There is a discrepancy in the quality of education worldwide, and it shouldn't come as a surprise that artificial intelligence (AI) education isn't an exception. While some students enjoy small classrooms with fancy electronic whiteboards and qualified instructors, others don't even have electricity. The world is digitally and economically divided, especially in the case of AI education. There are numerous barriers preventing students from acquiring AI skills besides a foundation in mathematics. Many underserved communities lack resources.

Here's everything our readers were most obsessed with in 2019. Purchases you make through our links may earn us a commission. As we head into the new year, we think it's fun to look at all the wonderful products that we bought in 2019. This year brought some incredible releases like Disney, Apple AirPods Pro, and the all-new Kindle--and honestly, some of these things were apart of what really make the year great. So we decided to roundup 100 of the most popular products that people bought over and over again. Whether it was a massive sale (looking at you Black Friday) or one of the hottest product people couldn't stop talking about (*cough* weighted blankets *cough*), our readers found something that caught their eyes. From robot vacuums to wireless headphones to streaming services, these are the most popular products that people couldn't stop buying in 2019. Everyone become obsessed with Disney in 2019. Although it was just released in November, the new streaming service Disney became the most popular product of the year. With it came nostalgia for the Disney classics, new original shows and movies, and plenty of Baby Yoda content. Seriously, if you're a fan of Marvel, Disney Princesses, Star Wars, Pixar, and all things Disney, you might want to consider following suit and getting a subscription for yourself. We still love the tried-and-true Instant Pot Duo. It's no surprise here--our readers were all about that Instant Pot life this year. The Duo 6 Quart, a.k.a. the most popular model out there, was far and away the biggest seller this year, and in no small part because of the deals that ran on it during Prime Day and Black Friday, respectively. If you were one of the lucky ducks who nabbed it when it was just $50, good on you. But you can still get it for a pretty good price right now, too. Nobody really wants to vacuum, but they also don't want to spend a fortune on a robot vacuum to do their dirty work. That's why the Eufy 11S was so popular this year. It's the best affordable robot vacuum we've ever tested because it balances great cleaning powering and a reasonable price. Our readers loved scooping it up--especially when it was on sale as it is right now.

Accurate short-term load forecasting is essential for efficient operation of the power sector. Predicting load at a fine granularity such as individual households or buildings is challenging due to higher volatility and uncertainty in the load. In aggregate loads such as at grids level, the inherent stochasticity and fluctuations are averaged-out, the problem becomes substantially easier. We propose an approach for short-term load forecasting at individual consumers (households) level, called {\em Forecasting using Matrix Factorization} (\textsc{FMF}). \textsc{FMF} does not use any consumers' demographic or activity patterns information. Therefore, it can be applied to any locality with the readily available smart meters and weather data. We perform extensive experiments on three benchmark datasets and demonstrate that \textsc{FMF} significantly outperforms the computationally expensive state-of-the-art methods for this problem. We achieve up to $26.5 \%$ and $24.4 \%$ improvement in \textsc{RMSE} over Regression Tree and Support Vector Machine, respectively and up to $36 \%$ and $73.2 \%$ improvement in \textsc{MAPE} over Random Forest and Long Short-Term Memory neural network, respectively.

Forecasting influenza-like illness (ILI) is of prime importance to epidemiologists and health-care providers. Early prediction of epidemic outbreaks plays a pivotal role in disease intervention and control. Most existing work has either limited long-term prediction performance or lacks a comprehensive ability to capture spatio-temporal dependencies in data. Accurate and early disease forecasting models would markedly improve both epidemic prevention and managing the onset of an epidemic. In this paper, we design a cross-location attention based graph neural network (Cola-GNN) for learning time series embeddings and location aware attentions. We propose a graph message passing framework to combine learned feature embeddings and an attention matrix to model disease propagation over time. We compare the proposed method with state-of-the-art statistical approaches and deep learning models on real-world epidemic-related datasets from United States and Japan. The proposed method shows strong predictive performance and leads to interpretable results for long-term epidemic predictions.

Two of the most widely quoted and shared poems in the closing years of this decade were William Butler Yeats's "The Second Coming" ("Things fall apart; the centre cannot hold"), and W.H. Auden's "September 1, 1939" ("Waves of anger and fear / Circulate over the bright / And darkened lands of the earth"). Yeats's poem, written just after World War I, spoke of a time when "The best lack all conviction, while the worst / Are full of passionate intensity." Auden's poem, written in the wake of Germany's invasion of Poland, described a world lying "in stupor," as democracy is threatened and "the enlightenment driven away." Apocalypse is not yet upon our world as the 2010s draw to an end, but there are portents of disorder. The hopes nourished during the opening years of the decade -- hopes that America was on a progressive path toward growing equality and freedom, hopes that technology held answers to some of our most pressing problems -- have given way, with what feels like head-swiveling speed, to a dark and divisive new era.

