Let's start with the one minute version: I was part of the EF12 London cohort in 2019, where I met my co-founder. A privacy-preserving medical-data marketplace and AI platform built around federated deep learning. The purpose of the platform would have been to allow data scientists to train deep learning models on highly sensitive healthcare data without that data ever leaving the hospitals. At the same time, thanks to a novel data monetization strategy and marketplace component, hospitals would have been empowered to make money from the data they are generating. We received pre-seed funding, valued at $1 million. Then the race for demo day began with frantic product building and non-stop business development.
ESI ThoughtLab, the thought leadership arm of Econsult Solutions, recently partnered with DataRobot and other AI leaders to produce a standard-setting study of senior executives in 1,200 companies focused on the impact of AI on ROI. The firms surveyed hail from 15 countries and 12 industries, portray a diverse array of AI maturity, and carry a combined revenue of $15.5 trillion. Even with companies increasing their AI investment by nearly 5% per year and projecting to bump that number up to 8.3% by 2023, 40% of AI projects are still currently showing no yield. It's time to move beyond "Experimental AI" -- AI that isn't value-oriented or trustworthy -- and translate data to value across the entire AI lifecycle, from creation to consumption. The report details how companies are tackling these five challenges and the specific steps they are taking to become AI-driven.
The 2020 presidential election in the United States is just around the corner. This year, the election has been particularly controversial in part because of the ongoing COVID-19 pandemic and the restrictions the virus has placed on in-person gatherings. In a world in which connected devices and IoT (Internet of Things) technologies have enabled everything from autonomous vehicles to robotic surgery, it seems like there should be other options for casting votes besides sending paper ballots in by mail or turning them in by hand. However, concerns (both legitimate and overblown) about election-outcome accuracy and voter privacy have held the election process back in many ways from the digital revolution that has permeated almost everything else. Will 2020 be a pivotal year in changing how the American people and "the powers that be" feel about advancing the voting process?
As a technologist, I am gung-ho about the widespread application of technology across any domain including healthcare. In Season 1 of the You AI Podcast, I spoke with 15 medical doctors and we explored together where AI may drive the most impact in healthcare. In this session, I share the lessons that I learnt from those conversations with doctors and the underlying absorbing stories that helped me be more nuanced about where and how AI should be applied in Healthcare.
A cohort of researchers from world-renowned academic institutions such as the University of Oxford, University of Warwick, University of Montpellier and credible research labs, have invented a methodology of detecting Covid-19 (SARS-CoV-2) and other respiratory pathogens within mere minutes. This feat is made possible through the utilisation of image analysis and machine learning techniques, more specifically convolutional neural networks to classify microscopic viruses of respiratory diseases based on structural features unique to the viruses. It is entirely, understandable that some terms and phrases within this article might be unfamiliar to some readers, so, at some points in this article, some sections provide definitions to words and key terms used. Common types of pathogens are viruses, bacterias, fungi, prion, and parasites.
The premarket hours are back in the red again on Wednesday. But we're seeing plenty of penny stocks shine. The fact that most small-cap stocks are disconnected from the overall market trend is something I think is overlooked. While not all penny stocks will respond this way, many do. We saw this today with several of the breakout, midstream oil stocks that were moving hugely in after-hours trading on Tuesday evening.
Motivation to learn new things and engage with life declines with age due to falling activity in a brain circuit that weighs costs and benefits, a study on mice suggested. US experts have been studying'striosomes' -- clusters of cells in the basal ganglia, a brain area linked to habit formation, movement control, emotion and addiction. They team found that striosomes are key to the decision making process when dealing with'approach-avoidance conflict' -- when a choice has both pros and cons. For example, one such thorny problem might be whether or not to take a new job that pays better, but would also call for a move away from family and friends. Working with mice, the researchers found that striosomal activity is correlated to the evaluation of costs and benefits -- but that this activity diminishes with age.
From text-reading applications for blind people to cancer screening with machine vision and early detection of disease outbreaks – AI can make this world a little bit better. But how to get started with your first AI for a good project. Developers and Data Scientists working on ideas that benefit society can, in the process, cultivate their skills and develop their careers. It creates opportunities to work on exciting stuff that might not be part of your regular day to day work, and what starts as a weekend project can someday become your dream job. It is essential to start with a problem in mind.
Huawei's TECH4ALL initiative aims to ensure nobody is left behind in the digital world by encouraging digital inclusion programmes and empowering technology adoption globally. The project is similar to some of the work happening within academia across Europe, where research projects are focused on harnessing technology for societal good. Professor van Ginneken, Professor of Medical Image Analysis at Radboud University Medical Centre in The Netherlands, is introducing digitized healthcare solutions to developing countries and believes that in ten years' time, all hospital pathology departments will be digitized. When did your work in medical imaging begin? I studied physics and completed a PhD in medical image analysis in 1996, developing computer programs that analyse chest x-rays using artificial intelligence (AI). At the end of the 1990s we wanted to put digital chest x-ray units with AI software in countries where there was a lot of tuberculosis, because it accommodates faster, more widespread screening, without the need to develop images on film.
It is official: GNU Solidario, the Spanish NGO behind GNU Health (GH) and Khadas Technologies have signed the "GNU Health Alliance of Academic and Research Institutions", to research and deliver Artificial Intelligence to the world of medicine. GNU Health covers genomics, medical genetics, Dx imaging and social medicine, areas where definitely we can use the power of AI for better diagnostics, personalized treatments, decision support, disease prevention and health promotion. What is truly revolutionary is that we will be using affordable single board computers, like the Khadas VIM3, where they could work alone or in the context of the GNU Health Federation, doing massive virtual parallel computing. Citizens, health professionals and institutions don't have to spend millions to access the latest in technology. In GNU Health, this has always been our philosophy.