Adopting and implementing digital automation technologies, including artificial intelligence (AI) models such as ChatGPT, robotic process automation (RPA), and other emerging AI technologies, will revolutionize many industries and business models. It is forecasted that the rise of AI will impact a wide range of job functions and roles. White-collar positions such as administrative, customer service, and back-office roles will all be impacted by AI-fueled digital automation. The adoption of digital workers is currently positioned in the early adopter phase of the product lifecycle.1 AI-driven digital workers are expected to substantially alter many white-collar tasks, including finance, customer support, human resources, sales, and marketing.42 A study from Oxford University and Deloitte predicts AI is a significant risk to the white-collar workforce.

This study examined the use of voice recognition technology in perioperative services (Periop) to enable Periop staff to record workflow milestones using mobile technology. The use of mobile technology to improve patient flow and quality of care could be facilitated if such voice recognition technology could be made robust. The goal of this experiment was to allow the Periop staff to provide care without being interrupted with data entry and querying tasks. However, the results are generalizable to other situations where an engineering manager attempts to improve communication performance using mobile technology. This study enhanced Google's voice recognition capability by using post-processing classifiers (i.e., bag-of-sentences, support vector machine, and maximum entropy). The experiments investigated three factors (original phrasing, reduced phrasing, and personalized phrasing) at three levels (zero training repetition, 5 training repetitions, and 10 training repetitions). Results indicated that personal phrasing yielded the highest correctness and that training the device to recognize an individual's voice improved correctness as well. Although simplistic, the bag-of-sentences classifier significantly improved voice recognition correctness. The classification efficiency of the maximum entropy and support vector machine algorithms was found to be nearly identical. These results suggest that engineering managers could significantly enhance Google's voice recognition technology by using post-processing techniques, which would facilitate its use in health care and other applications.

Human-robot collaboration has benefited users with higher efficiency towards interactive tasks. Nevertheless, most collaborative schemes rely on complicated human-machine interfaces, which might lack the requisite intuitiveness compared with natural limb control. We also expect to understand human intent with low training data requirements. In response to these challenges, this paper introduces an innovative human-robot collaborative framework that seamlessly integrates hand gesture and dynamic movement recognition, voice recognition, and a switchable control adaptation strategy. These modules provide a user-friendly approach that enables the robot to deliver the tools as per user need, especially when the user is working with both hands. Therefore, users can focus on their task execution without additional training in the use of human-machine interfaces, while the robot interprets their intuitive gestures. The proposed multimodal interaction framework is executed in the UR5e robot platform equipped with a RealSense D435i camera, and the effectiveness is assessed through a soldering circuit board task. The experiment results have demonstrated superior performance in hand gesture recognition, where the static hand gesture recognition module achieves an accuracy of 94.3\%, while the dynamic motion recognition module reaches 97.6\% accuracy. Compared with human solo manipulation, the proposed approach facilitates higher efficiency tool delivery, without significantly distracting from human intents.

Fox News senior national correspondent Kevin Corke and OutKick writer Mary Katharine Ham joined'MediaBuzz' to discuss the former president's sit-down interview with'Special Report' anchor Bret Baier. And few have a more recognizable one than Donald Trump. The media have gone into high-decibel mode over an audio recording, obtained by CNN, which appears to prove that he did show a highly classified document to one or more staffers, contradicting his past denials. You may have read part of the transcript of this 2021 conversation – it's included in the indictment – but there are new details on the tape (including the sound of Trump ruffling papers) that make it more newsworthy. Former President Trump remains the frontrunner for the 2024 Republican nomination.

Pakyong, 13 Feb: You might not realize it right away, but artificial intelligence (AI) actually powers many of your phone's features. Your phone's technology is always working in the background, handling various duties, even while you are not using it. It examines how your phone is used to maximize battery life, helps you take clear photographs, recognizes music, aids with language translation, and much more. AI was previously only found in pricey devices that incorporated the most cutting-edge technology. However, since AI is now such a crucial component of mobile applications, chipmakers saw the need to create AI processors specifically for machine learning and deep learning activities to speed up processing. The most widely used voice assistants at the moment are Google Assistant, Siri, and Bixby, and you can use at least one of them on any smartphone.

It features an AI camera in the robot arm that can capture up to 30 frames per second. It comes equipped with RISK-V-based processors and AI accelerators to support features like real-time face recognition, voice recognition, and object detection. Other features in AI cameras include image classification, color recognition, line tracking, human segmentation, and more. The robot arm works with Wi-Fi, and the Bluetooth feature allows users to pair their smartphones with it. It features an intuitive 2.4" touchscreen display and a microphone with voice recognition.

In recent times, voice assistants have become a part of our day-to-day lives, allowing information retrieval by voice synthesis, voice recognition, and natural language processing. These voice assistants can be found in many modern-day devices such as Apple, Amazon, Google, and Samsung. This project is primarily focused on Virtual Assistance in Natural Language Processing. Natural Language Processing is a form of AI that helps machines understand people and create feedback loops. This project will use deep learning to create a Voice Recognizer and use Commonvoice and data collected from the local community for model training using Google Colaboratory. After recognizing a command, the AI assistant will be able to perform the most suitable actions and then give a response. The motivation for this project comes from the race and gender bias that exists in many virtual assistants. The computer industry is primarily dominated by the male gender, and because of this, many of the products produced do not regard women. This bias has an impact on natural language processing. This project will be utilizing various open-source projects to implement machine learning algorithms and train the assistant algorithm to recognize different types of voices, accents, and dialects. Through this project, the goal to use voice data from underrepresented groups to build a voice assistant that can recognize voices regardless of gender, race, or accent. Increasing the representation of women in the computer industry is important for the future of the industry. By representing women in the initial study of voice assistants, it can be shown that females play a vital role in the development of this technology. In line with related work, this project will use first-hand data from the college population and middle-aged adults to train voice assistant to combat gender bias.

An expert on voice recognition and speech technologies responds to Ysabelle Cheung's "Galatea." When Joseph Faber invented the Euphonia, a mid-19th century analog voice synthesizer, people weren't impressed. They found Faber's invention to be a strange device with little to no purpose. In an attempt to create a machine that could mimic human speech, Faber was physically tethered to his invention, manipulating its bellows, gears, and hardware to produce human-like utterances--from short speeches to ghostly renditions of "God Save the Queen"--with a flat affect. One version of the machine was designed with a feminine face attached to its bellows, hair in ringlets and fair, smooth-looking skin.

While artificial intelligence (AI) models have been acknowledged for aiding imaging analysis or facilitating workflow enhancements, this author envisions AI as a potential workstation conceierge capable of turning common venting and gripes into actionable items for significant improvements.

But it is not user-friendly. At this point, we need to trigger the recording from the UI, and not from the console. Similarly, we need to process the speech predictions and display them on the page or use them somehow. To finalize our concise demo, we'll simply add a button to allow the user to start the recording and we'll display the predictions on the page, as a list.