Amid rising geopolitical tensions and intensifying polarisation, building a global consensus around the use of artificial intelligence (AI) is likely to be tough. Yet experts at a recent Science Business Data Rules workshop were cautiously optimistic that the necessary political will exists. If they fail to achieve some form of coordination, all of the world's major powers will suffer, according to MEP Brando Benifei, one of the European Parliament's rapporteurs for the EU's AI Act, which could arrive on the statute books next year. "I think it would be a problem, not just for Europe, but for all the players involved because artificial intelligence will be a very pervasive technology," he said. "Having two different contexts of application, standards and regulation will make it complicated to deal with all the activities that now interconnect the world. So I think we need really to put an effort into avoiding this situation."

Most research on question answering focuses on the pre-deployment stage; i.e., building an accurate model for deployment. In this paper, we ask the question: Can we improve QA systems further \emph{post-}deployment based on user interactions? We focus on two kinds of improvements: 1) improving the QA system's performance itself, and 2) providing the model with the ability to explain the correctness or incorrectness of an answer. We collect a retrieval-based QA dataset, FeedbackQA, which contains interactive feedback from users. We collect this dataset by deploying a base QA system to crowdworkers who then engage with the system and provide feedback on the quality of its answers. The feedback contains both structured ratings and unstructured natural language explanations. We train a neural model with this feedback data that can generate explanations and re-score answer candidates. We show that feedback data not only improves the accuracy of the deployed QA system but also other stronger non-deployed systems. The generated explanations also help users make informed decisions about the correctness of answers. Project page: https://mcgill-nlp.github.io/feedbackqa/

The Final Report also makes specific recommendations for the introduction of legislation which regulates the use of facial recognition and other biometric technology, and for a moratorium on the use of this technology in AI-informed decision-making until such legislation is enacted. The recommendations of the AHRC have been submitted to the Australian Government. The Australian Government has the ability to determine whether to adopt the recommendations of the Report or not. The adoption of the AHRC's recommendations for the introduction of specific legislation governing the use of AI would signal a change in the approach to the regulation of AI and other emerging technologies that has been adopted in Australia to date. Free data access is an issue in the use of AI tools in the provision of legal services in Australia. The success of an AI tool will be determined by the size and diversity of the sample data which is used to train that tool. There are a number of factors that contribute to free data access in Australia and generally these factors apply across the spectrum of different categories of AI tools discussed in question 2 (being litigation, transactional and knowledge management tools).

But when the technology is used to identify suspects in criminal cases, those flaws in the system can have catastrophic, life-changing consequences. People can be wrongly identified, arrested, and convicted, often without ever being told they were ID'd by a computer. It's especially troubling when you consider false identifications disproportionately affect women, young people, and people with dark skin--basically everyone other than white men. This content can also be viewed on the site it originates from. This week on Gadget Lab, WIRED senior writer Khari Johnson joins us to talk about the limits of facial recognition tech, and what happens to the people who get misidentified.

New research in which machine-learning models were trained to verify more than one million companies has demonstrated that artificial intelligence (AI) can precisely quantify the success and failure aspects of a startup. The outcome is a tool that allows investors to identify the next opportunities. A known fact is that about 90% of startups are unsuccessful - about 10% to 20% fail within their first year. This shows the notable risk to Venture Capitalists and other investors in early-stage companies. In an attempt to identify which companies are most likely to succeed, researchers have developed a machine-learning model trained on the historical performance of more than one million companies.

Both houses of the U.S. Congress now have significant legislation designed specifically to rein in AI created by contractors for use by the federal government. The news arrives as an influential non-governmental research institute has issued a major critique of the European Commission's proposed AI Act, citing problems and suggested solutions. A House of Representatives bill would create new rules for how the federal government buys artificial intelligence, presumably including biometric applications. The Senate already is deliberating its version of the legislation. Both bodies have to agree on bills for them to be sent to the president for a signature.

It is a great pleasure to be here for the formal opening of this Committee's – of the CAI's - first meeting. And I want to thank each one of you who have agreed to devote your time and expertise to the task that lies ahead. You are needed, and you are appreciated. That is based on the Council of Europe's standards on human rights, democracy and the rule of law – Well, it is no doubt a huge challenge. Ultimately, it could result in a legally binding convention that will shape and improve people's interaction with AI as it expands its reach further and further in the years ahead.

Don't look now, but using Google Analytics to track your website's audience might be illegal. That's the view of a court in Austria, which in January found that Google's data product was in breach of the European Union's General Data Protection Regulation (GDPR) as it was not doing enough to make sure data transferred from the EU to the company's servers in the US was protected (from, say, US intelligence agencies). Well for those working in AI and biotech, it matters, especially to those working outside of Europe with a view to expansion there. For a start, this is a major precedent that threatens to upend the way many tech companies work, since the tech sector relies heavily on the safe use and transfer of large quantities of data. Whether you use Google Analytics is neither here nor there; the case has shown that Privacy Shield -- the EU-US framework that governs the transfer of personal information in compliance with GDPR -- may not be compliant with European law after all.

I am regularly asked to summarize my many posts. I thought it would be a good idea to publish on this blog, every Monday, some of the most relevant articles that I have already shared with you on my social networks. Today I will share some of the most relevant articles about Artificial Intelligence and in what form you can find it in today's life. I will also comment on the articles. Artificial Intuition is the Next Phase of Artificial Intelligence.

Artificial Intelligence is everywhere: the rise of "thinking" machines has been one of the defining developments of the past two decades – and will only become more prominent as computing power increases. The European Union has been working on a framework to regulate AI for some time, starting way back in March 2018, as part of its broader Digital Decade regulations. Work on AI regulations has been relatively slow while the EU focuses on the Digital Markets Act and the Digital Services Act, which focus on reigning in the American tech giants, but the work definitely continues. Any worthwhile legislative process should be open to critique and analysis and the EU's AI Act is undergoing a thorough treatment by the UK-based Ada Lovelace Institute, an independent research institution working on data policy. The full report (via TechCrunch) includes a lot of detail on the pros and cons of the regulation, which is a global first, with the main takeaway is that the EU is setting itself up to have some pretty powerful tools at its disposal.