SHANGHAI (Reuters) - U.S. technology giants, facing tighter content rules in China and the threat of a trade war, are targeting an easier way into the world's second largest economy - artificial intelligence. Google (GOOGL.O), Microsoft Inc (MSFT.O) and Amazon Inc (AMZN.O) showcased their AI wares at a state-backed forum held in Shanghai this week against the backdrop of Beijing's plans to build a $400 billion AI industry by 2025. China's government and companies may compete against U.S. rivals in the global AI race, but they are aware that gaining ground won't be easy without a certain amount of collaboration. "Hey Google, let's make humanity great again," Tang Xiao'ou, CEO of Chinese AI and facial recognition unicorn Sensetime, said in a speech on Monday. Amazon and Microsoft announced plans on Monday to build new AI research labs in Shanghai.

Google's secret prototype search engine for China reportedly links users' mobile phone numbers to what search terms they've used. This feature would allow the Chinese government to simply associate searches with individuals, thereby putting Chinese citizens at increased risk of government repression if they search for topics that their government deems politically sensitive, according to the Intercept. The mobile-focused search engine prototype, code-named Dragonfly, was first revealed last month by the Intercept. Dragonfly is said to have been conceptualized as a joint venture between Google and a Chinese-based company. Both would have the ability to update a list of verboten search words, which could include expected subjects like "human rights" and "student protest" but could also extend to search terms such as "Nobel Prize", according to the Intercept's story.

Water managers in the western United States (U.S.) rely on longterm forecasts of temperature and precipitation to prepare for droughts and other wet weather extremes. To improve the accuracy of these longterm forecasts, the Bureau of Reclamation and the National Oceanic and Atmospheric Administration (NOAA) launched the Subseasonal Climate Forecast Rodeo, a year-long real-time forecasting challenge, in which participants aimed to skillfully predict temperature and precipitation in the western U.S. two to four weeks and four to six weeks in advance. Here we present and evaluate our machine learning approach to the Rodeo and release our SubseasonalRodeo dataset, collected to train and evaluate our forecasting system. Our system is an ensemble of two regression models. The first integrates the diverse collection of meteorological measurements and dynamic model forecasts in the SubseasonalRodeo dataset and prunes irrelevant predictors using a customized multitask model selection procedure. The second uses only historical measurements of the target variable (temperature or precipitation) and introduces multitask nearest neighbor features into a weighted local linear regression. Each model alone is significantly more accurate than the operational U.S. Climate Forecasting System (CFSv2), and our ensemble skill exceeds that of the top Rodeo competitor for each target variable and forecast horizon. We hope that both our dataset and our methods will serve as valuable benchmarking tools for the subseasonal forecasting problem.

In this work, a new approach for Sun tracking systems is presented. Due to the current system limitations regarding costs and operational problems, a new approach based on low cost, computer vision open hardware and deep learning has been developed. The preliminary tests carried out successfully in Plataforma solar de Almeria (PSA), reveal the great potential and show the new approach as a good alternative to traditional systems. The proposed approach can provide key variables for the Sun tracking system control like cloud movements prediction, block and shadow detection, atmospheric attenuation or measures of concentrated solar radiation, which can improve the control strategies of the system and therefore the system performance.

Multi-task learning (MTL) refers to the paradigm of learning multiple related tasks together. By contrast, single-task learning (STL) learns each individual task independently. MTL often leads to better trained models because they can leverage the commonalities among related tasks. However, because MTL algorithms will "transmit" information on different models across different tasks, MTL poses a potential security risk. Specifically, an adversary may participate in the MTL process through a participating task, thereby acquiring the model information for another task. Previously proposed privacy-preserving MTL methods protect data instances rather than models, and some of them may underperform in comparison with STL methods. In this paper, we propose a privacy-preserving MTL framework to prevent the information on each model from leaking to other models based on a perturbation of the covariance matrix of the model matrix, and we study two popular MTL approaches for instantiation, namely, MTL approaches for learning the low-rank and group-sparse patterns of the model matrix. Our methods are built upon tools for differential privacy. Privacy guarantees and utility bounds are provided. Heterogeneous privacy budgets are considered. Our algorithms can be guaranteed not to underperform comparing with STL methods. Experiments demonstrate that our algorithms outperform existing privacy-preserving MTL methods on the proposed model-protection problem.

