Haiku Tech Center

Grab your popcorn and some caffeine infused drink, sit back, and dive into this week's HTC Seminar.  Rodney Brooks will deliver his Shannon Luminary Lecture 'The Future of Innovation in Artificial Intelligence and Robotics'.  This talk was given at Nokia Bell Labs in December 2018.

We're continuing our exploration of the super nifty open source OAK hardware boards that allow you to run VPU accelerated neural net and computer vision algorithms. DepthAI is the term OAK's developers use for their complete embedded Spatial AI platform. So the open source hardware, firmware, and software interface api.  It allows you to run preconfigured neural net AI models ( openVINO models ) for constructing real time embedded computer vision systems. If you want to use custom neural net models, you can do so as well. Utilizing a set of instructions they provide here . SpatialAI is all about localizing objects in 3D space. So finding their positions in physical space as opposed to just pixel space in a 2D image. DepthAI provides 2 different methods for generating SpatialAI results.   -Monocular neural inference fused with stereo depth   -Stereo neural inference Monocular neural inference fused with stereo depth means that the neural net model is run on a single

OpenVINO is a toolkit specifically designed for deep learning neural net deployment.  It specifically targets hardware built with Intel chips, and just by coincidence it's development is funded by Intel.  So, it targets things like Intel CPUs, and the Intel Movidius VPU, found in such wonderful devices as the Neural Net Compute Stick, and the Vision Accelerator design.  And also OAK , which we are very excited about here at HTC and covered in a previous  post .

Hurray, team 'what is right and just'.  The patent on the SIFT algorithm has expired .  You may now use it in your for sale' software applications and hardware without fear from the threat of litigation. If you don't know what SIFT (scale-invariant feature transform) is, and profess to work in computer vision, get with the program.  David Lowe wrote a lot of great papers, but this is the one that launched an onslaught of additional referencing computer vision papers that used or at least acknowledged SIFT. Here is the original paper . Here are some additional David Lowe SIFT Keypoint Detector paper references . SIFT is part of a large number of different image 'feature point' detector algorithms. Feature points are also called  interest points  because your brain is apparently very interested in them. Feature or interest points are visually salient locations associated with an object in an image that can be used for all kinds of different purposes, includ

Today's HTC Seminar is by Andrej Karpathy on Visualizing and Understanding Recurrent Networks.  It was presented at the Deep Learning London Meetup, and can be seen here . The audio quality is not the greatest in this video, and i could not find it on you tube to embed directly into this post, but stick with watching it and you will learn quite a bit about how to build and use recurrent neural networks.  Including how to use them for sequential processing of a single image (building up sentence level descriptions of what objects are in an image would be an example of this). The talk covers the material presented in Andrej's web tutorial on The Unreasonable Effectiveness of Recurrent Neural Networks.  This tutorial and talk were put together in 2015, ancient times when it comes to the fast pace of neural network research, but the talk and associated tutorial are a great introduction to recurrent neural networks.  And the tutorial includes code examples you can run if you

Apple's world wide developer conference WWDC was held in June, as it is every year. Held virtually, which is quite unusual, but for the better i think. Since it's essentially impossible for anyone to actually get into WWDC these days, demand far outstrips the capacity of San Francisco or San Jose conference halls (augmented by the auditorium at the new giant spaceship headquarters in Cupertino). And mac developers wait anxiously every WWDC to see what surprises the mothership will drop onto their 'held in bondage and indentured servitude code serfs', i mean their application software developers.  I joke, but not really, because apple is notorious about never hesitating to provide their developers with new hurdles to climb if they want to keep their software running on the platform. And many times the hurdles themselves are in support of making the platform more proprietary and closed off from the rest of the world. And apple did not disappoint this year (WWDC recap

HTC is back for summer session. Lot's of fun high-tech developments and research to cover before we need to lock ourselves in the code dungeon again. And the first thing to focus on is a very new in-development open source inexpensive neural net AI embedded hardware platform.  Introducing the OAK, specifically OAK-1 and OAK-D. These 2 very inexpensive OAK hardware boards are tiny artificial intelligence (AI) and computer vision (CV) powerhouses, with OAK-D providing spatial AI leveraging stereo depth in addition to the 4K/30 12MP camera that both models share. They are supposedly absurdly easy to use, up and running in under 30 seconds. You can program your OAK's by just plugging them into your Mac or Ubuntu or Windows computer's USB port. OAK's modular, FCC/CE-approved, open-source hardware ecosystem can also be directly integrated into your products if you want to use them as embedded hardware in systems that you sell. OAK is currently a brand new kicksta