From'When Harry Met Sally' to'Black Swan', orgasms have been depicted in blockbuster hits for decades. But scientists have taken a new approach to visualise the'Big O' - by enlisting the help of artificial intelligence (AI). The team at LoveHoney used heart-monitors to record 10 volunteers as they climaxed, before using AI to bring the data to life. 'Ever wondered – at the height of climax, at the apex of sexual pleasure, the pique of existence (too far?) – I wonder what my orgasm looks like?' Lovehoney said. 'Well, you have to wonder no more, as alongside Womanizer we have created images of real orgasms using AI.' Scientists have taken a new approach to visualise the'Big O' - by enlisting the help of artificial intelligence (AI) Lovehoney and Womanizer set out to visualise the orgasm, having found that it had never successfully been put into an image.

While some of us were lucky enough to work only with great team leads, most of us have had both great and terrible experiences. And although terrible leadership can make the team members' life horrible, bitter experiences foster great team leads from the team members -- helping them learn what behaviours to avoid. Technical management of software engineering projects is very established, with multiple tools and techniques at the disposal of a team lead, such as Agile. Meanwhile, machine learning projects, where accurately predicting timelines, outcomes of the tasks, and task feasibility are challenging, are hard to fit into these paradigms. Navigating projects with high uncertainty at every step requires skills and knowledge that machine learning team leads need to gain through experience.

New Delhi: Angshuman Choudhury, used AI to draw up snow-clad pictures of Delhi and Kolkata and post it on the micro-blogging site. In one of the post, the user has shared a photo of Delhi's iconic India Gate and a historical gate in the bylanes of Old Delhi covered in a thick blanket of snow. While the other photo shows the streets of Kolkata with trams and old-age cars. "What would Delhi, both New and Old, look like during a heavy snowfall? And now, AI helped me visualise it," the caption of the post read.

Mitochondrial diseases are currently untreatable due to our limited understanding of their pathology. We study the expression of various mitochondrial proteins in skeletal myofibres (SM) in order to discover processes involved in mitochondrial pathology using Imaging Mass Cytometry (IMC). IMC produces high dimensional multichannel pseudo-images representing spatial variation in the expression of a panel of proteins within a tissue, including subcellular variation. Statistical analysis of these images requires semi-automated annotation of thousands of SMs in IMC images of patient muscle biopsies. In this paper we investigate the use of deep learning (DL) on raw IMC data to analyse it without any manual pre-processing steps, statistical summaries or statistical models. For this we first train state-of-art computer vision DL models on all available image channels, both combined and individually. We observed better than expected accuracy for many of these models. We then apply state-of-the-art explainable techniques relevant to computer vision DL to find the basis of the predictions of these models. Some of the resulting visual explainable maps highlight features in the images that appear consistent with the latest hypotheses about mitochondrial disease progression within myofibres.

Human creativity is the elixir that's powered civilisations down the ages. It's brought us untold breakthroughs in all sectors of the economy, from agriculture to healthcare, energy to mobility. Our imagination continues to be our saviour as the world's ageing population faces tough socioeconomic and environmental challenges. Imagination entails creating mental models of things that don't yet exist. This kind of innovation brought us the printing press, the steam engine, the light bulb, the telephone, the aeroplane, the TV and the PC.

When I first started working with the Internet of Things (IoT) nearly 10 years ago I used to lead presentations with a "the world is changing, and it's changing fast" mantra. Now, with the rise of new advanced technologies driven by artificial intelligence (AI) I simply start with "nothing is going to be like yesterday!". In this increasingly connected world, it is only by looking back that you can comprehend how quickly things have changed. In 1984, when I left secondary school and the original Apple MacIntosh computer went on sale, there were only 3,000 devices connected to the internet. In 2008, the number of connected devices surpassed the number of people on the planet – at nearly seven billion.

An important step in developing machine learning models is to evaluate the performance. Depending on the type of machine learning problem that you are dealing with, there is generally a choice of metrics to choose from to perform this step. However, simply looking at one or two numbers in isolation cannot always enable us to make the right choice for model selection. For example, a single error metric doesn't give us any information about the distribution of the errors. It does not answer questions like is the model wrong in a big way a small number of times, or is it producing lots of smaller errors?

