Much research has been done to apply auctions, markets, and negotiation mechanisms to solve the multiagent task allocation problem. However, there has been very little work on human-agent group task allocation. We believe that the notion of bounty hunting has good properties for human-agent group interaction in dynamic task allocation problems. We use previous experimental results comparing bounty hunting with auction-like methods to argue why it would be particularly adept at handling scenarios with unreliable collaborators and unexpectedly hard tasks: scenarios we believe highlight difficulties involved in working with humans collaborators.

The eagerly-awaited update for the popular machine learning framework TensorFlow was announced earlier this week by Martin Wicke from Google AI. He announced the news on his Google Group, adding that they are planning to release a preview version of TensorFlow 2.0 in late 2018. Since its open-source release in 2015, TensorFlow has become one of the most widely adopted ML frameworks, catering to a broad spectrum of users and use-cases. In this time, TensorFlow has evolved along with rapid developments in computing hardware, machine learning research, and commercial deployment. Wicke said on the group, "TensorFlow 2.0 will be a major milestone, with a focus on ease of use."