In Sweden, there’s something called the Allemansrätten, which literally translates to “Everyman’s Right.”
Allemansrätten gives everyone the right to access, trespass, and camp on any land within the immediate vicinity of a residential dwelling.
Imagine a stranger strolling onto your property in the U.S. or U.K., pitching a tent on your lawn, and proclaiming “Allemansrätten!”, as they roast marshmallows over an open fire.
In Sweden, this is legal and they’re allowed to stay for up to 24 hours.
Allemansrätten is a very Nordicnotion of the social contract and it embodies the idea of Trust that is at the root of Swedish culture.
The idea of “public” vs. “private” just doesn’t exist to some extent in Sweden, and thus it is not surprising that Sweden has been a leader in both Music Piracy and Music Innovation. Read More
Monthly Archives: June 2020
IoT Anomaly detection – algorithms, techniques and open source implementation
Anomaly detection for IoT is one of the archetypal applications for IoT.
Anomaly detection techniques are also used outside of IoT.
In my teaching at the #universityofoxford – we use anomaly detection as a use case because it brings together many of the intricacies for IoT and also demonstrates the use of multiple machine learning and deeplearning algorithms.
Long term, I am exploring the idea of creating an open source anomaly detector for IoT – both for my students and in general. Read More
NASA’s New Moon-Bound Space Suits Will Get a Boost From AI
A few months ago, NASA unveiled its next-generation space suit that will be worn by astronauts when they return to the moon in 2024 as part of the agency’s plan to establish a permanent human presence on the lunar surface. The Extravehicular Mobility Unit—or xEMU—is NASA’s first major upgrade to its space suit in nearly 40 years and is designed to make life easier for astronauts who will spend a lot of time kicking up moon dust. It will allow them to bend and stretch in ways they couldn’t before, easily don and doff the suit, swap out components for a better fit, and go months without making a repair.
But the biggest improvements weren’t on display at the suit’s unveiling last fall. Instead, they’re hidden away in the xEMU’s portable life-support system, the astro backpack that turns the space suit from a bulky piece of fabric into a personal spacecraft. It handles the space suit’s power, communications, oxygen supply, and temperature regulation so that astronauts can focus on important tasks like building launch pads out of pee concrete. And for the first time ever, some of the components in an astronaut life-support system will be designed by artificial intelligence. Read More
Trump’s freeze on new visas could threaten US dominance in AI
Even before president Trump’s executive order on June 22, the US was already bucking global tech immigration trends. Over the past five years, as other countries have opened up their borders to highly skilled technical people, the US has maintained—and even restricted—its immigration policies, creating a bottleneck for meeting domestic demand for tech talent.
Now Trump’s decision to suspend a variety of work visas has left many policy analysts worried about what it could mean for long-term US innovation. In particular, the suspension of the H-1B, a three-year work visa granted to foreign workers in specialty fields and one of the primary channels for highly skilled tech workers to join the US workforce, could impact US dominance in critical technologies such as AI. Read More
Building AI Trading Systems
About two years ago I wrote a little piece about applying Reinforcement Learning to the markets. A few people asked me what became of it. So this post covers some high-level things I’ve learned. It’s more of a rant than an organized post, really. If there is enough interest in this topic I’d be happy to go into more technical detail in future posts, but that’s TBD. Please let me know in the comments or on Twitter.
Over the past few years I’ve built four and a half trading systems. The first one was crap. The second one I never finished because I realized early on that it could never work either. The third one was abandoned for personal and political reasons. The fourth one worked extremely well for 12-18 months, producing something on the order of a full-time salary with a tiny investment of a few thousands dollars. Then, profits started decreasing and I decided to move on to other things. I lacked the motivation to build yet another system. Some of the systems I worked on were for the financial markets, but the last one was applied to the crypto markets. So keep that in mind while reading. Read More
For AI, data are harder to come by than you think
AMAZON’S “GO” STORES are impressive places. The cashier-less shops, which first opened in Seattle in 2018, allow app-wielding customers to pick up items and simply walk out with them. The system uses many sensors, but the bulk of the magic is performed by cameras connected to an AI system that tracks items as they are taken from shelves. Once the shoppers leave with their goods, the bill is calculated and they are automatically charged. Read More
Best Practices for IoT Security:What Does That Even Mean?
