With help from high resolution satellite imagery and some advanced artificial intelligence techniques, European scientists have been counting the trees in a parched African desert. They pored over 1.3 million square kilometres of the waterless western Sahara and the arid lands of the Sahel to the south, to identify what is in effect an unknown forest. This region a stretch of dunes and dryland larger than Angola, or Peru, or Niger proved to be home to 1.8 billion trees and shrubs with crowns larger than three square metres. "We were very surprised to see that quite a few trees actually grow in the Sahara Desert because up till now, most people thought that virtually none existed. We counted hundreds of millions of trees in the desert alone," said Martin Brandt, a geographer at the University of Copenhagen in Denmark, who led the research.

Early this spring as the pandemic began accelerating, AJ Venkatakrishnan took genetic data from 10,967 samples of the novel coronavirus and fed it into a machine. The Stanford-trained data scientist did not have a particular hypothesis, but he was hoping the artificial intelligence would pinpoint possible weaknesses that could be exploited to develop therapies. He was awed when the program reported back that the new virus appeared to have a snippet of DNA code -- "RRARSAS" -- distinct from its predecessor coronaviruses. This sequence, he learned, mimics a protein that helps the human body regulate salt and fluid balance. Venkatakrishnan, director of scientific research and partnerships at AI start-up Nference, wondered whether this change might allow the virus to act as a kind of Trojan horse. Could this explain its high infection and transmission rates?

Early this spring as the pandemic began accelerating, AJ Venkatakrishnan took genetic data from 10,967 samples of the novel coronavirus and fed it into a machine. The Stanford-trained data scientist did not have a particular hypothesis, but he was hoping the artificial intelligence would pinpoint possible weaknesses that could be exploited to develop therapies. He was awed when the program reported back that the new virus appeared to have a snippet of DNA code – "RRARSAS" – distinct from its predecessor coronaviruses. This sequence, he learned, mimics a protein that helps the human body regulate salt and fluid balance. Venkatakrishnan, director of scientific research and partnerships at AI start-up Nference, wondered whether this change might allow the virus to act as a kind of Trojan horse. Could this explain its high infection and transmission rates?

You are free to share this article under the Attribution 4.0 International license. There are far more trees in the West African Sahara Desert than you might expect, according to a study that combined artificial intelligence and detailed satellite imagery. Researchers counted over 1.8 billion trees and shrubs in the 1.3 million square kilometer (501,933 square miles) area that covers the western-most portion of the Sahara Desert, the Sahel, and what are known as sub-humid zones of West Africa. "We were very surprised to see that quite a few trees actually grow in the Sahara Desert, because up until now, most people thought that virtually none existed," says Martin Brandt, professor in the geosciences and natural resource management department at the University of Copenhagen and lead author of the study in Nature. "We counted hundreds of millions of trees in the desert alone. Doing so wouldn't have been possible without this technology. Indeed, I think it marks the beginning of a new scientific era."

Given a set $\mathcal{C}=\{C_i\}_{i=1}^m$ of square matrices, the matrix blind joint block diagonalization problem (BJBDP) is to find a full column rank matrix $A$ such that $C_i=A\Sigma_iA^\text{T}$ for all $i$, where $\Sigma_i$'s are all block diagonal matrices with as many diagonal blocks as possible. The BJBDP plays an important role in independent subspace analysis (ISA). This paper considers the identification problem for BJBDP, that is, under what conditions and by what means, we can identify the diagonalizer $A$ and the block diagonal structure of $\Sigma_i$, especially when there is noise in $C_i$'s. In this paper, we propose a ``bi-block diagonalization'' method to solve BJBDP, and establish sufficient conditions under which the method is able to accomplish the task. Numerical simulations validate our theoretical results. To the best of the authors' knowledge, existing numerical methods for BJBDP have no theoretical guarantees for the identification of the exact solution, whereas our method does.

Today is the era of intelligence in machines. With the advances in Artificial Intelligence, machines have started to impersonate different human traits, a chatbot is the next big thing in the domain of conversational services. A chatbot is a virtual person who is capable to carry out a natural conversation with people. They can include skills that enable them to converse with the humans in audio, visual, or textual formats. Artificial intelligence conversational entities, also called chatbots, conversational agents, or dialogue system, are an excellent example of such machines. Obtaining the right information at the right time and place is the key to effective disaster management. The term "disaster management" encompasses both natural and human-caused disasters. To assist citizens, our project is to create a COVID Assistant to provide the need of up to date information to be available 24 hours. With the growth in the World Wide Web, it is quite intelligible that users are interested in the swift and relatedly correct information for their hunt. A chatbot can be seen as a question-and-answer system in which experts provide knowledge to solicit users. This master thesis is dedicated to discuss COVID Assistant chatbot and explain each component in detail. The design of the proposed chatbot is introduced by its seven components: Ontology, Web Scraping module, DB, State Machine, keyword Extractor, Trained chatbot, and User Interface.

Subspace clustering methods have been widely studied recently. When the inputs are 2-dimensional (2D) data, existing subspace clustering methods usually convert them into vectors, which severely damages inherent structures and relationships from original data. In this paper, we propose a novel subspace clustering method for 2D data. It directly uses 2D data as inputs such that the learning of representations benefits from inherent structures and relationships of the data. It simultaneously seeks image projection and representation coefficients such that they mutually enhance each other and lead to powerful data representations. An efficient algorithm is developed to solve the proposed objective function with provable decreasing and convergence property. Extensive experimental results verify the effectiveness of the new method.

Commonsense knowledge (CSK) about concepts and their properties is useful for AI applications such as robust chatbots. Prior works like ConceptNet, TupleKB and others compiled large CSK collections, but are restricted in their expressiveness to subject-predicate-object (SPO) triples with simple concepts for S and monolithic strings for P and O. Also, these projects have either prioritized precision or recall, but hardly reconcile these complementary goals. This paper presents a methodology, called Ascent, to automatically build a large-scale knowledge base (KB) of CSK assertions, with advanced expressiveness and both better precision and recall than prior works. Ascent goes beyond triples by capturing composite concepts with subgroups and aspects, and by refining assertions with semantic facets. The latter are important to express temporal and spatial validity of assertions and further qualifiers. Ascent combines open information extraction with judicious cleaning using language models. Intrinsic evaluation shows the superior size and quality of the Ascent KB, and an extrinsic evaluation for QA-support tasks underlines the benefits of Ascent.

It's that time again for Americans to take to the polls. If you've lived long enough, you recognize the patterns… Each opposing political side, shades the other, scandals and leaks may pop, shortcomings are magnified, critics make the news, promises are doled out'rather-convincingly' and there's an overwhelming sense of'nationality and togetherness' touted by both sides… And often, we simply choose the'lesser of the two evils', because candidly the one is not significantly better than the other. So today, I'm going to analyze the presidential debates of President Trump and Vice-President Biden… The entire analysis is done by the Author, using scientific methods that do not assume faultlessness. This is a personal project devoid of any political affiliations, sentiments or undertones. The inferences expressed from this scientific process are entirely the Author's, based on the data.

The competitive business landscape is ripe for the use of artificial intelligence (AI)-driven to set organisations apart. Yet, many are still treating AI like a sidekick: underworked and underestimated. An AI strategy, operating model and solid execution framework all play a vital part in ensuring that the technology works in the interests of the organisation. Aligned to business objectives and singular in its commitment to learning powerful automated processes to get the job done, AI has the potential to be a team's ultimate superhero. During AI transformation projects, companies often make the mistake of separating the vision from the execution, resulting in disjointed and complicated AI programs that can take years to consolidate.