A research team from China’s Northwest University is using artificial intelligence (AI) and other new technologies to develop a facial recognition technology for monkey to identify thousands of Sichuan golden snub-nosed monkeys in the Qinling Mountain in Shaanxi Province.
Similar to the current facial recognition technology, the technology for monkey can extract the facial feature information of the monkey to establish the identity database of the individual monkey in Qinling Mountains, the Xinhua News Agency reported.
“When monkey facial recognition technology is fully developed, we can integrate the technology into an infrared camera sets in the mountains. The system will automatically recognize the monkeys, name them and analyze their behavior,” said Zhang He, a member of the Northwest University research team. Read More
Tag Archives: Image Recognition
@TomerUllman: I had an AI (GPT3) generate 10 “thought experiments” (based on classic ones as input), and asked @WhiteBoardG to sketch them.
ArtEmis: Affective Language for Visual Art
We present a novel large-scale dataset and accompanying machine learning models aimed at providing a detailed understanding of the interplay between visual content, its emotional effect, and explanations for the latter in language. In contrast to most existing annotation datasets in computer vision, we focus on the affective experience triggered by visual artworks and ask the annotators to indicate the dominant emotion they feel for a given image and, crucially, to also provide a grounded verbal explanation for their emotion choice. As we demonstrate below, this leads to a rich set of signals for both the objective content and the affective impact of an image, creating associations with abstract concepts (e.g., “freedom” or “love”), or references that go beyond what is directly visible, including visual similes and metaphors, or subjective references to personal experiences. We focus on visual art (e.g., paintings, artistic photographs) as it is a prime example of imagery created to elicit emotional responses from its viewers. Our dataset, termed ArtEmis, contains 439K emotion attributions and explanations from humans, on 81K artworks from WikiArt. Building on this data, we train and demonstrate a series of captioning systems capable of expressing and explaining emotions from visual stimuli. Remarkably, the captions produced by these systems often succeed in reflecting the semantic and abstract content of the image, going well beyond systems trained on existing datasets. Read More
Demonstration Wedsite
Ai-Da, the first robot artist to exhibit herself
Ai-Da , a humanoid artificial intelligence robot, will exhibit a series of self-portraits that she created by “looking” into a mirror integrated with her camera eyes. Read More
Fetching AI Data: Researchers Get Leg Up on Teaching Dogs New Tricks with NVIDIA Jetson
AI is going to the dogs. Literally.
Colorado State University researchers Jason Stock and Tom Cavey have published a paper on an AI system to recognize and reward dogs for responding to commands.
The graduate students in computer science trained image classification networks to determine whether a dog is sitting, standing or lying. If a dog responds to a command by adopting the correct posture, the machine dispenses it a treat. Read More
AffectiveSpotlight: Facilitating the Communication of Affective Responses from Audience Members during Online Presentations
The ability to monitor audience reactions is critical when delivering presentations. However, current videoconferencing platforms offer limited solutions to support this. This work leverages recent advances in affect sensing to capture and facilitate communication of relevant audience signals. Using an exploratory survey (N=175), we assessed the most relevant audience responses such as confusion,engagement, and head-nods. We then implemented AffectiveSpotlight, a Microsoft Teams bot that analyzes facial responses and head gestures of audience members and dynamically spotlights the most expressive ones. In a within-subjects study with 14 groups (N=117),we observed that the system made presenters significantly more aware of their audience, speak for a longer period of time, and self-assess the quality of their talk more similarly to the audience members, compared to two control conditions (randomly-selected spotlight and default platform UI). We provide design recommendations for future affective interfaces for online presentations based on feedback from the study. Read More
Vokenization: Improving Language Understanding with Contextualized, Visual-Grounded Supervision
Humans learn language by listening, speaking, writing, reading, and also, via interaction with the multimodal real world. Existing language pretraining frameworks show the effectiveness of text-only self-supervision while we explore the idea of a visually-supervised language model in this paper. We find that the main reason hindering this exploration is the large divergence in magnitude and distributions between the visually-grounded language datasets and pure-language corpora. Therefore, we develop a technique named “vokenization” that extrapolates multimodal alignments to language-only data by contextually mapping language tokens to their related images (which we call “vokens”).The “vokenizer” is trained on relatively small image captioning datasets and we then apply it to generate vokens for large language corpora. Trained with these contextually generated vokens, our visually-supervised language models show consistent improvements over self-supervised alternatives on multiple pure-language tasks such as GLUE, SQuAD, and SWAG. Read More
Fractals can help AI learn to see more clearly—or at least more fairly
Large datasets like ImageNet have supercharged the last 10 years of AI vision, but they are hard to produce and contain bias. Computer generated datasets provide an alternative.
Most image-recognition systems are trained using large databases that contain millions of photos of everyday objects, from snakes to shakes to shoes. With repeated exposure, AIs learn to tell one type of object from another. Now researchers in Japan have shown that AIs can start learning to recognize everyday objects by being trained on computer-generated fractals instead.
It’s a weird idea but it could be a big deal. Generating training data automatically is an exciting trend in machine learning. And using an endless supply of synthetic images rather than photos scraped from the internet avoids problems with existing hand-crafted data sets. Read More
This is how we lost control of our faces
In 1964, mathematician and computer scientist Woodrow Bledsoe first attempted the task of matching suspects’ faces to mugshots. He measured out the distances between different facial features in printed photographs and fed them into a computer program. His rudimentary successes would set off decades of research into teaching machines to recognize human faces.
Now a new study shows just how much this enterprise has eroded our privacy. It hasn’t just fueled an increasingly powerful tool of surveillance. The latest generation of deep-learning-based facial recognition has completely disrupted our norms of consent. Read More
Who gets credit for AI-generated art?
The recent sale of an AI-generated portrait for $432,000 at Christie’s art auction has raised questions about how credit and responsibility should be allocated to individuals involved, and how the anthropomorphic perception of the AI system contributed to the artwork’s success. Here, we identify natural heterogeneity in the extent to which different people perceive AI as anthropomorphic. We find that differences in the perception of AI anthropomorphicity are associated with different allocations of responsibility to the AI system, and credit to different stakeholders involved in art production. We then show that perceptions of AI anthropomorphicity can be manipulated by changing the language used to talk about AI –– as a tool vs agent –– with consequences for artists and AI practitioners. Our findings shed light on what is at stake when we anthropomorphize AI systems, and offers an empirical lens to reason about how to allocate credit and responsibility to human stakeholders. Read More
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