Rescued from the dusty interior of the Qumran Caves in 1947, the Dead Sea Scrolls contain the oldest manuscripts of the Old Testament and are a crucial piece of Biblical history that dates back to the 4th century BCE.
But despite these scrolls’ status as an unmovable piece of religious history, there are still many things that scholars don’t really know about their origin. For example, who actually wrote them down?More like thisInnovation4.16.2021 9:00 AMNASA’s InSight crisis reveals the most difficult part of exploring MarsBy Dave GershgornInnovation4.18.2021 8:00 AMRobotic lawnmowers could cut a huge swath in air pollutionBy Sarah WellsInnovation4.11.2021 8:00 AMCreepy robot skin answers 3 questions about the futureBy Sarah WellsEARN REWARDS & LEARN SOMETHING NEW EVERY DAY.
Using artificial intelligence and pattern recognition, a team of paleographers (scientists who study ancient handwriting) and computer scientists from the University of Groningen have now discovered hidden details in these scrolls that point toward not just one scribe, but two original scribes.
The research was published Wednesday in the journal PLOS One. Read More
Tag Archives: Image Recognition
Democratising deep learning for microscopy with ZeroCostDL4Mic
Deep Learning (DL) methods are powerful analytical tools for microscopy and can outperform conventional image processing pipelines. Despite the enthusiasm and innovations fueled by DL technology, the need to access powerful and compatible resources to train DL networks leads to an accessibility barrier that novice users often find difficult to overcome.Here, we present ZeroCostDL4Mic, an entry-level platform simplifying DL access by lever-aging the free, cloud-based computational resources of Google Colab. ZeroCostDL4Micallows researchers with no coding expertise to train and apply key DL networks to perform tasks including segmentation (using U-Net and StarDist), object detection (using YOLOv2), denoising (using CARE and Noise2Void), super-resolution microscopy (using Deep-STORM),and image-to-image translation (using Label-free prediction – fnet, pix2pix and CycleGAN). Importantly, we provide suitable quantitative tools for each network to evaluate model performance, allowing model optimisation. We demonstrate the application of the platform to study multiple biological processes. Read More
Geoffrey Hinton has a hunch about what’s next for AI
A decade ago, the artificial-intelligence pioneer transformed the field with a major breakthrough. Now he’s working on a new imaginary system named GLOM.
Back in November, the computer scientist and cognitive psychologist Geoffrey Hinton had a hunch. After a half-century’s worth of attempts—some wildly successful—he’d arrived at another promising insight into how the brain works and how to replicate its circuitry in a computer.
“It’s my current best bet about how things fit together,” Hinton says from his home office in Toronto, where he’s been sequestered during the pandemic. If his bet pays off, it might spark the next generation of artificial neural networks—mathematical computing systems, loosely inspired by the brain’s neurons and synapses, that are at the core of today’s artificial intelligence. His “honest motivation,” as he puts it, is curiosity. But the practical motivation—and, ideally, the consequence—is more reliable and more trustworthy AI.
