With international trade agreements becoming increasingly complex, UNCTAD is working with the Brazilian arm of the International Chamber of Commerce (ICC Brazil) to use artificial intelligence (AI) to help trade negotiators, especially those representing less powerful nations. "Artificial intelligence could help reduce the complexity of information and level the playing field between big and small players in trade negotiations," said Bonapas Onguglo, in charge of UNCTAD's trade analysis branch. A comparison of the 1985 US-Israel trade deal with the one that the United States and Singapore signed in 2004 shows how much such agreements have evolved. AI assists in trade negotiations The 1985 deal has less than 8,000 words and contains just 22 articles, mostly dedicated to tariffs, agricultural restrictions, import licensing and rules of origin – what Harvard economist Dani Rodrik calls conventional trade topics . While these issues are also covered in the US-Singapore deal, most of its 20 chapters and 70,000 or so words deal with other topics such as anti-competitive business conduct, e-commerce, intellectual property, investment rules, labour rights and the environment.

"We have the opportunity in this election in Brazil for the first time, here and around the world, to be very prepared to deal with the pitfalls of technology, such as fake news, social bots and macro-targets," said Rodrigo Helcer, CEO of Stilingue, a technology company specialized in artificial intelligence, during the talk "AI and Elections in Brazil" at the Path Festival in S--o Paulo. Stilingue was created to monitor social media posts and the media in Portuguese using artificial intelligence (AI). During the elections, marketing and advertising companies will use Stilingue technology to promote candidates and to help manage politicians' reputations. "AI brings politics closer to voters. Voters will be listened to, more protected and closer to their candidates," Helcer said.

The random-forest technique has emerged in recent years as a powerful way to analyze large data sets while avoiding some of the pitfalls of other data-mining methods. It is based on the idea that some future event can be determined by a decision tree in which an outcome is calculated at each branch by reference to a set of training data. However, decision trees suffer from a well-known problem. In the latter stages of the branching process, decisions can become severely distorted by training data that is sparse and prone to huge variation at this kind of resolution, a problem known as overfitting. The random-forest approach is different.

Provided significant future progress in artificial intelligence and computing, it may ultimately be possible to create multiple Artificial General Intelligences (AGIs), and possibly entire societies living within simulated environments. In that case, it should be possible to improve the problem solving capabilities of the system by increasing the speed of the simulation. If a minimal simulation with sufficient capabilities is created, it might manage to increase its own speed by accelerating progress in science and technology, in a way similar to the Technological Singularity. This may ultimately lead to large simulated civilizations unfolding at extreme temporal speedups, achieving what from the outside would look like a Temporal Singularity. Here we discuss the feasibility of the minimal simulation and the potential advantages, dangers, and connection to the Fermi paradox of the Temporal Singularity. The medium-term importance of the topic derives from the amount of computational power required to start the process, which could be available within the next decades, making the Temporal Singularity theoretically possible before the end of the century.

In this study, we predict the outcome of the football matches in the FIFA World Cup 2018 to be held in Russia this summer. We do this using classification models over a dataset of historic football results that includes attributes from the playing teams by rating them in attack, midfield, defence, aggression, pressure, chance creation and building ability. This last training data was a result of merging international matches results with AE games ratings of the teams considering the timeline of the matches with their respective statistics. Final predictions show the four countries with the most chances of getting to the semifinals as France, Brazil, Spain and Germany while giving Spain as the winner. The objective of this study is to build a predictive model that will allow us to make good predictions for the coming World Cup 2018 so we looked for dataset with historic data for match results, for this purpose we chose a dataset from Kaggle with data of almost 40,000 international matches played between 1872 and 2018.

A reinforcement learning agent that needs to pursue different goals across episodes requires a goal-conditional policy. In addition to their potential to generalize desirable behavior to unseen goals, such policies may also enable higher-level planning based on subgoals. In sparse-reward environments, the capacity to exploit information about the degree to which an arbitrary goal has been achieved while another goal was intended appears crucial to enable sample efficient learning. However, reinforcement learning agents have only recently been endowed with such capacity for hindsight. In this paper, we demonstrate how hindsight can be introduced to policy gradient methods, generalizing this idea to a broad class of successful algorithms. Our experiments on a diverse selection of sparse-reward environments show that hindsight leads to a remarkable increase in sample efficiency.

We consider fractional hedonic games, a subclass of coalition formation games that can be succinctly modeled by means of a graph in which nodes represent agents and edge weights the degree of preference of the corresponding endpoints. The happiness or utility of an agent for being in a coalition is the average value she ascribes to its members. We adopt Nash stable outcomes as the target solution concept; that is we focus on states in which no agent can improve her utility by unilaterally changing her own group. We provide existence, efficiency and complexity results for games played on both general and specific graph topologies. As to the efficiency results, we mainly study the quality of the best Nash stable outcome and refer to the ratio between the social welfare of an optimal coalition structure and the one of such an equilibrium as to the price of stability. In this respect, we remark that a best Nash stable outcome has a natural meaning of stability, since it is the optimal solution among the ones which can be accepted by selfish agents. We provide upper and lower bounds on the price of stability for different topologies, both in case of weighted and unweighted edges. Beside the results for general graphs, we give refined bounds for various specific cases, such as triangle-free, bipartite graphs and tree graphs. For these families, we also show how to efficiently compute Nash stable outcomes with provable good social welfare.

Every time the World Cup rolls around, there is always a random animal that can mysteriously predict the entire tournament. Previously we've seen the likes of Paul the Octopus, the physic turtle and this year we have'Mystic Marcus' the micro pig. Should we really be trusting an animal with our World Cup betting, though? Surely we should instead put all our faith in the things that now control our lives: technology. Technische Universitat Dortmund, Ghent University and the Technical University of Munich have developed an AI system that has analysed 100,000 simulations for this summer's tournament in Russia.

In the previous blog posts in this series, we discussed the motivation for clustering attacks and the data used and how to calculate the distance between two attacks using different methods on each feature we extracted. In this final blog post, we'll discuss the clustering algorithm itself – how to use the distance we calculated to create clusters from the data. We will discuss clustering in real time when only a small amount of data can be stored in memory. Finally, we'll show some results of the algorithm based on real data from Imperva customers. Now we have all the basic ingredients to input into the algorithm.

Washington: Scientists have developed an artificial intelligence system that can accurately diagnose Zika virus and several other viral, bacterial and even genetic diseases from the patient's blood. The platform developed by scientists at the University of Campinas (UNICAMP) in Brazil, can identify tens of thousands of molecules present in blood serum, with an artificial intelligence algorithm. "We used infection by Zika virus as a model to develop the platform and showed that in this case, diagnostic accuracy exceeded 95%. One of the main advantages is that the method doesn't lose sensitivity even if the virus mutates," said Rodrigo Ramos Catharino, principal investigator at UNICAMP. Another strength of the platform, he added, is the capacity to identify positive cases of Zika even in blood serum analysed 30 days after the start of infection, when the acute phase of the disease is over.