We suspect this is just the beginning of the long curvy road of a new layer of intelligence that can be applied everywhere. The question is how do we start? That was the first thing we realized back in late 2015: facing literally ten thousands of links, tutorials etc., it was like drinking from a firehose and we had a hard time to pick up the gems.
We decided to start our little AIDL group to see if we could get a community to help makes sense of the velocity of information. In less than one year, AIDL become the most active A.I. and deep learning group on Facebook. We hope to summarize, analyze, educate and disseminate and I think we have done a good job so far. This resulted in conversations flourishing in the group. We strived to have discussions one level deeper than others. For example, forum members including us fact check several pieces of news related to deep learning. This gives us a better edge in the rapidly changing field of A.I.
This newsletter follows exactly the same philosophy as our forum. We hope to summarize, analyze, educate and disseminate. We will keep an eye on the latest and most salient developments and present them in a coherent fashion to your mailbox.
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A recent paper published by DeepMinds, penned by Joel Z. Leibo et al, studies the use of reinforcement learning in few simulated games which social dilemmas involved. The authors find that conflict can emerge from competition over shared resources and shed light on how the sequential nature of real world social dilemmas affects cooperation.
While the paper is soley based on simulation results, DeepMind's work is watched by popular outlets and many over-sensational versions of the news spawned. e.g. Gizmodo's piece warned us that now AI has "killer instinct". This piece from TechTimes links DeepMind's simulation result with "survival of fittest". Of course, both pieces only have weak connections with DeepMind's actual experiments.
Ford is making a move in the self-driving car space! They are snapping up many ex-Googlers and Uber experts in self-driving. The result is a 1 billion dollar effort in the next 5 years. Would traditional autos make a difference in self-driving in United States? Perhaps they will have easier time to get approval from the different states?
Youtube Bounding Boxes (YoutubeBB) has 380k segments of videos, 5.6 bounding boxes for 23 objects. This dataset is ideal for video detection and perhaps useful for image localization/detection as well. The dataset is released under CC 4.0.
Last week, we have two big news on deep learning with Spark. One is Intel rolling out BigDL on Apache Spark, the other is Yahoo open source framework TensorflowOnSpark (Also see next link). While both Intel and Yahoo are not seen as major players in the deep learning landscape, you can see how big companies are trying to catch up in the space.
Since the release of tensorflow, multiple groups have been trying to integrate tensorflow into Spark. Apparently, Yahoo has done a rather thorough effort. TensorFlowSpark not only allow tensorflow runs on Spark, the Yahoo's team also enable tensor buffer transfer through remote direct memory access (RDMA) over Infiniband which speeds up distributed training.