"Please think forward to the year 2030. Analysts expect that people will become even more dependent on networked artificial intelligence (AI) in complex digital systems. Some say we will continue on the historic arc of augmenting our lives with mostly positive results as we widely implement these networked tools. Some say our increasing dependence on these AI and related systems is likely to lead to widespread difficulties. Our question: By 2030, do you think it is most likely that advancing AI and related technology systems will enhance human capacities and empower them? That is, most of the time, will most people be better off than they are today? Or is it most likely that advancing AI and related technology systems will lessen human autonomy and agency to such an extent that most people will not be better off than the way things are today? Please explain why you chose the answer you did and sketch out a vision of how the human-machine/AI collaboration will function in 2030.

About 3.85 million people studied Japanese at a record 18,604 institutions overseas in fiscal 2018, with the number of institutions soaring in Asia, according to a survey released this week. The number of Japanese language institutions jumped nearly fourfold to 818 in Vietnam from the previous survey in fiscal 2015 and nearly tripled to 400 in Myanmar, said the survey by the Japan Foundation, a government-backed organization conducting international cultural exchange programs. The number of Japanese learners overseas rose 5.2 percent to 3,846,773, led by a 169.0 percent surge to 174,461 in Vietnam, it said. The survey found a record high 142 countries and territories offering Japanese language education, five more than the fiscal 2015 level. The five include East Timor, Zimbabwe and Montenegro.

We study how to learn optimal interventions sequentially given causal information represented as a causal graph along with associated conditional distributions. Causal modeling is useful in real world problems like online advertisement where complex causal mechanisms underlie the relationship between interventions and outcomes. We propose two algorithms, causal upper confidence bound (C-UCB) and causal Thompson Sampling (C-TS), that enjoy improved cumulative regret bounds compared with algorithms that do not use causal information. We thus resolve an open problem posed by~\cite{lattimore2016causal}. Further, we extend C-UCB and C-TS to the linear bandit setting and propose causal linear UCB (CL-UCB) and causal linear TS (CL-TS) algorithms. These algorithms enjoy a cumulative regret bound that only scales with the feature dimension. Our experiments show the benefit of using causal information. For example, we observe that even with a few hundreds of iterations, the regret of causal algorithms is less than that of standard algorithms by a factor of three. We also show that under certain causal structures, our algorithms scale better than the standard bandit algorithms as the number of interventions increases.

The job sequencing and tool switching problem (SSP) has been extensively studied in the field of operations research, due to its practical relevance and methodological interest. Given a machine that can load a limited amount of tools simultaneously and a number of jobs that require a subset of the available tools, the SSP seeks a job sequence that minimizes the number of tool switches in the machine. To solve this problem, we propose a simple and efficient hybrid genetic search based on a generic solution representation, a tailored decoding operator, efficient local searches and diversity management techniques. To guide the search, we introduce a secondary objective designed to break ties. These techniques allow to explore structurally different solutions and escape local optima. As shown in our computational experiments on classical benchmark instances, our algorithm significantly outperforms all previous approaches while remaining simple to apprehend and easy to implement. We finally report results on a new set of larger instances to stimulate future research and comparative analyses.

An autonomous idea-creation system that already has invented patentable concepts has itself now been patented. The U.S. Patent and Trade Office has awarded a patent to Stephen L. Thaler, president and CEO of Imagination Engines Inc., for his Device for the Autonomous Bootstrapping of Unified Sentience (DABUS). Formally, the patent is titled "Electro‐Optical Device and Method for Identifying and Inducing Topological States Formed Among Interconnecting Neural Modules," which Thaler says constitutes a "successor to deep learning and the future of artificial general intelligence." With DABUS, "vast swarms of neural nets join to form chains that encode concepts gleaned from their environment," Thaler said in a press release. "It also teaches the noise‐stimulation of such neural chaining systems to generate derivative concepts from their accumulated experience (i.e., idea formation)."

The Research Insights has added an innovative statistics, titled as Artificial Intelligence (AI) In Construction Market. To explore the desired data, it uses primary and secondary exploratory techniques. Different aspects of the businesses are examined to provide the accurate data of market. The artificial intelligence in construction market was esteemed at USD 434 million out of 2018, and is relied upon to arrive at an estimation of USD 2,486 million by 2025, at a CAGR of 33%, during the conjecture time frame (2019 – 2025). Computerized reasoning enables PC frameworks to settle on keen choices by applying the required abilities.

Read on to discover how this definitive report can transform your research and save you time. The use of artificial intelligence based industrial and personal-service robots is on the rise and has led Visiongain to publish this timely report. The USD 2.3 billion Artificial Intelligence Robotics Market is expected to flourish in the next few years because of advances in specific areas of AI such as machine learning methods. The continuous advancements in computer power have enabled the development of more intelligent and stronger AI systems and this is expected to feed through in the latter part of the decade driving growth to new heights. If you want to be part of this growing industry, then read on to discover how you can maximise your investment potential.

As AI and machine learning transform industries by automating much of the work currently done by humans, women's careers will be disproportionately affected. That's according to a McKinsey Global Institute report published earlier this year ("The future of women at work: Transitions in the age of automation"), which found that women predominate in occupations that'll be adversely impacted. About 40% of jobs where men make up the majority in the 10 economies (Canada, France, Germany, Japan, the U.K., the U.S., China, India, Mexico, and South America) contributing over 60% of GDP collectively could be displaced by automation in our 2030, compared with the 52% of women-dominated jobs with high automation potential. Mekala Krishnan, a senior fellow at McKinsey's Boston-based business and economics research arm and a member of the board of the Global Fund for Women, spoke about the research (which she coauthored) at MIT Technology Review's EmTech MIT conference at the MIT Media Lab. Krishnan pointed out that monotonous or repetitive tasks are ripe for automation.

FRISS, a provider of AI-powered fraud and risk solutions for the property and casualty market, has announced that it has extended its partnership with Munich Re to support insurers worldwide in the fight against fraud risk. The extension of the agreement comes after FRISS and Munich Re successfully cooperated in Latin America and Iberia to mitigate fraud risk at P&C insurers. FRISS provides a combination of AI, predictive models, network analysis and text mining for more than 600 risk and fraud indicators that are used by insurers for real-time risk assessment in underwriting, fraud detection during claims and investigations at the special investigation unit. "Many of our insurance clients see the need to increase efficiencies, reduce claims costs and improve their competitive edge by an increased use of automation," said Joachim Mathe, head of Munich Re's global consulting. "To take full advantage of these new opportunities, insurers must arm themselves with relevant digital and data-analytics competency, including a modern IT system supporting such competencies. The combination of our strategy expertise and global player know-how with AI-powered fraud and risk solutions provided by FRISS will help our clients make effective big-picture decisions."

This week, we have launched the first in a series of reports on artificial intelligence (AI) and its potential impact in driving the digital transformation of biopharma. This overview report, Intelligent biopharma: Forging the links across the value chain, explores the challenges and opportunities in AI adoption and the potential ways that AI might impact the different segments of the biopharma value chain (see Figure 1).1 The pace and scale of medical and scientific innovation, together with increasing competition, lengthening R&D cycle times, shorter time in market, expiring patents, declining peak sales, pressure around reimbursement and mounting regulatory scrutiny are challenging the existing biopharma business and operating models. These challenges have also had a massively negative impact on the expected return on investment that large biopharma companies expect to achieve from their late-stage pipelines. Consequently, companies are looking to digital transformation as a key differentiator and essential part of their change management strategy. AI technologies are some of the most anticipated of these digital technologies.