Following on from a report that identified artificial intelligence (AI) as crucial in overcoming challenges in the legal industry, Information Age wanted to delve deeper into the subject. As a result, we got in touch Geoffrey Vance, the chair of Perkins Coie's E-Discovery Services and Strategy Practice, and Alvin Lindsay, partner at Hogan Lovells. Vance took over the Perkins Coie practice 3 years ago after leading McDermott Will & Emery's e-discovery group for 7 years, and has led Perkins Coie to be one of the first to use AI for various discovery, litigation and other legal needs. He's seen associates get more involved in this kind of work right at the start of their careers, and spoke to Information Age about the evolution of their roles with the rise of AI. At the same time, Lindsay has written about potential of AI in the legal industry and is well placed to comment on this encroaching trend.

Chemical transport models (CTMs), which simulate air pollution transport, transformation, and removal, are computationally expensive, largely because of the computational intensity of the chemical mechanisms: systems of coupled differential equations representing atmospheric chemistry. Here we investigate the potential for machine learning to reproduce the behavior of a chemical mechanism, yet with reduced computational expense. We create a 17-layer residual multi-target regression neural network to emulate the Carbon Bond Mechanism Z (CBM-Z) gas-phase chemical mechanism. We train the network to match CBM-Z predictions of changes in concentrations of 77 chemical species after one hour, given a range of chemical and meteorological input conditions, which it is able to do with root-mean-square error (RMSE) of less than 1.97 ppb (median RMSE = 0.02 ppb), while achieving a 250x computational speedup. An additional 17x speedup (total 4250x speedup) is achieved by running the neural network on a graphics-processing unit (GPU). The neural network is able to reproduce the emergent behavior of the chemical system over diurnal cycles using Euler integration, but additional work is needed to constrain the propagation of errors as simulation time progresses.

Predictions about the billions of dollars that drone technology represents are as pervasive as they are extreme. Drone industry experts are currently tracking over 75 firms that offer drone market reports or forecasts of some type, all of which offer various opinions and numbers around what sort of an impact the technology will enable in industries like construction and agriculture as well as maritime and offshore services. It's easy and in some cases justified to get excited about the potential of the technology, but many of these predictions are based on how the drones might be utilized, as opposed to the difference they're actually making. It's why figuring out the ROI of UAVs is a key consideration when it comes to adoption, and it's why the current applications of drones in maritime and offshore services are so important to consider. The use cases of today are what will make some of those billion dollar predictions possible, and makes it essential to see these uses explored in depth and detail at industry events.

Chinese artificial-intelligence startup CloudWalk Technology signed a deal in March with the Zimbabwean government, providing the authoritarian regime an advanced facial-recognition system that it can use to identify, track, and monitor citizens. In exchange, CloudWalk gains access to the facial data of the demographically distinct country, which provides the company much-needed data for improving its recognition algorithms. Arrangements such as this are common under China's Artificial Intelligence (AI) strategy, whereby Chinese private and state-controlled companies take advantage of the weak legal systems and low privacy standards of developing nations as part of the country's effort to become a world leader in artificial intelligence by 2030. But the vision of artificial intelligence that China is creating is a thoroughly illiberal one. Constant surveillance of citizens is powering initiatives such as the Social Credit System, which will rate citizens on their social and economic performance, increasing the power of the state to enforce its cultural vision.

Researchers who study stylometry--the statistical analysis of linguistic style--have long known that writing is a unique, individualistic process. The vocabulary you select, your syntax, and your grammatical decisions leave behind a signature. Automated tools can now accurately identify the author of a forum post for example, as long as they have adequate training data to work with. But newer research shows that stylometry can also apply to artificial language samples, like code. Software developers, it turns out, leave behind a fingerprint as well.

Chinese academic institutions are more prolific patent filers in the artificial intelligence (AI) area than domestic companies, according to China's State Intellectual Property Office (SIPO). SIPO shared the statement, based on a release from China IP News, on Wednesday, August 1. The release is based on "China's AI Development Report 2018", which was recently published by Tsinghua University, in Beijing. The university's report revealed that the most prolific filers in AI tend to come from research institutions, such as universities. Unlike in other countries, industry players in China file fewer patents in the AI sphere than those in research institutions. The country's "top IT giants" such as Alibaba and Tencent are "overwhelmed" by the filings of foreign companies, such as IBM and Microsoft, SIPO said.

"I will not sell the future for instant profit!" Werner von Siemens, 1884 In Atlas Shrugged (1957), by Ayn Rand, the system falls apart to the point that the remaining producers choose to simply withdraw rather than proliferate and disrupt the society from within. "In 1995, Fukuyama argued that only those societies with a high degree of social trust would be able to create the kind of flexible, large-scale business organizations that are needed for successful competition in the global economy." Carrying proudly the responsibility of its 170 years history and legacy, a Tech Giant, an Atlas of the modern era of turbulent markets and exponentially growing challenges, the largest industrial manufacturing company in Europe with its footprint in 180 countries around the globe, the German conglomerate company Siemens AG (German pronunciation: [ˈziːmɛns]) is shaping the future – the digital future. "With its Vision 2020, Siemens has recently once again clearly answered these questions: a company faces up to its responsibilities, furnishes lasting benefit and generates added value from a position of strength – for its shareholders, employees, customers, business partners and societies all over the world. Joe Kaeser, President and Chief Executive Officer of Siemens AG, puts it like this: "Only the strong can help the weak, take responsibility and then fulfill it.

Companies are using AI to prevent and detect everything from routine employee theft to insider trading. Many banks and large corporations employ artificial intelligence to detect and prevent fraud and money laundering. Social media companies use machine learning to block illicit content such as child pornography. Businesses are constantly experimenting with new ways to use artificial intelligence for better risk management and faster, more responsive fraud detection -- and even to predict and prevent crimes. While today's basic technology is not necessarily revolutionary, the algorithms it uses and the results they can produce are.

Disregard for how the research could undermine the tribe's interests led to a lawsuit and out-of-court settlement. Science is a social enterprise. Many scientific programs interact with a wide range of communities and stakeholders to secure various types of access and permission, to seek cooperation and collaboration for scientific studies, to fulfill regulatory and ethical requirements, and to try to shape research strategies and to improve the translation of their findings into policy or practice. But these interactions are motivated disproportionately by the interests and goals of the scientific programs and less by the need to elicit and understand their implications for stakeholders. However, there is increasing recognition that substantive community and stakeholder engagement (CSE) can improve the performance, and even make or break the success, of some science programs by providing a means of navigating, and responding to, the complex social, economic, cultural, and political settings in which science programs are conducted.

Companies are besieged by information and bedazzled by IT solutions. With the rapid advancements in information technology, high-speed Internet, mobile technology and artificial intelligence, we now have access to huge amounts of data about customers, their demographics, and their online behavior across all touch points. The advantage of access to so much information is not just increased revenue and long-lasting customer relationships, but also the ability to develop sensitivity to warning signals, which allow companies to prevent or mitigate disasters. The avoidance of conflict, the management of cyclical downturns or strategic missteps, and the management of the company's future are at the core of creating intelligent businesses. Companies have improved their practices with respect to capturing greater amounts of data.