In case the many books and films from the Twilight universe haven't provided enough fodder for your fandom, there's a new TV project in the works about the love-em-or-hate-em sparkly vampires of the Pacific Northwest. An animated series adaptation of Midnight Sun is currently in development at Netflix. Published in 2020, Midnight Sun is a companion to the original Twilight novel, telling the same events of that book from the perspective of Edward Cullen. Yes, the sick, masochistic lion gets to share his side of the story of how he falls for the stupid lamb known as Bella Swan. Author Stephanie Meyer will be an executive producer for the series, as she has been for most other projects in the Twilight realm.

Forget artificial intelligence breaking free of human control and taking over the world. A far more pressing concern is how today's generative AI tools will transform the labor market. Some experts envisage a world of increased productivity and job satisfaction; others, a landscape of mass unemployment and social upheaval. Someone with a bird's-eye view of the situation is Mary Daly, CEO of the Federal Reserve Bank of San Francisco, part of the national system responsible for setting monetary policy, maintaining a stable financial system, and ensuring maximal employment. Daly, a labor market economist by training, is especially interested in how generative AI might change the labor market picture.

Neglected tropical diseases (NTDs) continue to affect the livelihood of individuals in countries in the Southeast Asia and Western Pacific region. These diseases have been long existing and have caused devastating health problems and economic decline to people in low- and middle-income (developing) countries. An estimated 1.7 billion of the world's population suffer one or more NTDs annually, this puts approximately one in five individuals at risk for NTDs. In addition to health and social impact, NTDs inflict significant financial burden to patients, close relatives, and are responsible for billions of dollars lost in revenue from reduced labor productivity in developing countries alone. There is an urgent need to better improve the control and eradication or elimination efforts towards NTDs. This can be achieved by utilizing machine learning tools to better the surveillance, prediction and detection program, and combat NTDs through the discovery of new therapeutics against these pathogens. This review surveys the current application of machine learning tools for NTDs and the challenges to elevate the state-of-the-art of NTDs surveillance, management, and treatment.

The COVID-19 pandemic created enormous public health and socioeconomic challenges. The health effects of vaccination and non-pharmaceutical interventions (NPIs) were often contrasted with significant social and economic costs. We describe a general framework aimed to derive adaptive cost-effective interventions, adequate for both recent and emerging pandemic threats. We also quantify the net health benefits and propose a reinforcement learning approach to optimise adaptive NPIs. The approach utilises an agent-based model simulating pandemic responses in Australia, and accounts for a heterogeneous population with variable levels of compliance fluctuating over time and across individuals. Our analysis shows that a significant net health benefit may be attained by adaptive NPIs formed by partial social distancing measures, coupled with moderate levels of the society's willingness to pay for health gains (health losses averted). We demonstrate that a socially acceptable balance between health effects and incurred economic costs is achievable over a long term, despite possible early setbacks.

Welcome to episode 4 of New voices in AI. In this episode Maria De-Arteaga shares her work and journey into algorithmic fairness and human algorithm collaboration. You can find out more on Maria's website and follow her on Twitter, @mariadearteaga. Daly: Hello and welcome to New Voices in AI the series from Ai hub where we celebrate the voices of Masters and PhD students, early career researchers and those with a new perspective on AI. I am Joe Daly, engagement manager for AI hub and this week I am talking to Maria De-Arteaga about some of her research.

Welcome to the second episode of New voices in AI! This episode features Isabel Cachola talking about how she got into AI and her work on interpretability of NLP models. The music used is'Wholesome' by Kevin MacLeod, Licensed under Creative Commons Daly: Hello and welcome to the second episode of New Voices in AI, the new series from AI hub where we celebrate the voices of PhD students, early career researchers, and those with a new perspective on AI. I am Joe Daly, engagement manager for AI hub and in this episode I will be talking to Isabel Cachola. Without further ado, lets begin!

Product reviews provide insights in to real user experiences which can benefit others when making their purchasing decisions. Text-mining and NLP may be used to extract features and content that could influence a new user. Additionally, recommender systems and filtering interfaces rely on manufacturer reported data in order to support user preferences. In many instances this data may be absent or inaccurate. In this paper we focus on age related features mentioned in user reviews of baby and child related products in order to recommend the appropriate age range of a product. We demonstrate that manufacturer related information is frequently absent and when manufacturer specifications are available, we find they may not reflect real user experiences which could assist a buyer in their decision making process. As a result, we present a simple user interface to allow users assess the age appropriateness of the product.

Welcome to the first episode of New voices in AI! You can find David on Twitter @davlanade and find out more about Masakhane here. The music used is'Wholesome' by Kevin MacLeod, Licensed under Creative Commons Daly: Hello and welcome to new voices in AI, this a new series from AIhub where we celebrate the voices PhD students, early career researchers, and those with a new perspective on AI. And without further ado, let's begin. First up, a big welcome to our very first guest on "New voices in AI" and if you could introduce yourself, who are you? Adelani: Thank you very much for having me. So, Masakhane is this grassroots organization, whose mission is to strengthen and spur NLP research in African languages, by Africans for Africans, so, and currently the organization we are majorly operating on Slack we already have over 1000 Members. Of course, not everyone is active but we have more than 100 or close to 100 active members as well, yeah. So how did, how did you get into AI?

It's been six months since the PlayStation 5 launched, and they still fly out of stock minutes after appearing in stores. But anyone still waiting to pick one up can be comforted by the knowledge that as yet, there haven't been many games to show off what it can do. The only one that has felt strikingly next-generation is the superb horror-sci-fi-shooter Returnal, which is like Groundhog Day on an alien planet where everything is trying to kill you. Ratchet & Clank: Rift Apart, out 11 June, is also science-fiction, but unlike Returnal, it is more cuddly and approachable. Part of a long-running series about a furry big-eared alien and his unflappable robot companion having adventures in space with a wacky arsenal of weapons, it's made by Insomniac Games in California, the developer behind PS5 launch game Spider-Man: Miles Morales.

In this chapter we argue that discourses on AI must transcend the language of 'ethics' and engage with power and political economy in order to constitute 'Good Data'. In particular, we must move beyond the depoliticised language of 'ethics' currently deployed (Wagner 2018) in determining whether AI is 'good' given the limitations of ethics as a frame through which AI issues can be viewed. In order to circumvent these limits, we use instead the language and conceptualisation of 'Good Data', as a more expansive term to elucidate the values, rights and interests at stake when it comes to AI's development and deployment, as well as that of other digital technologies. Good Data considerations move beyond recurring themes of data protection/privacy and the FAT (fairness, transparency and accountability) movement to include explicit political economy critiques of power. Instead of yet more ethics principles (that tend to say the same or similar things anyway), we offer four 'pillars' on which Good Data AI can be built: community, rights, usability and politics. Overall we view AI's 'goodness' as an explicly political (economy) question of power and one which is always related to the degree which AI is created and used to increase the wellbeing of society and especially to increase the power of the most marginalized and disenfranchised. We offer recommendations and remedies towards implementing 'better' approaches towards AI. Our strategies enable a different (but complementary) kind of evaluation of AI as part of the broader socio-technical systems in which AI is built and deployed.