The Scandinavian leaders of AI in breast imaging presented their research at the ScreenPoint symposium at EUSOBI 2022 in Malmo, Sweden. Dr Kristina Lang presented the MASAI trial, the first prospective randomized controlled trial on the use of AI in breast screening as an alternative for double reading. Based on her previous retrospective studies, she is convinced that AI could lead to a more efficient and more effective screening programme. In the MASAI trial at Unilabs/Skane University Hospital Malmo, women are randomly assigned to a control arm where exams are double read as usual, or to the AI-based intervention arm: Transpara triages screening exams based on risk for malignancy and assigns 90% of all screening cases to single reading, and 10% to double reading. In addition, the top 1% most suspicious cases are automatically recalled.

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. To investigate how an artificial intelligence (AI) system performs on digital mammography (DM) from a screening population with ground truth defined by digital breast tomosynthesis (DBT), and whether AI could detect breast cancers on DM that had originally only been detected on DBT. In this secondary analysis of data from a prospective study, DM examinations from 14768 women (mean age, 57 years), examined with both DM and DBT with independent double reading in the Malmö Breast Tomosynthesis Screening Trial (MBTST; ClinicalTrials.gov

Reinforcement learning (RL) has recently shown impressive performance in complex game AI and robotics tasks. To a large extent, this is thanks to the availability of simulated environments such as OpenAI Gym, Atari Learning Environment, or Malmo which allow agents to learn complex tasks through interaction with virtual environments. While RL is also increasingly applied to natural language processing (NLP), there are no simulated textual environments available for researchers to apply and consistently benchmark RL on NLP tasks. With the work reported here, we therefore release NLPGym, an open-source Python toolkit that provides interactive textual environments for standard NLP tasks such as sequence tagging, multi-label classification, and question answering. We also present experimental results for 6 tasks using different RL algorithms which serve as baselines for further research. The toolkit is published at https://github.com/rajcscw/nlp-gym

This article is a summary of a talk I gave at the yearly Webstep's "Kompetensbio" event. Every year Webstep invites all developers to this free event that happens in some nice local cinema, where they can enjoy interesting tech-talks and watch some exciting movie. This year, for the first time, the event took place in three cities: Uppsala, Malmö and Stockholm. Ever since I was a child and to this day, I was a big Science-fiction fan. Growing up in a small town in former Eastern bloc country, was not really a lot of fun. Especially if you were smart and curious.

We are holding a competition on sample-efficient reinforcement learning using human priors. Standard methods require months to years of game time to attain human performance in complex games such as Go and StarCraft. In our competition, participants develop a system to obtain a diamond in Minecraft using only four days of training time. To facilitate solving this hard task with few samples, we provide a dataset of human demonstrations. This competition uses a set of Gym environments based on Malmo.

On the evening 5th of September Communities across the globe will drive events and engagements in Artificial Intelligence on Microsoft Azure, this is an initiative called Global AI Nights. Currently 92 communities are signed up and have some great agendas for the evening. In the Nordics there are currently only 3 locations available, Skåne Azure User Group (#AzureSkane) are happy to be one of these locations, venue is hosted by the excellent FooCafe. "Also check out the CloudBurst event we have 28th of August" We have a great agenda that will give you insights regardless the proficiency you have on the topic. This is a great opportunity to build or extend your knowledge about AI, Machine Learning, Cognitive services and more.

Welcome to the Global AI Night on 5 Sept 2019 Skåne Azure User Group (#AzureSkane on social media) is hosting this event alongside 87 other locations globally. This is a great opportunity to build or extend your knowledge about AI, Machine Learning, Cognitive services and more. We will have a short Global AI Keynote follwed by 2 sessions from industry AI leaders and end the night with an quick tour for a call to action on Hands-on labs created by Microsoft and the AI Community for this event, we will also be handing out Azure passes so you can execute the labs. The Sessions: "Making your ML / AI production ready" Do you know 3 out of 5 ML / AI engagement do not go to the production or can not operate in a enterprise production eco system. In this session we ll discuss about, what it takes to be production ready and how to avoid those pitfalls with some techno-functional example of the industries.

Though deep reinforcement learning has led to breakthroughs in many difficult domains, these successes have required an ever-increasing number of samples. As state-of-the-art reinforcement learning (RL) systems require an exponentially increasing number of samples, their development is restricted to a continually shrinking segment of the AI community. Likewise, many of these systems cannot be applied to real-world problems, where environment samples are expensive. Resolution of these limitations requires new, sample-efficient methods. To facilitate research in this direction, we introduce the MineRL Competition on Sample Efficient Reinforcement Learning using Human Priors. The primary goal of the competition is to foster the development of algorithms which can efficiently leverage human demonstrations to drastically reduce the number of samples needed to solve complex, hierarchical, and sparse environments. To that end, we introduce: (1) the Minecraft ObtainDiamond task, a sequential decision making environment requiring long-term planning, hierarchical control, and efficient exploration methods; and (2) the MineRL-v0 dataset, a large-scale collection of over 60 million state-action pairs of human demonstrations that can be resimulated into embodied trajectories with arbitrary modifications to game state and visuals. Participants will compete to develop systems which solve the ObtainDiamond task with a limited number of samples from the environment simulator, Malmo. The competition is structured into two rounds in which competitors are provided several paired versions of the dataset and environment with different game textures. At the end of each round, competitors will submit containerized versions of their learning algorithms and they will then be trained/evaluated from scratch on a hold-out dataset-environment pair for a total of 4-days on a prespecified hardware platform.

Dungeon Crawl Stone Soup is a popular, single-player, free and open-source rogue-like video game with a sufficiently complex decision space that makes it an ideal testbed for research in cognitive systems and, more generally, artificial intelligence. This paper describes the properties of Dungeon Crawl Stone Soup that are conducive to evaluating new approaches of AI systems. We also highlight an ongoing effort to build an API for AI researchers in the spirit of recent game APIs such as MALMO, ELF, and the Starcraft II API. Dungeon Crawl Stone Soup's complexity offers significant opportunities for evaluating AI and cognitive systems, including human user studies. In this paper we provide (1) a description of the state space of Dungeon Crawl Stone Soup, (2) a description of the components for our API, and (3) the potential benefits of evaluating AI agents in the Dungeon Crawl Stone Soup video game.

The Swedish Transport Agency Transportstyrelsen has given Volvo self-driving cars venture Zenuity approval to begin testing driverless cars on public roads. The cars will be tested at a maximum speed of 80km/hour (50mph) on three Swedish highways. Throughout all tests a trained driver will sit behind the wheel, although will keep their hands off it unless an intervention is required. Zenuity is a joint venture between car giant Volvo and Veoneer, a spin-off of vehicle safety company Autoliv specialising in autonomous driving software. The three roads that the self-driving cars will be tested on are the E4 between Stockholm and Malmö, the E6 between Gothenburg and Malmö and road 40 between Jönköping and Gothenburg.