This article is written by Maria Teresa Ronderos, director for the Program on Independent Journalism at the Open Society Foundation. Many large newsrooms and news agencies have, for some time, relegated sports, weather, stock exchange movements and corporate performance stories to computers. Machines can be more rigorous and comprehensive than some reporters. Software can import data from various sources, recognise trends and patterns and, using Natural Language Processing, put those trends into context, constructing sophisticated sentences with adjectives, metaphors and similes. These developments are why many in the journalistic profession fear Artificial Intelligence will leave them without a job. But, if instead of fearing it, journalists embrace AI, it could become the saviour of the trade -- making it possible for them to better cover the increasingly complex, globalised and information-rich world we live in.

Can effective international governance for artificial intelligence remain fragmented, or is there a need for a centralised international organisation for AI? We draw on the history of other international regimes to identify advantages and disadvantages in centralising AI governance. Some considerations, such as efficiency and political power, speak in favour of centralisation. Conversely, the risk of creating a slow and brittle institution speaks against it, as does the difficulty in securing participation while creating stringent rules. Other considerations depend on the specific design of a centralised institution. A well-designed body may be able to deter forum shopping and ensure policy coordination. However, forum shopping can be beneficial and a fragmented landscape of institutions can be self-organising. Centralisation entails trade-offs and the details matter. We conclude with two core recommendations. First, the outcome will depend on the exact design of a central institution. A well-designed centralised regime covering a set of coherent issues could be beneficial. But locking-in an inadequate structure may pose a fate worse than fragmentation. Second, for now fragmentation will likely persist. This should be closely monitored to see if it is self-organising or simply inadequate.

Latin America is a region unique for its cultural and geographical diversity, as well as for its set of unique set of social challenges and opportunities. In the last century, Latin America has been slow to develop compared with other regions of the world such as North America or Europe. Some have even named the region the forgotten continent. Artificial intelligence provides an opportunity to accelerate the development of Latin America in the near future. This will only be true if AI receives adequate support and if initiatives are developed rapidly in the region.

Climate change impacts and adaptations are the subjects to ongoing issues that attract the attention of many researchers. Insight into the wind power potential in an area and its probable variation due to climate change impacts can provide useful information for energy policymakers and strategists for sustainable development and management of the energy. In this study, spatial variation of wind power density at the turbine hub-height and its variability under future climatic scenarios are taken under consideration. An ANFIS based post-processing technique was employed to match the power outputs of the regional climate model with those obtained from the reference data. The near-surface wind data obtained from a regional climate model are employed to investigate climate change impacts on the wind power resources in the Caspian Sea. Subsequent to converting near-surface wind speed to turbine hub-height speed and computation of wind power density, the results have been investigated to reveal mean annual power, seasonal, and monthly variability for a 20-year period in the present (1981-2000) and in the future (2081-2100). The findings of this study indicated that the middle and northern parts of the Caspian Sea are placed with the highest values of wind power. However, the results of the post-processing technique using adaptive neuro-fuzzy inference system (ANFIS) model showed that the real potential of the wind power in the area is lower than those of projected from the regional climate model.

Causal inference often relies on the counterfactual framework, which requires that treatment assignment is independent of the outcome, known as strong ignorability. Approaches to enforcing strong ignorability in causal analyses of observational data include weighting and matching methods. Effect estimates, such as the average treatment effect (ATE), are then estimated as expectations under the reweighted or matched distribution, P . The choice of P is important and can impact the interpretation of the effect estimate and the variance of effect estimates. In this work, instead of specifying P, we learn a distribution that simultaneously maximizes coverage and minimizes variance of ATE estimates. In order to learn this distribution, this research proposes a generative adversarial network (GAN)-based model called the Counterfactual $\chi$-GAN (cGAN), which also learns feature-balancing weights and supports unbiased causal estimation in the absence of unobserved confounding. Our model minimizes the Pearson $\chi^2$ divergence, which we show simultaneously maximizes coverage and minimizes the variance of importance sampling estimates. To our knowledge, this is the first such application of the Pearson $\chi^2$ divergence. We demonstrate the effectiveness of cGAN in achieving feature balance relative to established weighting methods in simulation and with real-world medical data.

Innovation ecosystems can be naturally described as a collection of networked entities, such as experts, institutions, projects, technologies and products. Representing in a machine-readable form these entities and their relations is not entirely attainable, due to the existence of abstract concepts such as knowledge and due to the confidential, non-public nature of this information, but even its partial depiction is of strong interest. The representation of innovation ecosystems incarnated as knowledge graphs would enable the generation of reports with new insights, the execution of advanced data analysis tasks. An ontology to capture the essential entities and relations is presented, as well as the description of data sources, which can be used to populate innovation knowledge graphs. Finally, the application case of the Universidad Politecnica de Madrid is presented, as well as an insight of future applications.

Alcatel 3L may feature similar technology found in the leading smartphones, but it can be purchased for a sixth of the price. The handset, developed by TCL Communications, debuted at CES in Las Vegas with a price tag of $155 and includes an AI-powered triple rear cameras setup. The system includes a 48-megapixel sensor, a 12-megapixel and a 5-megapixel for ultra wide shots. The Alcatel 3L will be released in'select markets across Europe, Asia, Africa and the Middle East in the beginning of this year, reports CNET. Alcatel 3L may features similar technology found in the leading smartphones, but it can be purchased for a sixth of the price.

For a country that holds 12 percent of the planet's water supply, Brazil faces significant water management issues. In addition to its commonly known sanitation problems, the country's infrastructure lends itself to distribution issues, including fraudulent use. Fraudulent water use can be particularly hard to track and identify, and often goes unaddressed for significant periods of time – especially in highly populated areas where physically checking people's homes and water meters isn't an option. Instead, companies need to find ways to swiftly identify and eliminate fraudulent water activity which impacts an already scarce supply and costs communities money. To address this challenge, a utilities company from Mato Grosso, Brazil recently worked with a group of data engineers at ScientificCloud. The goal was to develop a solution that could better locate fraudulent water usage by tracking data patterns based on home location and property attributes. As a Sao Paolo-based data science company that develops and deploys machine learning (ML) and artificial intelligence (AI)-powered applications, ScientificCloud understood these problems first hand.

Rising number of contact centers and necessity for compliance and risk management across several verticals have led the companies to invent solutions in speech analytics which will aid companies to comprehend the changing necessities of customers. Several organizations functioning in diverse industrial domains have been evolving interests for the transcription and analyzing of customers and structural media and uptake rational decisions for the management of business and consumers with the help of speech and text intelligence. This is the main factor that is responsible for the growth of the speech analytics market and a protuberant driving factor in the growing demands for speech analytics in several industrial applications. This rising demand can also be accredited to the burdens on businesses for safeguarding their rational assets for improving agility and competence in business operations via the all-embracing insights quarried in the Voice of Customer (VoC). Speech analytics is used in sectors such as customer experience management, agent performance, business processes, compliance and risk management, and market intelligence.