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Realistic Face Restoration with GFP-GAN and DFDNet

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This article was published as a part of the Data Science Blogathon. Artificial Intelligence has made it possible for machines to learn from experience and adjust to new inputs and perform human-like tasks. The rising popularity of AI apps that can apply cool filters to the human face, edit videos, and create funny deepfakes have become viral on social media. In this article, we will look at one specific neural network architecture that can take old, blurry, and distorted photos of human faces and restore them into near-perfect, realistic images. Several neural network architectures can be used to achieve this – we will look at two of them specifically – GFP-GAN and DFDNet.



Traffic jams in your city are simple maths problem, claims Israel IT firm – HT Auto

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Using Artificial Intelligence or AI, the IT firm collects traffic-related data and then sends suggestions for traffic signal manipulation to ease …


IT Ministry Issues Draft Norms To Mobilise Non-Personal Citizen Data Available With Government

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… National Data Governance Framework is of interest for artificial intelligence (AI) startups, AI research entities and government departments.


We asked an AI tool to 'paint' images of Australia. Critics say they're good enough to sell

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The images are so crafted and "painterly" that you may not realise at first they have been dreamed up by a machine in just a few minutes. Maybe you've seen one already, but not realised what it was. It may have looked like something you'd seen before in an art book or a museum. These images are the product of a new AI-generated art scene that's exploded thanks to the development of free and easy-to-use tools that require (at the very least) short text prompts to create unique pictures. The image in the tweet above, for example, was created by giving the text prompt "a summer day" to an AI tool.


Development of a deep learning model that predicts Bi-level positive airway pressure failure - Scientific Reports

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Delaying intubation for patients failing Bi-Level Positive Airway Pressure (BIPAP) may be associated with harm. The objective of this study was to develop a deep learning model capable of aiding clinical decision making by predicting Bi-Level Positive Airway Pressure (BIPAP) failure. This was a retrospective cohort study in a tertiary pediatric intensive care unit (PICU) between 2010 and 2020. Three machine learning models were developed to predict BIPAP failure: two logistic regression models and one deep learning model, a recurrent neural network with a Long Short-Term Memory (LSTM-RNN) architecture. Model performance was evaluated in a holdout test set. 175 (27.7%) of 630 total BIPAP sessions were BIPAP failures. Patients in the BIPAP failure group were on BIPAP for a median of 32.8 (9.2–91.3) hours prior to intubation. Late BIPAP failure (intubation after using BIPAP > 24 h) patients had fewer 28-day Ventilator Free Days (13.40 [0.68–20.96]), longer ICU length of stay and more post-extubation BIPAP days compared to those who were intubated ≤ 24 h from BIPAP initiation. An AUROC above 0.5 indicates that a model has extracted new information, potentially valuable to the clinical team, about BIPAP failure. Within 6 h of BIPAP initiation, the LSTM-RNN model predicted which patients were likely to fail BIPAP with an AUROC of 0.81 (0.80, 0.82), superior to all other models. Within 6 h of BIPAP initiation, the LSTM-RNN model would identify nearly 80% of BIPAP failures with a 50% false alarm rate, equal to an NNA of 2. In conclusion, a deep learning method using readily available data from the electronic health record can identify which patients on BIPAP are likely to fail with good discrimination, oftentimes days before they are intubated in usual practice.


TigerGraph launches Workbench for graph neural network ML/AI modeling

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TigerGraph, maker of a graph analytics platform for data scientists, during its Graph & AI Summit event today introduced its TigerGraph ML (Machine Learning) Workbench, a new-gen toolkit that ostensibly will enable analysts to improve ML model accuracy significantly and shorten development cycles. Workbench does this while using familiar tools, workflows, and libraries in a single environment that plugs directly into existing data pipelines and ML infrastructure, TigerGraph VP Victor Lee told VentureBeat. The ML Workbench is a Jupyter-based Python development framework that enables data scientists to build deep-learning AI models using connected data directly from the business. Graph-enabled ML has proven to have more accurate predictive power and take far less run time than the conventional ML approach. Conventional machine learning algorithms are based on the learning of systems by training sets to develop a trained model.


IBM and MBZUAI join forces to advance AI research with new center of excellence

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MBZUAI has announced plans for a strategic collaboration with IBM (NYSE: IBM). Senior leaders from both organizations signed a Memorandum of Understanding aimed at advancing fundamental AI research, as well as accelerating the types of scientific breakthroughs that could unlock the potential of AI to help solve some of humanity's greatest challenges. Professor Eric Xing, President of MBZUAI, delivered short remarks, as did Jonathan Adashek, IBM's Senior Vice President and Chief Communications Officer, and Saad Toma, General Manager, IBM Middle East, and Africa. The agreement was then signed by Sultan Al Hajji, Vice President for Public Affairs and Alumni Relations at MBZUAI and Wael Abdoush, General Manager IBM Gulf and Levant. "The creation of a center of excellence between MBZUAI and IBM is a natural next step in the evolution of the UAE's groundbreaking AI university. The partnership will strengthen MBZUA's capacity to make practical contributions to the country's sustainable economic development. Working with IBM, MBZUAI will aim to deliver tangible results in wide-ranging sectors, including healthcare, biotech, digital and financial services. Importantly, this collaboration will help also develop the highly skilled talent we need to lead the Fourth Industrial Revolution," HE Dr. Sultan bin Ahmed Al Jaber, UAE Minister of State and Chairman of the Board of Trustees of MBZUAI said.


Amputees control a robotic arm with their mind

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University of Minnesota Twin Cities researchers have developed a more accurate, less invasive technology that allows amputees to move a robotic arm using their brain signals instead of their muscles. Many current commercial prosthetic limbs use a cable and harness system that is controlled by the shoulders or chest, and more advanced limbs use sensors to pick up on subtle muscle movements in a patient's existing limb above the device. But, both options can be cumbersome, unintuitive, and take months of practice for amputees to learn how to move them. Researchers in the University's Department of Biomedical Engineering, with the help of industry collaborators, have created a small, implantable device that attaches to the peripheral nerve in a person's arm. When combined with an artificial intelligence computer and a robotic arm, the device can read and interpret brain signals, allowing upper limb amputees to control the arm using only their thoughts. The researchers' most recent paper is published in the Journal of Neural Engineering, a peer-reviewed scientific journal for the interdisciplinary field of neural engineering.


Think you can spot content written on AI? The truth is you've probably already read a lot of it

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Analysis - Two years ago this weekend, GPT-3 was introduced to the world and although you may not have heard of it there's a good chance you've read its work. It is likely that you have already read work composed by AI model, GPT-3. Or you may have used a website that runs GPT-3 code, or even conversed with it through a chatbot or a character in a game. GPT-3 is an AI model - a type of artificial intelligence - and its applications have quietly trickled into our everyday lives over the past couple of years. In recent months, that trickle has picked up force: more and more applications are using AI like GPT-3, and these AI programmes are producing greater amounts of data, from words, to images, to code.