I've started a Newsletter for quantum computing news, if this field interests you, you can subscribe here. I cannot continue to write without tips, sponsorship and community support. A burgeoning quantum-computing ecosystem and emerging business use cases promise to create significant value for industries--if executives prepare for it and depending on how quantum computing evolves on the Cloud. The early 2020s have mostly seen a justification for the hype of Quantum computing with the in-company innovation teams we used to see a decade earlier when blockchain first came on the scene. The different is Quantum computing has more synergy with artificial intelligence.

Knowledge base question answering (KBQA) aims to answer a natural language question over a knowledge base (KB) as its knowledge source. A knowledge base (KB) is a structured database that contains a collection of facts in the form subject, relation, object, where each fact can have properties attached called qualifiers. For example, the sentence "Barack Obama got married to Michelle Obama on 3 October 1992 at Trinity United Church" can be represented by the tuple Barack Obama, Spouse, Michelle Obama, with the qualifiers start time 3 October 1992 and place of marriage Trinity United Church . Popular knowledge bases are DBpedia and WikiData. Early works on KBQA focused on simple question answering, where there's only a single fact involved.

Jeff Bezos went on 60 Minutes in 2013 and pledged to fill the skies with a fleet of delivery drones that could zip parcels to customers' homes in 30 minutes. Asked when this future would arrive, the Amazon.com Inc. founder said he expected drone deliveries to commence in the next five years or thereabouts. Almost a decade later, despite spending more than $2 billion and assembling a team of more than 1,000 people around the world, Amazon is a long way from launching a drone delivery service. A Bloomberg investigation based on internal documents, government reports and interviews with 13 current and former employees reveals a program beset by technical challenges, high turnover and safety concerns. A serious crash in June prompted federal regulators to question the drone's airworthiness because multiple safety features failed and the machine careened out of control, causing a brush fire.

Hope Speech Detection, a task of recognizing positive expressions, has made significant strides recently. However, much of the current works focus on model development without considering the issue of inherent imbalance in the data. Our work revisits this issue in hope-speech detection by introducing focal loss, data augmentation, and pre-processing strategies. Accordingly, we find that introducing focal loss as part of Multilingual-BERT's (M-BERT) training process mitigates the effect of class imbalance and improves overall F1-Macro by 0.11. At the same time, contextual and back-translation-based word augmentation with M-BERT improves results by 0.10 over baseline despite imbalance. Finally, we show that overlapping word removal based on pre-processing, though simple, improves F1-Macro by 0.28. In due process, we present detailed studies depicting various behaviors of each of these strategies and summarize key findings from our empirical results for those interested in getting the most out of M-BERT for hope speech detection under real-world conditions of data imbalance.

Argonne National Laboratory and NASA researchers have published a paper on the use of artificial intelligence to speed up simulations of air behavior surrounding supersonic and hypersonic aircraft propulsion systems. The partners trained a neural network to find shortcuts to answers about fuel combustion at environments faster than sound, reducing the memory and cost required to conduct computational fluid dynamics simulations, Argonne said Wednesday. The AI technology is expected to enhance the current process of running massive tables of combustion data through NASA's VULCAN-CFD hypersonic code and accelerate the development of barrier-breaking engines for supersonic and hypersonic aircraft. "Working with NASA gave us the opportunity to integrate our novel developments in a state-of-the-art CFD code, and also to further improve the developments for more efficient design and optimization of hypersonic jets," explained Sinan Demir, an Argonne computational scientist. The research paper was previously presented at the American Institute of Aeronautics and Astronautics SciTech Forum in January.

Artificial-intelligence algorithms are processing vast troves of data in electronic medical records, searching for patterns to predict future outcomes and recommend treatments. They are creating early-warning systems to help hospital staff spot subtle but serious changes in a patient's condition that aren't always visible or noticed in a busy unit, and predicting which patients about to be discharged from the hospital are at highest risk of being readmitted. These systems are just one effort in a vast array of AI projects in healthcare--from helping detect cancer in radiology images to identifying which drugs to test on patients with different diseases. But this prediction technology holds especially significant promise to transform care and improve patient safety in ER and ICU cases--as long as the systems can be designed to avoid some of the medical, technological and ethical concerns that have emerged in mixing the science of machine learning with the art of medicine. Sepsis Watch is based on data from 42,000 inpatient encounters, with 21.3% of those patients having experienced sepsis.

In November 2021, Oluwaseun Medayedupin was arrested by the Nigerian police in Lagos. An investigation found that he had been pursuing "disgruntled employees" from American companies and pushing them to release ransomware on internal enterprise servers, offering a percentage of the cut if they agreed to collaborate in the attack. This was a sophisticated social engineering scheme, far more advanced than the notorious "Nigerian prince" emails that have made the country of Nigeria synonymous with scams. The origins of these types of scams may be attributed to a boom in the establishment of cybercafes during the 1990s, coinciding with falling oil prices in Nigeria and a rise in unemployment. Add in a lack of national social security, and many Nigerians were forced to seek out alternative forms of employment--physical labor; gig work; and, most notoriously, cybercrime.

Fake faces created by artificial intelligence (AI) are considered more trustworthy than images of real people, a study has found. The results highlight the need for safeguards to prevent deep fakes, which have already been used for revenge porn, fraud and propaganda, the researchers behind the report say. The study – by Dr Sophie Nightingale from Lancaster University in the UK and Professor Hany Farid from the University of California, Berkeley, in the US – asked participants to identify a selection of 800 faces as real or fake, and to rate their trustworthiness. After three separate experiments, the researchers found the AI-created synthetic faces were on average rated 7.7% more trustworthy than the average rating for real faces. This is "statistically significant", they add.

Artificial intelligence has become an integral part of the day-to-day operations across most industries. And in great part, AI can be credited with condensing vast amounts of data into something more usable. But as companies come under greater public scrutiny for how algorithms are influencing corporate behavior, the question of how to ethically apply artificial intelligence is top of mind for commercial insurance leaders. Ethical use of technology is "not a problem that's exclusive to AI," Anthony Habayeb, founding CEO of Monitaur, an AI governance company, said. "Corporations have their corporate governance and need to have their opinion of what sort of ethics and practices they bring into the market as a company. And those principles should be implemented in their AI, software and practices overall," he explained.

It is true to say that the introduction of Artificial Intelligence, also known as AI models are a blessing to the IT industry. It has very well connected humans and the natural environment with technology in such a way that no one has ever expected. Machines have got enough power to replace humans. But, now the question arises is what is AI (Artificial Intelligence)? If it's playing an important role in the IT sector then why it's taking so much time to take over all the security management of the industry?