We study the robustness against adversarial examples of kNN classifiers and classifiers that combine kNN with neural networks. The main difficulty lies in the fact that finding an optimal attack on kNN is intractable for typical datasets. In this work, we propose a gradient-based attack on kNN and kNN-based defenses, inspired by the previous work by Sitawarin & Wagner [1]. We demonstrate that our attack outperforms their method on all of the models we tested with only a minimal increase in the computation time. The attack also beats the state-of-the-art attack [2] on kNN when k > 1 using less than 1% of its running time. We hope that this attack can be used as a new baseline for evaluating the robustness of kNN and its variants.

Over the last century, scientists have developed methods to map the structures within the Earth's crust, in order to identify resources such as oil reserves, geothermal sources, and, more recently, reservoirs where excess carbon dioxide could potentially be sequestered. They do so by tracking seismic waves that are produced naturally by earthquakes or artificially via explosives or underwater air guns. The way these waves bounce and scatter through the Earth can give scientists an idea of the type of structures that lie beneath the surface. There is a narrow range of seismic waves--those that occur at low frequencies of around 1 hertz--that could give scientists the clearest picture of underground structures spanning wide distances. But these waves are often drowned out by Earth's noisy seismic hum, and are therefore difficult to pick up with current detectors.

This is the most common form of AI that you'd find in the market now. These Artificial Intelligence systems are designed to solve one single problem and would be able to execute a single task really well. By definition, they have narrow capabilities, like recommending a product for an e-commerce user or predicting the weather. This is the only kind of Artificial Intelligence that exists today. They're able to come close to human functioning in very specific contexts, and even surpass them in many instances, but only excelling in very controlled environments with a limited set of parameters. AGI is still a theoretical concept. It's defined as AI which has a human-level of cognitive function, across a wide variety of domains such as language processing, image processing, computational functioning and reasoning and so on.

In our ever-advancing technological world, it's easy to get lost in the sea of terminology. Understanding these words and phrases is essential for business coverage since AI is everywhere. This post will explain the difference between four computing terms: artificial intelligence, neural networks, machine learning and deep learning. A note before we start: These definitions are drastically simplified to understand the basics. Perhaps one of the most used and least understood phrases in recent years, artificial intelligence, or AI, is a sort of blanket term.

Micron has acquired FWDNXT, a machine learning software and hardware startup that spun out of Purdue. Micron is integrating FWDNXT's artificial intelligence hardware and software technology with its advanced memory to explore deep learning solutions for data analytics, particularly in IoT and edge computing. Purdue provided the entrepreneurial resources to help me achieve my vision of taking our work on machine learning and deep learning technology to a much wider audience where we can have a bigger impact," said Eugenio Culurciello, Micron fellow and chief machine learning architect. "Micron has the leadership in memory, long history of innovation and drive to deliver power and performance capabilities that address the most complex and demanding edge applications at scale." Culurciello founded FWDNXT while working as an associate professor in Purdue's College of Engineering. Based in the Purdue Research Park, FWDNXT designed next-generation hardware and software for deep learning aimed at enabling computers to understand the world in the same way humans do. Culurciello worked closely with the Purdue Research Foundation Office of Technology Commercialization to secure and develop an intellectual property rights strategy for the AI technology that he developed at Purdue, which Micron licenses today. The FWDNXT acquisition is another strong show of confidence by industry in Purdue technology designed to make a difference for Indiana and beyond," said Brooke Beier, vice president of the Office of Technology Commercialization.

Welcome to "The Annotated GPT-2". One of the most brilliant and well-explained articles I have ever read is The Annotated Transformer. It introduced Attention like no other post ever written. The simple idea was to present an "annotated" version of the paper Attention is all you need along with code. Something I have come to realize with my little experience in Machine Learning, when you write things in code, the implementation and the secrets become clearer. It is not magic anymore.

In this Python tutorial, you'll learn how to facial recognition with Python and the face_recognition library Welcome to a tutorial for implementing the face recognition package for Python. The purpose of this package is to make facial recognition (identifying a face) fairly simple. Whether it's for security, smart homes, or something else entirely, the area of application for facial recognition is quite large, so let's learn how we can use this technology. To begin, we need to install everything. Installation instruction splits between Windows and Linux for some dependencies, then there is a common part for them.

In our world filled with unintended consequences, it turns out that saving memory space to help deal with GPU limitations, knowing it introduces performance penalties on matrix operations, can end up costing both performance and memory space. As reported in a paper at ISC19, researchers[i] recently rethought use of sparse matrix representations, originally motivated by GPU memory constraints, to use dense matrices in order to benefit from the larger memory capacities and scale-out capabilities of CPUs. The result was not only superior performance and scaling using CPUs, it also (perhaps surprisingly) included a reduction in memory footprint because of the interplay between using sparse representations to reduce memory and the increased memory usage due to algorithm inefficiencies. The researchers demonstrated the positive effects of their work in Horovod – an open source distributed Deep Learning framework for TensorFlow created by Uber Engineering. They also demonstrated its outstanding ability to scale-out, proving it using supercomputers run with large numbers of CPUs.

WEST LAFAYETTE, Ind. – Technology that combines machine learning with artificial intelligence from Purdue University has taken its next giant leap toward powering more Internet of Things and edge computing devices. FWDNXT, a software and hardware startup that spun out of Purdue, was acquired in October by Micron Technology Inc., an industry leader in innovative memory and storage solutions. Micron is integrating FWDNXT's artificial intelligence hardware and software technology with its advanced memory to explore deep learning solutions for data analytics, particularly in IoT and edge computing. "Purdue provided the entrepreneurial resources to help me achieve my vision of taking our work on machine learning and deep learning technology to a much wider audience where we can have a bigger impact," said Eugenio Culurciello, Micron fellow and chief machine learning architect. "Micron has the leadership in memory, long history of innovation and drive to deliver power and performance capabilities that address the most complex and demanding edge applications at scale."

With the rapid upsurge of deep learning tasks at the network edge, effective edge artificial intelligence (AI) inference becomes critical to provide low-latency intelligent services for mobile users via leveraging the edge computing capability. In such scenarios, energy efficiency becomes a primary concern. In this paper, we present a joint inference task selection and downlink beamforming strategy to achieve energy-efficient edge AI inference through minimizing the overall power consumption consisting of both computation and transmission power consumption, yielding a mixed combinatorial optimization problem. By exploiting the inherent connections between the set of task selection and group sparsity structural transmit beamforming vector, we reformulate the optimization as a group sparse beamforming problem. To solve this challenging problem, we propose a log-sum function based three-stage approach. By adopting the log-sum function to enhance the group sparsity, a proximal iteratively reweighted algorithm is developed. Furthermore, we establish the global convergence analysis and provide the ergodic worst-case convergence rate for this algorithm. Simulation results will demonstrate the effectiveness of the proposed approach for improving energy efficiency in edge AI inference systems.