Demis Hassabis talk on General Artificial Intelligence

Demis Hassabis: Towards General Artificial Intelligence – talk at Center for Brains, Minds and Machines (CBMM). [Background: r. Demis Hassabis is the Co-Founder and CEO of DeepMind, the world’s leading General Artificial Intelligence (AI) company, which was acquired by Google in 2014 in their largest ever European acquisition.

The talk  draws  on Demis’  eclectic experiences as an AI researcher, neuroscientist and video games designer.

see also.

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deep learning drizzle

u/kmario23 over at reddit points to a wonderful new resource m the deep learning drizzle. [on Github]

I have collected a list of freely available courses on Machine Learning, Deep Learning, Reinforcement Learning, Natural Language Processing, Computer Vision, Probabilistic Graphical Models, Machine Learning Fundamentals, and Deep Learning boot camps or summer schools.

So I checked it and immediately got involved watching Ian Goodfellow …

Ian and his advisor wrote this book …. take a look at it.

Goodfellow posted pdfs of his talks here

https://www.reddit.com/r/MachineLearning/  is worth following

AlphaGo Documentary 2017

the  1 1/2 movie about AlphaGo, Deep Mind’s AI program that beat the best GO player in the world. AMAZING .  MUST WATCH if you are really into real AI.

If you want to really understand what happened and how, and how Deepmind really works … you’ve got to watch the  documentary.

Lots of information about the game of GO … a great way to bring 8000 years of GO history and the future together

Natural Question Answering Research at Google

just announce on the Google AI Blog …/

Natural Questions: a New Corpus and Challenge for Question Answering Research

 

this is pretty exciting …hope to this grow and have fruitful implementation on the Google search engine.

this is what the Google AI researchers are saying

…. there are currently no large, publicly available sources of naturally occurring questions (i.e. questions asked by a person seeking information) and answers that can be used to train and evaluate QA models. This is because assembling a high-quality dataset for question answering requires a large source of real questions and significant human effort in finding correct answers.

To help spur research advances in QA, we are excited to announce Natural Questions (NQ), a new, large-scale corpus for training and evaluating open-domain question answering systems, and the first to replicate the end-to-end process in which people find answers to questions. NQ is large, consisting of 300,000 naturally occurring questions, along with human annotated answers from Wikipedia pages, to be used in training QA systems. We have additionally included 16,000 examples where answers (to the same questions) are provided by 5 different annotators,

I am really looking forward to digging into this …good questions and good answers are definitely part of the key for solving some great puzzles ….

have fun …