URL: Fight Earth’s problems with AI & Space Tech

COVID-19 ANNOUNCEMENT

The AI Moonshot Challenge is based on ambition and curiosity, aiming to leverage the unique capabilities of the AI community using satellite data to accelerate innovation in crucial areas for the future of our planet – starting with marine litter pollution.

We believe that now – more than ever – is the time to face global challenges and to develop answers that will benefit us all in the future. The challenges that we are facing right now validate and strengthen even more our commitment to this project and we’re redefining our strategy to take into account the restrictions imposed by the worldwide COVID-19 pandemic.

The safety and health of our collaborators and participants are above everything else.
We will keep you informed of any developments.

Expect to hear from the AI Moonshot Team soon.

THE CHALLENGE

The global competition to detect, locate and monitor maritime waste on a planetary scale.

Fifty years after landing on the moon, we’re calling a new challenge — this time back home. Our oceans are drowning in waste.

Marine Litter Pollution is an urgent problem we are far from solving and only starting to understand. More than 8 million tons of plastic end up in the ocean every year and most of it is unaccounted for.

At the same time, massive amounts of satellite data are being generated worldwide providing opportunities to improve health, economy and environment when leveraged with powerful tools such as Artificial Inteligence.

URL: https://openai.com/blog/procgen-minerl-competitions/

AIcrowd challenge: https://www.aicrowd.com/challenges/aicrowd-blitz-may-2020

Link: https://www.aicrowd.com/challenges/food-recognition-challenge

Overview

Recognizing food from images is an extremely useful tool for a variety of use cases. In particular, it would allow people to track their food intake by simply taking a picture of what they consume. Food tracking can be of personal interest, and can often be of medical relevance as well. Medical studies have for some time been interested in the food intake of study participants, but had to rely on food frequency questionnaires that are known to be imprecise.

Image-based food recognition has in the past few years made substantial progress thanks to advances in deep learning. But food recognition remains a difficult problem for a variety of reasons.

Problem Statement

The goal of this challenge is to train models which can look at images of food items and detect the individual food items present in them. We use a novel dataset of food images collected through the MyFoodRepo app where numerous volunteer Swiss users provide images of their daily food intake in the context of a digital cohort called Food & You. This growing data set has been annotated – or automatic annotations have been verified – with respect to segmentation, classification (mapping the individual food items onto an ontology of Swiss Food items), and weight / volume estimation.

This is an evolving dataset, where we will release more data as the dataset grows over time.

Github: https://github.com/pangeo-data/WeatherBench

Paper: https://arxiv.org/abs/2002.00469

Announcement: https://twitter.com/raspstephan/status/1229272564729614336

URL: https://www.aicrowd.com/challenges/snake-species-identification-challenge

Snakebite is the most deadly neglected tropical disease (NTD), being responsible for a dramatic humanitarian crisis in global health

Snakebite causes over 100,000 human deaths and 400,000 victims of disability and disfigurement globally every year. It affects poor and rural communities in developing countries, which host the highest venomous snake diversity and the highest burden of snakebite due to limited medical expertise and access to antivenoms

Antivenoms can be life‐saving when correctly administered but this depends first on the correct taxonomic identification (i.e. family, genus, species) of the biting snake. Snake identification is challenging due to:

• their high diversity
• the incomplete or misleading information provided by snakebite victims
• the lack of knowledge or resources in herpetology that healthcare professionals have

In this challenge we want to explore how Machine Learning can help with snake identification, in order to potentially reduce erroneous and delayed healthcare actions.

Dive into Deep Learning

An interactive deep learning book with code, math, and discussions, based on the NumPy interface. https://d2l.ai/

URL: https://idao.world/

Higher School of Economics and Yandex are proud to announce the 3rd international data analysts olympiad.

The event is open to all teams and individuals, be they undergraduate, postgraduate or PhD students, company employees, researchers or new data scientists, .

The event aims to bridge the gap between the all-increasing complexity of Machine Learning models and performance bottlenecks of the industry. The participants will strive not only to maximize the quality of their predictions, but also to devise resource-efficient algorithms.

This will be a team machine learning competition, divided into two stages. The first stage will be online, open to all participants. The second stage will be the offline on-site finals, in which the top 30 performing teams from the online round will compete at the Yandex office in Moscow.

LINK: https://www.drivendata.org/competitions/60/building-segmentation-disaster-resilience/

Link: https://www.drivendata.org/competitions/59/camera-trap-serengeti/

Overview

Leverage millions of images of animals on the Serengeti to build a classifier that distinguishes between gazelles, lions, and more!

In this competition, participants will predict the presence and species of wildlife in new camera trap data from the Snapshot Serengeti project, which boasts over 6 million images.

Camera traps are motion-triggered systems for passively collecting animal behavior data with minimal disturbance to their natural tendencies. Camera traps are an invaluable tool in conservation research, but the sheer amount of data they generate presents a huge barrier to using them effectively. This is where AI can help!

There are two immediate challenges where efforts like this competition are needed:

• Camera traps can’t automatically label the animals they observe, creating an immense (and sometimes prohibitive) burden on humans to determine where and what wildlife are present.
• Even when automated animal tagging models are available, the models that do exist don’t generalize well across time and locations, severely limiting their usefulness with new data.

To address these opportunities, we’re challenging data scientists, researchers, and developers from around the world to build the best algorithms for wildlife detection.

The competition is designed with a few objectives in mind:

• Innovation: Participants use state-of-the-art approaches in computer vision and AI and get live feedback on how well their solutions perform
• Generalization: This competition is designed to reward the best generalizable solutions. The private test data used to determine the winners will come entirely from the latest, unreleased season of data from the Snapshot Serengeti project (season 11). For more information on the competition timeline and evaluation, see the problem description.
• Execution: Models are trained locally and submitted to execute inference in the cloud – read on!
• Openness: All prize-winning models are released under an open source license for anyone to use and learn from

This is a brand new kind of DrivenData challenge! Previous models trained for camera trap images have often failed to generalize well. In this competition, we want to reward the models that generalize best to new images, so you won’t interact directly with the test set.  Rather than submitting your predicted labels for a test set you have, you’ll package up everything needed to do inference and send it to us. We’ll execute that code on Azure in a Docker container that has access to the test set images. By leveraging Microsoft Azure’s cloud computing platform and Docker containers, we’re moving our competition infrastructure one step closer to translating participants’ innovation into impact.

We can’t wait to run what you come up with!