As we enter a new era defined by artificial intelligence and machine learning, the very foundation of many modern technologies is being put under a microscope by policymakers. Data is required for the refinement of most cutting-edge technology, and it will only become more important in future as we develop more sophisticated AI and ML models, fueled by richer, higher quality data sets. However, there are strict regulations around how data can be used, particularly within the EU. The EU's General Data Protection Regulation (GDPR) mandates that businesses need consent to store subject data in order to preserve the privacy of its citizens online and offline. Get a weekly dose of entrepreneurial insights from TNW's founder Boris These regulations affect AI companies the world over, however, because they restrict how data is moved out of the bloc – to servers in the US, say.

Artificial intelligence (AI) technologies have become increasingly widespread over the last decade. As the use of AI has become more common and the performance of AI systems has improved, policymakers, scholars, and advocates have raised concerns. Policy and ethical issues such as algorithmic bias, data privacy, and transparency have gained increasing attention, raising calls for policy and regulatory changes to address the potential consequences of AI (Acemoglu 2021). As AI continues to improve and diffuse, it will likely have significant long-term implications for jobs, inequality, organizations, and competition. Premature deployment of AI products can also aggravate existing biases and discrimination or violate data privacy and protection practices.

The inference of politically-charged information from text data is a popular research topic in Natural Language Processing (NLP) at both text- and author-level. In recent years, studies of this kind have been implemented with the aid of representations from transformers such as BERT. Despite considerable success, however, we may ask whether results may be improved even further by combining transformed-based models with additional knowledge representations. To shed light on this issue, the present work describes a series of experiments to compare alternative model configurations for political inference from text in both English and Portuguese languages. Results suggest that certain text representations - in particular, the combined use of BERT pre-trained language models with a syntactic dependency model - may outperform the alternatives across multiple experimental settings, making a potentially strong case for further research in the use of heterogeneous text representations in these and possibly other NLP tasks.

AI-enabled smart toys come in an exponentially growing diversity and inhibit the most known social environments of children. The Market Research Future group, for instance, projects that the global market share of such toys will grow by 26% and reach 107.02 billion USD by 2030. Smart toys, as highlighted by the Generation AI Initiative of the World Economic Forum, can exhibit highly positive effects on children's development when designed responsibly. Complaints received by the Federal Trade Commission (FTC) as well as an investigation of the Norwegian Consumer Organisation and rich scholarly research however also point to the severe impacts that smart toys can have on children's development. The non-transparent ways in which some smart toys exchange data for algorithmic analysis with other AI-enabled devices, e.g., through Bluetooth connection, demonstrate how weak cyber security features can violate children's privacy and jeopardize safety.

Congress passed the CHIPS and Science Act on Thursday, a $280 billion package that includes $52 billion in funding available to companies that manufacture semiconductor chips stateside. It's a bipartisan push to reestablish American leadership in a technology that's increasingly vital to the US economy and its strategic goals. Although chipmaking was pioneered in the US and Intel dominated the global market for advanced computer chips for decades, competition from Asian firms and Intel's own missteps have seen that influence wane considerably in recent years. The proportion of chips made in the US has fallen from 37 percent in 1990 to 12 percent today. But while industry leaders are hopeful that the new funds will help fuel a resurgence, regaining an edge in chipmaking will require not just money, but spending it the right way.

In the UK, there have been a few cases of inventors putting forward patent applications that name AI as one of the co-inventors. However, although AI is certainly speeding up innovation and enabling greater human creativity, AI is not yet attributed with patents on its own. The UK's Intellectual Property Office which set out UK patent law has stated that all applications must name a human as the inventor or at least one of the inventors. The reason for this is down to the origins of an AI system and how it was created.

The method in which we select samples to learn more about characteristics in a given population is called hypothesis testing. Hypothesis testing is really a systematic way to test claims or ideas about a group or population. To illustrate, suppose we read an article stating that children in the United States watch an average of 3 hours of TV per week. To test whether this claim is true, we record the time (in hours) that a group of 20 American children (the sample), among all children in the United States (the population), watch TV. The mean we measure for these 20 children is a sample mean. We can then compare the sample mean we select to the population mean stated in the article. Hypothesis testing or significance testing is a method for testing a claim or hypothesis about a parameter in a population, using data measured in a sample. In this method, we test some hypothesis by determining the likelihood that a sample statistic could have been selected, if the hypothesis regarding the population parameter were true. To begin, we identify a hypothesis or claim that we feel should be tested. For example, we might want to test the claim that the mean number of hours that children in the United States watch TV is 3 hours.

An artificial intelligence program that has impressed the internet with its ability to generate original images from user prompts has also sparked concerns and criticism for what is now a familiar issue with AI: racial and gender bias. And while OpenAI, the company behind the program, called DALL·E 2, has sought to address the issues, the efforts have also come under scrutiny for what some technologists have claimed is a superficial way to fix systemic underlying problems with AI systems. "This is not just a technical problem. This is a problem that involves the social sciences," said Kai-Wei Chang, an associate professor at the UCLA Samueli School of Engineering who studies artificial intelligence. There will be a future in which systems better guard against certain biased notions, but as long as society has biases, AI will reflect that, Chang said.

Ghosting can hurt, for sure. When someone suddenly cuts off contact, doesn't show up at a date or just unmatches on one of those many dating apps, it sucks. One Filipino lawmaker is trying to make it stop, which could be a tall order. Arnolfo Teves Jr., a member of the Philippine House of Representatives, said ghosting was "a form of emotional cruelty and should be punished as an emotional offense." The bill -- yes there's proposed legislation -- doesn't offer specific penalties, but Teves suggested in an interview that community service might work.

Artificial intelligence (AI) has been the promise of healthcare for nearly a decade, but the industry has yet to adopt it widely. Applications of AI in arguably more difficult domains, such as search, language and image recognition, have seen massive success over the past decade. While neural net algorithms and compute power have improved dramatically, AI in healthcare is still lagging behind. The big reason these domains, and not healthcare, have been able to utilize AI tech is due to the internet's ability to make massive amounts of data available. Now data access via internet technologies is finally happening in healthcare through secure channels.