Theorists have suggested that emotions are canonical responses to situations ancestrally linked to survival. If so, then emotions may be afforded by features of the sensory environment. However, few computational models describe how combinations of stimulus features evoke different emotions. Here, we develop a convolutional neural network that accurately decodes images into 11 distinct emotion categories. We validate the model using more than 25,000 images and movies and show that image content is sufficient to predict the category and valence of human emotion ratings. In two functional magnetic resonance imaging studies, we demonstrate that patterns of human visual cortex activity encode emotion category–related model output and can decode multiple categories of emotional experience. These results suggest that rich, category-specific visual features can be reliably mapped to distinct emotions, and they are coded in distributed representations within the human visual system. Read More
Monthly Archives: July 2019
The AI Renaissance portrait generator isn't great at painting people of color
Surprise! Artificial intelligence-generated portraits based off artwork from 15th century Europe… kind of suck at depicting people of color.
Because we’re apparently always ready to hand over our photos for the sake of a trend, the internet’s current obsession is an AI portrait generator that deconstructs your selfies and rebuilds them as Renaissance and Baroque portraits.
Created by researchers at the MIT-IBM Watson AI Lab, AI Portrait Ars is a fun way to see how you would have been perceived if you lived in another time period.
“Portraits interpret the external beauty, social status, and then go beyond our body and face,” its creators wrote in the site’s “Why” section. “A portrait becomes a psychological analysis and a deep reflection on our existence.”
Unless, apparently, you’re not white. Read More
Facing your AI self at the ‘Neural Mirror’ art installation
Italian design studio Ultravioletto has created a mirror that lets you see yourself the way corporations see you: as a collection of data points. At first, the Neural Mirror installation (located at a former church in the Italian city of Spoleto), seems like an ordinary mirror. But after you’ve been duly scanned and processed (with the system estimating your age, sex and emotional state) you’ll quickly see something else; a ghostly vision of a machine’s idea of who you are. Read More
GLTR: Statistical Detection and Visualization of Generated Text
The rapid improvement of language models has raised the specter of abuse of text generation systems. This progress motivates the development of simple methods for detecting generated text that can be used by and explained to non-experts. We develop GLTR, a tool to support humans in detecting whether a text was generated by a model. GLTR applies a suite of baseline statistical methods that can detect generation artifacts across common sampling schemes. In a human-subjects study,we show that the annotation scheme provided by GLTR improves the human detection-rate of fake text from 54% to 72% without any prior training.GLTR is open-source and publicly deployed, and has already been widely used to detect generated outputs. Read More
America and China's Great AI Competition: What Is Driving It
For better or worse, the advancement and diffusion of artificial intelligence technology will come to define this century. Whether that statement should fill your soul with terror or delight remains a matter of intense debate. Techno-idealists and doomsdayers will paint their respective utopian and dystopian visions of machine-kind, making the leap from what we know now as “narrow AI” to “general AI” to surpass human cognition within our lifetime. On the opposite end of the spectrum, yawning skeptics will point to Siri’s slow intellect and the human instinct of Capt. Chesley “Sully” Sullenberger – the pilot of the US Airways flight that successfully landed on the Hudson River in 2009 – to wave off AI chatter as a heap of hype not worth losing sleep over.
The fact is that the development of AI – a catch-all term that encompasses neural networks and machine learning and deep learning technologies – has the potential to fundamentally transform civilian and military life in the coming decades. Regardless of whether you’re a businessperson pondering your next investment, an entrepreneur eyeing an emerging opportunity, a policymaker grappling with regulation or simply a citizen operating in an increasingly tech-driven society, AI is a global force that demands your attention. Read More
Deciphering China’s AI Dream
Marked by the State Council’s release of a national strategy for AI development in July 2017, China’s pursuit of AI has, arguably, been “the story” of the past year. Deciphering this story requires an understanding of the messy combination of two subjects, China and AI, both of which are already difficult enough to comprehend on their own. Toward that end, I outline the key features of China’s strategy to lead the world in AI and attempt to address a few misconceptions about China’s AI dream. Building off of the excellent reporting and analysis of others on China’s AI development, this report also draws on my translations of Chinese texts on AI policy, a compilation of metrics on China’s AI capabilities vis-à-vis other countries, and conversations with those who have consulted with Chinese companies and institutions involved in shaping the AI scene. Read More
China's AI "National Team" (Five Geese) Strategy
A “Collective Work Report” of the five major “National Team” members — BAT, iFlytek, and Sensetime
In November 2017, the Ministry of Science and Technology (MOST) assigned Baidu (autonomous driving), Alibaba (smart cities), Tencent (medical imaging), and iFlytek (intelligent voice) to lead the development of four national AI open innovation platforms; in September 2018, Sensetime (intelligent vision) was selected as the fifth member of this “national team” (guojiadui). This week’s feature translation is on a Leiphone report from an AI Expo held in Suzhou on May 10th, where each of the companies reported on the progress of their respective platforms (a “collective work report” of sorts). This fits with the Chinese government’s recognition of the importance of openness in spurring research and diffusion in the AI field, related to a previous white paper on AI Open Source Software, which we covered in a previous issue.
I find these “national team” platforms super interesting. Of course, they can tell us about leading Chinese companies in various domains of AI. But the contrast between a “national team” vs. “national champion” model may also be significant. Read More
Speech synthesis from neural decoding of spoken sentences — Article
Technology that translates neural activity into speech would be transformative for people who are unable to communicate as a result of neurological impairments. Decoding speech from neural activity is challenging because speaking requires very precise and rapid multi-dimensional control of vocal tract articulators. Here we designed a neural decoder that explicitly leverages kinematic and sound representations encoded in human cortical activity to synthesize audible speech. Recurrent neural networks first decoded directly recorded cortical activity into representations of articulatory movement, and then transformed these representations into speech acoustics. In closed vocabulary tests, listeners could readily identify and transcribe speech synthesized from cortical activity. Intermediate articulatory dynamics enhanced performance even with limited data. Decoded articulatory representations were highly conserved across speakers, enabling a component of the decoder to be transferrable across participants. Furthermore, the decoder could synthesize speech when a participant silently mimed sentences. These findings advance the clinical viability of using speech neuroprosthetic technology to restore spoken communication. Read More
Speech synthesis from neural decoding of spoken sentences
Why Chinese Artificial Intelligence Will Run The World
If you’ve been paying attention in the past year, it seems that all anyone can talk about is the coming artificial intelligence boom on the horizon. Whether it’s the Amazon, Google, or Facebook, everyone seems to be getting in on the AI game as fast as they can. And with good reason—they’re having to play catchup with the rapid growth of artificial intelligence in China.
The Rise of the BAT: Chinese Tech Giants Baidu, Alibaba, and Tencent
For the past few decades, China has developed a reputation as being the undisputed source of manufacturing for a whole host of well-established Western companies. Whether this bred Western complacency is debatable, but what is indisputable is that China has been diligently laying the groundwork to breakout into the tech world in its own right—with the power to start calling the shots on the world stage.
Three companies have been leading this charge: Baidu, Alibaba, and Tencent. To many in the West, these names aren’t familiar since their products and services have not made significant inroads into Western markets, but these three firms dominate the world’s 2nd largest economy. Read More
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