Today, Mozilla and the German Ministry for Economic Cooperation and Development (BMZ) have announced to join forces in the collection of open speech data in local languages, as well as the development of local innovation ecosystems for voice-enabled products and technologies. The initiative builds on the pilot project, which our Open Innovation team and the Machine Learning Group started together with the organization "Digital Umuganda" earlier this year. The Rwandan start-up collects language data in Kinyarwanda, an African language spoken by over 12 million people. Further languages in Africa and Asia are going to be added. Mozilla's projects Common Voice and Deep Speech will be the heart of the joint initiative, which aims at collecting diverse voice data and opening up a common, public database.

Could the same computer algorithms that teach autonomous cars to drive safely help identify nearby asteroids or discover life in the universe? NASA scientists are trying to figure that out by partnering with pioneers in artificial intelligence (AI)--companies such as Intel, IBM and Google--to apply advanced computer algorithms to problems in space science. Machine learning is a type of AI. It describes the most widely used algorithms and other tools that allow computers to learn from data in order to make predictions and categorize objects much faster and more accurately than a human being can. Consequently, machine learning is widely used to help technology companies recognize faces in photos or predict what movies people would enjoy.

DUBAI: We live in an age in which talking to a machine is no longer a futuristic concept. Many activities and fields are being shaped by innovations in artificial intelligence (AI) and technology, a phenomenon that is fundamentally altering the nature of human interaction, consumption of information and, more controversially, the nature of employment. Adoption of innovations such as voice recognition, virtual agents and biometrics could be the only way for humans to survive in the coming age of automation. Amid the surge in human-machine cooperation, many people nevertheless are eager to find out what the future holds. Will robots outsmart humans and steal their jobs?

Comprehensive decision-strategy space exploration for efficient territorial planning strategies Olivier Billaud, 1, Maxence Soubeyrand, 1, Sandra Luque, 1 and Maxime Lenormand 1, † 1 TETIS, Univ Montpellier, AgroParisTech, Cirad, CNRS, Irstea, Montpellier, France Multi-Criteria Decision Analysis (MCDA) is a well-known decision support tool that can be used in a wide variety of contexts. It is particularly useful for territorial planning in situations where several actors with different, and sometimes contradictory, point of views have to take a decision regarding land use development. While the impact of the weights used to represent the relative importance of criteria has been widely studied in the recent literature, the impact of order weights determination have rarely been investigated. This paper presents a spatial sensitivity analysis to assess the impact of order weights determination in Multi-Criteria Analysis by Ordered Weighted Averaging. We propose a methodology based on an efficient exploration of the decision-strategy space defined by the level of risk and tradeoff in the decision process. We illustrate our approach with a land use planning process in the South of France. The objective is to find suitable areas for urban development while preserving green areas and their associated ecosystem services. The ecosystem service approach has indeed the potential to widen the scope of traditional landscape-ecological planning by including ecosystem-based benefits, including social and economic benefits, green infrastructures and biophysical parameters in urban and territorial planning. We show that in this particular case the decision-strategy space can be divided into four clusters. Each of them is associated with a map summarizing the average spatial suitability distribution used to identify potential areas for urban development.

Artificial Intelligence and Machine Learning are increasingly seen as key technologies for buildin g more decentralised and resilient energy grids, but researchers must consider the ethical and social implications of their use E nergy grids are undergoing rapid changes, requiring new ways both to process the large amounts of data generated from the power system, but also - increasingly - to take smart operational decisions [1]. On the data side, the UK and most EU countries have committed to a target of offering a smart meter to every home by 2020 [ 2 ], with similar monitoring being installed in other parts of the energy network. This has led to some to refer to a "data tsunami", requiri ng development of new machine learning techniques to deal with the e nsuing challenge of extracting useful information from this data - often in real time. Another trend is the use of AI techniques (such as those from multi - agent systems, computational gam e theory and decision making under uncertainty) to take autonomous allocation and control decisions. This is driven increasingly by the moves towards more decentralised energy systems, where prosumers (consumers with own micro - generation and storage) can g enerate and source their own electricity through peer - to - peer (P2P) trading in local energy markets and community energy schemes.

