On Nov. 25, an article headlined "Spot the deepfake. The editors would not have placed this piece on the front page a year ago. If they had, few would have understood what its headline meant. This technology, one of the most worrying fruits of rapid advances in artificial intelligence (AI), allows those who wield it to create audio and video representations of real people saying and doing made-up things. As this technology develops, it becomes increasingly difficult to distinguish real audio and video recordings from fraudulent misrepresentations created by manipulating real sounds and images. "In the short term, detection will be reasonably effective," says Subbarao Kambhampati, a professor of computer science at Arizona State University. "In the longer run, I think it will be impossible to distinguish between the real pictures and the fake pictures."2 The longer run may come as early as later this year, in time for the presidential election.

Fungal Biosynthetic Gene Clusters (BGCs) of secondary metabolites are clusters of genes capable of producing natural products, compounds that play an important role in the production of a wide variety of bioactive compounds, including antibiotics and pharmaceuticals. Identifying BGCs can lead to the discovery of novel natural products to benefit human health. Previous work has been focused on developing automatic tools to support BGC discovery in plants, fungi, and bacteria. Data-driven methods, as well as probabilistic and supervised learning methods have been explored in identifying BGCs. Most methods applied to identify fungal BGCs were data-driven and presented limited scope. Supervised learning methods have been shown to perform well at identifying BGCs in bacteria, and could be well suited to perform the same task in fungi. But labeled data instances are needed to perform supervised learning. Openly accessible BGC databases contain only a very small portion of previously curated fungal BGCs. Making new fungal BGC datasets available could motivate the development of supervised learning methods for fungal BGCs and potentially improve prediction performance compared to data-driven methods. In this work we propose new publicly available fungal BGC datasets to support the BGC discovery task using supervised learning. These datasets are prepared to perform binary classification and predict candidate BGC regions in fungal genomes. In addition we analyse the performance of a well supported supervised learning tool developed to predict BGCs.

Studies of networked phenomena, such as interactions in online social media, often rely on incomplete data, either because these phenomena are partially observed, or because the data is too large or expensive to acquire all at once. Analysis of incomplete data leads to skewed or misleading results. In this paper, we investigate limitations of learning to complete partially observed networks via node querying. Concretely, we study the following problem: given (i) a partially observed network, (ii) the ability to query nodes for their connections (e.g., by accessing an API), and (iii) a budget on the number of such queries, sequentially learn which nodes to query in order to maximally increase observability. We call this querying process Network Online Learning and present a family of algorithms called NOL*. These algorithms learn to choose which partially observed node to query next based on a parameterized model that is trained online through a process of exploration and exploitation. Extensive experiments on both synthetic and real world networks show that (i) it is possible to sequentially learn to choose which nodes are best to query in a network and (ii) some macroscopic properties of networks, such as the degree distribution and modular structure, impact the potential for learning and the optimal amount of random exploration.

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.

We propose a novel method for fact-checking on knowledge graphs based on debate dynamics. The underlying idea is to frame the task of triple classification as a debate game between two reinforcement learning agents which extract arguments -- paths in the knowledge graph -- with the goal to justify the fact being true (thesis) or the fact being false (antithesis), respectively. Based on these arguments, a binary classifier, referred to as the judge, decides whether the fact is true or false. The two agents can be considered as sparse feature extractors that present interpretable evidence for either the thesis or the antithesis. In contrast to black-box methods, the arguments enable the user to gain an understanding for the decision of the judge. Moreover, our method allows for interactive reasoning on knowledge graphs where the users can raise additional arguments or evaluate the debate taking common sense reasoning and external information into account. Such interactive systems can increase the acceptance of various AI applications based on knowledge graphs and can further lead to higher efficiency, robustness, and fairness.

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.

While the default in amateur diagnostics has become a quick Google search, increasingly innovators are looking to curb potential health misinformation pitfalls. New research published last week by JAMA found that Buoy Health's free chatbot helped decrease the rate of uncertainty among patients. The study also found that patients using the platform were more likely to decrease their intended level of care after using the technology. "We're excited to have our results published -- clearing patient confusion and reducing unnecessary ER/urgent care visits have big implications as healthcare costs continue to rise," Dr. Andrew Le, CEO and cofounder of Buoy Health told MobiHealthNews in an email. "That said, we will be working on follow-on studies, always focused on proving our outcomes and safety. We believe in peer-reviewed science and will continue to pursue transparency."

Together with the rise of the Internet, access to large repositories of data has helped machine learning technology grow exponentially. The incredibly quick pace of growth was unprecedented. As a result, it is obvious that AI will make a significant impact on the world in the years to come. However, with the numerous established and emerging fields of AI around today, such a blanket statement doesn't provide much concrete meaning. What fields and applications of AI are receiving the most investment and development?

The US presidential election campaign is in its final days. Donald Trump is behind in the polls and the pundits are predicting a win for his Democrat challenger, former vice president Joe Biden. He boasts that he will win again. With two weeks to go, his campaign unleashes an offensive in the crucial swing states: adverts, Facebook posts, WhatsApp groups and tweets. They warn of violent crime and civil unrest driven by immigrants and gangs, playing up Trump's endorsement by evangelicals and smearing Biden as a closet atheist. The initiative works and Trump snatches another unlikely victory.

President Recep Tayyip Erdogan of Turkey has become the Tripoli government's last major patron, providing armed drones, armored vehicles and, in the past week, Turkish troops. Turkish officials say their troops will act mostly in an advisory role and avoid front-line combat. But there are indications, from American officials and from videos posted on the internet, that Ankara has deployed Syrian irregulars to Libya, drawn from units that fought the Kurds in northeastern Syria last year. The increasingly prominent foreign role drew an angry rebuke from the United Nations envoy to Libya, Ghassan Salamé, who told reporters on Monday that "probably thousands" of foreign mercenaries had arrived in Libya to participate in the fight. The battle has displaced 300,000 people and caused over 2,200 deaths.