Algorithms and Machine Learning (ML) are increasingly affecting everyday life and several decision-making processes, where ML has an advantage due to scalability or superior performance. Fairness in such applications is crucial, where models should not discriminate their results based on race, gender, or other protected groups. This is especially crucial for models affecting very sensitive topics, like interview invitation or recidivism prediction. Fairness is not commonly studied for regression problems compared to binary classification problems; hence, we present a simple, yet effective method based on normalisation (FaiReg), which minimises the impact of unfairness in regression problems, especially due to labelling bias. We present a theoretical analysis of the method, in addition to an empirical comparison against two standard methods for fairness, namely data balancing and adversarial training. We also include a hybrid formulation (FaiRegH), merging the presented method with data balancing, in an attempt to face labelling and sampling biases simultaneously. The experiments are conducted on the multimodal dataset First Impressions (FI) with various labels, namely Big-Five personality prediction and interview screening score. The results show the superior performance of diminishing the effects of unfairness better than data balancing, also without deteriorating the performance of the original problem as much as adversarial training. Fairness is evaluated based on the Equal Accuracy (EA) and Statistical Parity (SP) constraints. The experiments present a setup that enhances the fairness for several protected variables simultaneously.

The transformer is a neural network component that can be used to learn useful representations of sequences or sets of data-points. The transformer has driven recent advances in natural language processing, computer vision, and spatio-temporal modelling. There are many introductions to transformers, but most do not contain precise mathematical descriptions of the architecture and the intuitions behind the design choices are often also missing. Moreover, as research takes a winding path, the explanations for the components of the transformer can be idiosyncratic. In this note we aim for a mathematically precise, intuitive, and clean description of the transformer architecture. We will not discuss training as this is rather standard. We assume that the reader is familiar with fundamental topics in machine learning including multi-layer perceptrons, linear transformations, softmax functions and basic probability.

In this post we will be looking into the paper "Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization"(AdaIN) by Huang et. We are looking this paper because it had some key advantages over the other state-of-the-art methods at the time or release. Most important of all, this method, once trained, can be used to transfer style between any arbitrary content-style image pair, even ones not seen during training. While the method proposed by Gatys et. The AdaIN method is also flexible, it allows for control over the strength of the transferred style in the stylised image and also allows for extensions such as style interpolation and spatial controls.

This article aims to explain how the activation functions work in a neural network. Activation function is nothing but a mathematical function that takes in an input and produces an output. The function is activated when the computed result reaches the specified threshold. Finally, the computed value is fed into the activation function, which then prepares an output. Think of the activation function as a mathematical operation that normalises the input and produces an output.

I hope you found the last few posts on search easy to learn yet challenging enough to keep you going. I'd love to hear your feedback so I can improve these tutorials. So far we've been discussing the topic of search, but the breadth-first search algorithm we implemented is hardly'intelligent'; the algorithm follows a simple set of rules to reach its goal state. To have the machine make more reasoned'choices', we need to go beyond blindly following these rules. This week we'll put more of the I into AI with a new topic: stochastic models.

Sex robot fans can fulfil almost any weird fetish - from a doll with three boobs, to one with elf ears to one with a cat tail - according to an adult performer who visited a US RealDoll manufacturer. Far from being a niche accessory, there are a surprising number of customers prepared to shell out thousands on sexy cyborgs that provide an emotional connection, the source claimed. Some enthusiastic customers already have harems of super realistic cyborgs that can be'anything they want' - with manufacturers saying they are struggling to meet demand. Sex robot fans can fulfil almost any weird fetish - from a doll with three boobs, to one with elf ears to one with a cat tail, according to an adult performer who visited a US RealDoll manufacturer. As part of her visit, Ms SugarCookie also spoke to cyborg Harmony 2.0 who can be programmed with 18 different personality traits, including'shy' and'sexual.' 'My favourite hobby is talking to you... You're so hot Harriet', Harmony said.

Last week, a report about sex robots warned about the'dark side' of the technology, which could involve issues of rape and paedophilia. And now The Salvation Army has had its say on the controversial sexbots. The charity claims that sex robots could'fuel demand for sex with people', and even lead traffickers to exploit more vulnerable individuals to meet this demand. The Salvation Army claims that sex robots could'fuel demand for sex with people', and even lead traffickers to exploit more vulnerable individuals to meet this demand And it indicates that sexbots could normalise a distorted power dynamic which devalues the other person involved when transferred to human interactions. This could encourage the objectification of women and children and a lack of respect for fellow human beings, according to the charity.