Neuromorphic engineering, also known as neuromorphic computing,[1][2][3] is a concept developed by Carver Mead,[4] in the late 1980s, describing the use of very-large-scale integration (VLSI) systems containing electronic analog circuits to mimic neuro-biological architectures present in the nervous system.[5] In recent times, the term neuromorphic has been used to describe analog, digital, mixed-mode analog/digital VLSI, and software systems that implement models of neural systems (for perception, motor control, or multisensory integration). The implementation of neuromorphic computing on the hardware level can be realized by oxide-based memristors,[6] spintronic memories,[7] threshold switches, and transistors.[8] A key aspect of neuromorphic engineering is understanding how the morphology of individual neurons, circuits, applications, and overall architectures creates desirable computations, affects how information is represented, influences robustness to damage, incorporates learning and development, adapts to local change (plasticity), and facilitates evolutionary change. Neuromorphic engineering is an interdisciplinary subject that takes inspiration from biology, physics, mathematics, computer science, and electronic engineering to design artificial neural systems, such as vision systems, head-eye systems, auditory processors, and autonomous robots, whose physical architecture and design principles are based on those of biological nervous systems.[9]

We see the most promising applications in healthcare when real time decision making using simple data sets can relieve the burden on the caregiver and drive better outcomes for patients. For example, alarm fatigue in hospitals - both clinically and operationally - is a cause of lost productivity and decreased patient safety. AI technology can be applied to a smart pump to only sound an alarm when there is a critical need for intervention and to initiate an escalation process if there is no response. Trend: Digitization through the electronic health record brings a patient's entire clinical history to one place through notes, observations, lab results, imaging and other data. There is a risk that digitization creates only copies of the old analog data sets - electronic records without analytics and interoperability are just expensive paper.

The federal government on Monday announced it will invest AU$7.5 million for research into the use of artificial intelligence (AI) in healthcare. "Artificial intelligence will be critical in transforming the future of healthcare through improved preventive, diagnostic, and treatment approaches," a statement from acting Minister for Health Anne Ruston said. The new funding will be dispensed via grants to researchers through the Medical Research Future Fund. The government hopes the cash will be used to fully understand the potential benefits of AI in healthcare. "AI for better health, aged care, and disability services was recently identified as one of the top three areas where Australia is well positioned to transform existing industries and build new ones, including opportunities to export solutions worldwide," Ruston's statement continued.

Artificial Intelligence is using advances to help medical professionals detect and treat cancers; emergency responders predict and prepare for impending natural disasters; police identify criminals and safely disarm bombs; organisations improve products, services and processes and school children receive tailored help from virtual teachers suited to their learning style. Through the use of robots and software agents, the machine may even perform these tasks alone or as a team member collaborating with humans. If we are going to build machines that play roles that simulate human reasoning, behaviour and activities, as a society we should ensure that those machines benefit all members of society, regardless of their age, gender, religion or status in society, rather than replicate human biases, perpetuate disparities or widen the gap between the haves and have nots. If AI is a simulation of human intelligence, who does it simulate and does it have a gender? Whether you view gender as socially constructed by one's environment and culture, a biologically determined factor as in the essentialist perspective, or adhere to the theory of individual differences, gender plays a role in who we are.

Conflicting information in an agent's knowledge base may lead to a semantical defect, that is, a situation where it is impossible to draw any plausible conclusion. Finding out the reasons for the observed inconsistency (so-called diagnoses) and/or restoring consistency in a certain minimal way (so-called repairs) are frequently occurring issues in knowledge representation and reasoning. In this article we provide a series of first results for these problems in the context of abstract argumentation theory regarding the two most important reasoning modes, namely credulous as well as sceptical acceptance. Our analysis includes the following problems regarding minimal repairs/diagnoses: existence, verification, computation of one and enumeration of all solutions. The latter problem is tackled with a version of the so-called hitting set duality first introduced by Raymond Reiter in 1987. It turns out that grounded semantics plays an outstanding role not only in terms of complexity, but also as a useful tool to reduce the search space for diagnoses regarding other semantics.