Although deep convolutional networks have achieved improved performance in many natural language tasks, they have been treated as black boxes because they are difficult to interpret. Especially, little is known about how they represent language in their intermediate layers. In an attempt to understand the representations of deep convolutional networks trained on language tasks, we show that individual units are selectively responsive to specific morphemes, words, and phrases, rather than responding to arbitrary and uninterpretable patterns. In order to quantitatively analyze such an intriguing phenomenon, we propose a concept alignment method based on how units respond to the replicated text. We conduct analyses with different architectures on multiple datasets for classification and translation tasks and provide new insights into how deep models understand natural language.

For complex real-world systems, designing controllers are a difficult task. With the advent of neural networks as a proxy for complex function approximators, it has become popular to learn the controller directly. However, these controllers are specific to a given task and need to be relearned for a new task. Alternatively, one can learn just the model of the dynamical system and compose it with external controllers. Such a model is task (and controller) agnostic and must generalize well across the state space. This paper proposes learning a "sufficiently accurate" model of the dynamics that explicitly enforces small residual error on pre-defined parts of the state-space. We formulate task agnostic controller design for this learned model as an optimization problem with state and control constraints that is solved in an online fashion. We validate this approach in simulation using a challenging contact-based Ball-Paddle system.

V alidation is one of the most important aspects of clustering, but most approaches have been batch methods. Recently, interest has grown in providing incremental alternatives. This paper extends the incremental cluster validity index (iCVI) family to include incremental versions of Calinski-Harabasz (iCH), I index and Pakhira-Bandyopadhyay-Maulik (iI and iPBM), Silhouette (iSIL), Negentropy Increment (iNI), Representative Cross Information Potential (irCIP) and Representative Cross Entropy (irH), and Conn Index (iConn Index). Additionally, the effect of under-and over-partitioning on the behavior of these six iCVIs, the Partition Separation (PS) index, as well as two other recently developed iCVIs (incremental Xie-Beni (iXB) and incremental Davies-Bouldin (iDB)) was examined through a comparative study. Experimental results using fuzzy adaptive resonance theory (ART)-based clustering methods showed that while evidence of most under-partitioning cases could be inferred from the behaviors of all these iCVIs, over-partitioning was found to be a more challenging scenario indicated only by the iConn Index. The expansion of incremental validity indices provides significant novel opportunities for assessing and interpreting the results of unsupervised learning. L. E. Brito da Silva is with the Applied Computational Intelligence Laboratory, Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO 65409 USA, and also with the CAPES Foundation, Ministry of Education of Brazil, Bras ılia, DF 70040-020, Brazil (email: leonardoenzo@ieee.org). N. M. Melton is with the Applied Computational Intelligence Laboratory, Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO 65409 USA (email: niklasmelton@ieee.org). D. C. Wunsch II is with the Applied Computational Intelligence Laboratory, Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO 65409 USA (email: wunsch@ieee.org). I NTRODUCTION Cluster validation [1] is a critical topic in cluster analysis.

Many societal decision problems lie in high-dimensional continuous spaces not amenable to the voting techniques common for their discrete or single-dimensional counterparts. These problems are typically discretized before running an election or decided upon through negotiation by representatives. We propose a algorithm called Iterative Local Voting for collective decision-making in this setting. In this algorithm, voters are sequentially sampled and asked to modify a candidate solution within some local neighborhood of its current value, as defined by a ball in some chosen norm, with the size of the ball shrinking at a specified rate. We first prove the convergence of this algorithm under appropriate choices of neighborhoods to Pareto optimal solutions with desirable fairness properties in certain natural settings: when the voters' utilities can be expressed in terms of some form of distance from their ideal solution, and when these utilities are additively decomposable across dimensions. In many of these cases, we obtain convergence to the societal welfare maximizing solution.We then describe an experiment in which we test our algorithm for the decision of the U.S. Federal Budget on Mechanical Turk with over 2,000 workers, employing neighborhoods defined by various L-Norm balls. We make several observations that inform future implementations of such a procedure.

