A year-long BBC investigation into the brutal smuggling of chimpanzees found that as pets in wealthy homes or as performers in commercial zoos, baby chimps can command a price tag of $12,500, a little under £10,000.
But that might all change because the same software that recognises you in a friend’s social media post is being adapted to tackle the illegal trade in chimpanzees. Enter: Chimpface.
The idea came to conservationist Alexandra Russo when she contacted the non-profit Conservation X Labs and met computer vision expert Dr Colin McCormick.
“In 2030, the greatest set of questions will involve how perceptions of AI and their application will influence the trajectory of civil rights in the future. Questions about privacy, speech, the right of assembly and technological construction of personhood will all re-emerge in this new AI context, throwing into question our deepest-held beliefs about equality and opportunity for all,” says Sonia Katyal, co-director of the Berkeley Center for Law and Technology.
Google is issuing an open call to organizations around the world to submit their ideas for how they could use AI to help address societal challenges. Selected organizations will receive support from Google’s AI experts as well as grant funding from a 25 Million USD pool.
Google dot Org
Google.org is the charitable arm of Google. So far the organization has committed roughly US$100 million in investments and grants to nonprofits annually. Right now they’re looking for projects across a range of social impact domains and levels of technical expertise, from organizations that are experienced in AI to those with an idea for how they could be putting their data to better use.
Google is committing resources and $25 million to address societal challenges.
Another great example of how AI can be a force for Good. The forest monitoring project Global Forest Watch and the technology nonprofit Rainforest Connection use machine learning to identify factors that contribute to forest losses in the Congo and the Amazon. Read More
With suicide prevention, every minute of response time matters. That’s why the technology team at the well-known nonprofit Crisis Text Line in New York City analyzed some 65 million text messages to determine what words were most statistically associated with a high risk of suicide. This scale of analysis would clearly be infeasible without some form of automated analysis, and its results surprised the team. Read More
Last week our team was at the World Summit AI in Amsterdam (where else :-)) and we we’re blown away by the Afghan Women for Robotics Team. In 2017 this team won the Entrepreneur Challenge at the Robotex festival in Estonia.The afghan team faced off against 3700 other competitors and 1600 robots in a series of competitions, including robot basketball games, races, and mazes. Read More
What happens if you let data-scientists, hackers and coders play around with random medical data? Probably a lot. Probably a whole lot. You would see chaos arise within a couple of minutes!
But what if you would zoom in to a very specific medical data-set? For example DNA-samples of people who suffer from a specific type of leukemia… then you would end up with a briefing close to this:
We will give you a USB drive with datasets about sick (leukemia) and healthy people’s cells and hope you can tell the difference at the genomic level. We hope your data analysis can tell us more about why one patient responds better to treatment than other patients.
Date: 9 – 11 June 2017
Location: Sugar City (Halfweg)
Expert 1: Peter van der Spek (Erasmus Medical Center)
Expert 2: Jules Meijerink (Prinses Maxima Centrum voor kinderoncologie)
Leading up to the event Van der Spek and Meijerink gathered the data of 270 children with leukemia. Because these children have participated in medical research in the past, their DNA data could be used. These datasets were labeled with information of which therapy they have been treated and how effective this has been. There were close to 54.000 datapoints per patient. Needless to say, the data was anonymized before it was shared with the hackathon participants.
The participants were able to find a number of predictive indicators. They even managed to locate a “gene cluster” that can predict whether or not the treatment will be effective. This was something that (traditional) medical researchers weren’t able to find up to now.
Disclaimer: the findings of the hackathon still need to be scientifically validated before they can be used in practice.