Image captioning models have achieved impressive results on datasets containing limited visual concepts and large amounts of paired image-caption training data. However, if these models are to ever function in the wild, a much larger variety of visual concepts must be learned, ideally from less supervision. To encourage the development of image captioning models that can learn visual concepts from alternative data sources, such as object detection datasets, we present the first large-scale benchmark for this task. Dubbed 'nocaps', for novel object captioning at scale, our benchmark consists of 166,100 human-generated captions describing 15,100 images from the Open Images validation and test sets. The associated training data consists of COCO image-caption pairs, plus Open Images image-level labels and object bounding boxes. Since Open Images contains many more classes than COCO, more than 500 object classes seen in test images have no training captions (hence, nocaps). We evaluate several existing approaches to novel object captioning on our challenging benchmark. In automatic evaluations these approaches show modest improvements over a strong baseline trained only on image-caption data. However, even when using ground-truth object detections, the results are significantly weaker than our human baseline - indicating substantial room for improvement.

"When the world is destroyed, it will be destroyed not by its madmen but by the sanity of its experts and the superior ignorance of its bureaucrats." Decades before the advent of the Internet, Fernando António Nogueira Pessoa assumed a variety of identities with the ease that has become common in cyber-social platforms--those where cyber technologies play a part in human activity (for example, online banking, and social networks). Pessoa, a Portuguese poet, writer, literary critic, translator, publisher, and philosopher, wrote under his own name as well as 75 imaginary identities. He would write poetry or prose using one identity, then criticize that writing using another identity, then defend the original writing using yet another identity. Described by author Carmela Ciuraru as "the loving ringmaster, director, and traffic cop of his literary crew," Pessoa is one of the foremost Portuguese poets and a contributor to the Western canon. The story of Pessoa illustrates a key insight that holds true for the cyber-social systems of today: Identity costs little in the way of minting, forming, and maintaining yet demands a high price for its timely and accurate attribution to physical agency. Along with the low cost of minting and maintaining identities, a lack of constraints on using identities is a primary factor that facilitates adversarial innovations that rely on deception. With these factors in mind, we study the following problem: Will it be possible to engineer a decentralized system that can enforce honest usage of identity via mutual challenges and costly consequences when challenges fail? The success of such an approach will remedy currently deteriorating situations without requiring new infrastructure. For example, such a system should be able to reduce fake persons in social engineering attacks, malware that mimics the attributes of trusted software, and Sybil attacks that use fake identities to penetrate ad hoc networks. Note that many cyber-physical facilities--those where a physical mechanism is controlled or monitored by computer algorithms and tied closely to the internet and its users (for example, autonomous cars, medical monitoring)--also aim to enable users to remain anonymous and carry out certain tasks with only a persistent but pseudonymous identity. This form of short-term identity (especially in the networks that are ad hoc, hastily formed, and short lived) can remain uncoupled from a user's physical identity and allow them to maintain a strong form of privacy control.

The U.S. Government's National Science Foundation (NSF) started the Research Experiences for Undergraduates (REU) program in the mid-1980s to attract undergraduates in STEM fields into research careers and to consider going to graduate school. The REU program offers grants to universities to plan and oversee research experiences that enrich undergraduate students' educational experiences. It is believed these experiences encourage the participants to pursue leadership careers in the fields of science, technology, engineering, or mathematics. The University of Central Florida's (UCF) Computer Vision group was in the selected first group of sites: only three REU sites in NSF's Division of Computer and Information Science and Engineering (CISE) were awarded funding in 1987. The grant duration was one year, so continued funding would require a new application for renewal the following year.

On June 6, 2015, the U.S. Department of Justice brought the first-ever online market-place prosecution against a price-fixing cartel. One of the special features of the case was that prices were set by algorithms. Topkins and his competitors designed and shared dynamic pricing algorithms that were programmed to act in conformity with their agreement to set coordinated prices for posters sold online. They were found to engage in an illegal cartel. Following the case, the Assistant Attorney General stated that "[w]e will not tolerate anticompetitive conduct, [even if] it occurs...over the Internet using complex pricing algorithms."

