Many existing conversation models that are based on the encoder-decoder framework have focused on ways to make the encoder more complicated to enrich the context vectors so as to increase the diversity and informativeness of generated responses. However, these approaches face two problems. First, the decoder is too simple to effectively utilize the previously generated information and tends to generate duplicated and self-contradicting responses. Second, the complex encoder tends to generate diverse but incoherent responses because the complex context vectors may deviate from the original semantics of context. In this work, we proposed a conversation model named "THINK" (Teamwork generation Hover around Impressive Noticeable Keywords) to make the decoder more complicated and avoid generating duplicated and self-contradicting responses. The model simplifies the context vectors and increases the coherence of generated responses in a reasonable way. For this model, we propose Teamwork generation framework and Semantics Extractor. Compared with other baselines, both automatic and human evaluation showed the advantages of our model.

Approximation fixpoint theory (AFT) provides an algebraic framework for the study of fixpoints of operators on bilattices and has found its applications in characterizing semantics for various classes of logic programs and nonmonotonic languages. In this paper, we show one more application of this kind: the alternating fixpoint operator by Knorr et al. for the study of the well-founded semantics for hybrid MKNF knowledge bases is in fact an approximator of AFT in disguise, which, thanks to the power of abstraction of AFT, characterizes not only the well-founded semantics but also two-valued as well as three-valued semantics for hybrid MKNF knowledge bases. Furthermore, we show an improved approximator for these knowledge bases, of which the least stable fixpoint is information richer than the one formulated from Knorr et al.'s construction. This leads to an improved computation for the well-founded semantics. This work is built on an extension of AFT that supports consistent as well as inconsistent pairs in the induced product bilattice, to deal with inconsistencies that arise in the context of hybrid MKNF knowledge bases. This part of the work can be considered generalizing the original AFT from symmetric approximators to arbitrary approximators.

Most MCMC algorithms contain user-controlled hyperparameters which need to be carefully selected to ensure that the MCMC algorithm explores the posterior distribution efficiently. Optimal tuning rates for many popular MCMC algorithms such the random-walk (Gelman et al., 1997) or Metropolis-adjusted Langevin algorithms (Roberts and Rosenthal, 1998) rely on setting the tuning parameters according to the Metropolis-Hastings acceptance rate. Using metrics such as the acceptance rate, hyperparameters can be optimized on-the-fly within the MCMC algorithm using adaptive MCMC (Andrieu and Thoms, 2008; Vihola, 2012). However, in the context of stochastic gradient MCMC (SGMCMC), there is no acceptance rate to tune against and the trade-off between bias and variance for a fixed computational budget means that tuning approaches designed for target invariant MCMC algorithms are not applicable. Related work Previous adaptive SGMCMC algorithms have focused on embedding ideas from the optimization literature within the SGMCMC framework, e.g.

AI startup Merlin Labs today deactivated stealth mode to announce a $25 million funding round and a partnership with Dynamic Aviation to put a fleet of 55 King Air planes in the sky without humans aboard. What we're building is software that creates a think-for-itself-pilot … fully-autonomous flight take-off to touchdown. The big idea: See a need, fill a need. Merlin Labs is taking autonomous software technology and building an artificially intelligent pilot. Autonomous fixed-wing flight might sound familiar, but there's a huge difference between designing a remote or hybrid-controlled drone from the ground up and building a system that can fly nearly any fixed wing aircraft.

Privacy groups in Europe have filed complaints against Clearview AI for allegedly breaking privacy laws by scraping billions of photos from social media sites like Facebook, Bloomberg has reported. Watchdog groups like Privacy International have taken legal action against the company in Austria, France, Greece, Italy and the UK, telling regulators that the practices "are incredibly invasive and dangerous." "Extracting our unique facial features or even sharing them with the police and other companies goes far beyond what we could ever expect as online users," Privacy International's Ioannis Kouvakas told Bloomberg. Clearview has been controversial since it was first revealed. The company has an immense database of faces taken from social media and uses AI to compare those to images from security cameras or other sources.

