Lung cancer is the leading cause of cancer-related death worldwide. Early diagnosis of pulmonary nodules in Computed Tomography (CT) chest scans provides an opportunity for designing effective treatment and making financial and care plans. In this paper, we consider the problem of diagnostic classification between benign and malignant lung nodules in CT images, which aims to learn a direct mapping from 3D images to class labels. To achieve this goal, four two-pathway Convolutional Neural Networks (CNN) are proposed, including a basic 3D CNN, a novel multi-output network, a 3D DenseNet, and an augmented 3D DenseNet with multi-outputs. These four networks are evaluated on the public LIDC-IDRI dataset and outperform most existing methods. In particular, the 3D multi-output DenseNet (MoDenseNet) achieves the state-of-the-art classification accuracy on the task of end-to-end lung nodule diagnosis. In addition, the networks pretrained on the LIDC-IDRI dataset can be further extended to handle smaller datasets using transfer learning. This is demonstrated on our dataset with encouraging prediction accuracy in lung nodule classification.

The one-bit quantization is implemented by one single comparator that operates at low power and a high rate. Hence one-bit compressive sensing (1bit-CS) becomes attractive in signal processing. When measurements are corrupted by noise during signal acquisition and transmission, 1bit-CS is usually modeled as minimizing a loss function with a sparsity constraint. The one-sided $\ell_1$ loss and the linear loss are two popular loss functions for 1bit-CS. To improve the decoding performance on noisy data, we consider the pinball loss, which provides a bridge between the one-sided $\ell_1$ loss and the linear loss. Using the pinball loss, two convex models, an elastic-net pinball model and its modification with the $\ell_1$-norm constraint, are proposed. To efficiently solve them, the corresponding dual coordinate ascent algorithms are designed and their convergence is proved. The numerical experiments confirm the effectiveness of the proposed algorithms and the performance of the pinball loss minimization for 1bit-CS.

Dystopia starts with 23.6 inches of toilet paper. That's how much the dispensers at the entrance of the public restrooms at Beijing's Temple of Heaven dole out in a program involving facial-recognition scanners--part of the president's "Toilet Revolution," which seeks to modernize public toilets. If you go back to the scanner before nine minutes are up, it will recognize you and issue this terse refusal: "Please try again later." China is rife with face-scanning technology worthy of Black Mirror. Don't even think about jaywalking in Jinan, the capital of Shandong province.

In this paper, we consider solving a class of nonconvex and nonsmooth problems frequently appearing in signal processing and machine learning research. The traditional alternating direction method of multipliers encounters troubles in both mathematics and computations in solving the nonconvex and nonsmooth subproblem. In view of this, we propose a reweighted alternating direction method of multipliers. In this algorithm, all subproblems are convex and easy to solve. We also provide several guarantees for the convergence and prove that the algorithm globally converges to a critical point of an auxiliary function with the help of the Kurdyka-{\L}ojasiewicz property. Several numerical results are presented to demonstrate the efficiency of the proposed algorithm.

This report examines the intersection of two subjects, China and artificial intelligence, both of which are already difficult enough to comprehend on their own. It provides context for China's AI strategy with respect to past science and technology plans, and it also connects the consistent and new features of China's AI approach to the drivers of AI development (e.g. In addition, it benchmarks China's current AI capabilities by developing a novel index to measure any country's AI potential and highlights the potential implications of China's AI dream for issues of AI safety, national security, economic development, and social governance. The author, Jeffrey Ding, writes, "The hope is that this report can serve as a foundational document for further policy discussion and research on the topic of China's approach to AI." The report draws from the author's translations of Chinese texts on AI policy, a compilation of metrics on China's AI capabilities compared to other countries, and conversations with those who have consulted with Chinese companies and institutions involved in shaping the AI scene. To access the report, click here.

A strong declaration from a historically antagonist foe should put chills in the hearts of Americans preparing themselves for the world ahead: Russian President Vladimir Putin says the nation that leads in AI will be the ruler of the world [1]" … The ruler of the world! "The development of artificial intelligence has increasingly become a national security concern in recent years. It is China and the US (not Russia), which are seen as the two frontrunners, with China recently announcing its ambition to become the global leader in AI research by 2030. Many analysts warn that America is in danger of falling behind, especially as the [current US] administration prepares to cut funding for basic science and technology research." Elon Musk, one of America's foremost technology advocates, predicts that countries seeking leadership (and domination) from artificial intelligence will be the basis for World War III[2].

MUMBAI: Artificial intelligence (AI), a tool that uses logic to mimic the human brain, has been the buzzword in the advertising industry for quite some time now. AI was founded as an academic discipline in the year 1956, and in the years since, the technology has experienced several waves of optimism, followed by disappointment and the loss of funding (known as an AI winter), thereafter by new approaches and success. People often tend to use the term AI interchangeably with machine learning (ML), but they are completely different tools. While AI is the broad concept of teaching machines with data to do things in an efficient way, ML is the technique of using algorithms to process data, learn from insights and make predictions that train AI. As Wunderman AI's global leader Robbee Minicola rightly says, "You can have machine learning without AI, but you can't have AI without machine learning."

SenseTime, a leading Chinese startup specialized in artificial intelligence (AI) research and development, has established an alliance with Massachusetts Institute of Technology (MIT) to promote the further application of the technology widely utilized in facial recognition. The Chinese AI researcher and developer, currently valued at around $3 billion, said the cooperation aims to explore new avenues across MIT in areas like computer vision, human-intelligence inspired algorithms, medical imaging, and robotics. Tang Xiao'ou, a founder of SenseTime who is also a PhD '96 MIT alumnus specialized in computer vision and deep learning, said he expects the cooperation between the world's best and brightest talents will further promote AI's development and benefit society. Founded in 2014, the Chinese startup is currently working with a number of well-known Chinese brands including China Mobile, UnionPay, Sina Weibo, as well as major smartphone companies in China to provide machine learning technology. SenseTime's advanced facial recognition expertise has also helped attract leading investments from Qualcomm and CDH Investments.

A new artificial intelligence that is learning to understand the'thoughts' of others has been built by Google-owned research firm DeepMind. The software is capable of predicting what other AIs will do, and can even understand whether they hold'false beliefs' about the world around them. DeepMind reports its bot can now pass a key psychological test that most children only develop the skills for at around age four. Its proficiency in this'theory of mind' test may lead to robots that can think more like humans. DeepMind reports its bot can now pass a key psychological test that most children only develop the skills for around age four.

It has been nearly five years since Syrian government forces imposed a siege on the rebel-held Eastern Ghouta. The past month has been one of the deadliest in the enclave, with more 1,200 civilians killed since the aerial and ground bombardment began on February 18. As the campaign against Eastern Ghouta continues, schools and universities have either been destroyed or shut down, leaving students with few options for continuing their education. Some have enrolled in online universities, while others have joined new, start-up medical academies to address the extreme shortage of medical staff in the area. Three students spoke to Al Jazeera about the obstacles they face as they try to continue their education in Eastern Ghouta.