This study investigates ChatGPT for Japanese-English translation, exploring simple and enhanced prompts and comparing against commercially available translation engines. Performing both automatic and MQM-based human evaluations, we found that document-level translation outperforms sentence-level translation for ChatGPT. On the other hand, we were not able to determine if enhanced prompts performed better than simple prompts in our experiments. We also discovered that ChatGPT-3.5 was preferred by automatic evaluation, but a tradeoff exists between accuracy (ChatGPT-3.5) and fluency (ChatGPT-4). Lastly, ChatGPT yields competitive results against two widely-known translation systems.

Ultrasonic welding machines play a critical role in the lithium battery industry, facilitating the bonding of batteries with conductors. Ensuring high-quality welding is vital, making tool condition monitoring systems essential for early-stage quality control. However, existing monitoring methods face challenges in cost, downtime, and adaptability. In this paper, we present WeldMon, an affordable ultrasonic welding machine condition monitoring system that utilizes a custom data acquisition system and a data analysis pipeline designed for real-time analysis. Our classification algorithm combines auto-generated features and hand-crafted features, achieving superior cross-validation accuracy (95.8% on average over all testing tasks) compared to the state-of-the-art method (92.5%) in condition classification tasks. Our data augmentation approach alleviates the concept drift problem, enhancing tool condition classification accuracy by 8.3%. All algorithms run locally, requiring only 385 milliseconds to process data for each welding cycle. We deploy WeldMon and a commercial system on an actual ultrasonic welding machine, performing a comprehensive comparison. Our findings highlight the potential for developing cost-effective, high-performance, and reliable tool condition monitoring systems.

Tracking 3D human motion in real-time is crucial for numerous applications across many fields. Traditional approaches involve attaching artificial fiducial objects or sensors to the body, limiting their usability and comfort-of-use and consequently narrowing their application fields. Recent advances in Artificial Intelligence (AI) have allowed for markerless solutions. However, most of these methods operate in 2D, while those providing 3D solutions compromise accuracy and real-time performance. To address this challenge and unlock the potential of visual pose estimation methods in real-world scenarios, we propose a markerless framework that combines multi-camera views and 2D AI-based pose estimation methods to track 3D human motion. Our approach integrates a Weighted Least Square (WLS) algorithm that computes 3D human motion from multiple 2D pose estimations provided by an AI-driven method. The method is integrated within the Open-VICO framework allowing simulation and real-world execution. Several experiments have been conducted, which have shown high accuracy and real-time performance, demonstrating the high level of readiness for real-world applications and the potential to revolutionize human motion capture.

The last two years have been some of the most exciting and highly anticipated in Automatic Speech Recognition's (ASR's) long and rich history, as we saw multiple enterprise-level fully neural network-based ASR models go to market (e.g. The accelerated success of ASR deployments is due to many factors, including the growing ecosystem of freely available toolkits, more open source datasets, and a growing interest on the part of engineers and researchers in the ASR problem. This confluence of forces has produced an amazing momentum shift in commercial ASR. We truly are at the onset of big changes in the ASR field and of massive adoption of the technology. These developments are not only improving existing uses of the technology, such as Siri's and Alexa's accuracies, but they are also expanding the market ASR technology serves.

We have established a new data science practice at Canonical. The team will innovate in the open source data science technology stack, deliver advanced business analytics, support product roadmap decisions for Canonical through actionable insights, and lead by example in setting and publicly advocating for industry standards in open source data science. The team will have both Data Scientists and Data Engineers, apply here if you are most excited about the Data Engineer role! As a Data Engineer at Canonical you will act as a technical expert in an exciting field at the intersection of data engineering, data science, and machine learning technologies, with particular emphasis on the open source ecosystem of Canonical and Ubuntu. You will drive the organisation, instrumentation, ingestion, and transformation of data from a wide range of sources in the company.

The idea of designing an artificial woman is a tale as old as time. We can go back over 2000 years to Ancient Greece and find the myth of Galatea and Pygmalion. Pygmalion was a sculptor who fell in love with one of his statues which was granted life by Aphrodite. In modern times, we have countless examples of female AI personas, both in science fiction and in real-live systems. Some are embodied, such as Sophia, but some are purely voice-based, such as Amazon's Alexa, Microsoft's array of bots such as Cortana, Xiaoice and Zo.

MARKETING 24/06/2020 In English, Machine Translation Makes You Sound Like a Man in His Middle Age THREE BOCCONI SCHOLARS FOUND AN ALGORITHMIC BIAS IN THE SYSTEMS OF GOOGLE, BING, AND DEEPL, WHEN TRANSLATING FROM SEVERAL EUROPEAN LANGUAGES INTO ENGLISH Imagine a child raised in a village inhabited only by middle-aged men. For the first ten years of her life, she only hears males in their 60s talking of work, books, sports, health, and money. What kind of weird language do you think she will speak when she leaves the village? Something similar happens to the most common machine translation systems, according to a new study by Dirk Hovy, an Associate Professor of Computer Science at Bocconi, and two Postdoctoral Researchers in his lab, Federico Bianchi and Tommaso Fornaciari. To train a translation system based on machine learning, you feed it with large amounts of texts and let it learn by experience.

Ed. note: This is the first article in a two-part series about AI, its potential impact on how organizations approach security, and the accompanying considerations around implementation, efficacy, and compliance. Is Artificial Intelligence (AI) on track to help the world streamline and solve against tasks that are better left to a machine? One might think so, given everything we've seen and heard about the impact of AI on our society -- from our phones telling us the best way to drive home, to chatbots on e-commerce sites answering product questions, to devices as small as a thermostat or as large as an electric vehicle removing friction from everyday life. Now AI is entering the space of cybersecurity, promising to bring greater speed and accuracy in detecting and responding to breaches, user behavior analysis, or predicting new strains of malware. AI and machine learning technologies can help protect organizations from a continuously evolving threat landscape -- but AI is not just for sophisticated attacks, AI can also help protect against classic attack scenarios.

Ed. note: This is the first article in a two-part series about AI, its potential impact on how organizations approach security, and the accompanying considerations around implementation, efficacy, and compliance. Is Artificial Intelligence (AI) on track to help the world streamline and solve against tasks that are better left to a machine? One might think so, given everything we've seen and heard about the impact of AI on our society -- from our phones telling us the best way to drive home, to chatbots on e-commerce sites answering product questions, to devices as small as a thermostat or as large as an electric vehicle removing friction from everyday life. Now AI is entering the space of cybersecurity, promising to bring greater speed and accuracy in detecting and responding to breaches, user behavior analysis, or predicting new strains of malware. AI and machine learning technologies can help protect organizations from a continuously evolving threat landscape -- but AI is not just for sophisticated attacks, AI can also help protect against classic attack scenarios.

AMD just did Intel one better at Computex. Intel wowed the Taipei crowds on Tuesday with a 28-core Core chip, which the company promised by the end of the year. One day later, on Wednesday, AMD announced Threadripper 2--and at 32 cores and 64 threads, it will easily top what Intel promised. AMD's Threadripper 2 announcement was the highlight of the company's press conference, which didn't have much to offer in the way of new announcements in graphics. AMD did say that its Vega 56 Nano for mini-ITX systems is now shipping.