We investigate fundamental techniques in Graph Deep Learning, a new framework that combines graph theory and deep neural networks to tackle complex data domains in physical science, natural language processing, computer vision, and combinatorial optimization.
The aim of this research work was to investigate and quantify the risk of fatal and fracture injury for Thoroughbreds participating in flat racing in the US and Canada so that horses at particular risk can be identified and the risk of fatal injury reduced. Risk factors associated with fatalities and fractures were identified and predictive models for both fatalities and fractures were developed and their performance was evaluated. Our analysis was based on 188,269 Thoroughbreds that raced on 89 racecourses reporting injuries to the Equine Injury Database (EID) in the US and Canada from 1st January 2009 to 31st December 2015. This included 2,493,957 race starts and 4,592,162 exercise starts. The race starts reported to the EID represented the starts for 90.0% of all official Thoroughbred racing events in the United States and Canada during the 7-year observation period.
They say it takes a village, but in this digital age it actually just takes a chatroom to instantly connect you with worldwide resources that can turbocharge your career. We’ve combed through our contacts and data to bring you this list of some of the best data science and machine learning slack communities. Go ahead, sign in and spend some time with your peers discussing the issues that are blocking your in your workflow, pitfalls to avoid, or tips to make life easier. Happy slacking!
The danger of having artificially intelligent machines do our bidding is that we might not be careful enough about what we wish for. The lines of code that animate these machines will inevitably lack nuance, forget to spell out caveats, and end up giving AI systems goals and incentives that don’t align with our true preferences.
A now-classic thought experiment illustrating this problem was posed by the Oxford philosopher Nick Bostrom in 2003. Bostrom imagined a superintelligent robot, programmed with the seemingly innocuous goal of manufacturing paper clips. The robot eventually turns the whole world into a giant paper clip factory.
Everything on AI including futuristic robots with artificial intelligence, computer models of human intelligence and more.
With Artificial Intelligence playing a central role in the era of digital transformation, sharing information and knowledge about the advancements and the opportunities brought by AI is becoming crucial.
The influencers presented in this list are actively sharing information and knowledge about AI and disruptive technologies. They are the co-founders of AI ventures, machine learning professors, data scientists, and tech and digital transformation leaders.
Research paper：Authors and titles for recent submissions
Mainly Computer Vision (tasks/datasets/papers with code)