We developed a new class of physics-informed generative adversarial networks (PI-GANs) to solve in a unified manner forward, inverse and mixed stochastic problems based on a limited number of scattered measurements. Unlike standard GANs relying only on data for training, here we encoded into the architecture of GANs the governing physical laws in the form of stochastic differential equations (SDEs) using automatic differentiation. In particular, we applied Wasserstein GANs with gradient penalty (WGAN-GP) for its enhanced stability compared to vanilla GANs. We first tested WGAN-GP in approximating Gaussian processes of different correlation lengths based on data realizations collected from simultaneous reads at sparsely placed sensors. We obtained good approximation of the generated stochastic processes to the target ones even for a mismatch between the input noise dimensionality and the effective dimensionality of the target stochastic processes. We also studied the overfitting issue for both the discriminator and generator, and we found that overfitting occurs also in the generator in addition to the discriminator as previously reported. Subsequently, we considered the solution of elliptic SDEs requiring approximations of three stochastic processes, namely the solution, the forcing, and the diffusion coefficient. We used three generators for the PI-GANs, two of them were feed forward deep neural networks (DNNs) while the other one was the neural network induced by the SDE. Depending on the data, we employed one or multiple feed forward DNNs as the discriminators in PI-GANs. Here, we have demonstrated the accuracy and effectiveness of PI-GANs in solving SDEs for up to 30 dimensions, but in principle, PI-GANs could tackle very high dimensional problems given more sensor data with low-polynomial growth in computational cost.

Machine learning has been applied to a broad range of applications and some of them are available online as application programming interfaces (APIs) with either free (trial) or paid subscriptions. In this paper, we study adversarial machine learning in the form of back-box attacks on online classifier APIs. We start with a deep learning based exploratory (inference) attack, which aims to build a classifier that can provide similar classification results (labels) as the target classifier. To minimize the difference between the labels returned by the inferred classifier and the target classifier, we show that the deep learning based exploratory attack requires a large number of labeled training data samples. These labels can be collected by calling the online API, but usually there is some strict rate limitation on the number of allowed API calls. To mitigate the impact of limited training data, we develop an active learning approach that first builds a classifier based on a small number of API calls and uses this classifier to select samples to further collect their labels. Then, a new classifier is built using more training data samples. This updating process can be repeated multiple times. We show that this active learning approach can build an adversarial classifier with a small statistical difference from the target classifier using only a limited number of training data samples. We further consider evasion and causative (poisoning) attacks based on the inferred classifier that is built by the exploratory attack. Evasion attack determines samples that the target classifier is likely to misclassify, whereas causative attack provides erroneous training data samples to reduce the reliability of the re-trained classifier. The success of these attacks show that adversarial machine learning emerges as a feasible threat in the realistic case with limited training data.

The government is launching an inquiry into the use of personal data to set individual prices for holidays, cars and household goods, amid rising fears of a consumer rip-off. The research, supported by the competition watchdog, will explore the prevalence of "dynamic pricing" based on information gathered about an individual, such as location, marital status, birthday or travel history. With about 17% of retail sales now made online, according to the Office for National Statistics, there is rising concern about the use of technology, including artificial intelligence and bots, to "personalise" prices, to the disadvantage of some shoppers. It has become common for online prices to fluctuate depending on time of day or availability – whether for gig tickets or Uber taxis. Now digital labels have begun to appear in shops, offering the potential to bring "surge pricing" into analogue sales.

Microsoft founder and philanthropist Bill Gates predicts that as artificial intelligence and other technologies flourish, societies will use taxes to ensure there is still a place for humans in the workforce. "It is quite amazing, the progress the world has made during the last, I would say, 28 years," in tackling medical and poverty problems, Gates said in a wide-ranging interview with Nikkei. He emphasized the importance of global cooperation, as opposed to U.S. President Donald Trump's America First agenda, to resolve issues such as climate change. He also stressed that nurturing software talent is important for Japan to remain competitive. Microsoft has long been a leader of the global tech industry, accounting for a high share of computer operating systems.

