I've been talking about AI narration for several years now, but it's just starting to go mainstream and I've been getting emails every day recently asking the same questions, so this is a round-up article with the most important information. For context, I am an audiobook narrator. I absolutely value human narrators, and I have spent tens of thousands of dollars hiring professional narrators for my novels and non-fiction over the last decade. I am also a futurist and I embrace AI tools as part of my creative and business practice. This episode is sponsored by my patrons at Patreon.com/thecreativepenn. Thank you for enabling me to continue exploring the future of creativity and the author business model. You can find out lots more on how AI can help you create and earn more in my course on The AI-Assisted Author. You can find all my courses here on Teachable. I'd love to know what you think.

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A small team of researchers at Xiamen University has expressed alarm at the ease with which bad actors can now generate fake AI imagery for use in research projects. They have published an opinion piece outlining their concerns in the journal Patterns. When researchers publish their work in established journals, they often include photographs to show the results of their work. But now the integrity of such photographs is under assault by certain entities who wish to circumvent standard research protocols. Instead of generating photographs of their actual work, they can instead generate them using artificial-intelligence applications.

Biofourmis, a Boston-based global leader in virtual care and digital medicine, has announced its engagement with Wise Health System to launch a continuum-wide care-at-home initiative. Wise Health System is a four-hospital, integrated care network in the Dallas-Fort Worth metropolitan area. Wise Health System will launch the effort with a home hospital program leveraging Biofourmis' Hospital@Home end-to-end solution that combines artificial intelligence (AI)-based remote patient monitoring technology and clinical support services. Wise Health System has nearly 200 beds across its four-hospital campus, with over 200 physicians and more than 2,000 employees. The health system is launching a hospital at home program with Biofourmis as part of its progressive healthcare delivery philosophy, to enable participation in the Centers for Medicare and Medicaid Services' (CMS') Acute Hospital Care at Home program, for which it has already earned a waiver.

Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. In this post, I want to write what I shared when I got invited as a guest lecturer at the University of Indonesia for the Advanced Information Retrieval course. I shared several Information Retrieval implementation ideas that can be used in the real world.

The growth of the artificial intelligence (AI) industry worldwide -- and Canada specifically -- has revealed that its female researchers face many of the same challenges as they do in the other science, technology, engineering and math (STEM) fields. Underrepresentation, lower hiring rates and limited professional opportunities are all ongoing barriers. However, that may be changing, according to a new study published in the Journal of Informetrics. The authors present an analysis of gender patterns that evolved in the AI field over two decades, from 2000 to 2019. They used social network analysis, natural language processing, statistical analysis and machine learning to examine the space women occupy and the nature of their work in this ever-evolving and increasingly diverse field.

The Cine-CNNTrans achieved an average AUC of 0.88 0.10 for classifying benign versus malignant thyroid nodules. The Cine-CNNTrans showed higher AUC than the Static-2DCNN (P .03). For aggregating framewise outputs into nodulewise scores, the Cine-CNNTrans tended toward higher AUC compared with the Cine-CNNAvePool (P .17). Our system tended toward higher AUC than the Cine-Radiomics and the ACR TI-RADS level, though the difference did not achieve statistical significance (P .16

The sheer potential of facial recognition technology in various fields is almost unimaginable. However, certain errors that commonly creep into its functionality and a few ethical considerations need to be addressed before its most elaborate applications can be realized. An accurate facial recognition system uses biometrics to map facial features from a photograph or video. It compares the information with a database of known faces to find a match. Facial recognition can help verify a person's identity, but it also raises privacy issues. A few decades back, we could not have predicted that facial recognition would go on to become a near-indispensable part of our lives in the future.

I'm really excited to talk about advances in federated learning at the edge with you. When I think about the edge, I often think about small embedded devices, IoT, other types of things that might have a small computer in them, and I might not even realize that. I recently learned that these little scooters that are all over my city in Berlin, Germany, and maybe even yours as well, that they are collecting quite a lot of data and sending it. When I think about the data they might be collecting, and when I put on my data science and machine learning hat, and I think about the problems that they might want to solve, they might want to know about maintenance. They might want to know about road and weather conditions. They might want to know about driver performance. Really, the ultimate question they're trying to answer is this last one, which is, is this going to result in some problem for the scooter, or for the human, or for the other things around the scooter and the human? These are the types of questions we ask when we think about data and machine learning. When we think about it on the edge, or with embedded small systems, this often becomes a problem because traditional machine learning needs quite a lot of extra information to answer these questions. Let's take a look at a traditional machine learning system and investigate how it might go about collecting this data and answering this question. First, all the data would have to be aggregated and collected into a data lake. It might need to be standardized, or munged, or cleaned, or something done with it beforehand. Then, eventually, that data is pulled usually by a data science team or by scripts written by data engineering, or data scientists on the team.