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The machine-learning method works on most mobile devices and could be expanded to assess other motor disorders outside of the doctor’s office.
This paper presents a comprehensive survey of the taxonomy and evolution of multimodal foundation models that demonstrate vision and vision-language capabilities, focusing on the transition from specialist models to general-purpose assistants. The research landscape encompasses five core topics...
Blockwise self-attentional encoder models have recently emerged as one promising end-to-end approach to simultaneous speech translation. These models employ a blockwise beam search with hypothesis reliability scoring to determine when to wait for more input speech before translating further. However...
Unsupervised multi-view representation learning has been extensively studied for mining multi-view data. However, some critical challenges remain. On the one hand, the existing methods cannot explore multi-view data comprehensively since they usually learn a common representation between views...
World’s biggest bat colony gathers in Zambia every year: we used artificial intelligence to count them
Gamification is a technological, economic, cultural, and societal development toward promoting a more game-like reality. As this emergent phenomenon has been gradually consolidated into our daily lives, especially in educational settings, many scholars and practitioners face a major challenge ahead...
An emerging line of work has sought to generate plausible imagery from touch. Existing approaches, however, tackle only narrow aspects of the visuo-tactile synthesis problem, and lag significantly behind the quality of cross-modal synthesis methods in other domains. We draw on recent advances in...
A rapidly growing number of voices have argued that AI research, and computer vision in particular, is closely tied to mass surveillance. Yet the direct path from computer vision research to surveillance has remained obscured and difficult to assess. This study reveals the Surveillance AI pipeline...
Twenty years ago, nanotechnology was the artificial intelligence of its time. The specific details of these technologies are, of course, a world apart. But the challenges of ensuring each technology’s responsible and beneficial development are surprisingly alike. Nanotechnology, which is…
Study shows users can be primed to believe certain things about an AI chatbot’s motives, which influences their interactions with the chatbot.
AI audits are an increasingly popular mechanism for algorithmic accountability; however, they remain poorly defined. Without a clear understanding of audit practices, let alone widely used standards or regulatory guidance, claims that an AI product or system has been audited, whether by first-...
The widespread adoption of large language models (LLMs) makes it important to recognize their strengths and limitations. We argue that in order to develop a holistic understanding of these systems we need to consider the problem that they were trained to solve: next-word prediction over Internet...
MIT engineers develop a long, curved touch sensor that could enable a robot to grasp and manipulate objects in multiple ways.
How do practitioners who develop consumer AI products
scope, motivate, and conduct privacy work? Respecting privacy is a key principle for developing ethical, human-centered
AI systems, but we cannot hope to better support practitioners
without answers to that question. We interviewed 35 industry...
Large language models (LLMs) are trained on massive internet corpora that often contain copyrighted content. This poses legal and ethical challenges for the developers and users of these models, as well as the original authors and publishers. In this paper, we propose a novel technique for...
NLP is in a period of disruptive change that is impacting our methodologies, funding sources, and public perception. In this work, we seek to understand how to shape our future by better understanding our past. We study factors that shape NLP as a field, including culture, incentives, and...
Some researchers see formal specifications as a way for autonomous systems to "explain themselves" to humans. But a new study finds that we aren't understanding.
NZ police are using AI to catch criminals – but the law urgently needs to catch up too
We propose a novel taxonomy for bias evaluation of discriminative foundation models, such as Contrastive Language-Pretraining (CLIP), that are used for labeling tasks. We then systematically evaluate existing methods for mitigating bias in these models with respect to our taxonomy. Specifically, we...
Long-tailed object detection (LTOD) aims to handle the extreme data imbalance in real-world datasets, where many tail classes have scarce instances. One popular strategy is to explore extra data with image-level labels, yet it produces limited results due to (1) semantic ambiguity -- an image-level...
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