Not too long ago it was often said that computer vision could not compete with the visual abilities of a one-year-old. That is no longer true: computers can now recognize objects in images about as well as most adults can, and there are computerized cars on the road that drive themselves more safely than an average sixteen-year-old could. And rather than being told how to see or drive, computers have learned from experience, following a path that nature took millions of years ago. What is fueling these advances is gushers of data. Data are the new oil. Learning algorithms are refineries that extract information from raw data; information can be used to create knowledge; knowledge leads to understanding; and understanding leads to wisdom. Welcome to the brave new world of deep learning. Deep learning is a branch of machine learning that has its roots in mathematics, computer science, and neuroscience.

Editor's Note: We know artificial intelligence will change the very nature of war--but we don't know how. The United States, China, and other powers recognize this transformative potential and, even as they seek to exploit it, fear that others will gain the upper hand in an artificial intelligence arms race. My Brookings colleague Chris Meserole describes how artificial intelligence might produce a new security dilemma and proposes several ways to mitigate the risk. Recent breakthroughs in machine learning and artificial intelligence (A.I.) have prompted breathless speculation about their national security applications. Yet most of that work has focused narrowly on their implications for autonomous weapons systems, rather than on the broader security environment.

In September of 2017, Governor Scott Walker, Republican of Wisconsin, signed a contract that would make his state the home of the first U.S. factory of Foxconn, the world's largest contract electronics manufacturer. The company, which is based in Taiwan and makes products for Apple, Sony, Microsoft, and Nintendo, among others, would build a 21.5-million-square-foot manufacturing campus, invest up to ten billion dollars in Wisconsin, and hire as many as thirteen thousand workers at an average wage of fifty-four thousand dollars a year. For Walker, whose approval had fallen to the mid-thirties after his aborted Presidential run, the deal was seen as a crucial boost to his reëlection prospects. "The Foxconn initiative looked like something that could be a hallmark of Walker's reëlection campaign," Charles Franklin, a professor and pollster at Marquette University Law School, told me. "He could claim a major new manufacturing presence, one that would also employ blue-collar workers in a region where blue-collar jobs are more scarce than they used to be." The idea of putting the plant in southeastern Wisconsin originated in April of 2017, during a helicopter ride President Donald Trump took with Reince Priebus, a Wisconsin native and Trump's chief of staff at the time. Flying over Kenosha, Priebus's home town, they passed the empty lot that once held the American Motors Corporation plant.

Humanity continues to embark on a period of unparalleled technological advancement. The next 5, 10 and 20 years will present both significant challenges and opportunities. Private sectors, governments, academics and entrepreneurs are all seeking the roadmap for navigating these profound changes in the world of work. Such a road map must be created collaboratively by all stakeholders. At its core, an industrial revolution can be characterized by advancements in technology that humanity applies to improve the process of production.

Indian Institute of Technology Madras undertook a major effort to give a boost to the Artificial Intelligence (AI) and Machine Learning (ML) sectors in Chennai. The Robert Bosch Center for Data Science and Artificial Intelligence, IIT Madras, organized the'Artificial Intelligence and Machine Learning Conclave' focused on understand cutting-edge technology and innovation in the field with participation from top technology firms and think-tanks including Google, Amazon, Foxconn and TVS group among others. Prof Bhaskar Ramamurthi, Director, IIT Madras, inaugurated the Conclave, which was held on 23rd October 2018. The conclave aimed at generating a greater realization of the AI/ML ecosystem in and around Chennai and facilitated the stakeholders to have a brainstorming session about the needs for this ecosystem to thrive and grow further. This event for the first time brought together a significant number of AI/ML deep technology start-ups in Chennai in a single platform.

Tesla today released an earnings report that made shares soar -- and Wall Street analysts scratch their heads. Earnings came out to $2.90 a share versus expected losses of close to 20 cents per share. The company's net income, $311.5 million, is a far cry from its losses of $619.4 million this time last year. And quarterly revenue topped that of a year ago by more than 70 percent. This was Tesla's third-ever profitable quarter, as well as CEO Elon Musk's last full quarter, for the time being, as chairman of the company he founded 15 years ago.