PyTorch Image Models (timm) is a library for state-of-the-art image classification, containing a collection of image models, optimizers, schedulers, augmentations and much more; it was recently named the top trending library on papers-with-code of 2021! Whilst there are an increasing number of low and no code solutions which make it easy to get started with applying Deep Learning to computer vision problems, in my current role as part of Microsoft CSE, we frequently engage with customers who wish to pursue custom solutions tailored to their specific problem; utilizing the latest and greatest innovations to exceed the performance level offered by these services. Due to the rate that new architectures and training techniques are introduced into this rapidly moving field, whether you are a beginner or an expert, it can be difficult to keep up with the latest practices and make it challenging to know where to start when approaching new vision tasks with the intention of reproducing similar results to those presented in academic benchmarks. Whether I'm training from scratch or finetuning existing models to new tasks, and looking to leverage pre-existing components to speed up my workflow, timm is one of my favourite libraries for computer vision in PyTorch. However, whilst timm contains reference training and validation scripts for reproducing ImageNet training results and has documentation covering the core components in the official documentation and the timmdocs project, due to the sheer number of features that the library provides it can be difficult to know where to get started when applying these in custom use-cases. The purpose of this guide is to explore timm from a practitioner's point of view, focusing on how to use some of the features and components included in timm in custom training scripts. The focus is not to explore how or why these concepts work, or how they are implemented in timm; for this, links to the original papers will be provided where appropriate, and I would recommend timmdocs to learn more about timm's internals. Additionally, this article is by no means exhaustive, the areas selected are based upon my personal experience using this library. All information here is based on timm 0.5.4 which was recently released at the time of writing. Whilst this article can be read in order, it may also be useful as a reference for a particular part of the library. For ease of navigation, a table of contents is presented below. Tl;dr: If you just want to see some working code that you can use directly, all of the code required to replicate this post is available as a GitHub gist here. One of the most popular features of timm is its large, and ever-growing collection of model architectures.

The metaverse as described to us by science fiction is a world of infinite possibilities. The easiest way to conceptualise it is by looking at Hollywood blockbusters such as Avatar and Ready, Player One. In the movies, the metaverse is a three-dimensional digital universe where players can escape physical reality, engage with each other as an avatar of their creation and experience anything they want, only limited by the human imagination and technology, says Selina Yuan, general manager of international business unit, Alibaba Cloud Intelligence. Apart from being a wondrous twin digital reality of our physical world, the metaverse's true potential lies in its ability to make better use of the digital intelligence we are already gaining and visualising it in a way that uncovers new insights that might have otherwise remain hidden. This could be the key to helping us solve real-world problems and building a greener, more inclusive, and technically advanced world.

PyTorch Image Models (timm) is a library for state-of-the-art image classification, containing a collection of image models, optimizers, schedulers, augmentations and much more; it was recently named the top trending library on papers-with-code of 2021! Whilst there are an increasing number of low and no code solutions which make it easy to get started with applying Deep Learning to computer vision problems, in my current role as part of Microsoft CSE, we frequently engage with customers who wish to pursue custom solutions tailored to their specific problem; utilizing the latest and greatest innovations to exceed the performance level offered by these services. Due to the rate that new architectures and training techniques that are introduced into this rapidly moving field, whether you are a beginner or an expert, it can be difficult to keep up with the latest practices and make it challenging to know where to start when approaching new vision tasks with the intention of reproducing similar results to those presented in academic benchmarks. Whether I'm training from scratch or finetuning existing models to new tasks, and looking to leverage pre-existing components to speed up my workflow, timm is one of my favourite libraries for computer vision in PyTorch. However, whilst timm contains reference training and validation scripts for reproducing ImageNet training results and has documentation covering the core components in the official documentation and the timmdocs project, due to the sheer number of features that the library provides it can be difficult to know where to get started when applying these in custom use-cases. The purpose of this guide is to explore timm from a practitioner's point of view, focusing on how to use some of the features and components included in timm in custom training scripts. The focus is not to explore how or why these concepts work, or how they are implemented in timm; for this, links to the original papers will be provided where appropriate, and I would recommend timmdocs to learn more about timm's internals. Additionally, this article is by no means exhaustive, the areas selected are based upon my personal experience using this library. All information here is based on timm 0.5.4 which was recently released at the time of writing. Whilst this article can be read in order, it may also be useful as a reference for a particular part of the library. For ease of navigation, a table of contents is presented below. Tl;dr: If you just want to see some working code that you can use directly, all of the code required to replicate this post is available as a GitHub gist here. One of the most popular features of timm is its large, and ever-growing collection of model architectures.