A new report has claimed the Chinese government is now implementing cutting edge artificial intelligence to monitor the minds of dozens of Communist party officials.
Researchers in China claimed to have developed software that can acutely analyze facial expressions and brain waves to monitor if subjects were attentive to “thought and political education.”
China’s stringent police state has been radically upscaled over the past decade, using big data, machine learning, face recognition technology and artificial intelligence to build what many have labelled the world’s most complex digital dictatorship. ...
“On one hand, it can judge how party members have accepted thought and political education,” the article said. “On the other hand, it will provide real data for thought and political education so it can be improved and enriched.” ...
“Tapping on a person marked on the map reveals their personal information, including their full name, court-case number and the reason they have been labelled untrustworthy. Identity-card numbers and home addresses are also partially shown,” according to media reports.
There are also reports citizens whose social credit score falls too low are pre-emptively arrested and sent to re-education camps, not because they have committed a crime but because they are “likely to.” ...
In recent weeks, China has reportedly aimed at standardizing a rewards system to motivate the public to report crimes and boost their score. ...
Multi-modal AI is a new AI paradigm, in which various data types like image, text, speech and numerical data are combined with multiple intelligence processing algorithms to achieve higher performances. Multi-modal AI often outperforms single-modal AI in many real-world problems. ...
Multi-modal systems, with access to both sensory and linguistic modes of intelligence, process information the way humans do. ...
Multi-modal learning pieces together disjointed data into a single model. Since multiple sensors are used to observe the same data, multi-modal learning offers more dynamic predictions compared to a unimodal system processing more datasets translates to more intelligent insights. The ability to process multi-modal dataconcurrently is vital for advancements in AI. ...
DALL.E: It is an AI program developed by OpenAI that creates digital images from textual descriptions.
FLAVA: It is a multimodal model trained by Meta over images and 35 different languages.
NUWA: This model is trained on images, videos, and text, and given a text prompt or sketch, it can predict the next video frame and fill in incomplete images.
MURAL: It is a digital workspace for visual collaboration and helps everyone on the team imagine together to unlock new ideas, and solve hard problems.
ALIGN: It is an AI model trained by Google over a noisy dataset of a large number of image-text pairs.
CLIP: It is a multimodal AI system developed by OpenAI to successfully perform a wide set of visual recognition tasks.
Florence: It is released by Microsoft research and is capable of modeling space, time, and modality.
... The first model is a set of deep neural networks which predict respondents’ chronological age and psychological well-being over 10 years using information from psychological surveys. This model depicts the trajectories of the human mind as it ages. It also shows that the ability to form meaningful connections, as well as mental autonomy and environmental mastery, develop with age. He also notes that the focus on personal progress is constantly decreasing, but the sense of having a purpose in life fades after only 40-50 years. These findings add to the growing body of knowledge about social and emotional selectivity and tasteful adaptation in the context of adult personality development. ...
The second model is a self-organizing map created to serve as the basis for a recommendation engine for mental health applications. This unsupervised learning algorithm divides all responders into groups depending on the likelihood of developing depression and identifies the shortest path toward a set of mental stability for any individual. Alex Zhavoronkov, Chief Sustainability Officer at Deep Longevity, explains, “Existing mental health apps offer general advice that applies to everyone yet doesn’t work for anyone. We’ve built a scientifically sound system that provides ultra-customization.” ...
“This study provides an intriguing perspective on psychological age, future well-being, and depression risk, and demonstrates new application of machine learning approaches to mental health issues. It also broadens our view of aging and shifts across life stages and emotional states.” ...
... A recent challenge issued by NASA’s Spacesuit User Interface Technologies for Students (SUITS), launched in 2017, tasked students with designing and creating spacesuit information displays that share vitals, location and more within augmented reality environments.
USC’s and University of Arizona’s “Team Aegis” was one of 10 finalists in the SUITS challenge. Their display uses the Microsoft HoloLens 2 platform, a hands-free holographic device, and would host AR applications through the Unity game engine. For an astronaut exploring the moon, the user-friendly interface would share crucial information such as location tracking of the astronaut and rover, and vitals such as heart rate and oxygen levels. ...
Founded by Darlene Villicaña, a USC visual anthropology master’s student researching extravehicular interfaces, Team Aegis began as a group of 11 students. In December 2021, after its proposal was accepted by the NASA SUITS program, the team grew quickly to over 60 students from multiple disciplines and experience levels. Advised by USC Viterbi Professors David Barnhart and Garrett Reisman — as well as University of Arizona Assistant Professor Jessica Barnes — the students created an ambitious set of goals, a schedule and smaller teams that could work together to meet milestones. ...