The Stellar Daisy, a massive South Korean tanker that sank in March 2017, was spotted on the floor of the South Atlantic Ocean nearly two years later, the CEO of an ocean exploration company revealed Sunday. This discovery could shed new light on exactly what caused the vessel to tilt and sink and provide some closure to the families of the 22 crew members who died. "We are pleased to report that we have located Stellar Daisy, in particular for our client, the South Korean Government, but also for the families of those who lost loved ones in this tragedy," Ocean Infinity CEO Oliver Plunkett said. "Through the deployment of multiple state of the art (autonomous underwater vehicles), we are covering the seabed with unprecedented speed and accuracy." The Stellar Daisy sank on March 31, 2017, nearly 2,500 miles east of Uruguay, while transporting iron ore from Brazil to China.

According to Verified Market Research, the Global Smart Robot Market was valued at USD 4.83 Billion in 2018 and is projected to reach USD 26.25 Billion by 2026, growing at a CAGR of 23.6% from 2019 to 2026. Smart robots are defined as the robots that have been enhanced with advanced technologies such as artificial intelligence (AI) and IoT. These robots are capable of learning from its environment and further building its capabilities based on that knowledge. Smart robots act like a man's substitution in executing the tasks that are either dangerous or repetitive, where man is incapable of performing due to body limitations, or tasks that occur in extreme environments. Moreover, these smart robots are designed to carry out specific tasks for personal, professional, and industrial applications such as elderly assistance, pool cleaning, and robotic pets among others.

Veteran perfumer David Apel works on the AI-designed fragrance.IBM and Symrise Artificial intelligence, a buzzword across several sectors, may be about to shake up the fragrance industry. IBM Research and Symrise -- a major global producer of flavors and fragrances that counts among its clients Estee Lauder, Coty and Victoria's Secret parent L Brands -- have created what they described as the industry's first AI-designed perfume for sale, after the two parties came together over a year ago. The AI tool, named Philyra, uses a machine-learning algorithm to study Symrise's database of some 1.7 million formulas and can identify "white space" before suggesting not only formulas that may resonate with consumers but also combinations that perfumers may not have thought of before. For instance, when asked to come up with the "most creative" interpretation of a fragrance created 12 years ago, the AI system generated one formula that removed an outdated material and upped the dosage of a popular sandalwood scent. It also unexpectedly introduced to the mix cedar wood, another ingredient popular with today's consumers, said David Apel, Symrise's VP and senior perfumer of fine fragrance.

Researchers from North Carolina State University have developed an algorithm that could give pig farms advance notice of porcine epidemic diarrhea virus (PEDV) outbreaks. The proof-of-concept algorithm has potential for use in real-time prediction of other disease outbreaks in food animals. PEDV is a virus that causes high mortality rates in preweaned piglets. The virus emerged in the U.S. in 2013 and by 2014 had infected approximately 50 percent of breeding herds. PEDV is transmitted by contact with contaminated fecal matter.

AGM's belief revision is one of the main paradigms in the study of belief change operations. In this context, belief bases (prioritised bases) have been largely used to specify the agent's belief state - whether representing the agent's `explicit beliefs' or as a computational model for her belief state. While the connection of iterated AGM-like operations and their encoding in dynamic epistemic logics have been studied before, few works considered how well-known postulates from iterated belief revision theory can be characterised by means of belief bases and their counterpart in a dynamic epistemic logic. This work investigates how priority graphs, a syntactic representation of preference relations deeply connected to prioritised bases, can be used to characterise belief change operators, focusing on well-known postulates of Iterated Belief Change. We provide syntactic representations of belief change operators in a dynamic context, as well as new negative results regarding the possibility of representing an iterated belief revision operation using transformations on priority graphs.

An artificial intelligence project backed by SpaceX founder Elon Musk has been so successful its developers are not releasing it to the public for fear it will be misused. Research group Open AI developed a'large-scale unsupervised language model' that is able to generate news stories from a simple headline. But the group insists it will not be releasing details of the programme and instead has unveiled a much smaller version for research purposes. Its developers claim the technology is poised to rapidly advance in the coming years and the full specification and details of the project will only be released when the negative applications have been discussed by researchers. Elon Musk's AI research group Open AI announced in a paper yesterday that it has generated'a large-scale unsupervised language model' that can write news stories from little more than a headline.