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.

Can John Deere become one of the leading AI and robotics companies in the world alongside Tesla and Silicon Valley technology giants over the next decade? That notion may seem incongruous with the general perception of the 185-year-old company as a heavy-metal manufacturer of tractors, bulldozers and lawnmowers painted in the signature green and yellow colors. But that is what the company sees in its future, according to Jorge Heraud, vice president of automation and autonomy for Moline, Illinois-based Deere, a glimpse of which was showcased at last January's Consumer Electronics Show in Las Vegas, where Deere unveiled its fully autonomous 8R farm tractor, driven by artificial intelligence rather than a farmer behind the wheel. The autonomous 8R is the culmination of Deere's nearly two decades of strategic planning and investment in automation, data analytics, GPS guidance, internet-of-things connectivity and software engineering. While a good deal of that R&D has been homegrown, the company also has been on a spree of acquisitions and partnerships with agtech startups, harvesting know-how as well as talent.

The autonomous tractor is a version of Deere's existing 8R series machine, the largest of which has 410 horsepower. Current 8R users can upgrade their tractors with the autonomous driving system. The Deere tractor, unveiled at the CES 2022 tech conference in Las Vegas, isn't the world's first autonomous tractor. Smaller autonomous tractors are being used in specialty-crop farms. The application of the technology to larger vehicles is just getting started and promises to be highly consequential, according to Aron Cory, research manager for world-wide agriculture at International Data Corp. "The move from conventional tractors to autonomous tractors is going to be comparable from the move from horses to the combustion engine," he said.

To further explore the intersection of 5G and manufacturing, register for the 5G Manufacturing Forum. Global manufactuers are starting to adopt 5G to improve manufacturing processes. Low latency and high reliability are needed to support critical applications in the manufacturing field. Several top manufacturers are already taking advantage of 5G implementation to improve operations in different industrial environments. Here we briefly describe some implementations by large manufacturers globally.

This paper explores the hypothesis that the diversity of human languages, right now a barrier to interoperability in communication and trade, will become significantly less of a barrier as machine translation technologies are deployed over the next several years.But this new boundary-breaking technology does not reduce all boundaries equally, and it creates new challenges for the distribution of ideas and thus for innovation and economic growth.

Farming and advanced AI may seem antithetical, but they're not. The venerable farm equipment company has not only long embraced advanced technologies, the company for years has evangelized adoption of high performance clusters and simulation software for product design. And Deere freely states it's an extremely complex undertaking. In a recent article in IEEE, Deere's Julian Sanchez, who heads the Moline, IL, company's intelligent vehicles strategy, said that while the company is working on autonomous driving, "it's not just about driving tractors around." The more difficult problem, he said, is crop classification.