Miiskin has introduced automatic skin imaging, available in a consumer app, which will enable people to take full-body photographs of their own skin, at home, to make skin cancer self-examinations easier. The new feature applies a combination of Apple's computer vision and augmented reality technologies to audibly and visually assist the user in taking photos alone and hands-free. The user places their phone on a desk or table and are then guided by the Siri voice to pose whilst the photographs are taken. Latest statistics based on the number of photographs taken on the Miiskin app reveal that only 15% of photographs taken on the app are of areas on a person's back, despite it being an area that is often prone to skin cancer. This app will enable those living alone or those who prefer not to have help from others, to easily capture pictures of all their body.

Kaia Health, a digital therapeutics startup which uses computer vision technology for real-time posture tracking via the smartphone camera to deliver human-hands-free physiotherapy, has closed a $26 million Series B funding round. The funding was led by Optum Ventures, Idinvest and capital300 with participation from existing investors Balderton Capital and Heartcore Capital, in addition to Symphony Ventures -- the latter in an "investment partnership" with world famous golfer, Rory McIlroy, who knows a thing or two about chronic pain. Back in January 2019, when Kaia announced a $10M Series A, its business ratio was split 80:20 Europe to US. Now, says co-founder and CEO Konstantin Mehl -- speaking to TechCrunch by Zoom chat from New York where he's recently relocated -- it's flipped the other way. Part of the new funding will thus go on building out its commercial team in the US -- now its main market.

AI should be built on rigorous knowledge... Note: This is a follow-up to an earlier article on causal machine learning, "AI Needs More Why". There's much to be excited about with artificial intelligence (AI) in healthcare: Google AI is improving the workflow of clinicians with predictive models for diabetic retinopathy [2], many new approaches are achieving expert-level performance in tasks such as classification of skin cancer [3], and others surpassing the capabilities of doctors -- notably the recent report of DeepMind's AI for predicting acute kidney disease, capable of detecting potentially fatal kidney injuries 48 hours before symptoms are recognized by doctors [4]. Yet medical practitioners and researchers at the intersection of machine learning (ML) and medicine are quick to point out these successes are not representative of the more nuanced, non-trivial challenges presented by medical research and clinical applications. These ML success stories (notably all deep learning) are disease prediction problems, learning patterns that map well-defined inputs to well-labeled outputs [5]. Domains where instinctive pattern recognition works powerfully are what psychologist Robin Hogarth termed "kind learning environments" [6].

Mary Tolan, co-founder and managing partner at Chicago Pacific Founders, explains how healthcare providers can apply AI's diagnostic and logistical capabilities to create a protective buffer between emergency medical providers and COVID-19 patients. We are living through a near-unprecedented crisis. During the COVID-19 pandemic, healthcare workers have emerged as frontline heroes, working overtime to protect our communities from the spread of novel Coronavirus. But they aren't immune to the anxious, uncertain atmosphere the pandemic has fostered nor, indeed, the Coronavirus itself. We need to protect the first responders and hospital staff who put their wellbeing on the line to support their communities during a crisis.

Andrew Pery, is the Ethics Evangelist at ABBYY, a digital intelligence company. They empower organizations to access the valuable, yet often hard to attain, insight into their operations that enables true business transformation. ABBYY recently released a Global Initiative Promoting the Development of Trustworthy Artificial Intelligence. We decided to ask Andrew questions regarding ethics in AI, abuses of AI, and what the AI industry can do about these concerns moving forward. What is it that initially instigated your interest in AI ethics? What initially sparked my interest in AI ethics was a deep interest in the intersection of law and AI technology.

Covid-19 patients in the UK are being treated with dexamethasone today after a UK trial of the drug found it could save lives. "The treatment is immediately available and already in use on the NHS," said health minister Matt Hancock. "It is not by any means a cure but it is the best news we have had," Hancock told parliament today. The UK's chief medical officers say it should be used immediately, according to the BBC. A preliminary study found that the steroid, which is already widely prescribed for treating allergies and asthma, reduces the risk of dying from covid-19 by a third for patients on ventilators, and by a fifth for those receiving oxygen. Dexamethasone should only be taken if prescribed by a doctor. Officials in Beijing, China confirmed 31 new coronavirus cases today, bringing the total to 137 in the last six days. The city is again restricting all non-essential travel. Schools, swimming pools and gyms are all closed from today.

"For one moment you have the best technology handy, the next moment it is obsolete" Technology advances every single day, we might want to believe it or not, but this moment some other technology is arising whereas some might be obsoleting. It all depends upon the moment or situation we want technology to take part in. And, there is no doubt that technology is making things a little better than yesterday. In the race of AI, AR, VR, IoT, wearable technology trends are also taking part in making the lives of people monitored and strict. Tech giant Apple came up with the concept of Apple Watches is now a competitor to other brands' smartwatches. To measure heart rate count, blood pressure monitored, steps counted, a smartwatch is more than that. When we are saying smartwatch, there is another best wearable tech that is already working as the lead role in making the lives of people counted and monitored. Wearable technology trends 2020 are taking place in people's lives and expected to reach 614.31 million units in 2025.

In this session, we will be going through some of the fundamental methods that are used to tackle Natural Language Processing (NLP) problems. We will first be going through some NLP theory and then will walk through some code showing how a sentiment analysis model can be trained from scratch on a real-world IMDB movie review dataset.

Artificial intelligence (AI) has traditionally been deployed in the cloud, because AI algorithms crunch massive amounts of data and consume massive computing resources. But AI doesn't only live in the cloud. In many situations, AI-based data crunching and decisions need to be made locally, on devices that are close to the edge of the network. AI at the edge allows mission-critical and time-sensitive decisions to be made faster, more reliably and with greater security. The rush to push AI to the edge is being fueled by the rapid growth of smart devices at the edge of the network--smartphones, smart watches and sensors placed on machines and infrastructure.

In a future where artificial intelligence (AI) is universal, psychology will stay an asset for helping individuals adapt to vulnerability and change. As the world turns out to be progressively more innovative, so does the requirement for human-based advising and connection. Artificial intelligence is well along its way to outperforming capacities of human intelligence. The principal AI research project was launched in 1956 at Dartmouth and is commonly viewed as the introduction of artificial intelligence. The assembly of a few technology patterns has empowered AI analysts to accomplish breakthroughs and become commercially available.