The COVID-19 pandemic continues to bring up various topics discussed or debated on social media. In order to explore the impact of pandemics on people's lives, it is crucial to understand the public's concerns and attitudes towards pandemic-related entities (e.g., drugs, vaccines) on social media. However, models trained on existing named entity recognition (NER) or targeted sentiment analysis (TSA) datasets have limited ability to understand COVID-19-related social media texts because these datasets are not designed or annotated from a medical perspective. In this paper, we release METS-CoV, a dataset containing medical entities and targeted sentiments from COVID-19 related tweets. METS-CoV contains 10,000 tweets with 7 types of entities, including 4 medical entity types (Disease, Drug, Symptom, and Vaccine) and 3 general entity types (Person, Location, and Organization). To further investigate tweet users' attitudes toward specific entities, 4 types of entities (Person, Organization, Drug, and Vaccine) are selected and annotated with user sentiments, resulting in a targeted sentiment dataset with 9,101 entities (in 5,278 tweets). To the best of our knowledge, METS-CoV is the first dataset to collect medical entities and corresponding sentiments of COVID-19 related tweets.

Will.i.am has been embracing innovative technology for years. Now he is using artificial intelligence in an effort to transform how we listen to the radio. The musician, entrepreneur and tech investor has launched RAiDiO.FYI, a set of interactive radio stations themed around topics like sport, pop culture, and politics. Each station is fundamentally interactive: tune in and you'll be welcomed by name by an AI host "live from the ether," the Black Eyed Peas frontman tells TIME. Hosts talk about their given topic before playing some music.

In childhood, we used to write an essay on "Science is a miracle as well as a curse." Today, Artificial intelligence (A.I.) has changed the way we live, work, and communicate. Many industries have been transformed through it, like I.T., healthcare, finance, transportation, and manufacturing. The need for A.I. security has become more critical as it keeps evolving and becoming more sophisticated. A.I. can make our lives easier but can also be a cyber threat if misused.

I've published a couple of articles lately about the need for organizations to have an artificial intelligence (AI) strategy and how AI can help organizations with employee development. It's possible that with all the conversation about AI technologies in today's news, organizations are talking about what AI could mean for their operation and how to get started. So, I wanted to bring in another technology expert to talk specifically about the things that organizations need to consider when looking at AI tools. Matthew Geohring, MS, is a technology solutions consultant for global insurance brokerage Hub International's HUB People & Technology Consulting Practice. Prior to joining HUB, Matthew spent time as both a human resources generalist and an in-house senior HRIS analyst. I'm excited to be sharing his thoughts with you today.

Every government has a requirement to ensure that laws are not only equitable to all citizens but also applicable. Philosophers for centuries have argued and debated about the relationship of the individual in a society, and the concept of fairness and equality is generally a main driving force in democratic populations. As we've seen with government polarization, laws can become really slow to get adopted. Usually, elected officials pass a law to assign a set of responsibilities to an agency or to a department. This responsible body can update the regulations as they see fit for the duration of its mandate.

Deep-sea mining would cause "extensive and irreversible" damage to sensitive habitats.NOAA This story was originally published by the Guardian and is reproduced here as part of the Climate Desk collaboration. An investigation by conservationists has found evidence that deep-seabed mining of rare minerals could cause "extensive and irreversible" damage to the planet. The report, published on Monday by the international wildlife charity Fauna & Flora, adds to the growing controversy that surrounds proposals to sweep the ocean floor of rare minerals that include cobalt, manganese and nickel. Mining companies want to exploit these deposits--which are crucial to the alternative energy sector--because land supplies are running low, they say.

Welcome to insideBIGDATA's annual technology predictions round-up! The big data industry has significant inertia moving into 2023. In order to give our valued readers a pulse on important new trends leading into next year, we here at insideBIGDATA heard from all our friends across the vendor ecosystem to get their insights, reflections and predictions for what may be coming. We were very encouraged to hear such exciting perspectives. Even if only half actually come true, Big Data in the next year is destined to be quite an exciting ride. There are many reasons why a customer would choose to implement their architecture on multiple clouds whether it's technology, market, or business-driven. When this happens, many times this leads to transactional and operational data being stored on multiple cloud platforms. The challenge this brings is how to gain insight into these without resorting to implementing multiple disparate data platforms. Historically data virtualization tools have been ...

First line: Management (process/model owners) has the primary responsibility to own and manage risks associated with development and day-to-day operational activities. Management should have a baseline understanding of risks in AI applications and where they manifest themselves in the specific models and data relevant to the organization's use cases. Second line: Risk management provides oversight in the form of frameworks, policies, procedures, methodologies, and tools. The second-line function should have a deep understanding of the AI-specific risks and related controls and mitigation. In assessing the first-line functions, internal audit should assess whether AI development and monitoring adheres to the organization's policies, best practices for model development and relevant regulations.

One of the most critical components in machine learning projects is the quality of an organization's database management system. And as artificial intelligence (AI) continues to grow more complex, access to adequate data is an increasingly important component of a company's success. For deep learning, forward-thinking companies must choose to upgrade to more robust and efficient databases. As reported by the World Economic Forum, the "deep" in deep learning refers to the depth of layers in a neural network. A neural network consisting of more than three layers--which would be inclusive of the inputs and the output--can be considered a deep learning algorithm.

To many leaders, it comes as a surprise to learn that the investment needed to develop AI solutions cannot realize a return through the deployment of single, disconnected use cases, or even a handful.1 This is why it's so important to have an AI strategy that is connected and coordinated across the enterprise, in tight alignment with the overarching business strategy. All too often, however, business leaders get the planning process out of order, focusing too much on use cases or abdicating leadership of the AI strategy to IT or data sciences. This can be a slippery slope, diminishing the organization's ability to use AI to create new ways of competing for customers, launching products, accelerating time-to-market, securing supply chains, and beyond. The strongest AI strategies tend to begin without ever mentioning AI.