Purely AI News: For AI professionals in a hurry
July 21, 2020
WordCraft: A Reinforcement Learning environment for enabling common-sense based agents
A sample episode of WordCraft: The agent needs to create the goal entity (cyborg) from a set of starting entities.
The human ability to solve a wide variety of real-world problems usually includes a common-sense understanding of the world around us. While advanced Reinforcement Learning algorithms have been developed, it remains an open challenge to better extract such common-sense information from natural language corpora and combine it with agents in RL. Researchers at University College London and the University of Oxford claim that is in part due to a lack of lightweight simulation environments that accurately represent real-world semantics and include established sources of information with respect to RL observations.

In their paper which was accepted to the International Conference on Machine Learning (ICML 2020), they propose a new benchmarking tool to enable research on agents making use of common sense knowledge. The framework they are calling "WordCraft", is an RL environment based on the game Little Alchemy 2 (a game that tasks players with mixing ingredients to create new items). Similar to its inspiration, WordCraft measures the reasoning capabilities of RL agents by providing them more than 700 different entities (ingredients), and letting them combine previously discovered entities like “water” and “earth” to create “mud.”

The environment starts off with a set of four basic items, the agent is tasked with generating as many different items as possible. Each non-starter item can be created by combining two other items. For example, combining “moon” and “butterfly” yields “moth”, and combining “human” and “medusa” yields “statue”. There are 3,417 valid item combinations in WordCraft, and an agent must use information about concept relationships to solve the game efficiently without trying out all the other possible combinations. Every task is generated by random sampling of a target entity, true constituent entities, and distracting entities, and the complexity of the task can be changed by increasing the number of distracting entities or raising the number of intermediate entities that must be formed.

Along with the WordCraft benchmarking environment, the researchers also present an agent architecture that makes use of information from an external knowledge-graph to guide the agent’s policy. This agent architecture is that of an actor-critic network, based on the concepts of self-attention and an external knowledge-graph link prediction model. To illustrate how well the tasks represent real-world relationships between entities, when this actor-critic agent model was evaluated they used GloVe embedding representations of the entities to capture real semantic information about them. They conclude that this agent architecture with a full knowledge-graph and GloVe embeddings can perform at par with a human at the same task with up to 8 distracting entities.
LinkedIn



Aug. 2, 2020

Sample Factory, a new training framework for Reinforcement Learning slashes the level of compute required for state-of-the-art results

23
July 31, 2020

Intel joins hands with researchers from MIT and Georgia Tech to work on a code improvement recommendation system, develops "An End-to-End Neural Code Similarity System"

22
July 25, 2020

Google's tensorflow-lite framework for deep learning is now more than 2x faster on average, using operator fusion and optimizations for additional CPU instruction sets

21
July 23, 2020

Fawkes: An AI system that puts an 'invisibility cloak' on images so that facial recognition algorithms are not able to reveal identities of people without permission

20
July 22, 2020

Researchers from Austria propose an AI system that reads sheet music from raw images and aligns that to a given audio accurately

19
July 20, 2020

A designer who worked on over 20 commercial projects for a year turns out to be an AI built by the Russian design firm Art. Lebedev Studio

17
July 19, 2020

Microsoft is developing AI to improve camera-in-display technology for natural perspectives and clearer visuals in video calls

16
July 18, 2020

Microsoft and Zhajiang Univ. researchers create AI Model that can sing in several languages including both Chinese and English

15
July 18, 2020

New event-based learning algorithm 'E-Prop' inspired by the Human brain is more efficient than conventional Deep Learning

14
July 17, 2020

Scientists from the University of California address the false-negative problem of MRI Reconstruction Networks using adversarial techniques

13
July 16, 2020

A new technique of exposing DeepFakes uses the classical signal processing technique of frequency analysis

12
July 16, 2020

New AI model By Facebook researchers can recognize five different voices speaking simultaneously, pushes state-of-the-art forward

11
July 15, 2020

Researchers from Columbia Univ. and DeepMind propose a new framework for Taylor Expansion Policy Optimization (TayPO)

10
July 14, 2020

Federated Learning is finally here; Presagen's new algorithm creates higher performing AI than traditional centralized learning

9
July 14, 2020

Fujitsu designed a new Deep Learning based method for dimensionality reduction inspired by compression technology

8
July 12, 2020

Databricks donates its immensely popular MLflow framework to the Linux Foundation

7
July 12, 2020

Microsoft Research restores old photos that suffer from severe degradation with a new deep learning based approach

6
July 12, 2020

Amazon launches a new AI based automatic code review service named CodeGuru

5
July 12, 2020

IBM launches new Deep Learning project: Verifiably Safe Reinforcement Learning (VSRL) framework

4
July 12, 2020

DevOps for ML get an upgrade with new open-source CI/CD library, "Continuous Machine Learning (CML)"

3
July 12, 2020

DeepMind's new open-sourced Reinforcement-Learning library, dm_control, packs a simple interface to common RL utilities

2
July 12, 2020

Learning to learn: Google's AutoML-Zero learns to evolve new ML algorithms from scratch

1