In March 2016, space MMO EVE Online added the Project Discovery minigame which let players classify proteins in their downtime to help researchers. After collecting data for over two years, the project's team recently uploaded it to the publicly-available Human Protein Atlas database for scientists to use all over the world. Players participating in Project Discovery matched images of proteins from a separate database of 13 million human cells. EVE Online studio CCP Games offered in-game rewards to incentivize gamers to take part. Players worked alongside an AI to identify images for a team of the KTH Royal Institute of Technology and Massive Multiplayer Online Science.
Reykjavík, Iceland-- "This is probably the first time high-profile scientific journals are publishing screenshots from EVE Online," says Attila Szantner, co-founder of Massively Multiplayer Online Science (MMOS). "This is going to be the next big revolution in citizen science." Szantner is standing on-stage at EVE Fanfest 2016 talking about Project Discovery, a minigame in EVE Online that's quite a bit more than it appears. In-game, Project Discovery is a "classified research program" run by the Sisters of Eve to analyze samples of the Drifters, a mysterious and threatening faction in New Eden lore. Players are told that these biological samples hold the key to Drifter technology.
They know all about saving fictional worlds, but gamers are now being called upon by researchers to lend a hand in one of humankind's biggest crises--the Covid-19 pandemic. So far, they have risen to the occasion and delivered the equivalent of 471 years of work. In the multiplayer space opera EVE Online, a mini-game called Project Discovery doubles as a citizen science platform, studying the human immune system's response to the novel coronavirus. Participants take on data analysis through gameplay that helps researchers isolate specific patterns as predictors of disease severity. The project is a collaboration with McGill University, the British Columbia Cancer Research Centre, the University of Pennsylvania, and the University of Modena and Reggio Emilia.
The buzz surrounding artificial intelligence (AI) is hard to ignore. Huge data sets and large amounts of compute are the perfect match for deep learning, wowing us with algorithms that have beaten grandmasters at games of chess and Go. Today, with access to no more than a web browser, the benefits enabled by breakthroughs in image and speech recognition – not to mention machine translation and more – are just a few clicks away. There's the growth of the cloud to consider too. We're depositing more and more of our data on virtual drives that make it easier to share, back-up and transfer information – providing us with new opportunities both in the office and at home.
With all of the discussion about Big Data these days, there is frequest reference to the 3 V's that represent the top big data challenges: Volume, Velocity, and Variety. These 3 V's generally refer to the size of the dataset (Volume), the rate at which data is flowing into (or out of) your systems (Velocity), and the complexity (dimensionality) of the data (Variety). Most practitioners agree that big data volume is indeed huge, but that is not necessarily big data's biggest challenge, at least not in terms of data storage capacities, which are growing rapidly also and keeping pace with data volume. The velocity of big data is also a very big challenge, though primarily for applications and use cases that specifically demand near-real-time analysis and response to dynamic data streams. However, unlike volume and velocity, most will agree that the variety (complexity) of the data is truly big data's biggest mega-challenge at all scales and in most applications.