Here is Sam Altman, CEO of OpenAI, talking about their business model (31:26 – 32:13)

https://youtu.be/TzcJlKg2Rc0?t=1886

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When asked about how the business model is going to work, he replies:

“The honest answer is that we have no idea. We have never made any revenue. We have no current plans to make revenue. We have no idea about how we may one day generate revenue. We have made a soft promise to investors that once we build this sort-of generally intelligent system, basically we will ask it to figure out a way to generate an investment return.”

After reading “Gauge Equivariant Convolutional Networks and the Icosahedral CNN” by Cohen et. al. posted here yesterday, I thought I’d share a non-math summary for everyone who doesn’t have the time to read up on an entire field of physics. I really think the fundamental idea of the paper is really cool for everyone doing deep learning.

https://medium.com/@kayzaks/an-easy-guide-to-gauge-equivariant-convolutional-networks-9366fb600b70

Summary: Create a bot that can play chess blind but with the ability to sense a 3×3 square of the board each turn, as part of a fun, online AI competition of NeurIPS 2019!

Participants do not need to attend the NeurIPS conference and there is no cost to participate. (Play now.)

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Slightly Longer Story: All are invited to participate in a computer science competition that will be part of the 2019 Conference on Neural Information Processing Systems (NeurIPS, https://nips.cc/), Reconnaissance Blind Chess.

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Many of the favorite studied games in artificial intelligence (AI) such as checkers, chess, and Go lack something that is common and critical in real-life decision making, uncertainty.

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This is a competition with a simple but powerful twist on what may be considered the most classic game in AI history, chess. Reconnaissance Blind Chess (RBC) is like chess except a player cannot see where her opponent’s pieces are a priori. Rather, she learns partial information about them with the ability to sense a 3×3 square of the board each turn and from the results of moves.

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In comparison to poker, which seems to be the most popularly studied game of imperfect information, RBC includes a critical component of long-term planning. Compared to phantom games like Kriegspiel, in RBC players have much more ability to manage their uncertainty, which we believe makes the game more interesting from an AI perspective and more realistic for most scenarios; players are not completely blind, but rather, metaphorically, they simply cannot look everywhere at once.

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For more information on the NeurIPS competition, the game itself, or the API, or to play the game to get a feel for it, visit our website below.

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All are welcome to create the best RBC bot they can, and see how well it can play against other bots in the tournament starting on October 21, 2019!

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http://rbc.jhuapl.edu

A new type of dataset for all of the ML fellows here to work on (inquiry by email in paper, institution + use case). Longitudinal warehousing storage dataset – first of its kind (AFAAK). Try to predict how long it will stay in the warehouse! (Spoiler: You may first want to properly define what “how long” means first)

Not to mention the company transports 30% of American’s frozen food, so….see if what you eat is in there!

https://arxiv.org/abs/1903.05063

Workshop Website: https://scalableroboticlearning.github.io/

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When: June 22, 2019

Where: Freiburg, Germany @Robotics: Science and Systems (RSS) Conference

Wanted: Extended Abstracts (2-4 pages) are due June 7th, 2019

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Topics include

• Automatized training procedures
• Interactions between sensing, manipulation and planning
• Scalable approaches for grasping and generalizing functional grasps
• End-to-end vs. modular approaches (introspection, suitable metrics, ..)
• Automatic recovery and failure proof systems

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Invited Speakers

• Kai Arras, Bosch Robotics Research Program
• Michael Beetz, University Bremen
• Renaud Detry, NASA JPL
• Dieter Fox, University of Washington, as well as NVIDIA AI Robotics Research Lab
• Maxim Likhachev, Robotics Institute Carnegie Mellon University
• Gilwoo Lee, School of Computer Science & Engineering at the University of Washington
• Peter Pastor, X

I was told to use PCA from my postdoc supervisor, but I realized it is best for continuous features. I’ve looked into splitting my data to do a PCA with continuous features and MCA for binary. My postdoc supervisor said he combined the results – I was wondering how? A PCA returns PC1 and PC2 with coordinates (length = dataframe size). My MCA returns dim1 and dim2 but with 24 coordinates for the binary data. I’m not sure how to proceed?

But if there is another method that someone could recommend would be much appreciated! I am using R currently. But I can use Python too.

CoreML offers pose estimation on IOS.

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– Are there any resources for Pose Estimation on Android?

– Also, when is TF lite pose estimation going to be released?

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Thanks

Since fundamental frequency is added to whisper speech to produce a normal speech. How is Generative model (GAN, WaveNet etc) are able to capture features of whisper speech?