A lot has changed in the world of automation since Apple first launched its virtual assistant, Siri, on October 4, 2011. It's a date that musician, actress, and public speaker, Susan Bennett, remembers well because she's the original female, American voice behind Siri. "It is still weird," she said on this episode of The Insuring Cyber Podcast. "It's like, 'How many millions of people know my voice?' …I don't really think about that aspect of it." After getting her start in entertainment as a musician working on commercial jingles and singing backup vocals on tour with Burt Bacharach and Roy Orbison, Bennett began voiceover work at Doppler Studios in Atlanta.

Last week Microsoft Corp said it would stop selling software that guesses a person's mood by looking at their face. The reason: It could be discriminatory. Computer vision software, which is used in self-driving cars and facial recognition, has long had issues with errors that come at the expense of women and people of color. Microsoft's decision to halt the system entirely is one way of dealing with the problem. But there's another, novel approach that tech firms are exploring: training AI on "synthetic" images to make it less biased.

Melbourne/Canberra: Private companies and public authorities are quietly using facial recognition systems around Australia. Despite the growing use of this controversial technology, there is little in the way of specific regulations and guidelines to govern its use. Spying on shoppers We were reminded of this fact recently when consumer advocates at CHOICE revealed that major retailers in Australia are using the technology to identify people claimed to be thieves and troublemakers. There is no dispute about the goal of reducing harm and theft. But there is also little transparency about how this technology is being used.

Research on Fairness and Bias Mitigation in Machine Learning often uses a set of reference datasets for the design and evaluation of novel approaches or definitions. While these datasets are well structured and useful for the comparison of various approaches, they do not reflect that datasets commonly used in real-world applications can have missing values. When such missing values are encountered, the use of imputation strategies is commonplace. However, as imputation strategies potentially alter the distribution of data they can also affect the performance, and potentially the fairness, of the resulting predictions, a topic not yet well understood in the fairness literature. In this article, we investigate the impact of different imputation strategies on classical performance and fairness in classification settings. We find that the selected imputation strategy, along with other factors including the type of classification algorithm, can significantly affect performance and fairness outcomes. The results of our experiments indicate that the choice of imputation strategy is an important factor when considering fairness in Machine Learning. We also provide some insights and guidance for researchers to help navigate imputation approaches for fairness.

For three decades now, carbon emissions from cars have been a political and social issue; there are reporting obligations for manufacturers, government regulation, and much accompanying research. A similar approach might be taken with a modern product that is spreading at an enormous pace and also has an impact on the climate: "artificial intelligence" (AI), software based on adaptive algorithms for various purposes, from self-driving cars to automatic image recognition and translation tools to optimizing logistics. Here, too, climate research is now beginning to measure the carbon footprint. A framework for this is provided by a new study with contributions from the Berlin-based climate research institute MCC (Mercator Research Institute on Global Commons and Climate Change). The study is published in the journal Nature Climate Change.

Artificial intelligence technology has led to some surprising changes in the field of accounting. Justin Hatch of the Forbes Technology Council reports that AI helps accountants streamline many mundane tasks. Therefore, they can use it to boost productivity as much as 40%. Unfortunately, it can be difficult to know how to use AI effectively. There are a number of great software applications that use AI to help accountants.

The 1st of May is celebrated as International Labor Day, as it historically marks the relentless struggle of the working class to get the workday reduced to 8 hours and the workweek to 40 hours (Al Jazeera, 2019). The history of International Labor Day is rooted in the struggle for freedom and rights. It was initially called the "day of demonstrations," as peaceful protests for the demand of reducing working hours by workers in Chicago were countered by violence by the state. It also led to the sentencing to death of revolutionary leaders, who were tried only because of their political beliefs, without any evidence linking them to violence. Although this movement for labor rights started in the West, it soon reached other parts of the globe as well, where non-Western countries like India, Bangladesh, and Pakistan also initiated similar demonstrations to support better labor rights and opportunities.

Koomey's law This law posits that the energy efficiency of computation doubles roughly every one-and-a-half years (see Figure 1–7). In other words, the energy necessary for the same amount of computation halves in that time span. To visualize the exponential impact this has, consider the face that a fully charged MacBook Air, when applying the energy efficiency of computation of 1992, would completely drain its battery in a mere 1.5 seconds. According to Koomey's law, the energy requirements for computation in embedded devices is shrinking to the point that harvesting the required energy from ambient sources like solar power and thermal energy should suffice to power the computation necessary in many applications. Metcalfe's law This law has nothing to do with chips, but all to do with connectivity. Formulated by Robert Metcalfe as he invented Ethernet, the law essentially states that the value of a network increases exponentially with regard to the number of its nodes (see Figure 1–8).

The skies overhead could soon be filled with constellations of commercial space stations occupying low earth orbit while human colonists settle the Moon with an eye on Mars, if today's robber barons have their way. But this won't result in the same freewheeling Wild West that we saw in the 19th century, unfortunately, as tomorrow's interplanetary settlers will be bringing their lawyers with them. In their new book, The End of Astronauts: Why Robots Are the Future of Exploration, renowned astrophysicist and science editor, Donald Goldsmith, and Martin Rees, the UK's Astronomer Royal, argue in favor of sending robotic scouts -- with their lack of weighty necessities like life support systems -- out into the void ahead of human explorers. But what happens after these synthetic astronauts discover an exploitable resource or some rich dork declares himself Emperor of Mars? In the excerpt below, Goldsmith and Rees discuss the challenges facing our emerging exoplanetary legal system.

With the rapid advancements in artificial intelligence technology, how are AI-generated inventions recognised when it comes to patents? Innovation funding and Patent Box experts ABGI UK have recently looked into where inventions created by artificial intelligence (AI) systems currently stand in regard to intellectual property, and how potential changes will affect U.K. businesses. The output has been shared with Digital Journal. One important point that arises from AI, is that as artificial intelligence becomes increasingly advanced, how is AI-generated innovation considered when it comes to intellectual property? The issue is perhaps more pertinent than ever following the case earlier this year of Thaler v Comptroller General of Patents, Trade Marks and Designs.