Google has been forced to abandon its specialist Chinese search engine that censors results in line with the strict government, reports have claimed. The firm is believed to have shut down an internal data analysis system which was being used to develop the search engine, known as Dragonfly. According to a report from The Intercept, this has'effectively ended' the entire project. Members of Google's privacy team raised concerns about the project back in August and it is now extremely unlikely the search engine can be built without the system, according to sources close to the project. Google has been forced to abandon its plan to launch a specialist Chinese search engine that censors results in line with the strict government.

We propose a variant of iterated belief revision designed for settings with limited computational resources, such as mobile autonomous robots. The proposed memory architecture---called the {\em universal memory architecture} (UMA)---maintains an epistemic state in the form of a system of default rules similar to those studied by Pearl and by Goldszmidt and Pearl (systems $Z$ and $Z^+$). A duality between the category of UMA representations and the category of the corresponding model spaces, extending the Sageev-Roller duality between discrete poc sets and discrete median algebras provides a two-way dictionary from inference to geometry, leading to immense savings in computation, at a cost in the quality of representation that can be quantified in terms of topological invariants. Moreover, the same framework naturally enables comparisons between different model spaces, making it possible to analyze the deficiencies of one model space in comparison to others. This paper develops the formalism underlying UMA, analyzes the complexity of maintenance and inference operations in UMA, and presents some learning guarantees for different UMA-based learners. Finally, we present simulation results to illustrate the viability of the approach, and close with a discussion of the strengths, weaknesses, and potential development of UMA-based learners.

Kalashnikov has revealed a pair of smart drones designed to protect Russian assets in the Arctic. The drones will offer'round-the-clock protection of the perimeters' according the the arms makers. The two'ZALA' drones have an automatic identification system that can gather information about a vessel at a distance of 62 miles, it claims. The ZALA 421-16Ev2, one of two new'ZALA' drones that have an automatic identification system that can gather information about a vessel at a distance of 62 miles, it is claimed The two drones will able to give operators information about each vessel: its name, size, course and speed. It also has its own alternative navigation system for when GPS or its Russian equivalent, is unavailable.

The rate of progress in the field of artificial intelligence is one of the most hotly contested aspects of the ongoing boom in teaching computers and robots how to see the world, make sense of it, and eventually perform complex tasks both in the physical realm and the virtual one. And just how fast the industry is moving, and to what end, is typically measured not just by actual product advancements and research milestones, but also by the prognostications and voiced concerns of AI leaders, futurists, academics, economists, and policymakers. AI is going to change the world -- but how and when are still open questions. Today, findings from a group of experts were published in an ongoing effort to help answer those questions. The experts include members of Harvard, MIT, Stanford, the nonprofit OpenAI, and the Partnership on AI industry consortium, among others, and they were put together as part of the second annual AI Index.

A new report released Monday reveals that the Internet Research Agency, the troll farm linked to Russian intelligence, actually increased its social media activity after the 2016 election. The report, which took seven months to complete and is the most comprehensive of its kind to date, comes from researchers at Oxford University and analytics firm Graphika. Their data shows the volume of IRA activity doubling between 2016 and 2017 on Facebook, Instagram, and Twitter, even as the number of ads purchased by the agency decreased. The amount of activity increased the most on Facebook-owned Instagram, where it more than doubled from 2,611 posts in 2016 to 5,956 posts in 2017. The research is based on Facebook data from 2015-2017, Twitter data from 2009-2018, and YouTube data from 2014-2018 that was provided by the companies to the Senate Intelligence Committee and relayed to the researchers.

While Japan is already a leading manufacturer of industrial robots, Groove X is trying to expand the fledgling market for household robots. It has raised 8 billion yen ($71.1 million) from investors including a Toyota Motor Corp-backed fund, chat app operator Line Corp and the Japanese government.