We introduce the use of rectified linear units (ReLU) as the classification function in a deep neural network (DNN). Conventionally, ReLU is used as an activation function in DNNs, with Softmax function as their classification function. However, there have been several studies on using a classification function other than Softmax, and this study is an addition to those. We accomplish this by taking the activation of the penultimate layer $h_{n - 1}$ in a neural network, then multiply it by weight parameters $\theta$ to get the raw scores $o_{i}$. Afterwards, we threshold the raw scores $o_{i}$ by $0$, i.e. $f(o) = \max(0, o_{i})$, where $f(o)$ is the ReLU function. We provide class predictions $\hat{y}$ through argmax function, i.e. argmax $f(x)$.

Deep neural networks are vulnerable to adversarial examples, which poses security concerns on these algorithms due to the potentially severe consequences. Adversarial attacks serve as an important surrogate to evaluate the robustness of deep learning models before they are deployed. However, most of existing adversarial attacks can only fool a black-box model with a low success rate. To address this issue, we propose a broad class of momentum-based iterative algorithms to boost adversarial attacks. By integrating the momentum term into the iterative process for attacks, our methods can stabilize update directions and escape from poor local maxima during the iterations, resulting in more transferable adversarial examples. To further improve the success rates for black-box attacks, we apply momentum iterative algorithms to an ensemble of models, and show that the adversarially trained models with a strong defense ability are also vulnerable to our black-box attacks. We hope that the proposed methods will serve as a benchmark for evaluating the robustness of various deep models and defense methods. With this method, we won the first places in NIPS 2017 Non-targeted Adversarial Attack and Targeted Adversarial Attack competitions.

The article focuses mainly on patients with cancer, who often undergo treatments that can cause profound alterations in the gut microbiota and potentially contribute to the development of complications. Because research on the human microbiome is an emerging science and the application of artificial intelligence in medicine is in its infancy, it is important to consider ethical, legal, and social issues simultaneously with technical refinements required for applying these technologies to the clinic. "Artificial intelligence algorithms have the potential to change the everyday medical practices and offer the prospect of identifying new associations not yet detected by humans, which will be very useful for better understanding the complexity of the human microbiota," said author Dr. J. Luis Espinoza, of the Kindai University Faculty of Medicine, in Japan.

By its own reckoning, Bitmain built 70 percent of all the computers on the Bitcoin network. It makes specialized chips to perform the critical hash functions involved in mining and trading bitcoin and packages those chips into the top mining rig; the Antminer S9. Now the tech giant is looking to Artificial Intelligence. Apart from Bitcoin and cryptocurrency mining, Jihan Wu, the Co-founder of Bitmain envisions a use-case for Bitmain beyond blockchain and cryptocurrency. In an interview with the Institute of Electronics and Electrical Engineers (IEEE), Wu expressed that while Bitcoin's success is personal to him, Bitmain can't solely rely on Bitcoin and has to search other avenues for continual success.

UiPath is creating a strong ecosystem of partners to help deliver increasingly efficient enterprise RPA deployment by ensuring that a rich marketplace of leading partner technologies can offer a powerful, pre-integrated, value-added function to the already highly successful UiPath solutions in place. "Conversational Natural Language engagement is a huge growth area for both personal and business use across the globe," said Boris Krumrey, Chief Robotics Officer of UiPath. "Speech and text driven access to initiate and operate robotic process automations is a new channel of user interaction to RPA using conversation. Humley has demonstrated how a virtual assistant can be deployed to support RPA management and build Voice or Chat User Interfaces which will help customers innovate in regards to the'how and where' they access robotic process automations. What we like about Humley is that it works with other leading technologies such as IBM Watson and Microsoft Cognitive Services."

US President Donald Trump often tweets from his iPhone about pressuring China to address its $375bn trade surplus with the United States. But a closer look at the Apple smartphone reveals how the headline figure is distorted. The big trade imbalance - at the heart of a potential trade war, with Trump expected to impose tariffs on Chinese imports this week - exists in large part because of electrical goods and tech, the biggest US import item from China. Apple Inc's iPhone, however, illustrates how a big portion of that imbalance is due to imports of American-branded products - many of which use global suppliers for parts, but are put together in China and shipped around the world. Take a look at the iPhone X. IHS Markit estimates its components cost a total of $370.25.

The Fourth Industrial Revolution is well underway in India – many people just haven't realized it yet. Entrepreneurs, CEOs, company founders and start-ups are rapidly adopting technologies involving AI, the Internet of Things (IoT), 3D-printing, advanced robotics and neuroscience. Quietly and with firm resolve, visionary leadership in Indian industry is bringing the country into the arc of progress sweeping the world. From water pumps, railway coaches and chai shops; to burger chains, telecom towers and warehouses, the technology of the Fourth Industrial Revolution is taking root in different businesses, from global corporations to domestic firms and start-ups.

Best listening experience is on Chrome, Firefox or Safari. In the last Cold War, nations tried to see who could make the biggest nuclear bomb. In the current international rivalry, the race is aimed at something you can't see. In fact, China is spending big on AI as one component in its strategy for building a force-projecting military. Chris Taylor, CEO of the data and analytics firm Govini, joined Federal Drive with Tom Temin to talk more about China's mission.

In June of 1956, A few dozen scientists and mathematicians from all around the country gathered for a meeting on the campus of Dartmouth College. Most of them settled into the red-bricked Hanover Inn, then strolled through the famously beautiful campus to the top floor of the math department, where groups of white-shirted men were already engaged in discussions of a "strange new discipline"--so new, in fact, that it didn't even have a name. "People didn't agree on what it was, how to do it or even what to call it," Grace Solomonoff, the widow of one of the scientists, recalled later. The talks--on everything from cybernetics to logic theory--went on for weeks, in an atmosphere of growing excitement. What the scientists were talking about in their sylvan hideaway was how to build a machine that could think. The "Dartmouth workshop" kicked off the decades-long quest for artificial intelligence. In the following years, the pursuit faltered, enduring several "winters" where it seemed doomed to dead ends and baffling disappointments. But today nations and corporations are pouring billions into AI, whose recent advancements have startled even scientists working in the field. What was once a plot device in sci-fi flicks is in the process of being born. Hedge funds are using AI to beat the stock market, Google is utilizing it to diagnose heart disease more quickly and accurately, and American Express is deploying AI bots to serve its customers online.

Today one book attracted my interest. The author of the book is a Japanese researcher of AI. She tried to develop an AI which could pass the entrance exam of Tokyo university, which is regarded as the best college in Japan. The story of the AI development is introduced, but her focus is more on people who took lower score than that AI in exams. She points out that they lacked an ability of understanding meanings from text, and they relied on an ability of memorizing.