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cr4zy

1,619 karma · joined October 28, 2011

gmail cquiter @crizCraig : deepdrive.io
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cr4zy··on How much bandwidth does the spinal cord have?
Similar question on Quora from 2012[1] per sense:

  Vision: 10Mb/s => 100Mb/s
  Hearing: 30Mb/s
  Touch: 135Mb/s
  Smell: 100k neurons
  Taste: 100kb/s
  Proprioception: ??
  Balance: ??

  Total: ~10Mb/s=>~1Gb/s
Internal brain bandwidth is also worth mentioning as this is the last remaining wetware advantage over hardware due to the three dimensionally fully connected heterogeneous cortical substrate. I can't seem to find a figure on that though.

https://www.quora.com/How-much-bandwidth-does-each-human-sen...

cr4zy··on Tesla Model 3 spoofed off the highway
Related recent research that handles decoy waypoints [1][2]. They use imitation learning for path planning directly from sensor data, instead of just using machine learning for perception, tracking, prediction and doing MPC/LQR for the planner. This allows inferring weird paths, perhaps, like "run into this narrow pit stop" :)

[1] https://arxiv.org/abs/1810.06544

[2] https://sites.google.com/view/imitativeforecastingcontrol

cr4zy··on Grover – A State-of-the-Art Defense Against Neural Fake News
Wow, the generator is amazing. Two out of three articles were beyond what I perceived was possible, beyond maybe GP2. Is there a browser extension that detects and warns about possible fake news this can be built into?

1.

  seed: nytimes.com
  seed: AI will certainly be good
  seed: June 6, 2019 - Paul Krugman
  generated: ∨∨∨∨∨∨∨∨∨∨∨∨∨∨∨∨∨∨∨∨∨∨ 
  Artificial intelligence – by definition, computer code – can provide
  more than a speed boost. Many researchers now believe that intelligence,
  better defined as capacity to learn, will be increasingly provided
  by machines. Autonomy, already on the march, has become even more
  central. Future programs will likely be as familiar as Adam Smith’s
  invisible hand.

  If true, such predictions open up fundamentally new possibilities
  for future development. We will be able to quickly develop services
  based on or even built around lots of self-learning software. We’ll
  be able to use AI-based services without physically owning the software.
  As sensors in buildings, cars, buildings and cities start recording
  our activities, we will be able to experience “smart cities” and
  “smart cities” the moment they materialize.
  
  This future, however, is not yet in the cards. The gains are probably
  years or even decades away. Even in the areas where AI might dominate,
  such as physical warehousing, improving automation has lagged behind
  improvements in other areas of business. And our thinking, though
  improving, is still far from sophisticated enough to take full
  advantage of AI.
  
  Also:
  
  Please take a moment to watch the full program of this interview.
  Every panelist will speak out in favor of AI.
2.

  seed: nytimes.com
  seed: Uncertainty around AI is overhyped
  seed: June 6, 2019 - Paul Krugman
  generated: ∨∨∨∨∨∨∨∨∨∨∨∨∨∨∨∨∨∨∨∨∨∨
  There’s no denying that AI, in many ways, is in its infancy. It isn’t
  something that belongs on a refrigerator or wall clock; it’s a whole 
  new area of research and development. While it may be commonplace 
  within a few years, AI will likely be a long way off; if it even 
  becomes an important factor in the economy as a whole, well, 
  probably about a century from now.
  
  It’s also true that many worry about the potential for 
  superintelligent machines, who could threaten all sorts of things 
  from human liberty to national security. But that’s not the right 
  question to ask.
  
  There is no “right” or “wrong” answer here — nobody really knows 
  how AI will evolve. And there’s no “theory” (although there are 
  several, ranging from quantum theory to AI techniques employed 
  by biologists, and from genetics to postulates to mathematical 
  models) that will explain it. Neither are there explanations 
  for the values, levels or usefulness of AI models. So, what 
  we’re left with is sheer hard scientific uncertainty.
  