Noname manuscript No. (will be inserted by the editor) Abstract We formalize networks with evolving structures as temporal networks and propose a generative link prediction model, Generative Link Sequence Modeling (GLSM), to predict future links for temporal networks. GLSM captures the temporal link formation patterns from the observed links with a sequence modeling framework and has the ability to generate the emerging links by inferring from the probability distribution on the potential future links. To avoid overfitting caused by treating each link as a unique token, we propose a self-tokenization mechanism to transform each raw link in the network to an abstract aggregation token automatically. The self-tokenization is seamlessly integrated into the sequence modeling framework, which allows the proposed GLSM model to have the generalization capability to discover link formation patterns beyond raw link sequences. We compare GLSM with the existing state-of-art methods on five real-world datasets. The experimental results demonstrate that GLSM obtains future positive links effectively in a generative fashion while achieving the best performance (2-10% improvements on AUC) among other alternatives. Keywords Temporal link prediction, sequence modeling, recurrent neural network, self-tokenization mechanism 1 Introduction Many real-world applications could be modeled as link prediction problems. Lu Bai is the corresponding author, Email: bailucs@cufe.edu.cn 1. Central University of Finance and Economics, Beijing, P.R. China. Two mainstream categories in link prediction are either based on the statistical patterns of the link formation behaviors of the network [10, 2, 17] or the graph representation learning [31, 33] methods which embed nodes as vectors with respect to the network topological information. Most of these methods are discriminative models that verify whether an unknown link given during the test time is rational by training a classifier on existing links and negative samples [19].

Significant advances have been made in artificial systems by using biological systems as a guide. However, there is often little interaction between computational models for emergent communication and biological models of the emergence of language. Many researchers in language origins and emergent communication take compositionality as their primary target for explaining how simple communication systems can become more like natural language. However, there is reason to think that compositionality is the wrong target on the biological side, and so too the wrong target on the machine-learning side. As such, the purpose of this paper is to explore this claim. This has theoretical implications for language origins research more generally, but the focus here will be the implications for research on emergent communication in computer science and machine learning---specifically regarding the types of programmes that might be expected to work and those which will not. I further suggest an alternative approach for future research which focuses on reflexivity, rather than compositionality, as a target for explaining how simple communication systems may become more like natural language. I end by providing some reference to the language origins literature that may be of some use to researchers in machine learning.

Community Question Answering (cQA) provides new interesting research directions to the traditional Question Answering (QA) field, e.g., the exploitation of the interaction between users and the structure of related posts. In this context, we organized SemEval-2015 Task 3 on "Answer Selection in cQA", which included two subtasks: (a) classifying answers as "good", "bad", or "potentially relevant" with respect to the question, and (b) answering a YES/NO question with "yes", "no", or "unsure", based on the list of all answers. We set subtask A for Arabic and English on two relatively different cQA domains, i.e., the Qatar Living website for English, and a Quran-related website for Arabic. We used crowdsourcing on Amazon Mechanical Turk to label a large English training dataset, which we released to the research community. Thirteen teams participated in the challenge with a total of 61 submissions: 24 primary and 37 contrastive. The best systems achieved an official score (macro-averaged F1) of 57.19 and 63.7 for the English subtasks A and B, and 78.55 for the Arabic subtask A.

The term "uncanny valley" refers to that unsettling feeling you get when looking at an android that has been made to appear human. Of course, the problem goes away when we can make robots that are indistinguishable from humans. A paper published last week by New Yawk University claims that "bots are more efficient than humans at certain human-machine interactions, but only if they are allowed to hide their non-human nature." In other words, once we're past that whole uncanny valley problem, we're better served letting people think they're interacting with a human when in fact it's just artificial intelligence perfected. This raises a very important question.

In Japan, one restaurant is exploring artificial intelligence (AI) robotics technology to enable paralyzed employees to remotely pilot robotic waiters. JPMorgan Chase, Microsoft and Ford are hosting virtual career fairs tailored to the needs of neurodiverse candidates. Enterprise Rent-A-Car integrated braille-reader technology into its reservations system for blind employees. Using AI to increase accessibility at work is one of the Gartner Top 10 strategic predictions for 2020 and beyond. The predictions examine how technology is changing the definition of what it means to be human, and IT leaders must be prepared to adapt in a changing environment.