Microorganisms play a vital role in human life. Therefore, microorganism detection is of great significance to human beings. However, the traditional manual microscopic detection methods have the disadvantages of long detection cycle, low detection accuracy in large orders, and great difficulty in detecting uncommon microorganisms. Therefore, it is meaningful to apply computer image analysis technology to the field of microorganism detection. Computer image analysis can realize high-precision and high-efficiency detection of microorganisms. In this review, first,we analyse the existing microorganism detection methods in chronological order, from traditional image processing and traditional machine learning to deep learning methods. Then, we analyze and summarize these existing methods and introduce some potential methods, including visual transformers. In the end, the future development direction and challenges of microorganism detection are discussed. In general, we have summarized 137 related technical papers from 1985 to the present. This review will help researchers have a more comprehensive understanding of the development process, research status, and future trends in the field of microorganism detection and provide a reference for researchers in other fields.

This chapter outlines the relation between artificial intelligence (AI) / machine learning (ML) algorithms and digital games. This relation is two-fold: on one hand, AI/ML researchers can generate large, in-the-wild datasets of human affective activity, player behaviour (i.e. actions within the game world), commercial behaviour, interaction with graphical user interface elements or messaging with other players, while games can utilise intelligent algorithms to automate testing of game levels, generate content, develop intelligent and responsive non-player characters (NPCs) or predict and respond player behaviour across a wide variety of player cultures. In this work, we discuss some of the most common and widely accepted uses of AI/ML in games and how intelligent systems can benefit from those, elaborating on estimating player experience based on expressivity and performance, and on generating proper and interesting content for a language learning game.

Data augmentation has recently seen increased interest in NLP due to more work in low-resource domains, new tasks, and the popularity of large-scale neural networks that require large amounts of training data. Despite this recent upsurge, this area is still relatively underexplored, perhaps due to the challenges posed by the discrete nature of language data. In this paper, we present a comprehensive and unifying survey of data augmentation for NLP by summarizing the literature in a structured manner. We first introduce and motivate data augmentation for NLP, and then discuss major methodologically representative approaches. Next, we highlight techniques that are used for popular NLP applications and tasks. We conclude by outlining current challenges and directions for future research. Overall, our paper aims to clarify the landscape of existing literature in data augmentation for NLP and motivate additional work in this area.

An institution, be it a body of government, commercial enterprise, or a service, cannot interact directly with a person. Instead, a model is created to represent us. We argue the existence of a new high-fidelity type of person model which we call a digital voodoo doll. We conceptualize it and compare its features with existing models of persons. Digital voodoo dolls are distinguished by existing completely beyond the influence and control of the person they represent. We discuss the ethical issues that such a lack of accountability creates and argue how these concerns can be mitigated.

This article reviews the "Once learning" mechanism that was proposed 23 years ago and the subsequent successes of "One-shot learning" in image classification and "You Only Look Once - YOLO" in objective detection. Analyzing the current development of Artificial Intelligence (AI), the proposal is that AI should be clearly divided into the following categories: Artificial Human Intelligence (AHI), Artificial Machine Intelligence (AMI), and Artificial Biological Intelligence (ABI), which will also be the main directions of theory and application development for AI. As a watershed for the branches of AI, some classification standards and methods are discussed: 1) Human-oriented, machine-oriented, and biological-oriented AI R&D; 2) Information input processed by Dimensionality-up or Dimensionality-reduction; 3) The use of one/few or large samples for knowledge learning.

The first image of China's rouge Long March 5B rocket in orbit has been released by astronomers. The Italy-based Virtual Telescope Project captured the craft, which appears like a glowing light, as it passed above the group's'Elena' robotic telescope. The Chinese rocket made headlines this week when new surfaced the massive 21-ton vehicle would make an uncontrolled reentry weekend, with the possibility of landing in inhabited areas. The rocket was moving'extremely fast' when it soared 435 miles above the Virtual Telescopes Project's telescope Wednesday evening, researchers said. Gianluca Masi, an astronomer with the Virtual Telescope Project who snapped the image, stated that'while the Sun was just a few degrees below the horizon, so the sky was incredibly bright: these conditions made the imaging quite extreme, but our robotic telescope succeeded in capturing this huge debris.' 'This is another bright success, showing the amazing capabilities of our robotic facility in tracking these objects.'