The different sets of regulations existing for different agencies within the government make the task of creating AI enabled solutions in government difficult. Regulatory restrictions inhibit sharing of data across different agencies, which could be a significant impediment to training AI models. We discuss the challenges that exist in environments where data cannot be freely shared and assess technologies which can be used to work around these challenges. We present results on building AI models using the concept of federated AI, which allows creation of models without moving the training data around.

Abstract--Drought forecasting and prediction is a complicated process due to the complexity and scalability of the environmental parameters involved. Hence, it required a high level of expertise to predict. In this paper, we describe the research and development of a rule-based drought early warning expert systems (RB-DEWES) for forecasting drought using local indigenous knowledge obtained from domain experts. The system generates inference by using rule set and provides drought advisory information with attributed certainty factor (CF) based on the user's input. The system is believed to be the first expert system for drought forecasting to use local indigenous knowledge on drought. The architecture and components such as knowledge base, JESS inference engine and model base of the system and their functions are presented. The intricate complexity of drought has always been a stumbling block for drought forecasting and prediction systems [1]. This is mostly due to the web of environmental events (such as climate variability) that directly/indirectly triggers this environmental phenomenon. There are six broad categories of drought: meteorological, climatological, atmospheric, agricultural, hydrologic and water drought [1]. Nevertheless, irrespective of the category of drought, there is a consensus amongst scientist that drought is a disastrous condition of lack of moisture caused by a deficit in precipitation in a certain geographical region over some time period [2]. The effect of drought can be quantified based on the frequency, duration and intensity in the affected region subject to established timescales.

As machine learning systems move from computer-science laboratories into the open world, their accountability becomes a high priority problem. Accountability requires deep understanding of system behavior and its failures. Current evaluation methods such as single-score error metrics and confusion matrices provide aggregate views of system performance that hide important shortcomings. Understanding details about failures is important for identifying pathways for refinement, communicating the reliability of systems in different settings, and for specifying appropriate human oversight and engagement. Characterization of failures and shortcomings is particularly complex for systems composed of multiple machine learned components. For such systems, existing evaluation methods have limited expressiveness in describing and explaining the relationship among input content, the internal states of system components, and final output quality. We present Pandora, a set of hybrid human-machine methods and tools for describing and explaining system failures. Pandora leverages both human and system-generated observations to summarize conditions of system malfunction with respect to the input content and system architecture. We share results of a case study with a machine learning pipeline for image captioning that show how detailed performance views can be beneficial for analysis and debugging.

Recent research in extensions of Answer Set Programming has included a renewed interest in the language of Epistemic Specifications, which adds modal operators K ("known") and M ("may be true") to provide for more powerful introspective reasoning and enhanced capability, particularly when reasoning with incomplete information. An epistemic logic program is a set of rules in this language. Infused with the research has been the desire for an efficient solver to enable the practical use of such programs for problem solving. In this paper, we report on the current state of development of epistemic logic program solvers.

If North Korea's dear leader wakes up tomorrow, takes a crazy pill and decides to lob a nuclear missile at the US mainland, there's a good chance the military will be relying on artificial intelligence to protect us. Reuters reported earlier this summer on the existence of a secretive military effort -- actually, of multiple classified programs in various stages that are all focused on the development of AI-reliant systems to help us anticipate the launch of a missile, as well as to track launchers. Fears about runaway AI notwithstanding, the Pentagon is now apparently taking that kind of an effort and planning to crank it up to 11. The Pentagon's research agency DARPA announced Monday it will be spending $2 billion on AI, the focus of which, according to CNN, will include "creating systems with common sense, contextual awareness and better energy efficiency. Advances could help the government automate security clearances, accredit software systems and make AI systems that explain themselves."