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How to Thwart Facial Recognition and Other Surveillance

WIRED

Facial-recognition tech can see around hoodies or big shades, so pair them with a face covering. Plus, you'll get protection against coronavirus particles and tear gas. There are makeup tutorials online for edgy face paint intended to trick face-recognizing algorithms, but these designs are unproven. Also, it's probably easier for humans to track you if you look like a member of Insane Clown Posse. Make yourself less memorable to both humans and machines by wearing clothing as dark and pattern-free as your commitment to privacy.


Angry Nerd: If You're Dating Online in a Pandemic, Ghost or Be Ghosted

WIRED

Please don't complain to me about literally anything if you've touched human flesh since March. Being very single, I have not, and my Grubhub guy doesn't want a hug. So I am doomed, instead, to online dating in the context of a pandemic. Let me walk you through the torture. It starts typically enough, with endless scrolling through profiles of now-offensively-irrelevant travel photos.


QAnon Is Like a Game--a Most Dangerous Game

WIRED

When QAnon emerged in 2017, the game designer Adrian Hon felt a shock of recognition. QAnon, as you very likely know, is the right-wing conspiracy theory that revolves around a figure named Q. This supposedly high-ranking insider claims that the deep state--an alleged cabal led by Barack Obama, Hillary Clinton, and George Soros and abetted by decadent celebrities--is running a global child-sex-trafficking ring and plotting a left-wing coup. Only Donald Trump heroically stands in the way. But what intrigued Hon was the style of nonsense.




Artificial Intelligence Advances Food Safety

#artificialintelligence

Landing AI is helping food producers overcome not only the limitations of their human workforce but of traditional machine vision as well, using machine …


Why do birds crash into solar panels?

#artificialintelligence

Billions of birds die annually from collisions with windows, communication towers, wind turbines, and other human-made objects. One reason is that birds see a reflection of the sky in the object and think they're flying into an unobstructed path. This is even a problem for solar panel facilities, which see up to 138,000 bird deaths per year in the US from collisions with equipment. Though damage to the solar panels is minimal, officials worry about the impact these structures have on local wildlife. To combat the problem, the Department of Energy (DOE) has awarded Argonne National Laboratory $1.3 million to develop a system that can automatically monitor bird activity.


Embedded computing development kit for artificial intelligence (AI)-based machine vision offered by AAEON

#artificialintelligence

TAIPEI, Taiwan – AAEON Technology in Taipei, Taiwan, and Aotu.ai in Santa Clara, Calif., are introducing the BrainFrame Edge AI Developers Kit (DevKit) for an Intel artificial intelligence (AI) computer to enable system integrators rapidly to create and deploy smart machine vision applications. The BrainFrame Edge AI DevKit helps create solutions such as machine vision-based access control, uniform compliance, manufacturing automation, and video analytics. BrainFrame scales and configures easily and enables a connected camera to become a continuously monitoring Smart Vision system. BrainFrame's automatic algorithm fusion and optimization engine has VisionCapsules, Aotu.ai's open source algorithm packaging format. These self-contained capsules have a negligible memory footprint and include all necessary code, files, and metadata to describe and implement a machine learning algorithm.


Exploring exploration: comparing children with RL agents in unified environments

AIHub

Despite recent advances in artificial intelligence (AI) research, human children are still by far the best learners we know of, learning impressive skills like language and high-level reasoning from very little data. Children's learning is supported by highly efficient, hypothesis-driven exploration: in fact, they explore so well that many machine learning researchers have been inspired to put videos like the one below in their talks to motivate research into exploration methods. However, because applying results from studies in developmental psychology can be difficult, this video is often the extent to which such research actually connects with human cognition. Why is directly applying research from developmental psychology to problems in AI so hard? For one, taking inspiration from developmental studies can be difficult because the environments that human children and artificial agents are typically studied in can be very different. Traditionally, reinforcement learning (RL) research takes place in grid-world-like settings or other 2D games, whereas children act in the real world which is rich and 3-dimensional.


Training our humans on the wrong dataset

#artificialintelligence

I really don't want to say that I've figured out the majority of what's wrong with modern education and how to fix it, BUT When we train (fit) any given ML model for a specific problem, on which we have a training dataset, there are several ways we go about it, but all of them involve using that dataset. Say we're training a model that takes a 2d image of some glassware and turn it into a 3d rendering. We have images of 2000 glasses from different angles and in different lighting conditions and an associated 3d model. How do we go about training the model? Well, arguable, we could start small then feed the whole dataset, we could use different sizes for test/train/validation, we could use cv to determine the overall accuracy of our method or decide it would take to long... etc But I'm fairly sure that nobody will ever say: I know, let's take a dataset of 2d images of cars and their 3d rendering and train the model on that first.