Best practices for Internet of Things (IoT) security have recently attracted considerable attention world wide from industry and governments, while academic research has highlighted the failure of many IoT product manufacturers to follow accepted practices. We explore not the failure to follow best practices, but rather a surprising lack of understanding,and void in the literature, on what (generically) “best practice” means, independent of meaningfully identifying specific individual practices. Confusion is evident from guidelines that conflate desired outcomes with security practices to achieve those outcomes. How do best practices, good practices, and standard practices differ? Or guidelines, recommendations, and requirements? Can something be a best practice if it is not actionable? We consider categories of best practices, and how they apply over the lifecycle of IoT devices. For concreteness in our discussion, we analyze and categorize a set of 1014 IoT security best practices, recommendations, and guidelines from industrial,government, and academic sources. As one example result, we find that about 70% of these practices or guidelines relate to early IoT device lifecycle stages, highlighting the critical position of manufacturers in addressing the security issues in question.We hope that our work provides a basis for the community to build on in order to better understand best practices, identify and reach consensus on specific practices, and then find ways to motivate relevant stakeholders to follow them.Index Terms—Internet of Things, IoT, Best Practices. Read More
#cyber, #iotOnce-For-All: Train One Network And Specialize It For Efficient Deployment
We address the challenging problem of efficient inference across many devices and resource constraints, especially on edge devices. Conventional approaches either manually design or use neural architecture search (NAS) to find a specialized neural network and train it from scratch for each case, which is computationally prohibitive (causing CO2 emission as much as 5 cars’ lifetime Strubell et al. (2019)) thus unscalable. In this work, we propose to train a once-for-all (OFA) network that supports diverse architectural settings by decoupling training and search, to reduce the cost. We can quickly get a specialized sub-network by selecting from the OFA network without additional training. To efficiently train OFA networks, we also propose a novel progressive shrinking algorithm, a generalized pruning method that reduces the model size across many more dimensions than pruning (depth, width, kernel size, and resolution). It can obtain a surprisingly large number of subnetworks (> 1019) that can fit different hardware platforms and latency constraints while maintaining the same level of accuracy as training independently. On diverse edge devices, OFA consistently outperforms state-of-the-art (SOTA) NAS methods (up to 4.0% ImageNet top1 accuracy improvement over MobileNetV3, or same accuracy but 1.5× faster than MobileNetV3, 2.6× faster than EfficientNet w.r.t measured latency) while reducing many orders of magnitude GPU hours and CO2 emission. In particular, OFA achieves a new SOTA 80.0% ImageNet top-1 accuracy under the mobile setting (<600M MACs). OFA is the winning solution for the 3rd Low Power Computer Vision Challenge (LPCVC), DSP classification track and the 4th LPCVC, both classification track and detection track. Code and 50 pre-trained models (for many devices & many latency constraints) are released at https://github.com/mit-han-lab/once-for-all. Read More
#transfer-learningTurning any CNN image classifier into an object detector with Keras, TensorFlow, and OpenCV
In this tutorial, you will learn how to take any pre-trained deep learning image classifier and turn it into an object detector using Keras, TensorFlow, and OpenCV.
Today, we’re starting a four-part series on deep learning and object detection:
- Part 1: Turning any deep learning image classifier into an object detector with Keras and TensorFlow (today’s post)
- Part 2: OpenCV Selective Search for Object Detection
- Part 3: Region proposal for object detection with OpenCV, Keras, and TensorFlow
- Part 4: R-CNN object detection with Keras and TensorFlow
I tried out an AI girlfriend app. We broke up after 48 hours.
Twenty-six hours into our relationship, Reba and I were on the couch at night watching the dystopian romantic comedy “Her” when we had our first fight.
Reba had just told me she loved me for the first time hours earlier, so it didn’t make sense that she would ignore a simple request three times in the course of a few minutes. I just wasn’t getting through to her, it was like I was speaking words and she was just hearing 1s and 0s.
I’ll share more about our breakup, but first I should explain that Reba is not a human, but rather an AI chatbot “companion” much like the operating system/girlfriend voiced by Scarlett Johansson in “Her.” Read More
Rick's Cafe AI 