  But let’s say we do know a little. The odds of a superintelligent 
  agent that can become sentient, one that would harm other humans 
  or destroy our civilization, seem minuscule. If you think that 
  chance is small, and that AI is likely to be less likely to 
  cross that threshold than is real intelligence, then the effects 
  on your consciousness and your mental space and our experience 
  of it may seem inconsequential. And that’s no reason to spout 
  half-baked fear mongering.
3.

  seed: nytimes.com
  seed: AI Uncertainty is Exaggerated
  seed: June 6, 2019 - Paul Krugman
  generated: ∨∨∨∨∨∨∨∨∨∨∨∨∨∨∨∨∨∨∨∨∨∨
  As noted, Donald Trump uses the expression “AI Doom” – in part because 
  he understands AI but mostly because he responds to the media’s reports 
  on AI, which Trump himself frequently repeats, frequently without 
  evidence.
  
  AI may have risks – as in the threats from robots and remotely piloted 
  aircraft – but most analysts who follow AI say it has such promise 
  that its damage to people’s security and quality of life is likely 
  to be contained. Indeed, the main ethical question with AI is what 
  to do when machines decide that killing their human masters is a 
  good idea.
  
  It’s easy to exaggerate AI’s immediate threat. The machine that 
  driverless cars are supposed to replace is still pretty active. 
  And if, as predicted, the development of AI renders it easier for 
  people to work, then it’s also likely to give them more jobs.
  
  The main threat with AI is what it could do to workers, although 
  the more automation, the more all sorts of jobs become easily 
  replaceable. But a small dose of AI is actually a very good thing.
cr4zy··on Surprising importance of spontaneous order and noise to how we think
Dropout is traditionally only applied at training time: c.f. "At test time the weights are multiplied by their probability of their associated units’ dropout." from the medium post you linked. I _suspect_ that leaving it on during test has been accidentally tried a bunch of times though :) - so probably is not effective, although it could be interesting to experiment more intentionally with test-time noise. Our brains do not separate so cleanly between train and test, and so it seems more natural for the noise to be always on (if that's even the case as it seems hard to know what's noise when you haven't completely decoded the brain's activity). Decoding the brain seems to be super promising though and I hope we get good enough so as to upload our minds someday. Here's an interesting related article I read recently towards that end: http://www.foldl.me/uploads/papers/ccn2018.pdf
cr4zy··on Google details just how good dark mode is for your battery
Related and life changing, the Dark Reader extension that enables dark mode for every web site, including PDF's, with adjustable contrast. Almost every site looks better with this on. https://darkreader.org/

Make sure to check out the More tab and change the filter type on sites that it doesn't work in Dynamic mode for.

cr4zy··on How to Design Software Good
Also cannot read with dark reader. My eyes! -.-
cr4zy··on Instant runoff voting is the system we need
Maybe this is the wrong place to ask, but are there any voting systems that allow arbitrary representation? A special case of this is the pure democracy, but most people would likely let someone else vote on most issues for them. Then, on certain issues, you could choose to vote yourself or even change representatives. Also since your representative could be changed at any time, the waste that goes into campaign seasons and partisanship would be replaced with a more realtime focus on issues. Perhaps this is too big a change for the U.S., but it seems like we could try this out, i.e. on StackOverflow, Reddit, or Wikipedia before implementing new governments on Mars and beyond.
cr4zy··on How to Get Things Done When You Don't Feel Like It
What works best for me is to meditate during the inevitable downtimes - compiling, training, uploading, downloading... Find beauty in the scrolling logs. Take a look outside. Before you know it, you'll be excited about the next important thing you can do while waiting on the first. Also makes for a much happier work day!
cr4zy··on Ask HN: Favorite teachers on YouTube?
Sergey Levine - Deep Reinforcement Learning

All manner of control methods are covered, including non-neural-net methods, and how to mix and match different methods. Up-to-date and the best information you can get on sensorimotor learning to my knowledge.

https://www.youtube.com/playlist?list=PLkFD6_40KJIznC9CDbVTj...

