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Man arrested after using AI to beat Japan's smut censorship

#artificialintelligence

In brief A man was detained in Japan for selling uncensored pornographic content that he had, in a way, depixelated using machine-learning tools. Masayuki Nakamoto, 43, was said to have made about 11 million yen ($96,000) from peddling over 10,000 processed porn clips, and was formally accused of selling ten hardcore photos for 2,300 yen ($20). Explicit images of genitalia are forbidden in Japan, and as such its porn is partially pixelated. Don't pretend you don't know what we're talking about. Nakamato flouted these rules by downloading smutty photos and videos, and reportedly used deepfake technology to generate fake private parts in place of the pixelation.


Cutting through the noise: AI enables high-fidelity quantum computing

#artificialintelligence

Researchers led by the Institute of Scientific and Industrial Research (SANKEN) at Osaka University have trained a deep neural network to correctly determine the output state of quantum bits, despite environmental noise. The team's novel approach may allow quantum computers to become much more widely used. Modern computers are based on binary logic, in which each bit is constrained to be either a 1 or a 0. But thanks to the weird rules of quantum mechanics, new experimental systems can achieve increased computing power by allowing quantum bits, also called qubits, to be in "superpositions" of 1 and 0. For example, the spins of electrons confined to tiny islands called quantum dots can be oriented both up and down simultaneously. However, when the final state of a bit is read out, it reverts to the classical behavior of being one orientation or the other. To make quantum computing reliable enough for consumer use, new systems will need to be created that can accurately record the output of each qubit even if there is a lot of noise in the signal.


Cutting through the Noise

#artificialintelligence

A team of scientists used a machine learning method called a deep neural network to discern the signal created by the spin orientation of electrons on quantum dots. Researchers led by the Institute of Scientific and Industrial Research (SANKEN) at Osaka University have trained a deep neural network to correctly determine the output state of quantum bits, despite environmental noise. The team's novel approach may allow quantum computers to become much more widely used. Modern computers are based on binary logic, in which each bit is constrained to be either a 1 or a 0. But thanks to the weird rules of quantum mechanics, new experimental systems can achieve increased computing power by allowing quantum bits, also called qubits, to be in "superpositions" of 1 and 0. For example, the spins of electrons confined to tiny islands called quantum dots can be oriented both up and down simultaneously. However, when the final state of a bit is read out, it reverts to the classical behavior of being one orientation or the other.


"Learn Amazon SageMaker", 2nd edition

#artificialintelligence

I'm very happy to announce that the second edition of "Learn Amazon SageMaker" is now available for pre-order on Amazon (US, India, UK, France, Japan, etc.) and elsewhere. This book is for software engineers, machine learning developers, data scientists, and AWS users who are new to using Amazon SageMaker and want to build high-quality machine learning models without worrying about infrastructure. Knowledge of AWS basics is required to grasp the concepts covered in this book more effectively. A solid understanding of machine learning concepts and the Python programming language will also be beneficial.


NYK Tests AI System to Automatically Identify Navigation Hazards

#artificialintelligence

Efforts are continuing to explore the use of automation, artificial intelligence, and image recognition to improve the navigation and safety of ship operations. Earlier this year, Japan's Mitsui O.S.K. Lines demonstrated its efforts are using augmented reality (AR) technology to enhance navigational awareness and now NYK announced that it has begun a trial on the system that can monitor the horizon to recognize dangerous objects that might be within a ship's range. NYK working with its strategic research and development subsidiary MTI Co. installed the Automatic Ship Target recognition System developed in Israel by Orca AI on one of NYK's vessels. The goal is to verify the detection capability and the contribution the system can make to the role of the lookout on a ship's bridge. Working with Orca, NYK also plans to improve the target detection algorithm through the use of data collection and machine learning on the Israeli company's servers.


