Google planning to bring AI and ML tools to Raspberry Pi
bbc.co.uk
bbc.co.uk
I'm working on the next version to make it more useful, but all the technology is not there yet, I want the robot to be able to understand speech and talk back to users. I also want the robot to be able to play games with people. I think the platform has a lot of potential. I want Google to release a low power tensor processing unit made for the pi to make this more useful. This will open up a lot of doors for robot and AI enthusiasts. I'm looking to turn this into a platform, contact me if this is of interest to you.
It's probably cheaper to hire a landscape maintenance company, or a neighborhood teenager.
Then don't! If it's autonomous it can be out there all the time rather than once a week/whatever, no spinning blade needed. Plan B? Buy a goat.
Nah. What we really want and need is a robot that while it mows the lawn, turns it into: its entire energy source, its entire pool of building blocks for daily (nightly?) self-repairs, for reproduction, for producing oh maybe fluffy warm wool and delicious milk/cheese/butter as a side product, and fertilizes the land from the occasional .. discharge resulting from perpetual energy production-consumption and internal cleanups/repairs, finally as it still does wear down as all physical assemblages are wont to with time, it leaves behind highly durable inputs for sturdy clothing and stylish home decoration (horns, hides, leather etc)!
All from a friggin lawn.
Roboruminant 2.0 baby. Need to reinvent mammalian evolution before we can really reinvent the wheel!
I'd call that safe enough to operate unsupervised in an enclosed yard.
And if in doubt, just wear under-armour instead..
I've heard good things about the Husqvarna 450 especially.
Spoiler alert: there is no computer vision involved, still works perfectly even for large lawns; our local university even uses them in campus parks.
http://www.superhouse.tv/19-husqvarna-automower-basics/
http://www.superhouse.tv/superhouse-vlog-47-husqvarna-automo...
http://www.superhouse.tv/superhouse-vlog-48-pausing-the-husq...
http://www.superhouse.tv/superhouse-vlog-49-automower-securi...
http://www.husqvarna.com/us/products/robotic-lawn-mowers/
http://www.deere.com/en_INT/products/equipment/robotic_mower...?
http://www.honda.co.uk/lawn-and-garden/products/miimo-2015/o...
Donkey: a self driving library and control platform for small scale DIY vehicles
"What do you want?"
"Coffee."
"I CAN'T HEAR YOU! I SAID WHAT DO YOU WANT?!"
"Give me coffee or I will end you."
This is not what I expected when I read your comment. I was expecting a platform where you upload the parameters of a hardware system, the platform digitizes that system into a virtual sandbox, and people can then write arbitrary programs for that system and showcase them in the sandbox, where the uploader can buy, comment, and rate those programs. Something along the lines of https://openai.com/blog/
And it suggests tools to aid face and emotion recognition, speech-to-text translation, natural language processing and sentiment analysis.
Google has previously developed a range of tools for machine learning, internet of things devices, wearables, robotics and home automation."
That's the meat of it. Google put out a survey - speculation ensues.
Yes.
This is the actual announcement with the link to the survey at the bottom:
https://www.raspberrypi.org/blog/google-tools-raspberry-pi/
I play with RPis and I make NNs with TensorFlow, so I took the survey. Pretty standard "tell us what you think" type of thing. If it leads to Google maintaining an official binary release of TF for the RPi, that would be great.
There are two basic groups of users. People looking for a cheap small computer to use for education, gaming, etc. And those who use the Pi as a controller for other projects.
I'm using one as the controller for an open source coffee maker called Mugsy as well as another start up in the music education space.
They've just announced that they might release something ML/AI related in 2017 for the PI.
I've been thinking about building a few of these as a class/group project at work.
There's another one that goes into more detail on the "how" of running other algorithms: https://rpiplayground.wordpress.com/tag/raspberry-pi-gpu/
I'm not sure if there are limitations that would keep it from being interesting for TF or not; I don't know enough about it.
There are a few "Android on the Pi" projects around. They all need work before they're remotely useable. Frankly, I wish there was at least one high-quality project to point people to.
are you talking about the tons of cheap Android phones?
TensorFlow is not suitable for anything practical on the Pi. You can certainly get it to run there, but CPU vector math on resource constrained devices is not going to be a forte for a framework designed primarily for quickly iterating over models on a GPU workstation or a multi-GPU server. TF very much likes to have a very beefy GPU.
Remember when Wolfram came to the Pi? Runs too slow to be of use for anyone but it ships with every copy of raspbian.
>Perhaps the use-case is a fleet of Raspberry Pis?
This would be a waste of money, I know they're cheap but for a space that is working on GPU power anything CPU based isn't cost effective at all.
The Pi does have a GPU. Nothing amazing, but better than the CPU. Given this is public knowledge, why is the GPU being ignored in comments like yours?
You obviously wont do any training on the pi, but low power devices have been used for inference for years now. For example here it is made to run on a phone: https://github.com/tensorflow/tensorflow/tree/master/tensorf...
You give it problems and it generates answers -> that's inference.
An example on inference would be feeding it an image and classifying it with a neural net.
The example I linked to above has inception running, which classifies things you point the camera into 1000 different categories.
It is very easy to set up (I have done it, and it only took a few minutes)
It's a perfectly valid statement to say that a pi will run inference on 90% of the models out there, and I have experience with the same. It would be similar to claiming that (if it could), a pi could run 90% of the games out there.
Once again, I am speaking from practical experience from having implemented tensorflow neural nets on low power devices. And I sincerely get the feeling that although you're enthusiastic, you have no clue what you're talking about.
Rather than making offhand comments like facepalm, I would challenge you to either offer up some evidence to the contrary (you could start by trying to find a tensorflow model that a pi wont run), or spend more of your time doing something more practical than acting like a clueless rabid fanboy.
Besides which, they have been implementing things like 8 bit graphs for processing on low power devices. That should result in a large performance increase for these devices. I tried it on mobile and I got decent FPS (I can't remember the exact figure) by using it. https://www.tensorflow.org/versions/r1.0/how_tos/quantizatio...
There was a side by side with Google's online voice recognition and it out performed in speed and accuracy on a mobile GPU/CPU. Complete with an actual learning system. That is truer to an AI for the Raspberry Pi, not to mention addressing privacy concerns.
If this is a glorified API / Cloud adapter rather than a true AI, what is it really?
edit: found it https://www.youtube.com/watch?v=Fwzs8SvOI3Y
I have a strong feeling that quite a bit of R&D has been going toward Apple's upcoming chip, which will likely have a custom GPU architecture optimized for deep learning (of which Siri will also greatly benefit from) and augmented reality - like the custom HPU in Hololens. Apple's "Lens" wearable will probably pair via W1 with an iPhone which will handle most of the processing. Perhaps they'll even have a custom 3D/depth sensor based on the PrimeSense tech they purchased....
We're on the cusp of consumer AR going mainstream, and it's exciting.
Hopefully it will be an efficient implementation of 1bit-weight NNs like XNOR.ai (which has been pushing on the research on 8bit & 3bit nets).
ARM SIMD (NEON) is not as great as x86's but this can turn out to work & be very cache-efficient!
EDIT: for CPUs, at least.