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.

Traditional machine learning especially supervised learning follows the assumptions of closed-world learning i.e., for each testing class a training class is available. However, such machine learning models fail to identify the classes which were not available during training time. These classes can be referred to as unseen classes. Whereas, open-world machine learning deals with arbitrary inputs (data with unseen classes) to machine learning systems. Moreover, traditional machine learning is static learning which is not appropriate for an active environment where the perspective and sources, and/or volume of data are changing rapidly. In this paper, first, we present an overview of open-world learning with importance to the real-world context. Next, different dimensions of open-world learning are explored and discussed. The area of open-world learning gained the attention of the research community in the last decade only. We have searched through different online digital libraries and scrutinized the work done in the last decade. This paper presents a systematic review of various techniques for open-world machine learning. It also presents the research gaps, challenges, and future directions in open-world learning. This paper will help researchers to understand the comprehensive developments of open-world learning and the likelihoods to extend the research in suitable areas. It will also help to select applicable methodologies and datasets to explore this further.

Causal inference methods are widely applied in various decision-making domains such as precision medicine, optimal policy and economics. Central to causal inference is the treatment effect estimation of intervention strategies, such as changes in drug dosing and increases in financial aid. Existing methods are mostly restricted to the deterministic treatment and compare outcomes under different treatments. However, they are unable to address the substantial recent interest of treatment effect estimation under stochastic treatment, e.g., "how all units health status change if they adopt 50\% dose reduction". In other words, they lack the capability of providing fine-grained treatment effect estimation to support sound decision-making. In our study, we advance the causal inference research by proposing a new effective framework to estimate the treatment effect on stochastic intervention. Particularly, we develop a stochastic intervention effect estimator (SIE) based on nonparametric influence function, with the theoretical guarantees of robustness and fast convergence rates. Additionally, we construct a customised reinforcement learning algorithm based on the random search solver which can effectively find the optimal policy to produce the greatest expected outcomes for the decision-making process. Finally, we conduct an empirical study to justify that our framework can achieve significant performance in comparison with state-of-the-art baselines.

Densest subgraph detection is a fundamental graph mining problem, with a large number of applications. There has been a lot of work on efficient algorithms for finding the densest subgraph in massive networks. However, in many domains, the network is private, and returning a densest subgraph can reveal information about the network. Differential privacy is a powerful framework to handle such settings. We study the densest subgraph problem in the edge privacy model, in which the edges of the graph are private. We present the first sequential and parallel differentially private algorithms for this problem. We show that our algorithms have an additive approximation guarantee. We evaluate our algorithms on a large number of real-world networks, and observe a good privacy-accuracy tradeoff when the network has high density.