The impact of AI will be felt in both developed and developing countries, especially where primary care physicians are in increasingly short supply, notes Mr. Kalis. "How do we create new primary care delivery models where machines and humans work together to save labour and produce better patient outcomes?" he asks. Health-tech innovators that are using AI to deliver automated medical advice may soon have an answer. In the UK, Babylon Health and Your.MD are digital health assistants that perform triage and diagnosis of patient illnesses based on data input into an app. Both work with the National Health Service in different ways but both companies say their apps are helping make general practitioners' (GPs') lives easier by handling simpler cases and giving them time to focus on more complex ones.

While Mongolia may be most sparsely populated independent country in the world, in the European Union that claim goes to Finland. With just 5.5 million people (that's about the same population as Houston and Chicago put together, just without that whole deadly crime thing) Finland has many claims to fame. She is a country of great natural beauty that has influenced generations of minimalist industrial designers. More importantly though, the country follows the Nordic model of capitalism and has thus became one of the few working examples of a progressive, socially sensitive state with superb welfare, education, and healthcare services. It's no surprise then that the country's liberal administration is keen to explore the possibilities offered by artificial intelligence.

Deep learning is a technology with a lot of promise: helping computers "see" the world, understand speech, and make sense of language. But away from the headlines about computers challenging humans at everything from spotting faces in a crowd to transcribing speech -- real-world performance has been more mixed. One deep-learning technology whose real-world results have often disappointed has been facial-recognition. In the UK, police in Cardiff and London used facial-recognition systems on multiple occasions in 2017 to flag persons of interest captured on video at major events. Unfortunately, more than 90% of people picked out by these systems were false matches.

LISBON: Forget swiping through endless profiles. Dating apps are using artificial intelligence to suggest where to go on a first date, recommend what to say and even find a partner who looks like your favourite celebrity. Until recently smartphone dating apps - such as Tinder which lets you see in real time who is available and "swipe" if you wish to meet someone - left it up to users to ask someone out and then make the date go well. But to fight growing fatigue from searching through profiles in vain, the online dating sector is turning to artificial intelligence (AI) to help arrange meetings in real life and act as a dating coach. These new uses for AI - the science of programming computers to reproduce human processes like thinking and decision making - by dating apps were highlighted at the four-day Web Summit which wraps up Thursday in Lisbon.

In 1913, the largest and most influential art show in history took place; The 1913 Armory Show. Packed into New York's 69th Regiment Armory on Lexington Avenues between 25th and 26th streets were over 1200 works of art that ranged from sculptures, paintings and decorative works by over 300 artists from America and Europe. The show introduced Picasso, Matisse, Duchamp and modernism to American audiences. The event was so radical at the time, critics, who were used to realism in their art, questioned the sanity of the artists whose works were represented in the show. But the experimental art was eventually embraced by America and made way for great American artists such as Jackson Pollock, Mark Rothko and Andy Warhol.

Scientists have designed a model using Artificial Intelligence that can predict risk of death in patients with coronary heart disease (CHD) better than expert-constructed models. According to a new study published in PLOS One, scientists from the Francis Crick Institute, working with University College London Hospitals NHS Foundation Trust and the Farr Institute of Health Informatics Research, developed the AI model using the data of 80,000 patients, available for researchers through UCL's CALIBER platform, which links four sources of electronic health data in England. The model that the AI one was compared to made predictions based on 27 variables chosen by medical experts, while the Crick team got their AI algorithms to train themselves, look for patterns and select the most relevant variables from a set of 600. Both machine learning and AI are picking up steam in healthcare, with hospitals testing or deploying the tech for a range of use cases from treating patients with pancreatic cancer to reducing surgical site infections while experts are saying the next generation of clinical decision support tools will include AI in workflow to improve diagnostics, imaging, radiology and pathology, among other functions. Consultancy McKinsey said last month that hospitals need a solid digital base comprising a modern infrastructure with cloud, mobile and web capabilities in place before starting down the road to AI and machine learning.

