Microsoft reveals'what if' face mashup system that can you show everything from Voldemort in Kiss to Donald Trump in Game of Thrones The chatbot has since taken the internet by storm, with users creating'what if' images for every imaginable situation. This includes'What if Trump is Cersei Lannister' proposed by Twitter user Jeremy Randall Pictured is a terrifying baby-Yoda mashup it created when asked'What if Yoda were BB-8?' The bot created an image to visualize'What if Chewbacca were Yoda?' There are often those moments in life that cause us to wonder, 'what if' – but, Microsoft's new chatbot might make you wish you never had. The new bot called'Murphy' generates mashup images for any hypothetical face combination, with hilarious, and often terrifying, results Twitter user Stephen Bell asked the bot, 'What if Voldemort was in Kiss?' Valley Stream Best Buy associates gift a teen with a Wii U'I'm going to wing walk!' Schofield talks to Duke about wing walk Prince Philip reminisces about expansion of Duke of Edinburgh awards Homeowner trolls bungling burglar with Mission Impossible theme'They make each other laugh': Countess Sophie on the Duke and Queen Hunters forced to shoot a wild bear dead as it charges towards them'I wanted the painting!': Joanna Lumley jokes about Duke's artwork Documentary director attacked by gang of immigrants in Stockholm Adorable baby dressed as Lion comes face to face with real one Hammer wielding thugs smash car windows and threaten man Adorable dog won't allow owner to stop scratching his belly Ferrari crashes into pedestrians while racing near Battersea Dogs Home Adorable dog won't allow owner to stop scratching his belly Terminally-ill boy, five, dies in Santa Claus' arms after... Missing North Carolina girl who was last seen aged 15... Trump's Iran stance could threaten a WORLD WAR and the... Woman left with huge bill after Plenty of Fish date eats... Model, 32, claims her MIT-grad hedge-funder boyfriend, 29,... Blood-spattered walls, unbearable odours and houses where... Best Buy employees in Long Island chip in to buy a $300 WiiU... Nothing like retail therapy!
BART is trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text. It uses a standard Tranformer-based neural machine translation architecture which, despite its simplicity, can be seen as generalizing BERT (due to the bidirectional encoder), GPT (with the left-to-right decoder), and many other more recent pretraining schemes. We evaluate a number of noising approaches, finding the best performance by both randomly shuffling the order of the original sentences and using a novel in-filling scheme, where spans of text are replaced with a single mask token. BART is particularly effective when fine tuned for text generation but also works well for comprehension tasks. It matches the performance of RoBERTa with comparable training resources on GLUE and SQuAD, achieves new state-of-the-art results on a range of abstractive dialogue, question answering, and summarization tasks, with gains of up to 6 ROUGE. BART also provides a 1.1 BLEU increase over a back-translation system for machine translation, with only target language pretraining. We also report ablation experiments that replicate other pretraining schemes within the BART framework, to better measure which factors most influence end-task performance.
Topic models, such as Latent Dirichlet Allocation (LDA), posit that documents are drawn from admixtures of distributions over words, known as topics. The inference problem of recovering topics from admixtures, is NP-hard. Assuming separability, a strong assumption,  gave the first provable algorithm for inference. For LDA model,  gave a provable algorithm using tensor-methods. But [4,6] do not learn topic vectors with bounded $l_1$ error (a natural measure for probability vectors). Our aim is to develop a model which makes intuitive and empirically supported assumptions and to design an algorithm with natural, simple components such as SVD, which provably solves the inference problem for the model with bounded $l_1$ error. A topic in LDA and other models is essentially characterized by a group of co-occurring words. Motivated by this, we introduce topic specific Catchwords, group of words which occur with strictly greater frequency in a topic than any other topic individually and are required to have high frequency together rather than individually. A major contribution of the paper is to show that under this more realistic assumption, which is empirically verified on real corpora, a singular value decomposition (SVD) based algorithm with a crucial pre-processing step of thresholding, can provably recover the topics from a collection of documents drawn from Dominant admixtures. Dominant admixtures are convex combination of distributions in which one distribution has a significantly higher contribution than others. Apart from the simplicity of the algorithm, the sample complexity has near optimal dependence on $w_0$, the lowest probability that a topic is dominant, and is better than . Empirical evidence shows that on several real world corpora, both Catchwords and Dominant admixture assumptions hold and the proposed algorithm substantially outperforms the state of the art .
For the second time in months, Syrian President Bashar al-Assad has said "we will fight on to liberate every inch of our land". The last time Assad made a similar statement, he was scolded by the Russian ambassador to the UN who said this was not in line with the Kremlin's policies. At the time, it wasn't - Russia was pushing for a political settlement and was involved in efforts with the United States to bring about a cessation of hostilities to create a conducive atmosphere for peace talks. This time around, however, Assad has so far not been told off. Instead, Russia sent its defence minister to Iran's capital Tehran to take part in talks with his Syrian and Iranian counterparts.
Future Tense is a partnership of Slate, New America, and Arizona State University that examines emerging technologies, public policy, and society. It sounds like the stuff of spy novels. A secretive company backed by an eccentric billionaire taps into sensitive data gathered by a University of Cambridge researcher. The company then works to help elect an ultranationalist presidential candidate who admires Russian President Vladimir Putin. Oh, and that Cambridge researcher, Aleksandr Kogan, worked briefly for St. Petersburg State University.