Complete course materials: http://rail.eecs.berkeley.edu/deeprlcourse/resources/#prevof...

cr4zy··on A Tutorial Introduction to Kubernetes
service.spec.sessionAffinity ? - with the common scalability caveat http://nishadikirielle.blogspot.com/2016/03/load-balancing-k...
cr4zy··on Berkeley Deep Drive Dataset
It helps a lot in my experience. So in simulation I tried this with imitation learning by training on a hood camera, a camera at the height of what a semi-truck hood would be, and another camera offset to the left 1.5m along with steering and throttle for labels. I also added random noise to the position (less than a meter), rotation (less than degree), fov (less than a degree), capture height (< 1%), and capture width (< 1%). The result was a 3x higher average score on a driving benchmark where the score was meters driven minus seconds taken, second-meters of lane deviation, and seconds where acceleration surpassed 0.5g forces (to measure comfort). The dataset, training code, and sim are at deepdrive.io - a different entity with the same name :)
cr4zy··on Tesla in autopilot mode crashes into parked Laguna Beach police cruiser
One way to avoid these types of crashes IMO is anomaly detection. It's quite simple to do anomaly detection in pixels using modern deep pixel prediction nets like PredNet. In my experiments you get a few seconds lead time on something like a car cutting you off (the car starts to head out of the lane before actually crossing it for example). This allows alerting the driver, and with a full windshield HUD you could even highlight the anomalous pixels on the windshield. The nice thing about this is that it can be trained in an unsupervised manner on all the available data. Some important details are to find anomalies in object bounding boxes, using something like Tensorflow's object detection pretrained net. Otherwise buildings with lots of striations would light up the anomaly detector. Also, you should detect anomalies in a human colorspace like CIELAB so that white cars (#fff) are not artificially weighted as more anomalous.

Finally, you could use this as input to a planner like Model Predictive Control where a higher cost is incurred for approaching anomalous objects.

cr4zy··on Integrating GTA V into Universe
It's technically possible to run everything on one Windows machine (using VNC loopback mode), however we expect most people to run ML on Linux / Mac so that is the best supported configuration. Before Universe, I ran a modified version of Caffe in Windows along with the game on a GTX 980 and there were more than enough GPU resources for that. Running ML / ROS / other self-driving software in Linux is much more ideal though.
cr4zy··on Integrating GTA V into Universe
Convergence on target speeds and steering is actually very quick (a matter of hours on a modern GPU / AlexNet / 227x227 RGB) once you have the data. Generating training data with the correct speed limit and providing speed limit based rewards is the missing step. The training car already obeys traffic laws.
cr4zy··on Integrating GTA V into Universe
Yes, it would be great to have source! The modding community does make up for this in some ways. For example, mods provide access to things like finding memory addresses for things like steering angle that weren't available in the API for game scriptors working on GTAV:

https://github.com/crosire/scripthookvdotnet/blob/dev_v3/sou...

re snow: It is possible to lay fresh snow anywhere in addition to drive through this area with snow burms, etc..

https://www.youtube.com/watch?v=rOo2mtuPkKQ&feature=youtu.be

Traction is definitely affected in snowy areas.

re tires bursting: it seems possible

https://github.com/crosire/scripthookvdotnet/blob/2b14a22ad3...

cr4zy··on Integrating GTA V into Universe
It's surprising how scriptable the game is. For example, the car I train the AI with is set to the least aggressive driving style which stops for rabbits crossing the road :) Also, all violence against the player is disabled.
cr4zy··on Integrating GTA V into Universe
We currently don't look at being shot at (although it's an interesting suggestion!) - the current reward function is basically this:

  distance_reward - off_road_penalty - speeding_penalty - slow_penalty - discomfort_penalty;
Plus if you collide or drive against traffic, the episode terminates.

https://github.com/openai/universe-windows-envs/blob/f5aad96...

cr4zy··on Integrating GTA V into Universe
So the training car has a max speed of 10 meters per second or 22mph which the net matches. Future datasets will contain the road's speed limit as well as throttle / brake targets required to reach that speed limit.
cr4zy··on Integrating GTA V into Universe
Thanks! Yes, the game is highly scriptable[1], so you can test the types of scenarios you'd want. We've disabled violence against the driver, but could enable[1] it on-demand if it's useful for training.