Over the next decade, homes will become greener and smarter, with wardrobes folding clothes, toilets checking waste, and a space for video calls, a futurologist has claimed. Tom Cheesewright claims that trends were already pointing towards a more remote, flexible and sustainable life, but the pandemic and lockdown are making it happen faster. Research funded by Hive found that 88 per cent of people wanted to live in a more sustainable future but 41 per cent didn't know how to go about making it happen. There is also a push towards smart homes, with smart assistants, video doorbells and smart lights becoming more popular as people spent time indoors over lockdown. Speaking exclusively to MailOnline, Mr Cheesewright said: 'The pressure of the pandemic brought that forward,' adding that homes are going to change to reflect these trends over the next decade. These changes will include a rise in'smart technology', including things like smart wardrobes that can iron and fold your clothes, or a medical toilet that can analyse your waste for signs of cancer or other health problems and report back to doctors, according to the futurologist.

Researchers on the OcéanIA project are developing new artificial intelligence and mathematical modelling tools to contribute to the understanding of the oceans and their role in regulating and sustaining the biosphere, and tackling climate change. You may have seen our recent interview with the director of the project, and of Inria Chile, Nayat Sánchez-Pi. She explained the challenges of research in the field, what they are working on as part of the project, and the role that AI methods play. A key part of the project is data, and much of this is being collected by the Tara Microbiome-CEODOS expedition. The objective of this expedition is to study the marine microorganisms which play a fundamental role in ocean ecosystems.

Ruth the Cookie Coach is a digital human being introduced by the Toll House brand of Nestle Global to provide baking assistance on a 24-7 basis, using an avatar incorporating AI that exhibits a degree of emotional intelligence, according to the company. Ruth is named after the creator of the Nestle Toll House original chocolate chip cookie, Ruth Wakefield. The avatar is the culmination of two years of effort between Soul Machines, which offers a Human OS platform with a Digital Brain, and Nestle. Founded in 2016 in Auckland, New Zealand, Soul Machines has raised $65 million to date, according to Crunchbase. The company was spun out of the University of Auckland by Mark Sagar, CEO and Greg Cross, chief business officer.

Inpatient falls are a serious safety issue in hospitals and healthcare facilities. Recent advances in video analytics for patient monitoring provide a non-intrusive avenue to reduce this risk through continuous activity monitoring. However, in-bed fall risk assessment systems have received less attention in the literature. The majority of prior studies have focused on fall event detection, and do not consider the circumstances that may indicate an imminent inpatient fall. Here, we propose a video-based system that can monitor the risk of a patient falling, and alert staff of unsafe behaviour to help prevent falls before they occur. We propose an approach that leverages recent advances in human localisation and skeleton pose estimation to extract spatial features from video frames recorded in a simulated environment. We demonstrate that body positions can be effectively recognised and provide useful evidence for fall risk assessment. This work highlights the benefits of video-based models for analysing behaviours of interest, and demonstrates how such a system could enable sufficient lead time for healthcare professionals to respond and address patient needs, which is necessary for the development of fall intervention programs.

Recent advances in deep learning have enabled the development of automated frameworks for analysing medical images and signals, including analysis of cervical cancer. Many previous works focus on the analysis of isolated cervical cells, or do not offer sufficient methods to explain and understand how the proposed models reach their classification decisions on multi-cell images. Here, we evaluate various state-of-the-art deep learning models and attention-based frameworks for the classification of images of multiple cervical cells. As we aim to provide interpretable deep learning models to address this task, we also compare their explainability through the visualization of their gradients. We demonstrate the importance of using images that contain multiple cells over using isolated single-cell images. We show the effectiveness of the residual channel attention model for extracting important features from a group of cells, and demonstrate this model's efficiency for this classification task. This work highlights the benefits of channel attention mechanisms in analyzing multiple-cell images for potential relations and distributions within a group of cells. It also provides interpretable models to address the classification of cervical cells.