Automated Fact-Checking (AFC) is the automated verification of claim accuracy. AFC is crucial in discerning truth from misinformation, especially given the huge amounts of content are generated online daily. Current research focuses on predicting claim veracity through metadata analysis and language scrutiny, with an emphasis on justifying verdicts. This paper surveys recent methodologies, proposing a comprehensive taxonomy and presenting the evolution of research in that landscape. A comparative analysis of methodologies and future directions for improving fact-checking explainability are also discussed.

Recent progress towards theoretical interpretability guarantees for AI has been made with classifiers that are based on interactive proof systems. A prover selects a certificate from the datapoint and sends it to a verifier who decides the class. In the context of machine learning, such a certificate can be a feature that is informative of the class. For a setup with high soundness and completeness, the exchanged certificates must have a high mutual information with the true class of the datapoint. However, this guarantee relies on a bound on the Asymmetric Feature Correlation of the dataset, a property that so far is difficult to estimate for high-dimensional data. It was conjectured in W\"aldchen et al. that it is computationally hard to exploit the AFC, which is what we prove here. We consider a malicious prover-verifier duo that aims to exploit the AFC to achieve high completeness and soundness while using uninformative certificates. We show that this task is $\mathsf{NP}$-hard and cannot be approximated better than $\mathcal{O}(m^{1/8 - \epsilon})$, where $m$ is the number of possible certificates, for $\epsilon>0$ under the Dense-vs-Random conjecture. This is some evidence that AFC should not prevent the use of interactive classification for real-world tasks, as it is computationally hard to be exploited.

Generalized fiducial inference(GFI) Hannig et al.(2016), a modern reincarnation of R. A. Fisher's fiducial inference(Fisher, 1930), provides inferentially meaningful probability statements about subsets of parameter space without the need for subjective prior information. GFI specifies a generalized fiducial distribution (GFD) by defining a data-dependent measure on the parameter space through an inverse of a data-generating algorithm (see Sections 2). Data-generating algorithm plays the role of a model and is sometimes called data-generating equation or data-generating function. With GFD as a distribution estimator for the fixed parameter, we can further define approximate confidence (fiducial) sets which are often shown in simulation to have very desired properties. Given the data-generating algorithm and the corresponding density of the GFD, one could form point estimate and asymptotic confidence sets similarly as with a Bayesian posterior density. Standard MCMC-type sampling techniques have already been successfully implemented in many situations; see Hannig et al. (2016) and the references therein.

Job training is always important, but in the military it can mean the difference between life and death. Researchers at NC State University are working with the U.S. Army Futures Command (AFC) to develop artificial intelligence (AI) tools that can be used to improve squad training – and save lives. "We're developing AI programs that address two aspects of training, specifically for the synthetic training environments the Army uses to prepare its personnel," says Randall Spain, a research scientist in NC State's Center for Educational Informatics (CEI) who is working on the project. "One tool is focused on assessing team-level communication, which is critical to mission success and soldier safety. The second tool is focused on identifying the most effective ways of providing feedback to trainees."

As Amazon inches closer to establishing a drone delivery system, the e-commerce giant may one day keep its inventory in the sky. The US Patent Office earlier this year awarded Amazon a patent for an airborne fulfillment center (AFC) from which drones could launch to make local deliveries. The AFC could sit at an altitude of about 45,000 feet, the patent explains, allowing unmanned aerial vehicles (UAVs) to be stocked and deployed as necessary. "As the UAVs descend, they can navigate horizontally toward a user specified delivery location using little to no power, other than to stabilize the UAV and/or guide the direction of descent," the patent says. "Shuttles (smaller airships) may be used to replenish the AFC with inventory, UAVs, supplies, fuel, etc. Likewise, the shuttles may be utilized to transport workers to and from the AFC."

Amazon has filed a patent for flying warehouses that could use a fleet of drones to make deliveries to customers. A patent document filed in 2014 in the US describes giant airships as "airborne fulfilment centres" (AFCs) that could be stationed above metropolitan areas and used to store and quickly deliver items at times of high demand, using drones dispatched directly from the airship. The technology and e-commerce giant is already testing drone deliveries in the UK, and made its first commercial delivery under the trial in Cambridgeshire this month. The patent filing also suggests smaller aircraft and other unmanned aerial vehicles (UAVs) could ferry workers to and from the ship as well as replenish stock. "The AFC may be an airship that remains at a high altitude (eg 45,000ft) and UAVs with ordered items may be deployed from the AFC to deliver ordered items to user-designated delivery locations," the filing reads.

Amazon plans to use giant flying warehouses to help its drones make deliveries, a new patent reveals. Described as'airborne fulfilment centres' (AFC), these airships will hover over cities at 45,000ft before releasing drones to deliver goods. The patent follows news earlier this month that Amazon had made the first successful delivery by drone, after shipping a parcel to a customer in Cambridge. Amazon plans to use giant flying warehouses to help its drones make deliveries, a new patent reveals. Described as'airborne fulfilment centres' (AFC), these airships will hove over cities at 45,000ft before releasing drones to deliver goods The latest patent was filed back in May of this year and recently uncovered by Zoe Leavitt of CB Insights.

Earlier this month, Amazon celebrated the first successful drone delivery made by its Prime Air service. But that's just the beginning of Amazon's aerial assault into our homes, if a recently unearthed patent filing is to be believed. A possible endgame for the drone delivery agenda: The use of roving "airborne fulfillment centers" (AFCs) to make Prime deliveries. Giant, possibly autonomous motherships full of your favorite products that then spit out delivery drones to bring stuff to your front door. SEE ALSO: Amazon's grocery store disruption has a very human problem The patent, which was filed back in May of this year for Amazon Technologies Inc., was brought to our attention by Zoe Leavitt of CB Insights.