Garrett Reisman, a former NASA astronaut, said, “Our USC/Arizona team did a great job and learned some very important real-world engineering lessons. Plus we had the best sizzle-reel video. ...
... As architectural designer Xiaohang Hou outlined, the infinite nature of XR space allows people to interact with the virtual world through the real world, changing the demand for public space and making urban design more complex. "When people perceive metaverse architecture as an extension of the real world, it influences their perceptions and expectations, causing a revolution in the real world." ...
Fologram has recently built the world’s first pavilion-scale steel structure using the HoloLens, displaying the possibilities of integrating standard CAD workflow with augmented reality. By displaying the generative design model through holographic instructions rather than traditional 2D drawings, it explores the potential of revolutionizing the bridge between design and construction. ...
... The image is just that, with paintings of two crowned raccoons — one wearing what looks like a yellow dress, the other in a blue-and-gold jacket — in ornate gold frames. But as Holland Michel noted, the raccoons are sporting Western-style royal outfits, even though the prompt didn't specify anything about how they should appear beyond looking "royal."
Even such "subtle" manifestations of bias are dangerous, Holland Michel said.
"In not being flagrant, they're really hard to catch," he said.
Today I report news that I’m pretty excited to write about: the CEO of Mojo Vision has worn in his eye a functioning smart contact lens and used it for around one hour. ...
Artificial intelligence poses a lot of ethical risks to businesses: It may promote bias, lead to invasions of privacy, and in the case of self-driving cars, even cause deadly accidents. Because AI is built to operate at scale, when a problem occurs, the impact is huge. Consider the AI that many health systems were using to spot high-risk patients in need of follow-up care. Researchers found that only 18% of the patients identified by the AI were Black—even though Black people accounted for 46% of the sickest patients. And the discriminatory AI was applied to at least 100 million patients.
The sources of problems in AI are many. For starters, the data used to train it may reflect historical bias. The health systems’ AI was trained with data showing that Black people received fewer health care resources, leading the algorithm to infer that they needed less help. The data may undersample certain subpopulations. Or the wrong goal may be set for the AI. Such issues aren’t easy to address, and they can’t be remedied with a technical fix. You need a committee—comprising ethicists, lawyers, technologists, business strategists, and bias scouts—to review any AI your firm develops or buys to identify the ethical risks it presents and address how to mitigate them. This article describes how to set up such a committee effectively.
... Research teams at the University of Tokyo, Keio University and Toyohashi University of Technology in Japan have developed a virtual robotic limb system which can be operated by users’ feet in a virtual environment as extra, or supernumerary, limbs. After training, users reported feeling like the virtual robotic arms had become part of their own body. This study focused on the perceptual changes of the participants, understanding of which can contribute to designing real physical robotic supernumerary limb systems that people can use naturally and freely just like our own bodies. ...
... this research as a way to explore the limits of human “plasticity” — in other words, our brain’s ability to alter and adapt to external and internal changes. ...
Participants wore a head-mounted display to give them a first-person view of their own arms represented in VR, as well as the additional virtual robotic arms. They then had to perform tasks using only the virtual robotic arms, which were controlled by moving their toes. Tactile devices returned sensations from the virtual robotic arms to the tops and soles of their feet when they touched an object, like a virtual ball. ...
The team also found that the participant’s “peripersonal space” (the area around our bodies which we perceive as being our personal space) extended to include the area around the virtual robotic arms. ...
Next, the team wants to look at the potential for cooperative behavior between participants’ own arms in virtual reality and the virtual robotic arms. ...
... Earlier studies have shown that facial micro-expressions are more reliable than facial macro-expressions for revealing emotions. They are subtle, involuntary movements responding to external stimuli that cannot be controlled. This paper proposes using facial micro-expressions combined with brain and physiological signals to more reliably detect underlying emotions. We describe our models for measuring arousal and valence levels from a combination of facial micro-expressions, Electroencephalography (EEG) signals, galvanic skin responses (GSR), and Photoplethysmography (PPG) signals. We then evaluate our model using the DEAP dataset and our own dataset based on a subject-independent approach. Lastly, we discuss our results, the limitations of our work, and how these limitations could be overcome. We also discuss future directions for using facial micro-expressions and physiological signals in emotion recognition. ...
