Our homes and offices are only as solid as the ground beneath them. When that solid ground turns to liquid--as sometimes happens during earthquakes--it can topple buildings and bridges. This phenomenon is known as liquefaction, and it was a major feature of the 2011 earthquake in Christchurch, New Zealand, a magnitude 6.3 quake that killed 185 people and destroyed thousands of homes. An upside of the Christchurch quake was that it was one of the most well-documented in history. Because New Zealand is seismically active, the city was instrumented with numerous sensors for monitoring earthquakes.

Artificial intelligence is a promising technology, that has made significant changes in the 21st century. Starting from self-driving cars and robotic assistants to automated disease diagnosis and drug discovery, the stronghold of artificial intelligence is no joke. Along with artificial intelligence, data science has also shifted the way we live and work. With the demand for data science and artificial intelligence spiralling, the job market is opening its door to AI and data science jobs. The tech sphere has ensured that artificial intelligence jobs and data science jobs provide limitless opportunities for professionals to explore cutting edge solutions.

Elena works in the field of Natural Language Processing. She graduated with a degree from Saint-Petersburg State University in Russia first and then acquired PhD from Macquarie University in Sydney, Australia, where she works currently. Now she applies theoretical concepts developed in the field of Natural Language Processing to solve business problems of different big and small enterprises. As an early adopter of BigData tools and concepts she finds existing BigData frameworks to be attractive means of working with data. She started using such tools and advising other people to adopt BigData concepts way before Hadoop, Spark and other related technologies became "must to know" tools for many IT professionals.

The Australian Government has released the Artificial Intelligence (AI) Action Plan in a bid to accelerate the development and adoption of AI technologies. "AI could contribute more than $20 trillion to the global economy by 2030, and the AI Action Plan will help us leverage opportunities for AI to further strengthen the economy and improve the quality of life of all Australians, while ensuring that the development and adoption of AI is guided by appropriate safeguards, privacy and ethical considerations," said Minister for Industry, Science and Technology Christian Porter. The Action Plan is backed by $124.1 million in funding announced in the May Budget and sets out four key focus areas for government that will help to position Australia as a global leader in AI. Focus one: Developing and adopting AI to transform Australian businesses -- support to help businesses develop and adopt AI technologies to create jobs and increase their productivity and competitiveness. Focus two: Creating an environment to grow and attract the world's best AI talent -- support to ensure our businesses have access to talent and expertise.

This volume contains revised versions of the papers selected for the second volume of the Online Handbook of Argumentation for AI (OHAAI). Previously, formal theories of argument and argument interaction have been proposed and studied, and this has led to the more recent study of computational models of argument. Argumentation, as a field within artificial intelligence (AI), is highly relevant for researchers interested in symbolic representations of knowledge and defeasible reasoning. The purpose of this handbook is to provide an open access and curated anthology for the argumentation research community. OHAAI is designed to serve as a research hub to keep track of the latest and upcoming PhD-driven research on the theory and application of argumentation in all areas related to AI.

A British-built solar powered drone with a 115ft wingspan that can stay in the air for over a year will be an alternative to low Earth orbit satellites, its developers claim. PHASA-35 is a cutting edge drone being developed by BAE systems at their facility in Warton, Lancashire that can fly about at 70,000ft above the surface for 20 months. It harnesses power from the Sun to stay airborne, charging a bank of small batteries during the day to keep it flying overnight, allowing for longer operations. The 150kg drone is able to carry a payload of up to 15kg including cameras, sensors and communications equipment to allow troops to talk to each other or provide internet access to rural locations during a natural disaster or emergency. BAE systems say it will be available by the middle of the decade and provide a'persistent and affordable alternative to satellite technology.'

Hey there Noonies! Hope the afternoon is going great with lots of code and coffee. Even I was just sitting by the window enjoying rain when suddenly the sky turned dark and I wanted to switch on the light to read my book better, but the switch is on the other side of the room! So, I just said, Hey Alexa, switch on the lights, and voila! After a while I switched on my TV and there it was, Gracie, helping out the covid patients and our front line superheroes amidst the pandemic. From a light switch to a pandemic, seems like artificial intelligence is slowly winning the world. Well, if you wanna join the race, here you go with the top stories on Artificial Intelligence on Hacker Noon.

Self-training is an effective approach to semi-supervised learning. The key idea is to let the learner itself iteratively generate "pseudo-supervision" for unlabeled instances based on its current hypothesis. In combination with consistency regularization, pseudo-labeling has shown promising performance in various domains, for example in computer vision. To account for the hypothetical nature of the pseudo-labels, these are commonly provided in the form of probability distributions. Still, one may argue that even a probability distribution represents an excessive level of informedness, as it suggests that the learner precisely knows the ground-truth conditional probabilities. In our approach, we therefore allow the learner to label instances in the form of credal sets, that is, sets of (candidate) probability distributions. Thanks to this increased expressiveness, the learner is able to represent uncertainty and a lack of knowledge in a more flexible and more faithful manner. To learn from weakly labeled data of that kind, we leverage methods that have recently been proposed in the realm of so-called superset learning. In an exhaustive empirical evaluation, we compare our methodology to state-of-the-art self-supervision approaches, showing competitive to superior performance especially in low-label scenarios incorporating a high degree of uncertainty.

We build on the In this paper we present our submission which tries to address unsupervised representations of speech proposed by the organizers all four tasks. We extend the baseline solution in several as a baseline, derived from CPC and clustered with the k-directions: we refine the intermediate representations, extracted means algorithm. We demonstrate that simple methods of refining with CPC, to directly improve the ABX scores. We show that those representations can narrow the gap, or even improve such representations can be used to perform simple fuzzy lookups upon the solutions which use a high computational budget. The in a large dataset, and even extract some common patterns results lead to the conclusion that the CPC-derived representations that serve as pseudo-words. Our approach to the semantic word are still too noisy for training language models, but stable similarity task is also based on pseudo-words.

The article proposes a new type of AI system that does not give solutions directly but rather points toward it, friendly prompting the user with questions and adjusting messages. Models of AI - human collaboration can be deduced from the classic literary example of interaction between Mr. Holmes and Dr. Watson from the stories by Conan Doyle, where the highly qualified expert Mr. Holmes, answers questions posed by Dr. Watson. Here Mr. Holmes, with his rule-based calculations, logic and memory management apparently plays the role of an AI system and Dr. Watson is the user. Looking into the same Holmes-Watson interaction, we find and promote another model in which the AI behaves like Dr. Watson, who, by asking questions and acting in a particular way, helps Holmes (the AI user) to make the right decisions. We call the systems based on this principle "Dr.Watson-type systems". The article describes the properties of such systems and introduces two particular - Patient Management System for intensive care physicians and Data Error Prevention System.