Sign Language Production (SLP) is a challenging task, given the limited resources available and the inherent diversity within sign data. As a result, previous works have suffered from the problem of regression to the mean, leading to under-articulated and incomprehensible signing. In this paper, we propose using dictionary examples and a learnt codebook of facial expressions to create expressive sign language sequences. However, simply concatenating signs and adding the face creates robotic and unnatural sequences. To address this we present a 7-step approach to effectively stitch sequences together. First, by normalizing each sign into a canonical pose, cropping, and stitching we create a continuous sequence. Then, by applying filtering in the frequency domain and resampling each sign, we create cohesive natural sequences that mimic the prosody found in the original data. We leverage a SignGAN model to map the output to a photo-realistic signer and present a complete Text-to-Sign (T2S) SLP pipeline. Our evaluation demonstrates the effectiveness of the approach, showcasing state-of-the-art performance across all datasets. Finally, a user evaluation shows our approach outperforms the baseline model and is capable of producing realistic sign language sequences.

Microsoft allegedly plans to bring Copilot, its generative-AI-powered personal assistant, to late adopters. Windows Central's Zac Bowden reports the Copilot button and sidebar from Windows 11 will "soon" arrive in Windows 10. The AI assistant for Windows 11 launched in beta in August and officially in September. Bowden says the Windows 10 Copilot will include plugins that work across both operating systems. "I understand the experience and capabilities of Copilot across Windows 10 and Windows 11 will be roughly the same, including plugin compatibility across both versions of the OS," the editor reported.

The flexibility to present and process insurance data in a manner that is easy to work with is of vital importance. The best machine learning models are built from clean, high-quality data that has been effectively and skilfully processed. Quite often, Bowden said, this task requires the heaviest lifting and has led to a running joke that most data scientists spend 80% of their time cleaning data and only 20% calibrating models. Although the core of EDA involves summary statistics, Bowden stressed that there is often more to it. Understanding the data types is often the first step and identifying which fields will be numerical and which are categorical is the crucial next step.

Business-to-consumer (B2C) businesses have made it a priority to incorporate machine learning into customer-facing functions, integrating it into sales and marketing. For business-to-business (B2B) companies, however, translating data into actionable marketing strategies can be a more difficult proposition. Selling to organizations invariably requires embarking on a much longer and more complex journey, culminating in an order of much higher value than in the consumer realm. With hundreds of thousands, if not millions, of dollars at stake, a misguided marketing investment could lead to financial losses. "The availability of data and the importance of having the focus on the full customer journey is coming a little later to the B2B world," says Laura Beaudin, a partner at Bain & Co. "A lot of expectations in terms of customers manifested themselves in the consumer world before they brought those expectations to their business-purchasing world."

Business-to-consumer (B2C) businesses have made it a priority to incorporate machine learning into customer-facing functions, integrating it into sales and marketing. For business-to-business (B2B) companies, however, translating data into actionable marketing strategies can be a more difficult proposition. Selling to organizations invariably requires embarking on a much longer and more complex journey, culminating in an order of much higher value than in the consumer realm. With hundreds of thousands, if not millions, of dollars at stake, a misguided marketing investment could lead to financial losses. "The availability of data and the importance of having the focus on the full customer journey is coming a little later to the B2B world," says Laura Beaudin, a partner at Bain & Co. "A lot of expectations in terms of customers manifested themselves in the consumer world before they brought those expectations to their business-purchasing world."