AI Weekly 2 Mar 2018

//AI Weekly 2 Mar 2018

AI Weekly 2 Mar 2018

Hi! New AI Weekly is here! This week brought us couple great articles, definitely worth reading is the first from series about Harvard progress in AI, their robots are really amazing. Also worth to mention that Google announced online Machine Learning Course, it looks really promising and is must check this week. That’s not all of course… just enjoy your weekend reading other AI news and don’t forget to share it with your friends 😉

GENERAL 

An AI just beat top lawyers at their own game – The nation’s top lawyers recently battled artificial intelligence in a competition to interpret contracts — and they lost. A new study, conducted by legal AI platform LawGeex in consultation with law professors from Stanford University, Duke University School of Law, and University of Southern California, pitted twenty experienced lawyers against an AI trained to evaluate legal contracts. Competitors were given four hours to review five non-disclosure agreements (NDAs) and identify 30 legal issues, including arbitration, confidentiality of relationship, and indemnification. They were scored by how accurately they identified each issue.http://on.mash.to/2oCX3Zw

Behind the Chat: How E-commerce Robot Assistant AliMe Workshttp://bit.ly/2F6t2Y9

Onward and upward, robots – Harvard scientists help drive new age of machines, aiming for transformative impact in medicine, on Main Street, and beyond. This is first in a series of articles on cutting-edge research at Harvardhttp://bit.ly/2FhJGY9

Artificial Intelligence – What it is and why it matters by SAS http://bit.ly/2FM8K7r

VIDEOS 

Variational Autoencoders – In this episode, Arxiv Insights dives into Variational Autoencoders, a class of neural networks that can learn to compress data completely unsupervised! http://bit.ly/2FLGXnJ

LEARNING

Machine Learning Crash Course by Google – A self-study guide for aspiring machine learning practitioners. Learn Machine Learning concepts by taking the same course that over 10,000 Google engineers have completed (available in English, Spanish, French, Korean, and Mandarin). http://bit.ly/2FLldbu

RESOURCES 

Google-Landmarks: A New Dataset and Challenge for Landmark Recognition – a new dataset for landmark recognition containing 2M+ images depicting 30K unique landmarks from across the world. http://bit.ly/2tcVwxW

PROGRAMMING

Multi-Agent Deep Deterministic Policy Gradient (MADDPG) – This is the code for implementing the MADDPG algorithm presented in the paper: Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments. It is configured to be run in conjunction with environments from the Multi-Agent Particle Environments (MPE). http://bit.ly/2H194yH

Ingredients for Robotics Research – OpenAI released eight simulated robotics environments and a Baselines implementation of Hindsight Experience Replay, all developed for their research over the past year. They’ve used these environments to train models which work on physical robots. They’ve also released a set of requests for robotics research. http://bit.ly/2CTkJgo

PAPERS 

Learning by playing – DeepMind proposes a new learning paradigm called ‘Scheduled Auxiliary Control (SAC-X)’ which seeks to overcome this exploration issue. SAC-X is based on the idea that to learn complex tasks from scratch, an agent has to learn to explore and master a set of basic skills first. Just as a baby must develop coordination and balance before she crawls or walks—providing an agent with internal (auxiliary) goals corresponding to simple skills increases the chance it can understand and perform more complicated tasks.http://bit.ly/2oCnMW2

Machine Theory of Mind – Theory of mind (ToM; Premack & Woodruff, 1978) broadly refers to humans’ ability to represent the mental states of others, including their desires, beliefs, and intentions. DeepMind team proposes to train a machine to build such models too. They design a Theory of Mind neural network – a ToMnet – which uses meta-learning to build models of the agents it encounters, from observations of their behaviour alone. Through this process, it acquires a strong prior model for agents’ behaviour, as well as the ability to bootstrap to richer predictions about agents’ characteristics and mental states using only a small number of behavioural observations. http://bit.ly/2te4paB

By |2018-03-27T18:22:19+00:00March 2nd, 2018|AI Weekly|0 Comments

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