Recording just got a lot easier with PLAUD's devices. In the rapidly evolving landscape of AI, there are productivity tools that truly help you better succeed in life. Among them is PLAUD, the creator of products like NotePin and Note, two AI recording devices that help students and professionals easily record lectures, meetings and conversations. This comes as interest in AI transcription has grown by more than 250% in the past two years. These cutting-edge devices are designed to streamline notetaking and enhance productivity, offering users the ability to record, transcribe and summarize content effortlessly.

It's not having to pretend that you're paying attention; it's actually having to pay attention--and take notes. It's a necessary evil if you want to keep your boss happy or get a passing grade, but this AI audio recorder could make your life a lot easier. Imagine this: Instead of frantically trying to keep up with writing notes, this voice-to-text recorder captures every spoken word and summarizes all key points with AI. Get one here for 99.99 with free shipping (reg. A lot of people already record meetings and lectures, but this gadget goes two steps further.

Marmoset, a highly vocalized primate, has become a popular animal model for studying social-communicative behavior and its underlying mechanism comparing with human infant linguistic developments. In the study of vocal communication, it is vital to know the caller identities, call contents, and vocal exchanges. Previous work of a CNN has achieved a joint model for call segmentation, classification, and caller identification for marmoset vocalizations. However, the CNN has limitations in modeling long-range acoustic patterns; the Transformer architecture that has been shown to outperform CNNs, utilizes the self-attention mechanism that efficiently segregates information parallelly over long distances and captures the global structure of marmoset vocalization. We propose using the Transformer to jointly segment and classify the marmoset calls and identify the callers for each vocalization.

If you want to coast through meetings, keep track of everyone you meet, or just remember the name of that obscure dog food your veterinarian told you to feed your pooch, there's a necklace for that. Plaud is an AI company that makes the creatively named Plaud Note--a slim ChatGPT-enabled audio recorder that can be stuck on the back of your phone or slipped into a shirt pocket to record, transcribe, and summarize your conversations. The company's newest offering is called the Plaud NotePin (the naming scheme doesn't get any better here), and it takes basically all the same features of the Note and packs them into a wearable device about the size of a lipstick tube. The NotePin can be worn as a necklace, a wristwatch, or a pin, or clipped onto something like a lapel. It costs 169 and lets you record up to 300 minutes of audio per month.

In the thrilling gaming world, recording games can help players better enhance their gaming experience. Gamers can use robust game recorders to capture and save those victories and action-packed moments. Recording and editing the wonderful game achievement, you can make it into a tutorial, share it with your friends, or post it on social media and streaming platforms for greater enjoyment. This guide will walk you through how to record gameplay on PC in high quality using screen recorder utilities like EaseUS RecExperts, whether you're playing games on Steam, Roblox, GOG Galaxy, or other platforms. There are more factors to consider when selecting the right game screen recorder for PC.

Foundation models have had a big impact in recent years and billions of dollars are being invested in them in the current AI boom. The more popular ones, such as Chat-GPT, are trained on large amounts of data from the Internet, and then reinforcement learning, RAG, prompt engineering and cognitive modelling are used to fine-tune and augment their behavior. This technology has been used to create models of individual people, such as Caryn Marjorie. However, these chatbots are not based on people's actual emotional and physiological responses to their environment, so they are, at best, surface-level approximations to the characters they are imitating. This paper describes how a new type of foundation model - a first-person foundation model - could be created from recordings of what a person sees and hears as well as their emotional and physiological reactions to these stimuli. A first-person foundation model would map environmental stimuli to a person's emotional and physiological states, and map a person's emotional and physiological states to their behavior. First-person foundation models have many exciting applications, including a new type of recommendation engine, personal assistants, generative adversarial networks, dating and recruitment. To obtain training data for a first-person foundation model, we have developed a recording rig that captures what the wearer is seeing and hearing as well as their emotional and physiological states. This novel source of data could help to address the shortage of new data for building the next generation of foundation models.

Foundation models have had a big impact in recent years and billions of dollars are being invested in them in the current AI boom. The more popular ones, such as Chat-GPT, are trained on large amounts of Internet data. However, it is becoming apparent that this data is likely to be exhausted soon, and technology companies are looking for new sources of data to train the next generation of foundation models. Reinforcement learning, RAG, prompt engineering and cognitive modelling are often used to fine-tune and augment the behaviour of foundation models. These techniques have been used to replicate people, such as Caryn Marjorie. These chatbots are not based on people's actual emotional and physiological responses to their environment, so they are, at best, a surface-level approximation to the characters they are imitating. To address these issues, we have developed a recording rig that captures what the wearer is seeing and hearing as well as their skin conductance (GSR), facial expression and brain state (14 channel EEG). AI algorithms are used to process this data into a rich picture of the environment and internal states of the subject. Foundation models trained on this data could replicate human behaviour much more accurately than the personality models that have been developed so far. This type of model has many potential applications, including recommendation, personal assistance, GAN systems, dating and recruitment. This paper gives some background to this work and describes the recording rig and preliminary tests of its functionality. It then suggests how a new type of foundation model could be created from the data captured by the rig and outlines some applications. Data gathering and model training are expensive, so we are currently working on the launch of a start-up that could raise funds for the next stage of the project.

While various sensors have been deployed to monitor vehicular flows, sensing pedestrian movement is still nascent. Yet walking is a significant mode of travel in many cities, especially those in Europe, Africa, and Asia. Understanding pedestrian volumes and flows is essential for designing safer and more attractive pedestrian infrastructure and for controlling periodic overcrowding. This study discusses a new approach to scale up urban sensing of people with the help of novel audio-based technology. It assesses the benefits and limitations of microphone-based sensors as compared to other forms of pedestrian sensing. A large-scale dataset called ASPED is presented, which includes high-quality audio recordings along with video recordings used for labeling the pedestrian count data. The baseline analyses highlight the promise of using audio sensors for pedestrian tracking, although algorithmic and technological improvements to make the sensors practically usable continue. This study also demonstrates how the data can be leveraged to predict pedestrian trajectories. Finally, it discusses the use cases and scenarios where audio-based pedestrian sensing can support better urban and transportation planning.

Have you ever asked, why can't I just tell the PC what I want it to do? If you're in that camp, there's good news: A new AI update to the Windows Power Automate app may make that future a reality. Microsoft is finally adding Copilot-like capabilities to Power Automate, the "power macro" Windows app that you likely have never used. And one of the best additions will be the ability to literally talk the AI through the process. It's not here yet -- it's part of an early access program -- but this could be an extremely powerful update when it finally arrives.

Autonomous systems are becoming increasingly prevalent in new vehicles. Due to their environmental friendliness and their remarkable capability to significantly enhance road safety, these vehicles have gained widespread recognition and acceptance in recent years. Automated Driving Systems (ADS) are intricate systems that incorporate a multitude of sensors and actuators to interact with the environment autonomously, pervasively, and interactively. Consequently, numerous studies are currently underway to keep abreast of these rapid developments. This paper aims to provide a comprehensive overview of recent advancements in ADS technologies. It provides in-depth insights into the detailed information about how data and information flow in the distributed system, including autonomous vehicles and other various supporting services and entities. Data validation and system requirements are emphasised, such as security, privacy, scalability, and data ownership, in accordance with regulatory standards. Finally, several current research directions in the AVs field will be discussed.