Event Detection (ED) aims to identify event trigger words from a given text and classify it into an event type. Most of current methods to ED rely heavily on training instances, and almost ignore the correlation of event types. Hence, they tend to suffer from data scarcity and fail to handle new unseen event types. To address these problems, we formulate ED as a process of event ontology population: linking event instances to pre-defined event types in event ontology, and propose a novel ED framework entitled OntoED with ontology embedding. We enrich event ontology with linkages among event types, and further induce more event-event correlations. Based on the event ontology, OntoED can leverage and propagate correlation knowledge, particularly from data-rich to data-poor event types. Furthermore, OntoED can be applied to new unseen event types, by establishing linkages to existing ones. Experiments indicate that OntoED is more predominant and robust than previous approaches to ED, especially in data-scarce scenarios.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - August 3. Join AI and data leaders for insightful talks and exciting networking opportunities. Until recently, there was no visceral sense that the largest challenge we face is fixing the planet. Responding to environmental problems was for too long viewed by big companies as a marketing strategy to target consumers who were more environmentally conscious than others. Today, the tides are, literally, changing, and sustainability is now mission critical for businesses as new wisdom has emerged that illustrates how being'green' is a catalyst for innovation and market opportunity. Climate tech companies can now leverage advances in visual data collection, computer vision and AI to bolster their bottom line by focusing on enhancing sustainable practices.

This article was published as a part of the Data Science Blogathon. MLOps, as a new area, is quickly gaining traction among Data Scientists, Machine Learning Engineers, and AI enthusiasts. MLOps are required for anything to reach production. Here's everything you need to know about MLOps and why it's so important for getting the most out of machine learning. When organizations needed to adopt Machine Learning solutions in the early 2000s, they used vendor-licensed software like SAS, SPSS, and FICO.

AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner How to Regulate #ArtificialIntelligence the Right Way? #AI must be regulated to protect the positive progress of the technology. Legislators across the globe have to this day failed to design laws that specifically regulate the use of artificial intelligence. This allows profit-oriented companies to develop systems that may cause harm to individuals and to the broader society. We need to regulate artificial intelligence for two reasons, First, because governments and companies use AI to make decisions that can have a significant impact on our lives. Second, because whenever someone takes a decision that affects us, they have to be accountable to us.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - August 3. Join AI and data leaders for insightful talks and exciting networking opportunities. Despite the need to maintain the integrity and security of data in enterprise artificial intelligence (AI) systems, an alarming number of organizations lack proper AI governance policies and tools to protect themselves from potential legal issues, O'Reilly Media researchers report. The Boston-based publisher and researcher today announced the results of its annual "AI Adoption in the Enterprise" survey. The benchmark report explores trends in how AI is being implemented, including the techniques, tools and practices organizations are using, in order to better understand the outcomes of enterprise adoption over the past year. Among respondents with AI products in production, the number of those whose organizations had a governance plan in place to oversee how projects are created, measured and observed was roughly the same as those that didn't (49% yes, 51% no).

AI is being increasingly used to make important decisions. Many AI experts (including Jeff Dean, head of AI at Google, and Andrew Ng, founder of Coursera and deeplearning.ai) I am an AI researcher, and I'm worried about some of the societal impacts that we're already seeing. At the end, I'll briefly share some positive ways that we can try to address these. Before we dive in, I need to clarify one point that is important to understand: algorithms (and the complex systems they are a part of) can make mistakes. These mistakes come from a variety of sources: bugs in the code, inaccurate or biased data, approximations we have to make (e.g.

The European Union's planned risk-based framework for regulating artificial intelligence includes powers for oversight bodies to order the withdrawal of a commercial AI system or require that an AI model be retrained if it's deemed high risk, according to an analysis of the proposal by a legal expert. That suggests there's significant enforcement firepower lurking in the EU's (still not yet adopted) Artificial Intelligence Act -- assuming the bloc's patchwork of Member State-level oversight authorities can effectively direct it at harmful algorithms to force product change in the interests of fairness and the public good. The draft Act continues to face criticizm over a number of structural shortcomings -- and may still fall far short of the goal of fostering broadly "trustworthy" and "human-centric" AI, which EU lawmakers have claimed for it. But, on paper at least, there looks to be some potent regulatory powers. The European Commission put out its proposal for an AI Act just over a year ago -- presenting a framework that prohibits a tiny list of AI use cases (such as a China-style social credit scoring system), considered too dangerous to people's safety or EU citizens' fundamental rights to be allowed, while regulating other uses based on perceived risk -- with a subset of "high risk" use cases subject to a regime of both ex ante (before) and ex post (after) market surveillance.

The Ada Lovelace Institute is an independent research institute, based in the UK and Brussels, with a mission to ensure data and AI work for people and society. Centring those affected by AI, Ada recommends enshrining legal rights for complaint and collective action and giving civil society a voice within standards setting. Ada recommends expanding and reshaping the role of risk in the Act. Risk should be based on'reasonably foreseeable' purpose and extended beyond individual rights and safety, to also include systemic and environmental risks. The Ada Lovelace Institute, has today published a series of proposed amendments to the EU AI Act aimed at recognising and empowering those affected by AI, expanding and reshaping the meaning of'risk' and accurately reflecting the nature of AI systems and their lifecycle.

ANJA KASPERSEN: Today I am very pleased to be joined by Pascale Fung. Pascale is a;rofessor in the Department of Electronic and Computer Engineering and Department of Computer Science and Engineering at The Hong Kong University of Science and Technology. She is known globally for her pioneering work on conversational artificial intelligence (AI), computational linguistics, and was one of the earliest proponents of statistical and machine-learning approaches for natural language processing (NLP). She is now leading groundbreaking research on how to build intelligent systems that can understand and empathize with humans. I have really been looking forward to this conversation with you. Your professional accolades are many, most of which we will touch on during our conversation. However, for our listeners to get to know you a bit better, I would like us to go back to your upbringing during what I understand to be a very tenuous political period in China. I was born, spent my childhood, ...

The applications of Artificial Intelligence (AI) are constantly expanding, opening up new possibilities in workflows, processes, and technological solutions. In a digitally connected future, where machines and humans work together for remarkably impressive results, companies that successfully adopt ethical AI will have an advantage. As AI becomes more pervasive in people's daily lives, the conversation shifts from technological advancement to the ramifications of using AI. When it comes to AI applications, security, privacy, ethics and bias are becoming increasingly important. Artificial intelligence (AI) helps decision-making, posing risks such as mimicking or amplifying human biases.