A Google engineering fellow and cofounder of the Vector Institute for Artificial Intelligence, Hinton wrote up his hunch in fits and starts, and at the end of February announced via Twitter that he’d posted a 44-page paper on the arXiv preprint server. He began with a disclaimer: “This paper does not describe a working system,” he wrote. Rather, it presents an “imaginary system.” He named it, “GLOM.” The term derives from “agglomerate” and the expression “glom together.” Read More
Self-Supervised Equivariant Scene Synthesis from Video
We propose a self-supervised framework to learn scene representations from video that are automatically delineated into background, characters, and their animations. Our method capitalizes on moving characters being equivariant with respect to their transformation across frames and the background being constant with respect to that same transformation. After training, we can manipulate image encodings in real time to create unseen combinations of the delineated components. As far as we know, we are the first method to perform unsupervised extraction and synthesis of interpretable background, character, and animation. We demonstrate results on three datasets: Moving MNIST with backgrounds, 2D video game sprites, and Fashion Modeling. Read More
#image-recognitionA Study of Face Obfuscation in ImageNet
Face obfuscation (blurring, mosaicing, etc.) has been shown to be effective for privacy protection; nevertheless, object recognition research typically assumes access to complete, unobfuscated images. In this paper, we explore the effects of face obfuscation on the popular ImageNet challenge visual recognition benchmark. Most categories in the ImageNet challenge are not people categories; however, many incidental people appear in the images, and their privacy is a concern. We first annotate faces in the dataset. Then we demonstrate that face blurring—a typical obfuscation technique—has minimal impact on the accuracy of recognition models. Concretely, we benchmark multiple deep neural networks on face-blurred images and observe that the overall recognition accuracy drops only slightly (≤0.68%). Further,we experiment with transfer learning to 4 downstream tasks (object recognition, scene recognition, face attribute classification, and object detection) and show that features learned on face-blurred images are equally transferable. Our work demonstrates the feasibility of privacy-aware visual recognition, improves the highly-used ImageNet challenge benchmark,and suggests an important path for future visual datasets. Read More
Can AI read your emotions? Try it for yourself
Emotion recognition AI is bunk.
Don’t get me wrong, AI that recognizes human sentiment and emotion can be very useful. For example, it can help identify when drivers are falling asleep behind the wheel. But what it cannot do, is discern how a human being is actually feeling by the expression on their face.
You don’t have to take my word for it, you can try it yourself here. Read More
I Dream My Painting and I Paint My Dream
Dutch photographer Bas Uterwijk used artificial intelligence to create a realistic portrait of Vincent van Gogh on van Gogh’s 168th birthday.
The neural network has completed the faces of people from the past. The surviving portraits.
Big Self-Supervised Models Advance Medical Image Classification
Self-supervised pretraining followed by supervised fine-tuning has seen success in image recognition, especially when labeled examples are scarce, but has received limited attention in medical image analysis. This paper studies the effectiveness of self-supervised learning as a pretraining strategy for medical image classification. We conduct experiments on two distinct tasks: dermatology skin condition classification from digital camera images and multi-label chest X-ray classification, and demonstrate that self-supervised learning on ImageNet, followed by additional self-supervised learning on unlabeled domain-specific medical images significantly improves the accuracy of medical image classifiers. We introduce a novel Multi-Instance Contrastive Learning (MICLe) method that uses multiple images of the underlying pathology per patient case, when available, to construct more informative positive pairs for self-supervised learning. Combining our contributions, we achieve an improvement of 6.7% in top-1 accuracy and an improvement of 1.1% in mean AUC on dermatology and chest X-ray classification respectively, outperforming strong supervised baselines pretrained on ImageNet. In addition, we show that big self-supervised models are robust to distribution shift and can learn efficiently with a small number of labeled medical images. Read More
Bottleneck Transformers for Visual Recognition
We present BoTNet, a conceptually simple yet powerful backbone architecture that incorporates self-attention for multiple computer vision tasks including image classification, object detection and instance segmentation. By just replacing the spatial convolutions with global self-attentionin the final three bottleneck blocks of a ResNet and no other changes, our approach improves upon the baselines significantly on instance segmentation and object detection while also reducing the parameters, with minimal overhead in latency. Through the design of BoTNet, we also point out how ResNet bottleneck blocks with self-attention can be viewed as Transformer blocks. Without any bells and whistles, BoTNet achieves 44.4% Mask AP and 49.7% Box AP on the COCO Instance Segmentation benchmark using the Mask R-CNN framework; surpassing the previous best published single model and single scale results of ResNeSt [72] evaluated on the COCO validation set. Finally, we present a simple adaptation of the BoTNet design for image classification, resulting in models that achieve a strong performance of 84.7% top-1 accuracy on the ImageNet benchmark while being up to 2.33x faster in “compute” time than the popular EfficientNet models on TPU-v3 hardware. We hope our simple and effective approach will serve as a strong baseline for future research in self-attention models for vision. Read More
#image-recognition
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