Google’s AI teams used the comparatively quiet post-easter days to get ML practitioners up to speed with their latest research in reinforcement learning, natural language processing, and computer vision.
In “An optimistic perspective on offline reinforcement learning”, a team of researchers has looked into ways to use a fixed offline dataset of logged interactions to teach agents how to handle themselves in real world situations. While agents normally learn by getting live feedback from their environment, this approach is meant to be useful in certain robotics use cases or autonomous driving, where enough recorded interaction data is available and other ways of collecting information either seem insufficient or are too expensive to realise.
These are usually seen as tricky to implement, since there’s no real way of knowing how an agent should be rewarded when it takes an action that differs from the dataset provided. To tackle that, Google’s AI team added some supervised learning methods into the mix which helps to improve generalisation and make the whole system more robust. The results are called Ensemble-DQN and Random Ensemble Mixture and can be investigated here.
Meanwhile another team has been busy improving the way objects are detected. The outcome has been dubbed EfficientDet and will be presented at the renowned computer vision conference CVPR in Seattle in June – if COVID refrains from putting a spoke in their wheel. It aims at introducing a “new family of scalable and efficient object detectors” to the computer vision community, building upon earlier work concerning the scaling of neural networks (EfficientNet).
In EfficientDet, EfficientNet is used as a backbone to more effectively extract features from images, while a new bi-directional feature network in combination with a fresh normalised fusion technique is meant to get to image characteristics faster at a lower computation cost.
If you’re more interested in NLP, Google has also been busy setting up a benchmark to make comparing multilingual representations easier. XTREME covers 40 languages from 12 language families and includes nine tasks ranging from sentence classification to question answering to evaluate methods making the most of the shared structures of languages. The project can be found at GitHub.
Git pushes out security updates to stop tricksters
This week, Git maintainer Junio C Hamano has unleashed versions v2.26.1, v2.25.3, v2.24.2, v2.23.2, v2.22.3, v2.21.2, v2.20.3, v2.19.4, v2.18.3, and v2.17.4 of the version control system onto the coding masses.
Updating is strongly advised, since the security fixes mediate an issue which “allowed a crafted URL to trick a Git client to send credential information for a wrong host to the attacker’s site”.
Gloo lures admins with new dev portal
Envoy-based API gateway Gloo hit version 1.3 earlier this week, focusing on performance, stability and extensibility improvements. However, the release also includes a developer portal, so that admins have an easier way of controlling who gets access to which APIs.
Once set up, they can select which interfaces should be shared at all and decide which users and groups get to see them once they’ve logged into the portal. The whole apparatus is designed for self-service, with the Gloo team promising easy integration into continuous delivery processes.