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Diagnosing Mental Health Disorders Through AI Facial Expression Evaluation

#artificialintelligence

Researchers from Germany have developed a method for identifying mental disorders based on facial expressions interpreted by computer vision. The new approach can not only distinguish between unaffected and affected subjects, but can also correctly distinguish depression from schizophrenia, as well as the degree to which the patient is currently affected by the disease. The researchers have provided a composite image that represents the control group for their tests (on the left in the image below) and the patients who are suffering from mental disorders (right). Individuals with affective disorders tend to have raised eyebrows, leaden gazes, swollen faces and hang-dog mouth expressions. To protect patient privacy, these composite images are the only ones made available in support of the new work.


Pega to Hold Third Annual Global Hackathon for Citizen and Professional App Makers

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Pegasystems Inc., the low-code platform provider that builds agility into the world's leading organizations, announced its 2022 Pega Community Hackathon is now open for registration. In its third year, this global contest invites both professional and citizen developers to compete in building meaningful new apps that help solve real-world business and social problems that continue to emerge. Open to the entire Pega community, individuals and teams will leverage the intuitive, low-code Pega Platform environment throughout the development process -- from ideation and design to building and execution. Pega will offer extensive technology resources and mentorship, including office hours access with Pega experts for guidance and technical help. Registered participants can start building today and must complete their projects by October 7, 2022 with a solution concept and prototype that's ready for implementation.


DMS and Seekr Announce New Strategic Partnership

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Seekr, an internet technology and content evaluation company, and Digital Media Solutions, Inc., a leading provider of digital performance advertising solutions, announced a multi-year agreement to support Seekr's media strategy, revenue and advertising operations across its global search advertising platform and vertical content sponsorships. The alliance will accelerate the build out and monetization of new Seekr verticals in concert with expanding its global audience and reach. Seekr prioritizes transparency and empowers users with choice and control by streamlining access to reliable information. Powered by AI and machine learning, Seekr offers the first fully transparent search engine that reimagines what web results can look like when they are free of bias or manipulation. Seekr plans to increase consumer engagement across its platforms and scale audience engagement by leveraging the power of the award-winning DMS toolset, inclusive of the DMS first-party data, expansive media reach and proprietary technologies.


Deep Learning and Compression

#artificialintelligence

This is part of a series of articles in AI Codecs. While digital media are transmitted in a wide variety of settings, the available codecs are "one-size-fits-all": they are hard-coded, and cannot be customized to particular use cases beyond high-level hyperparameter tuning [10]. In the last few years, deep learning has revolutionized many tasks such as machine translation, speech recognition, face recognition, natural language processing and photo-realistic image generation. Given unlabeled training data, deep learning based models generate new samples from the input data distribution [6]. These are called deep generative models and have powerful capabilities such as extracting features by learning a low-dimension feature representation of the input space and sampling to generate, restore, predict or compress data.


Minerva - Google's Language Model for Quantitative Reasoning

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This article was published as a part of the Data Science Blogathon. Google presented Minerva; a neural network created in-house that can break calculation questions and take on other delicate areas like quantitative reasoning. The model for natural language processing is called Minerva. Recently, experimenters have developed a very sophisticated natural language processing model. You can use it to restructure textbooks or write essays.


North America is seeing a hiring boom in tech industry AI roles

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North America extended its dominance for artificial intelligence (AI) hiring among tech industry companies in the three months ending June, according to GlobalData. The number of roles in North America made up 56.1% of total AI jobs – up from 53.9% in the same quarter last year. That was followed by Europe, which saw a 1 year-on-year percentage point change in AI roles. The figures are compiled by GlobalData, who track the number of new job postings from key companies in various sectors over time. Using textual analysis, these job advertisements are then classified thematically.


AI regulation: A state-by-state roundup of AI bills

#artificialintelligence

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Wondering where AI regulation stands in your state? Today, the Electronic Privacy Information Center (EPIC) released The State of State AI Policy, a roundup of AI-related bills at the state and local level that were passed, introduced or failed in the 2021-2022 legislative session. Within the past year, according to the document, states and localities have passed or introduced bills "regulating artificial intelligence or establishing commissions or task forces to seek transparency about the use of AI in their state or locality."


Google's Read Along App and AI Assistant Launches Web Version - Voicebot.ai

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Google has introduced a web version of its popular Read Along educational app for teaching children to read. The website includes Read Along the virtual assistant Diya, a voice-enabled AI guiding and correcting kids as they practice reading. The Read Along website operates much like the Android app. Children can pick from different stories and word games. Diya monitors the child, using Google's speech recognition technology to spot mistakes and places where they are having trouble.


Baidu rolls out driverless taxi service in two Chinese cities

ZDNet

Baidu has rolled out commercial driverless taxi services in the Chinese cities of Wuhan and Chongqing, expanding the transport option beyond the country's capital Beijing. The launch comes this week with the government releasing China's first draft guidelines on the use of self-driving vehicles for public transport. Baidu said in a statement that it secured regulatory approvals to collect fares for its driverless taxi service Apollo Go in the two Chinese cities. The autonomous vehicle manufacturer's vice president and chief safety operation officer of intelligent driving group, Wei Dong, said: "Fully driverless cars providing rides on open roads to paying customers means we have finally come to the moment the industry has been longing for. We believe these permits are a key milestone on the path to the inflection point when the industry can finally roll out fully autonomous driving services at scale."


#002 Machine Learning - Linear Regression Models - Master Data Science 18.07.2022

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Highlights: Welcome back to the all-new series on Machine Learning. In the previous post, we gave you a sneak peak into the basics of Machine Learning, the two types of Machine Learning, viz., Supervised & Unsupervised, and implemented some examples using various algorithms in each of the techniques. In this new tutorial post, we will explore one of the most widely used Supervised Learning algorithms in the world today – Linear Regression. We will start off with some theory and go on to build a simple model in Python, from scratch. In our previous post (also the first post of this Machine Learning tutorial series), we brushed the fundamentals of Linear Regression using the example of housing price prediction, given the size of the house. If you remember, the prediction was based on the linear relationship that existed between the house price and the size of the house. Have a look at the image below. In the graph above, the size of the house is shown along the horizontal axis and the price of a house is shown along the vertical axis. Here, each data point is a house with its respective size and the price that the house was recently sold for.