The resident robot from Middlesex University was asked by chairman Robert Halfon to explain the artificial intelligence (AI) Careses project, which promotes independent living and aims to reduce pressure on health care services.

A contemporary artist has (almost) succeeded in bringing Queen Elizabeth I back to life. In a new exhibit titled'The Mask of Youth', English artist Mat Collishaw has created a strikingly realistic recreation of the Tudor Queen's head with eyes that follow you around the room, blink expectantly and a mouth that opens periodically. The exhibit takes on a surrealist feeling as the animatronic head sits suspended in a mirrored recess, facing the Armada Portrait, one of the most famous paintings of Queen Elizabeth I. In a new exhibit titled'The Mask of Youth', English artist Mat Collishaw has created a strikingly realistic recreation of the Tudor Queen's head with eyes that follow you around the room, blink expectantly and a mouth that opens periodically'The Mask of Youth' is now on view at the Queen's House, Greenwich and was commissioned by Royal Museums Greenwich. When viewers stand in front of the suspended head, they immediately see things from her point of view, reflected in the mirror behind her.

LONDON (Reuters) – Softbank's Vision Fund plans to pump more money into insurance, a sector it sees as both ripe for disruption and a potential booster for its bigger bets in cars, health and financial services, a Vision Fund executive told Reuters. In the past year, the world's biggest private technology investor has backed China's largest online insurer ZhongAn 6060.HK as well as PolicyBazaar, India's biggest online insurance distributor, and app-based U.S. home insurer Lemonade. And these and other insurance bets totaling nearly $3 billion are just the start, Vision Fund dealmaker David Thevenon said. The Vision Fund has raised nearly $100 billion, almost half of it from Saudi Arabia's sovereign wealth fund. "We believe that technology and how data is used, processed and collected is going to transform insurance," Thevenon said.

LONDON (Reuters) - Softbank's Vision Fund plans to pump more money into insurance, a sector it sees as both ripe for disruption and a potential booster for its bigger bets in cars, health and financial services, a Vision Fund executive told Reuters. In the past year, the world's biggest private technology investor has backed China's largest online insurer ZhongAn (6060.HK) as well as PolicyBazaar, India's biggest online insurance distributor, and app-based U.S. home insurer Lemonade. And these and other insurance bets totaling nearly $3 billion are just the start, Vision Fund dealmaker David Thevenon said. The Vision Fund has raised nearly $100 billion, almost half of it from Saudi Arabia's sovereign wealth fund. "We believe that technology and how data is used, processed and collected is going to transform insurance," Thevenon said.

Graphs Future Agenda First published September 2018 by: Future Agenda Limited 84 Brook Street London W1K 5EH www.futureagenda.org

You can query this question in a search engine and check what answers you get. As opposed to doing that the question will be answered here essentially by "revising" history, so its safe to say it is "based on a true story". During World War 2, Alan Turing and his partners at Bletchley Park succeeded with regards to building a machine that utilized electromechanical elements to translate the communications to and from German submarines working in the Atlantic. The German messages were encoded utilizing the celebrated Enigma machine. Arguably, the accomplishment of Turning and his associates was a definitive factor that helped Britain to win the war.

A patient has become the first in Britain to be operated on for major cancer surgery by a robot. Dean Walter, 41, had a full pelvic extraction in which his bladder, prostate, rectum and lower colon were removed through a cut in his abdomen only 2in wide. The operation usually requires a surgeon and three assistants to cut the patient open from their chest to their groin. Mr Walter, a former fitness model, would have needed three weeks in hospital to recover from traditional surgery – but he was ready to go home eight days after the robotic procedure for rectal cancer. It was done using a £2million Da Vinci Xi robot, which has four arms for cutting tissue, sealing blood vessels and filming inside the body with a 3D camera.

Gaussian Process Regression (GPR) and Gaussian Process Latent Variable Models (GPLVM) offer a principled way of performing probabilistic non-linear regression and dimensionality reduction. In this paper we propose a hybrid between the two, the covariate-GPLVM (c-GPLVM), to perform dimensionality reduction in the presence of covariate information (e.g. continuous covariates, class labels, or censored survival times). This construction lets us adjust for covariate effects and reveals meaningful latent structure which is not revealed when using GPLVM. Furthermore, we introduce structured decomposable kernels which will let us interpret how the fixed and latent inputs contribute to feature-level variation, e.g. identify the presence of a non-linear interaction. We demonstrate the utility of this model on applications in disease progression modelling from high-dimensional gene expression data in the presence of additional phenotypes.

Zero-shot learning (ZSL) can be defined by correctly solving a task where no training data is available, based on previous acquired knowledge from different, but related tasks. So far, this area has mostly drawn the attention from computer vision community where a new unseen image needs to be correctly classified, assuming the target class was not used in the training procedure. Apart from image classification, only a couple of generic methods were proposed that are applicable to both classification and regression. These learn the relation among model coefficients so new ones can be predicted according to provided conditions. So far, up to our knowledge, no methods exist that are applicable only to regression, and take advantage from such setting. Therefore, the present work proposes a novel algorithm for regression problems that uses data drawn from trained models, instead of model coefficients. In this case, a shape analyses on the data is performed to create a statistical shape model and generate new shapes to train new models. The proposed algorithm is tested in a theoretical setting using the beta distribution where main problem to solve is to estimate a function that predicts curves, based on already learned different, but related ones.

This paper describes FBK's submission to the end-to-end English-German speech translation task at IWSLT 2018. Our system relies on a state-of-the-art model based on LSTMs and CNNs, where the CNNs are used to reduce the temporal dimension of the audio input, which is in general much higher than machine translation input. Our model was trained only on the audio-to-text parallel data released for the task, and fine-tuned on cleaned subsets of the original training corpus. The addition of weight normalization and label smoothing improved the baseline system by 1.0 BLEU point on our validation set. The final submission also featured checkpoint averaging within a training run and ensemble decoding of models trained during multiple runs. On test data, our best single model obtained a BLEU score of 9.7, while the ensemble obtained a BLEU score of 10.24.