The influence of Artificial Intelligence (AI) and Artificial Life (ALife) technologies upon society, and their potential to fundamentally shape the future evolution of humankind, are topics very much at the forefront of current scientific, governmental and public debate. While these might seem like very modern concerns, they have a long history that is often disregarded in contemporary discourse. Insofar as current debates do acknowledge the history of these ideas, they rarely look back further than the origin of the modern digital computer age in the 1940s-50s. In this paper we explore the earlier history of these concepts. We focus in particular on the idea of self-reproducing and evolving machines, and potential implications for our own species. We show that discussion of these topics arose in the 1860s, within a decade of the publication of Darwin's The Origin of Species, and attracted increasing interest from scientists, novelists and the general public in the early 1900s. After introducing the relevant work from this period, we categorise the various visions presented by these authors of the future implications of evolving machines for humanity. We suggest that current debates on the co-evolution of society and technology can be enriched by a proper appreciation of the long history of the ideas involved.

Intelligent systems for the annotation of media content are increasingly being used for the automation of parts of social science research. In this domain the problem of integrating various Artificial Intelligence (AI) algorithms into a single intelligent system arises spontaneously. As part of our ongoing effort in automating media content analysis for the social sciences, we have built a modular system by combining multiple AI modules into a flexible framework in which they can cooperate in complex tasks. Our system combines data gathering, machine translation, topic classification, extraction and annotation of entities and social networks, as well as many other tasks that have been perfected over the past years of AI research. Over the last few years, it has allowed us to realise a series of scientific studies over a vast range of applications including comparative studies between news outlets and media content in different countries, modelling of user preferences, and monitoring public mood. The framework is flexible and allows the design and implementation of modular agents, where simple modules cooperate in the annotation of a large dataset without central coordination.

There are tons of neural style transfer papers that came out over the past years, so there are a lot of different repos implementing them. Which one do you think works the best? That would be for some HD pictures containing people.

Just like they do every week, the Season 4, Week 6 challenges have leaked online, courtesy of the dataminers at Fortnite Tracker. We're more than halfway through the season now, and many players are closing in on the ultimate goals of the season: for some that might mean unlocking and upgrading the Omega skin, which requires Battle Pass Tier 100 and Season Level 80. For others, that might mean unlocking the Blockbuster skin, which requires finishing seven complete weeks of challenges. Whatever you're working towards, the weekly challenges are your best source of Battle Pass tiers and experience, so let's take a look at what we've got. Take note that these are subject to change, and have done so in the past: last week one of the collection challenges wound up replaced by the charming disco ball dance challenge.

Starting with the idea that sentiment analysis models should be able to predict not only positive or negative but also other psychological states of a person, we implement a sentiment analysis model to investigate the relationship between the model and emotional state. We first examine psychological measurements of 64 participants and ask them to write a book report about a story. After that, we train our sentiment analysis model using crawled movie review data. We finally evaluate participants' writings, using the pretrained model as a concept of transfer learning. The result shows that sentiment analysis model performs good at predicting a score, but the score does not have any correlation with human's self-checked sentiment.

In many practical problems, a learning agent may want to learn the best action in hindsight without ever taking a bad action, which is significantly worse than the default production action. In general, this is impossible because the agent has to explore unknown actions, some of which can be bad, to learn better actions. However, when the actions are combinatorial, this may be possible if the unknown action can be evaluated by interleaving it with the production action. We formalize this concept as learning in stochastic combinatorial semi-bandits with exchangeable actions. We design efficient learning algorithms for this problem, bound their n-step regret, and evaluate them on both synthetic and real-world problems. Our real-world experiments show that our algorithms can learn to recommend K most attractive movies without ever violating a strict production constraint, both overall and subject to a diversity constraint.

Digital texts have become an increasingly important source of data for social studies. However, textual data from open platforms are vulnerable to manipulation (e.g., censorship and information inflation), often leading to bias in subsequent empirical analysis. This paper investigates the problem of data distortion in text classification when controlling type I error (a relevant textual message is classified as irrelevant) is the priority. The default classical classification paradigm that minimizes the overall classification error can yield an undesirably large type I error, and data distortion exacerbates this situation. As a solution, we propose the Neyman-Pearson (NP) classification paradigm which minimizes type II error under a user-specified type I error constraint. Theoretically, we show that while the classical oracle (i.e., optimal classifier) cannot be recovered under unknown data distortion even if one has the entire post-distortion population, the NP oracle is unaffected by data distortion and can be recovered under the same condition. Empirically, we illustrate the advantage of NP classification methods in a case study that classifies posts about strikes and corruption published on a leading Chinese blogging platform.

As artificial intelligence (AI) technologies and platforms become integral to advanced operations in nearly every industry, blockchain is inserting itself as a means to enhance AI applications in both form and function. Blockchain has the potential to allow AI technologies to become more collaborative in nature and therefore increase their operating efficiency. Additionally, the potential for bolstered revenue streams is also apparent, as blockchain is projected to grow to $20 billion by 2024 according to Transparency Market Research and the Grand View Research projects the AI market will be worth more than $35 billion by 2025. As previously noted, leaders in the AI landscape are turning to blockchain to finetune various applications. Active tech companies in the markets this week include Gopher Protocol Inc. (OTC:GOPH), Overstock.com

Budding authors face a minefield when it comes to publishing their work. For a large fee, as much as $3,000, they can make their work available to anyone who wants to read it. Or they can avoid the fee and have readers pay the publisher instead. Often it is libraries that foot this bill through expensive annual subscriptions. This is not the lot of wannabe fiction writers, it's the business of academic publishing.

JX Press has designed a newsroom staffed by engineers and artificial intelligence instead of employing journalists. According to Bloomberg, once the Japenese media firm finds news, it uses algorithms to create stories and filters out 99% of fake news stories. The firm has developed a tool called NewsDigest – a free mobile news app which generates the firm's advertising revenue, as well as filters fake news and finds breaking stories on social media programmes. Fast Alert is another one of its products which analyses social media posts and photos to find Japan's breaking news and reports major international developments that it deems as trustworthy. The team consists of 24 people, of which 17 are engineers and the others are in relevant business functions, not journalists or reporters.