YouTube AI deletes war crime videos as ‘extremist material’
YouTube is facing criticism after a new artificial intelligence program monitoring “extremist” content began flagging and removing masses of videos and blocking channels that document war crimes in the Middle East.
Middle East Eye, the monitoring organisation Airwars and the open-source investigations site Bellingcat are among a number of sites that have had videos removed for breaching YouTube’s Community Guidelines.
The removals began days after Google, which owns YouTube, trumpeted the arrival of an artificial intelligence program that it said could spot and flag “extremist” videos without human involvement.
But since then vast tracts of footage, including evidence used in the Chelsea Manning court case and videos documenting the destruction of ancient artifacts by Islamic State, have been flagged as “extremist” and deleted from its archives.
Why Everyone Is Hating on IBM Watson—Including the People Who Helped Make It
The interview was representative of IBM’s marketing strategy: promote Watson as a world-changing technology, describe it in quixotic ways, then let reporters run wild with clickbait-y headlines about a robot helping humans do their jobs—for example: “IBM’s Watson Supercomputer May Soon Be The Best Doctor In The World,” “IBM’s Watson Helped Design Karolina Kurkova’s Light-Up Dress for the Met Gala.”
[…]
Perlich, who knew the team that built the original Watson, said she has a “truly deep respect for the technology” in that Watson computer. “I knew the components in the technology very well,” she said. “The reality however is what Watson as a commercial product represents is very different, even from a technology perspective, from Watson who won Jeopardy!”
DeepMind and Blizzard open StarCraft II as an AI research environment
Testing our agents in games that are not specifically designed for AI research, and where humans play well, is crucial to benchmark agent performance. That is why we, along with our partner Blizzard Entertainment, are excited to announce the release of SC2LE, a set of tools that we hope will accelerate AI research in the real-time strategy game StarCraft II.
New AI algorithm monitors sleep with radio waves
To make it easier to diagnose and study sleep problems, researchers at MIT and Massachusetts General Hospital have devised a new way to monitor sleep stages without sensors attached to the body. Their device uses an advanced artificial intelligence algorithm to analyze the radio signals around the person and translate those measurements into sleep stages: light, deep, or rapid eye movement (REM).
“Imagine if your Wi-Fi router knows when you are dreaming, and can monitor whether you are having enough deep sleep, which is necessary for memory consolidation,” says Dina Katabi, the Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science, who led the study. “Our vision is developing health sensors that will disappear into the background and capture physiological signals and important health metrics, without asking the user to change her behavior in any way.”
[…]
The researchers will present their paper at the International Conference on Machine Learning on Aug. 9.
How to train your own Object Detector with TensorFlow’s Object Detector API
This is a follow-up post on “Building a Real-Time Object Recognition App with Tensorflow and OpenCV” where I focus on training my own classes. Specifically, I trained my own Raccoon detector on a dataset that I collected and labeled by myself. The full dataset is available on my Github repo.
One of the bots, named BabyQ, made by the Beijing-based company Turing Robot, was asked, “Do you love the Communist Party?” To which it replied simply, “No.” Another bot named XiaoBing, which is developed by Microsoft, told users, “My China dream is to go to America.” When the bot was then quizzed on its patriotism, it dodged the question and replied, “I’m having my period, wanna take a rest.”
🤔
In two new papers, we describe a new family of approaches for imagination-based planning. We also introduce architectures which provide new ways for agents to learn and construct plans to maximise the efficiency of a task. These architectures are efficient, robust to complex and imperfect models, and can adopt flexible strategies for exploiting their imagination.
The agents we introduce benefit from an ‘imagination encoder’- a neural network which learns to extract any information useful for the agent’s future decisions, but ignore that which is not relevant.
AI Is Inventing Languages Humans Can’t Understand. Should We Stop It?
This conversation occurred between two AI agents developed inside Facebook. At first, they were speaking to each other in plain old English. But then researchers realized they’d made a mistake in programming.
“There was no reward to sticking to English language,” says Dhruv Batra, visiting research scientist from Georgia Tech at Facebook AI Research (FAIR). As these two agents competed to get the best deal–a very effective bit of AI vs. AI dogfighting researchers have dubbed a “generative adversarial network”–neither was offered any sort of incentive for speaking as a normal person would. So they began to diverge, eventually rearranging legible words into seemingly nonsensical sentences.
“Agents will drift off understandable language and invent codewords for themselves,” says Batra, speaking to a now-predictable phenomenon that Facebook as observed again, and again, and again. “Like if I say ‘the’ five times, you interpret that to mean I want five copies of this item. This isn’t so different from the way communities of humans create shorthands.”
[…]
So should we let our software do the same thing? Should we allow AI to evolve its dialects for specific tasks that involve speaking to other AIs? To essentially gossip out of our earshot? Maybe; it offers us the possibility of a more interoperable world, a more perfect place where iPhones talk to refrigerators that talk to your car without a second thought.
The tradeoff is that we, as humanity, would have no clue what those machines were actually saying to one another.
NoScope: 1000x Faster Deep Learning Queries over Video
Recent advances in deep learning enable automated analysis of this growing amount of video data, allowing us to query for objects of interest, detect unusual and abnormal events, and sift through lifetimes of video that no human would ever want to watch. However, these deep learning methods are extremely computationally expensive: state-of-the-art methods for object detection run at 10-80 frames per second on a state-of-the-art NVIDIA P100 GPU. This is fine for one video, but it is untenable for real deployments at scale; to put these computational overheads in context, it would cost over $5 billion USD in hardware alone to analyze all the CCTVs in the UK in real time.
To address this enormous gap between our ability to acquire video and the cost to analyze it, we’ve built a system called NoScope, which is able to process video feeds thousands of times faster compared to current methods. Our key insight is that video is highly redundant, containing a large amount of temporal locality (i.e., similarity in time) and spatial locality (i.e., similarity in appearance in a scene). To harness this locality, we designed NoScope from the ground up for the task of efficiently processing video streams. Employing a range of video-specific optimizations that exploit video locality dramatically reduces NoScope’s amount of computation over each frame—while still retaining high accuracy for common queries.
Meow Generator
I experimented with generating faces of cats using Generative adversarial networks (GAN). I wanted to try DCGAN, WGAN and WGAN-GP in low and higher resolutions. I used the CAT dataset (yes this is a real thing!) for my training sample. This dataset has 10k pictures of cats. I centered the images on the kitty faces and I removed outliers (I did this from visual inspection, it took a couple of hours…). I ended up with 9304 images bigger than 64 x 64 and 6445 images bigger than 128 x 128.
kreuzaderny