How much cooler would basketball footage be if software could automatically shine a spotlight on key players? How much easier would it be to understand plays if arrows could indicate movement before the players actually move? How much improvement would be possible if one could see how distance from a basket affects shot trajectory?How much cooler would basketball footage be if software could automatically shine a spotlight on key players? How much easier would it be to understand plays if arrows could indicate movement before the players actually move? How much improvement would be possible if one could see how distance from a basket affects shot trajectory?[#item_full_content]

Machine learning researchers at Apple have developed an application that can accept a simple drawing and a text description to animate the drawing in desired ways. Tiffany Tseng, Ruijia Cheng and Jeffrey Nichols have published a paper describing the new app, called Keyframer, on the arXiv preprint server.Machine learning researchers at Apple have developed an application that can accept a simple drawing and a text description to animate the drawing in desired ways. Tiffany Tseng, Ruijia Cheng and Jeffrey Nichols have published a paper describing the new app, called Keyframer, on the arXiv preprint server.[#item_full_content]

It is winter, the typical time for colds. What if you could simulate how the disease may spread? At the Cluster of Excellence Collective Behavior at the University of Konstanz, Julia Klein, a doctoral student in computer science, and colleagues investigated how using strict, rule-based methods can help better estimate the parameters for Markov chains. The findings are published in the journal PLOS ONE.It is winter, the typical time for colds. What if you could simulate how the disease may spread? At the Cluster of Excellence Collective Behavior at the University of Konstanz, Julia Klein, a doctoral student in computer science, and colleagues investigated how using strict, rule-based methods can help better estimate the parameters for Markov chains. The findings are published in the journal PLOS ONE.[#item_full_content]

One of the features of 5G networks is so-called slicing, which is segmentation of the network. Physically, the network remains the same but is logically divided into slices depending on current requests. This approach guarantees a given level of signal quality. Resources are allocated dynamically to a specific segment: If some resources are not currently being used, they can be redirected to another segment.One of the features of 5G networks is so-called slicing, which is segmentation of the network. Physically, the network remains the same but is logically divided into slices depending on current requests. This approach guarantees a given level of signal quality. Resources are allocated dynamically to a specific segment: If some resources are not currently being used, they can be redirected to another segment.[#item_full_content]

Sam Altman, chief executive of ChatGPT-maker OpenAI, is reportedly trying to find up to US$7 trillion of investment to manufacture the enormous volumes of computer chips he believes the world needs to run artificial intelligence (AI) systems. Altman also recently said the world will need more energy in the AI-saturated future he envisions—so much more that some kind of technological breakthrough like nuclear fusion may be required.Sam Altman, chief executive of ChatGPT-maker OpenAI, is reportedly trying to find up to US$7 trillion of investment to manufacture the enormous volumes of computer chips he believes the world needs to run artificial intelligence (AI) systems. Altman also recently said the world will need more energy in the AI-saturated future he envisions—so much more that some kind of technological breakthrough like nuclear fusion may be required.[#item_full_content]

Large language models (LLMs) are deep learning artificial intelligence programs, like OpenAI’s ChatGPT. The capabilities of LLMs have developed into quite a wide range, from writing fluent essays, through coding to creative writing. Millions of people worldwide use LLMs, and it would not be an exaggeration to say these technologies are transforming work, education and society.Large language models (LLMs) are deep learning artificial intelligence programs, like OpenAI’s ChatGPT. The capabilities of LLMs have developed into quite a wide range, from writing fluent essays, through coding to creative writing. Millions of people worldwide use LLMs, and it would not be an exaggeration to say these technologies are transforming work, education and society.[#item_full_content]

When a human-AI conversation involves many rounds of continuous dialogue, the powerful large language machine-learning models that drive chatbots like ChatGPT sometimes start to collapse, causing the bots’ performance to rapidly deteriorate.When a human-AI conversation involves many rounds of continuous dialogue, the powerful large language machine-learning models that drive chatbots like ChatGPT sometimes start to collapse, causing the bots’ performance to rapidly deteriorate.[#item_full_content]

Artificial intelligence (AI) is seemingly everywhere. Right now, generative AI in particular—tools like Midjourney, ChatGPT, Gemini (previously Bard) and others—is at the peak of hype.Artificial intelligence (AI) is seemingly everywhere. Right now, generative AI in particular—tools like Midjourney, ChatGPT, Gemini (previously Bard) and others—is at the peak of hype.[#item_full_content]

The landscape of artificial intelligence (AI) applications has traditionally been dominated by the use of resource-intensive servers centralized in industrialized nations. However, recent years have witnessed the emergence of small, energy-efficient devices for AI applications, a concept known as tiny machine learning (TinyML).The landscape of artificial intelligence (AI) applications has traditionally been dominated by the use of resource-intensive servers centralized in industrialized nations. However, recent years have witnessed the emergence of small, energy-efficient devices for AI applications, a concept known as tiny machine learning (TinyML).[#item_full_content]

In several applications of computer vision, such as augmented reality and self-driving cars, estimating the distance between objects and the camera is an essential task. Depth from focus/defocus is one of the techniques that achieve such a process using the blur in the images as a clue. Depth from focus/defocus usually requires a stack of images of the same scene taken with different focus distances, a technique known as “focal stack.”In several applications of computer vision, such as augmented reality and self-driving cars, estimating the distance between objects and the camera is an essential task. Depth from focus/defocus is one of the techniques that achieve such a process using the blur in the images as a clue. Depth from focus/defocus usually requires a stack of images of the same scene taken with different focus distances, a technique known as “focal stack.”[#item_full_content]