Following the announcement earlier this week of Google Cloud's AI Hub and Kubeflow Pipelines tools, Rajen Sheth, director of product management for Cloud AI, has outlined how the technology giant is working to ensure that its AI work is ethical and fair. In a blog post earlier this week titled'steering the right course for AI', he outlined what is seen as the main industry challenges to be overcome in order to make AI not just a reality, but one that is for the net good of society. Engaging with each of these in turn, he first suggests that unfair, or confirmation bias must be tackled "on multiple fronts," starting with awareness. "To foster a wider understanding of the need for fairness in technologies like machine learning, we've created educational resources like ml-fairness.com Google is also encouraging thorough documentation "as a means to better understand what goes on inside a machine learning solution". Within Google this takes the form of'model cards': "a standardised format for describing the goals, assumptions, performance metrics, and even ethical considerations of a machine learning model." Embedded documentation tools from Google Cloud, like the Inclusive ML Guide, integrated throughout AutoML, and TensorFlow Model Analysis (TFMA) and the What-If Tool all help with this. "I'm proud of the steps we're taking, and I believe the knowledge and tools we're developing will go a long way towards making AI more fair," he said, before reiterating that this is an industry-wide problem to be tackled. "No single company can solve such a complex problem alone.

The paper proposes an on-line monitoring framework for continuous real-time safety/security in learning-based control systems (specifically application to a unmanned ground vehicle). We monitor validity of mappings from sensor inputs to actuator commands, controller-focused anomaly detection (CFAM), and from actuator commands to sensor inputs, system-focused anomaly detection (SFAM). CFAM is an image conditioned energy based generative adversarial network (EBGAN) in which the energy based discriminator distinguishes between proper and anomalous actuator commands. SFAM is based on an action condition video prediction framework to detect anomalies between predicted and observed temporal evolution of sensor data. We demonstrate the effectiveness of the approach on our autonomous ground vehicle for indoor environments and on Udacity dataset for outdoor environments.

Artificial Intelligence (AI) systems sometimes make errors and will make errors in the future, from time to time. These errors are usually unexpected, and can lead to dramatic consequences. Intensive development of AI and its practical applications makes the problem of errors more important. Total re-engineering of the systems can create new errors and is not always possible due to the resources involved. The important challenge is to develop fast methods to correct errors without damaging existing skills. We formulated the technical requirements to the 'ideal' correctors. Such correctors include binary classifiers, which separate the situations with high risk of errors from the situations where the AI systems work properly. Surprisingly, for essentially high-dimensional data such methods are possible: simple linear Fisher discriminant can separate the situations with errors from correctly solved tasks even for exponentially large samples. The paper presents the probabilistic basis for fast non-destructive correction of AI systems. A series of new stochastic separation theorems is proven. These theorems provide new instruments for fast non-iterative correction of errors of legacy AI systems. The new approaches become efficient in high-dimensions, for correction of high-dimensional systems in high-dimensional world (i.e. for processing of essentially high-dimensional data by large systems).

Over the past few years many research efforts have been devoted to the field of affect analysis. Various approaches have been proposed for: i) discrete emotion recognition in terms of the primary facial expressions; ii) emotion analysis in terms of facial Action Units (AUs), assuming a fixed expression intensity; iii) dimensional emotion analysis, in terms of valence and arousal (VA). These approaches can only be effective, if they are developed using large, appropriately annotated databases, showing behaviors of people in-the-wild, i.e., in uncontrolled environments. Aff-Wild has been the first, large-scale, in-the-wild database (including around 1,200,000 frames of 300 videos), annotated in terms of VA. In the vast majority of existing emotion databases, their annotation is limited to either primary expressions, or valence-arousal, or action units. In this paper, we first annotate a part (around $234,000$ frames) of the Aff-Wild database in terms of $8$ AUs and another part (around $288,000$ frames) in terms of the $7$ basic emotion categories, so that parts of this database are annotated in terms of VA, as well as AUs, or primary expressions. Then, we set up and tackle multi-task learning for emotion recognition, as well as for facial image generation. Multi-task learning is performed using: i) a deep neural network with shared hidden layers, which learns emotional attributes by exploiting their inter-dependencies; ii) a discriminator of a generative adversarial network (GAN). On the other hand, image generation is implemented through the generator of the GAN. For these two tasks, we carefully design loss functions that fit the examined set-up. Experiments are presented which illustrate the good performance of the proposed approach when applied to the new annotated parts of the Aff-Wild database.