Using AI to enhance robot programming methods. Production, warehouse, shipping – where goods are produced, stored, sorted or packed, picking also takes place. This means that several individual goods are removed from storage units such as boxes or cartons and reassembled. With the FLAIROP (Federated Learning for Robot Picking) project Festo and researchers from the Karlsruhe Institute of Technology (KIT), together with partners from Canada, want to make picking robots smarter using distributed AI methods. To do this, they are investigating how to use training data from multiple stations, from multiple plants, or even companies without requiring participants to hand over sensitive company data.

Modzy, the leading enterprise AI platform, announced that DarwinAI is now a model partner at the Modzy AI Model Marketplace. DarwinAI is expected to deploy numerous models using its GenSynth platform, including COVID-Net, an open source deep neural network for detecting COVID-19 infections from chest X-rays. DarwinAI has been featured in the MIT Technology Review, AI in Healthcare, and VentureBeat for their innovations in combating COVID-19 using AI. "We're really excited for this partnership with DarwinAI," said Norm Litterini, Head of Partnerships at Modzy. "The quality of their work is reflected in their solid reputation and industry acknowledgment. DarwinAI's models will enable customers to quickly operationalize AI into strategic initiatives while building out our marketplace offering, particularly for biomedical applications, where there is critical need."

Explainable artificial intelligence is an emerging method for boosting reliability, accountability, and dependence in critical areas. This is done by merging machine learning approaches with explanatory methods that reveal what the decision criteria are or why they have been established and allow people to better understand and control AI-powered tools. Below here, we have discussed some of the important milestones, in no particular order, on explainable AI (XAI) in 2020. Fairlearn is a popular explainable AI toolkit that enables data scientists as well as developers to evaluate and enhance the fairness of their AI systems. The toolkit has two components, an interactive visualisation dashboard and unfairness mitigation algorithms.

As the number of COVID-19 infections are again spiking around the U.S., health care workers struggling to stay ahead have a tool with a novel approach to add to their arsenal in COVID-Net, an open source AI-based platform that uses radiological lung images to determine COVID-19-specific lung damage, as well as assess the degree of that damage. The technology was developed in March, during the early days of the pandemic, but has been gaining more notice as an example of artificial intelligence in health care as more organizations have adopted it. Although the nonprofit project is being led by Red Hat, Boston Children's Hospital and DarwinAI (a 3-year-old proprietary artificial intelligence startup headquartered in Waterloo, Ontario), it began as a collaboration between Canada's University of Waterloo and DarwinAI. "COVID-Net was an initiative to try to contribute to the whirlwind of the pandemic in March," DarwinAI CEO Sheldon Fernandez told ITPro Today. "We open sourced it and we didn't want it to be commercial.

DarwinAI, the explainable artificial intelligence (XAI) company, and Red Hat, the world's leading provider of open source solutions, announced a collaboration to accelerate the deployment of COVID-Net--a suite of deep neural networks for COVID-19 detection and risk stratification via chest radiography--to hospitals and other healthcare facilities. DarwinAI and Red Hat are also leveraging the expertise of a computation research group, the Fetal Neonatal Neuroimaging and Developmental Science Center (FNNDSC) at Boston Children's Hospital to better focus the software for real world clinical and research use. "The COVID-Net system is a promising tool, but needs to be coupled with a compelling GUI to be effective -- Boston Children's ChRIS framework and the Red Hat OpenShift platform provides an effective way to get COVID-Net into the hands of health care professionals on the front lines." Since the launch of COVID-Net by DarwinAI and the University of Waterloo's Vision and Imaging Processing (VIP) Lab, the project has continued to evolve with assistance, participation and collaboration from researchers and clinicians around the world. The initiative eventually led to a collaboration between DarwinAI and Red Hat, using underlying technology from Boston Children's, the number one pediatric hospital in the nation.

Since the White House issued a "call to action" to AI researchers to help fight the coronavirus spread, researchers have stepped up in multiple ways. Lots of data is available. The Covid-19 Open Research Dataset (CORD-19) is a collection of research studies published in both peer-reviewed journals and non-peer-reviewed pre-print websites such as bioRxiv and medRxiv. Currently, it consists of over 13,000 full-text papers and abstracts for another 16,000 papers and is expected to be updated with new research as it becomes available, according to an account in Forbes. The account was written by Kashyap Kompella, the CEO of the technology industry analyst firm RPA2AI Research.

The news: An open-access neural network called COVID-Net, released to the public this week, could help researchers around the world in a joint effort to develop an AI tool that can test people for Covid-19. You can read all of our coverage of the coronavirus/Covid-19 outbreak for free, and also sign up for our coronavirus newsletter. But please consider subscribing to support our nonprofit journalism.. COVID-Net is a convolutional neural network, a type of AI that is particularly good at recognizing images. Developed by Linda Wang and Alexander Wong at the University of Waterloo and the AI firm DarwinAI in Canada, COVID-Net was trained to identify signs of Covid-19 in chest x-rays using 5,941 images taken from 2,839 patients with various lung conditions, including bacterial infections, non-Covid viral infections, and Covid-19. The data set is being provided alongside the tool so that researchers--or anyone who wants to tinker--can explore and tweak it. Don't believe the hype: Several research teams have announced AI tools that can diagnose Covid-19 from x-rays in the last few weeks.

Find a hospital taking in coronavirus cases, and you'll most likely find departments often in need of more staff and without enough testing kits. Now one Canadian AI startup is hoping to develop tools that will automatically detect COVID-19 infections from X-rays, and help guide medical professionals on how seriously the infection has taken hold. DarwinAI, which spun out of work at the University of Waterloo, normally works on AI explainability. The company makes a tool that can show why deep-learning modules make the decisions they do, enabling users to correct the inputs that lead to wrong decisions, and fix the architecture or retrain the system to prevent the same mistakes in future. The idea is that, by getting an insight into why AI does what it does, companies can speed up the development of their AI products.

A chest X-ray, analyzed by Qure.ai's software, picks up on abnormalities that suggest the likelihood of COVID-19 infection. X-rays are one of the quickest, simplest ways to diagnose the disease, and an army of AI specialists around the world are trying to speed up how the images are used to find cases. Most cite the lack of data as the prime obstacle to broader adoption of AI. For all the frantic effort to coordinate life-saving work around the globe during the COVID-19 pandemic, the digital age finds itself hampered in one very specific respect: information. Teams of artificial intelligence researchers are trying to bring decades of technology to bear on the problem of diagnosing and treating the disease, but the data they need to develop their software programs is scattered around the globe, making it practically inaccessible. The painful lack of data is evident in one particular use case for AI, the development of diagnostic tests for COVID-19 based on X-rays or on "computed tomography" scans of the lungs.

Canadian startup DarwinAI and researchers from the University of Waterloo are open-sourcing COVID-Net, a convolutional neural network that aims to detect COVID-19 in X-ray imagery. In response to the pandemic, a global community of health care and AI researchers have produced a number of AI systems for identifying COVID-19 in CT scans. Companies like Alibaba and AI startups RadLogics and Lunit claim they've created systems capable of recognizing COVID-19 in X-ray or CT scans with more than 90% accuracy. Early work from Chinese medical researchers and a system published in the journal Radiology last week demonstrated similar results. Like other companies making AI to detect COVID-19 from chest X-rays, DarwinAI said it's creating COVID-Net and the accompanying COVIDx data set to give doctors a way to quickly triage and screen potential cases.