MURAL: Multimodal, Multitask Retrieval Across Languages

arXiv.org Artificial Intelligence

Both image-caption pairs and translation pairs provide the means to learn deep representations of and connections between languages. We use both types of pairs in MURAL (MUltimodal, MUltitask Representations Across Languages), a dual encoder that solves two tasks: 1) image-text matching and 2) translation pair matching. By incorporating billions of translation pairs, MURAL extends ALIGN (Jia et al. PMLR'21)--a state-of-the-art dual encoder learned from 1.8 billion noisy image-text pairs. When using the same encoders, MURAL's performance matches or exceeds ALIGN's cross-modal retrieval performance on well-resourced languages across several datasets. More importantly, it considerably improves performance on under-resourced languages, showing that text-text learning can overcome a paucity of image-caption examples for these languages. On the Wikipedia Image-Text dataset, for example, MURAL-base improves zero-shot mean recall by 8.1% on average for eight under-resourced languages and by 6.8% on average when fine-tuning. We additionally show that MURAL's text representations cluster not only with respect to genealogical connections but also based on areal linguistics, such as the Balkan Sprachbund.


GPUs May Be Better, Not Just Faster, at Training Deep Neural Networks

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Researchers from Poland and Japan, working with Sony, have found evidence that machine learning systems trained on GPUs rather than CPUs may contain fewer errors during the training process, and produce superior results, contradicting the common understanding that GPUs simply perform such operations faster, rather than any better. The research, titled Impact of GPU Uncertainty on the Training of Predictive Deep Neural Networks, comes from the Faculty of Psychology and Cognitive Sciences at Adam Mickiewicz University and two Japanese universities, together with SONY Computer Science Laboratories. The study suggests that'uncertainties' which deep neural networks exhibit in the face of various hardware and software configurations favor more expensive (and increasingly scarce) graphics processing units, and found in tests that a deep neural network trained exclusively on CPU produced higher error rates over the same number of epochs (the number of times that the system reprocesses the training data over the course of a session). In this supplemental example from the paper, we see (bottom two rows), similar result quality obtained from a variety of GPUs, and (first row), the inferior results obtained from a range of otherwise very capable CPUs. These preliminary findings do not apply uniformly across popular machine learning algorithms, and in the case of simple autoencoder architectures, the phenomenon does not appear.


Tokyo Paralympics: Sarah Storey wins record-breaking 17th gold in women's C4-5 road race

BBC News

Sarah Storey wins her 17th Paralympic gold by defending her women's C4-5 road race title to become Great Britain's most successful Paralympian of all time.


Convolutional Neural Networks - AI Summary

#artificialintelligence

Research by Hubel and Wiesel [2,3] analyzed the striate cortex of cats and monkeys, revealing two key findings that would come to heavily influence Fukushima's work [1]. The next significant implementation of a convolution neural network was LeNet-5 proposed in 1999 by Le Cun et al. in their work "Object Recognition with Gradient Based Learning'' [4]. Their proposed network, LeNet-5 performed well on the MNIST data set and was shown to do better than state of the art (at the time) SVMs and K-nearest neighbor based approaches. Their final implementation outperformed other state of the art image classification algorithms with error rates which were 10% lower than its competitors on the ImageNet dataset. This application of a discrete convolution precisely represents local receptive fields observed by Hubel and Wiesel [2,3] and implemented in early CNNs by Fukushima and Le Cun [1,4]. Research by Hubel and Wiesel [2,3] analyzed the striate cortex of cats and monkeys, revealing two key findings that would come to heavily influence Fukushima's work [1]. The next significant implementation of a convolution neural network was LeNet-5 proposed in 1999 by Le Cun et al. in their work "Object Recognition with Gradient Based Learning'' [4].


AI's time to shine as manufacturers respond to shocks

#artificialintelligence

Even the most skilled inspector might have an off day. Here it can be useful to outsource mundane or mechanical tasks to'intelligent' machines. Poorly defined quality-control procedures were blamed for one of the most extensive automotive parts recalls in history, involving the airbag manufacturer Takata. The firm's inflators, which contained the chemical ammonium nitrate, were found to be unsafe – leading 19 US carmakers to recall 69m of the products. Similar recalls were issued in Japan, China and Oceania.