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"... the research area that studies the operation and design of systems that recognize patterns in data." It includes statistical methods like discriminant analysis, feature extraction, error estimation, cluster analysis.
– Pattern Recognition Laboratory at Delft University of Technology
Video-meeting giant Zoom is rolling out a set of new features including its refreshed digital whiteboard, a backstage for events, gesture recognition, and more. Zoom announced the whiteboard overhaul six months ago and is now rolling the Zoom Whiteboard out to users as a collaboration feature that's always on and built into the desktop client, Zoom Meetings, and Zoom Rooms for third-party touch devices as dedicated whiteboards, such as the DTEN D7 and Neat Board. Which video conferencing platform is right for your business? We've gathered details about 10 leading services. Support for Zoom Chat is coming soon, according to Zoom, but there's no news of the proposed Zoom Whiteboard integration with Meta's Oculus Horizon Workrooms, which was supposed to ship in early 2022.
You no longer need to bring out an iPad or iPhone just to use Zoom's gesture recognition. Zoom has updated its Mac and Windows apps with visual gesture support. Raise your hand or give a thumbs-up and you'll send the appropriate reaction. As you might imagine, this promises more natural interaction in virtual classrooms and meetings than you'd get from clicking buttons. The feature requires the latest version of Zoom as of this writing (5.10.3).
Google on Thursday began rolling out a new Search feature that will let users search for information using both text and images at the same time. The new multisearch feature is part of Google's ongoing efforts to use AI to "create information experiences that are truly conversational, multimodal and personal," as Google CEO Sundar Pichai said recently. These Chromebook laptops feature low prices and long battery lives. The multisearch feature is embedded in Google Lens, the image recognition tool that's accessible via the Google app. For now, the feature is available in beta for US users searching with text in English.
We've been overcomplicating machine learning for years. Sometimes we confuse it with the over-hyped artificial intelligence, talking about replacing humans with robotic reasoning when really ML is about augmenting human intelligence with advanced pattern recognition. Or we burrow into deep learning when more basic SQL queries would get the job done. But perhaps the greatest problem with ML today is how incredibly complicated we make the tooling because, as Confetti AI co-founder Mihail Eric has posited, the ML "tooling landscape with constantly shifting responsibilities and new lines in the sand is especially hardest for newcomers to the field," making it "a pretty rough time to be taking your first steps into MLOps." Normally we look to tooling to make tech easier.
You are free to share this article under the Attribution 4.0 International license. A new data-driven approach is offering insight into people with type 1 diabetes, who account for about 5-10% of all diabetes diagnoses. The researchers gathered information through health informatics and applied artificial intelligence (AI) to better understand the disease. In the study, they analyzed publicly available, real-world data from about 16,000 participants enrolled in the T1D Exchange Clinic Registry. By applying a contrast pattern mining algorithm, researchers were able to identify major differences in health outcomes among people living with type 1 diabetes who do or do not have an immediate family history of the disease.
This stock forecast is designed for investors and analysts who need predictions of the best stocks for the whole Pharmaceutical sector (see Pharma Stocks Package). Package Name: Pharma Stocks Forecast Recommended Positions: Long Forecast Length: 7 Days (3/16/22 – 3/23/22) I Know First Average: 25.8% The algorithm correctly predicted 10 out of 10 the suggested trades in the Pharma Stocks Forecast Package for this 7 Days forecast. The prediction with the highest return was SPPI, at 85.74%. YMAB and BBIO also performed well for this time horizon with returns of 40.69% and 26.44%, respectively.
An interdisciplinary team of researchers from the University of Missouri, Children's Mercy Kansas City, and Texas Children's Hospital has used a new data-driven approach to learn more about persons with Type 1 diabetes, who account for about 5-10% of all diabetes diagnoses. The team gathered its information through health informatics and applied artificial intelligence (AI) to better understand the disease. In the study, the team analyzed publicly available, real-world data from about 16,000 participants enrolled in the T1D Exchange Clinic Registry. By applying a contrast pattern mining algorithm developed at the MU College of Engineering, the team was able to identify major differences in health outcomes among people living with Type 1 diabetes who do or do not have an immediate family history of the disease. Chi-Ren Shyu, the director of the MU Institute for Data Science and Informatics (MUIDSI), led the AI approach used in the study and said the technique is exploratory.
A simple interface that allows users to upload images or transcripts to analyze, all within the same window. Dealing with thousands of transcripts to process each semester can put a massive load on your Admissions team, not just to process those transcripts, but to do so with no errors while trying to meet deadlines. With an influx of transcript requests to process on time, room for manual/human error increases. Due to the unique nature of every transcript, processing time can be skyrocketing especially with manual efforts where it can take weeks or more, delaying the whole process. With out-of-the-box, intricate Image Recognition Algorithms, Sia can read, detect and extract courses, programs, credits & GPA-related metrics from any transcript instantly, with just images of the transcripts.
Artificial intelligence has been used to analyse thousands of written reports of personal experiences with psychoactive drugs to gain a better understanding of their subjective effects and how they work in the brain. Psychedelic drugs such as LSD, ketamine and psilocybin – the active compound in magic mushrooms – are being investigated as treatments for a range of conditions, including depression, addiction and post-traumatic stress disorder. The experiences they induce, which may be important for their therapeutic effects, are highly variable, and can include visual and auditory hallucinations, an altered sense of self and a distorted perception of time. Danilo Bzdok at McGill University in Montreal, Canada, and his colleagues used a pattern-recognition algorithm to scour 6850 accounts of experiences submitted on the website Erowid, involving 27 different drugs. They linked words used in the accounts for each drug, such as "euphoria", "nausea" or "visuals", with any of 40 receptors in the brain that the drug is known to interact with, and mapped drug effects onto areas of the brain where these receptors are most active.
Gesture recognition is one of the most popular techniques in the field of computer vision today. In recent years, many algorithms for gesture recognition have been proposed, but most of them do not have a good balance between recognition efficiency and accuracy. Therefore, proposing a dynamic gesture recognition algorithm that balances efficiency and accuracy is still a meaningful work. Currently, most of the commonly used dynamic gesture recognition algorithms are based on 3D convolutional neural networks. Although 3D convolutional neural networks consider both spatial and temporal features, the networks are too complex, which is the main reason for the low efficiency of the algorithms. To improve this problem, we propose a recognition method based on a strategy combining 2D convolutional neural networks with feature fusion. The original keyframes and optical flow keyframes are used to represent spatial and temporal features respectively, which are then sent to the 2D convolutional neural network for feature fusion and final recognition. To ensure the quality of the extracted optical flow graph without increasing the complexity of the network, we use the fractional-order method to extract the optical flow graph, creatively combine fractional calculus and deep learning. Finally, we use Cambridge Hand Gesture dataset and Northwestern University Hand Gesture dataset to verify the effectiveness of our algorithm. The experimental results show that our algorithm has a high accuracy while ensuring low network complexity.