Our Morning Risk Report features insights and news on governance, risk and compliance. Adobe's AI ethics committee, launched two years ago, has been able to review new features for potential bias before those features are deployed, Mr. Rao said Wednesday at The Wall Street Journal's Risk & Compliance Forum. The committee is made up of employees of various ethnicities and genders from different parts of the company, including legal, government relations and marketing. "It takes a lot of people across your company to help figure this out," he said. "Sometimes we might look at it and say there's not an issue here," he said, but getting a diverse group of people together can help identify issues product developers might miss.

The rapid progress in artificial intelligence (AI) and machine learning has opened unprecedented analytics possibilities in various team and individual sports, including baseball, basketball, and tennis. More recently, AI techniques have been applied to football, due to a huge increase in data collection by professional teams, increased computational power, and advances in machine learning, with the goal of better addressing new scientific challenges involved in the analysis of both individual players' and coordinated teams' behaviors. The research challenges associated with predictive and prescriptive football analytics require new developments and progress at the intersection of statistical learning, game theory, and computer vision. In this paper, we provide an overarching perspective highlighting how the combination of these fields, in particular, forms a unique microcosm for AI research, while offering mutual benefits for professional teams, spectators, and broadcasters in the years to come. We illustrate that this duality makes football analytics a game changer of tremendous value, in terms of not only changing the game of football itself, but also in terms of what this domain can mean for the field of AI. We review the state-of-the-art and exemplify the types of analysis enabled by combining the aforementioned fields, including illustrative examples of counterfactual analysis using predictive models, and the combination of game-theoretic analysis of penalty kicks with statistical learning of player attributes. We conclude by highlighting envisioned downstream impacts, including possibilities for extensions to other sports (real and virtual).

In this independent report fAshIon after fashion, we examine the development of fAshIon (artificial intelligence (AI) in fashion) and explore its potentiality to become a major disruptor of the fashion industry in the near future. To do this, we investigate AI technologies used in the fashion industry through several lenses. We summarise fAshIon studies conducted over the past decade and categorise them into seven groups: Overview, Evaluation, Basic Tech, Selling, Styling, Design, and Buying. The datasets mentioned in fAshIon research have been consolidated on one GitHub page for ease of use. We analyse the authors' backgrounds and the geographic regions treated in these studies to determine the landscape of fAshIon research. The results of our analysis are presented with an aim to provide researchers with a holistic view of research in fAshIon. As part of our primary research, we also review a wide range of cases of applied fAshIon in the fashion industry and analyse their impact on the industry, markets and individuals. We also identify the challenges presented by fAshIon and suggest that these may form the basis for future research. We finally exhibit that many potential opportunities exist for the use of AI in fashion which can transform the fashion industry embedded with AI technologies and boost profits.

Elger: I'm going to talk to you about AI as a service, fast track to AI with serverless. I'm not going to go deep into training models and all of that stuff. Really, the takeaway I hope you get from this talk is that adopting machine learning in your day-to-day work is really not as difficult as you might think. That you maybe come away from this talk able to go and start experimenting at a low cost with AI as a service. Because in a lot of cases, the ability to do machine learning or to run inferences is, these days, just an API call away. At fourTheorem, we do work in the serverless space, obviously, and we work in machine learning. My own particular area of research with regard to machine learning is, how do we apply machine learning to the process of software transformation? Although, as we heard, the monolith is not the enemy, but can that be treated as a big data problem? I'm not going to talk to you about that. Some sausages, some rashers, black pudding, eggs, wash it down with some coffee and some orange juice. I want to go to the shops. I want to buy myself that breakfast. It's going to cost me about £15, £16 to buy these. I'll probably get two breakfasts out of it as well, and I can reuse the coffee. Let's say that I wanted to DIY my own breakfast. I want to build it from scratch. What does that look like? It looks significantly more expensive.