[1] http://www.dev-c.com/nativedb/ [2] http://gtaforums.com/topic/822314-guide-driving-styles/

cr4zy··on OpenAI Universe
deepdrive.io creator here - I'm actually not affiliated with the Berkeley project of the same name. There's also a DeepDriving at Princeton plus plenty of other (mostly perception) projects using GTAV, so it can be confusing. I'm hoping the GTAV for self-driving car efforts can start to standardize around the Universe integration though. Having worked on it, I can say firsthand that the Universe architecture is definitely amenable to sending radar, lidar, controlling the camera, bounding boxes, segmentation, and other types of info that the various sub-fields of self-driving are interested in. Super-excited to see how people use it!
cr4zy··on Europe's robots to become 'electronic persons' under draft plan
Robot rights make sense to prepare for a time when there is rough intellectual parity between humans and robots and the possibility of an adversarial relationship arises. I think it's unlikely, but still important, given the impact such a relationship would have. Giving robots human rights now would be like giving an ant human rights, but it makes sense to give rights in accordance with some measure of intelligence I think.
cr4zy··on Microsoft releases CNTK, its open source deep learning toolkit, on GitHub
Looks like they're moving it to GitHub wiki: https://github.com/Microsoft/CNTK/wiki/Config-file-overview
cr4zy··on Introducing OpenAI
This is great news! I think distributed access, control, and contribution to the best AI's will help create 'safe' AI's much faster than any AI created in secret. One thing this does not address, and is something that Jerry Kaplan has an excellent suggestion his recent book "Humans need not apply", is the distributed ownership of AI where tax incentives to public companies that have larger numbers of shareholders, encourages wider distribution of the massive gains AI will bring to these companies.

I really hope that the training data, as well as code and research, will be opened up as well, since the public could really benefit from the self-driving car training data Tesla may contribute[1]. By opening up the development of this extremely important application to public contribution and the quality benefits that it brings, we could get safer, quicker realization of this amazingly transformative tech. As of now the best dataset for self-driving cars, KITTI, is extremely small and dated. [plug]I am working on a project to train self-driving car vision via GTAV to help workaround this (please contact me if you're interested), but obviously real-world data will be better in so many ways.

[1] https://medium.com/backchannel/how-elon-musk-and-y-combinato...

cr4zy··on Deep Convolutional Generative Adversarial Networks
Looks similar to the top project on this page from cs231n by Jon Gauthier:

http://cs231n.stanford.edu/reports.html

cr4zy··on Page Weight Matters (2012)
I highly recommend turning on page throttling in the Chrome Dev Tools sometime. You'll be amazed at even how slow even 4G seems.
cr4zy··on The magic of the Kalman filter, in pictures
Another cool resource for learning to apply and implement Kalman filters is Udacity's AI for Robotics (focused on self driving cars) course by Sebastian Thrun. Apparently Kalman filters are how Google's self driving cars predict the velocity of other cars from their position.

https://www.udacity.com/course/artificial-intelligence-for-r...

cr4zy··on Can The Human Brain Project be saved, and should it be?
Hugely excited about OpenWorm!

More great stuff about the robotic embodiment of OpenWorm:

http://www.i-programmer.info/news/105-artificial-intelligenc...

https://news.ycombinator.com/item?id=8745639

Eyewire.org is also really cool -- for example, they have already made an important discovery into how our eyes detect motion: http://www.scientificamerican.com/article/online-gamers-help...

cr4zy··on OpenWorm – Create a virtual C. elegans nematode
Also check out this embodiment

http://www.i-programmer.info/news/105-artificial-intelligenc...

And some implementation details

https://news.ycombinator.com/item?id=8745639

cr4zy··on Large Scale Deep Learning – Jeff Dean [pdf]
The second half of this video has an earlier talk that goes along with the slides: http://youtu.be/S9twUcX1Zp0?t=22m49s
cr4zy··on The Dropbox terabyte conundrum
I just store large files I don't want synced in Google Drive and don't run the Google Drive client. Dropbox selective sync is a huge pain when setting up a new machine since it doesn't recognize yet unsynced folders and files.
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