-generate fake houses, fake cities, fake situations, fake pets, fake disputes, fake arguments, fake injuries, and fake police responding to whatever fake stuff supposedly happened. Not only can AI create realistic videos of people doing and saying things they'd never say or do in real life, it can add convincing human faces that never existed in the first place. We are about to become deluged with tons of fake testimonials hoping to persuade us to buy some new product or service.

Artificial Intelligence (AI) will create as many jobs in the UK as it will displace over the next 20 years, a report has said. The analysis, by accountancy giant PwC, found AI would boost economic growth, creating new roles as others fell away. But it warned there would be "winners and losers" by industry sector, with many jobs likely to change. Opinion is split over AI's potential impact, with some warning it could leave many out of work in future. The pessimists argue AI is different to previous forms of technological change, because robots and algorithms will be able to do intellectual as well as routine physical tasks.

LINZ (Austria): Can artificial intelligence turn out symphonies to match one of the greats of classical music? That was the question posed by one unusual orchestra performance in the Austrian city of Linz on Friday, in which Gustav Mahler's unfinished Symphony No 10 was played -- immediately followed by six minutes of "Mahleresque" music written by software. The project's creator says that the two are clearly distinguishable but not everyone in the audience agreed. "I couldn't really feel the difference... I believe it was really well done," Maria Jose Sanchez Varela, 34, a science and philosophy researcher from Mexico, said.

"Well, let's train it [AI] on all of the stock prices …

Both datasets are being shared by Google AI researchers to supply the training material necessary to model natural language systems that achieve human-level performance. Google researchers call CCPE a new way to collect voice data. It includes 500 dialogues with people about their movie preferences -- 10,000 in total, across 12,000 utterances. Movie preferences were chosen as a topic because of the value of metadata such as the names of actors and directors. "We do not restrict the workers to detailed scripts or to a small knowledge base and hence we observe that our dataset contains more realistic and diverse conversations in comparison to existing datasets," a paper published covering CCPE reads.

In recent years, with the development of deep learning technology, text generation technology has undergone great changes and provided many kinds of services for human beings, such as restaurant reservation and daily communication. The automatically generated text is becoming more and more fluent so researchers begin to consider more anthropomorphic text generation technology, that is the conditional text generation, including emotional text generation, personalized text generation, and so on. Conditional text generation (c-TextGen) has thus become a research hotspot. As a promising research field, we find that many efforts have been paid to researches of c-TextGen. Therefore, we aim to give a comprehensive review of the new research trends of c-TextGen. We first give a brief literature review of text generation technology, based on which we formalize the concept model of c-TextGen. We further make an investigation of several different c-TextGen techniques, and illustrate the advantages and disadvantages of commonly used neural network models. Finally, we discuss the open issues and promising research directions of c-TextGen.