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New machine learning method allows hospitals to share patient data -- privately

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PHILADELPHIA - To answer medical questions that can be applied to a wide patient population, machine learning models rely on large, diverse datasets from a variety of institutions. However, health systems and hospitals are often resistant to sharing patient data, due to legal, privacy, and cultural challenges. An emerging technique called federated learning is a solution to this dilemma, according to a study published Tuesday in the journal Scientific Reports, led by senior author Spyridon Bakas, PhD, an instructor of Radiology and Pathology & Laboratory Medicine in the Perelman School of Medicine at the University of Pennsylvania. Federated learning -- an approach first implemented by Google for keyboards' autocorrect functionality -- trains an algorithm across multiple decentralized devices or servers holding local data samples, without exchanging them. While the approach could potentially be used to answer many different medical questions, Penn Medicine researchers have shown that federated learning is successful specifically in the context of brain imaging, by being able to analyze magnetic resonance imaging (MRI) scans of brain tumor patients and distinguish healthy brain tissue from cancerous regions.


Neural Nets Aren't Black Boxes

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If you think neural nets are black boxes, you're certainly not alone. While they may not be as interpretable as something like a random forest (at least not yet), we can still understand how they process data to arrive at their predictions. In this post we'll do just that as we build our own network from scratch, starting with logistic regression. If you think neural nets are black boxes, you're certainly not alone. While they may not be as interpretable as something like a random forest (at least not yet), we can still understand how they process data to arrive at their predictions.


EDN - Embedding AI in smart sensors

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In 2018, the smart sensor market was valued at $30.82 billion and is expected to reach $85.93 billion by the end of 2024, registering an increase of 18.82% per year during the forecast period 2019-2024. With the growing roles that IoT applications, vehicle automation, and smart wearable systems play in the world's economies and infrastructures, MEMS sensors are now perceived as fundamental components for various applications, responding to the growing demand for performance and efficiency. Connected MEMS devices have found applications in nearly every part of our modern economy, including in our cities, vehicles, homes, and a wide range of other "intelligent" systems. As the volume of data produced by smart sensors rapidly increases, it threatens to outstrip the capabilities of cloud-based artificial intelligence (AI) applications, as well as the networks that connect the edge and the cloud. In this article, we will explore how on-edge processing resources can be used to offload cloud applications by filtering, analyzing, and providing insights that improve the intelligence and capabilities of many applications.


microsoft/knowledge-extraction-recipes-forms

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Retrieving information from documents and forms has long been a challenge, and even now at the time of writing, organisations are still handling significant amounts of paper forms that need to be scanned, classified and mined for specific information to enable downstream automation and efficiencies. Automating this extraction and applying intelligence is in fact a fundamental step toward digital transformation that organisations are still struggling to solve in an efficient and scalable manner. An example could be a bank that receives hundreds of kilograms of very diverse remittance forms a day that need to be processed manually by people in order to extract a few key fields. Or medicinal prescriptions need to be automated to extract the prescribed medication and quantity. Typically organisations will have built text mining and search solutions which are often tailored for a scenario, with baked in application logic, resulting in an often brittle solution that is difficult and expensive to maintain.


Multi-Label Image Classification in TensorFlow 2.0

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The 2.2M parameters in MobileNet are frozen, but there are 1.3K trainable parameters in the dense layers. You need to apply the sigmoid activation function in the final neurons to ouput a probability score for each genre apart. By doing so, you are relying on multiple logistic regressions to train simultaneously inside the same model. Every final neuron will act as a seperate binary classifier for one single class, even though the features extracted are common to all final neurons. When generating predictions with this model, you should expect an independant probability score for each genre and that all probability scores do not necessarily sum up to 1. This is different from using a softmax layer in multi-class classification where the sum of probability scores in the output is equal to 1.


Artificial Intelligence and Its Partners

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The creation of the Global Partnership on Artificial Intelligence (GPAI) reflects the growing interest of states in AI technologies. The initiative, which brings together 14 countries and the European Union, will help participants establish practical cooperation and formulate common approaches to the development and implementation of AI. At the same time, it is a symptom of the growing technological rivalry in the world, primarily between the United States and China. Russia's ability to interact with the GPAI may be limited for political reasons, but, from a practical point of view, cooperation would help the country implement its national AI strategy. The Global Partnership on Artificial Intelligence (GPAI) was officially launched on June 15, 2020, at the initiative of the G7 countries alongside Australia, India, Mexico, New Zealand, South Korea, Singapore, Slovenia and the European Union. According to the Joint Statement from the Founding Members, the GPAI is an "international and multistakeholder initiative to guide the responsible development and use of AI, grounded in human rights, inclusion, diversity, innovation, and economic growth."


How Blockchain Benefits From Artificial Intelligence – Tech Check News

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From performing simple commands on smartphones using Alexa or Siri to high-end technical operations in big tech firms, one thing is sure: Ease is a necessity in the modern human experience. The 21st century has marked a rapid advancement of technology in every aspect of human life and interactions. Despite being around for many decades, the replication of human intelligence in machines -- artificial intelligence -- has now become popularized.


How AI can accelerate the search for treatments for emerging and intractable diseases – Tech Check News

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AI can save time and money in the search for treatments for emerging diseases, including COVID-19. Artificial intelligence (AI) has been a powerful tool in the search for COVID-19 treatments. In January, BenevolentAI identified a drug for rheumatoid arthritis as a potential therapy for the novel coronavirus. It's now being tested in large-scale trials around the world. AI models and algorithms can save time and money in the search for potential drug leads for emerging diseases.


VIEW: Digitisation in pathology and the promise of artificial intelligence – Tech Check News

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The COVID-19 pandemic has had a profound impact across industries and healthcare in particular--every aspect of it is undergoing change--from diagnosis to treatment and through the entire continuum of care. This has also created an urgency in the healthcare industry, to look for innovative solutions and a boost to the faster, efficient application of technologies like Artificial Intelligence (AI) and Deep Learning. Pathology is one area which stands to greatly benefit from these applications.


Artificial Intelligence: Trends & Applications To Watch In 2020 - Simpliv Blog

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For movie buffs, the work that the factory machines do in Charlie Chaplin's 1936 classic, Modern Times, may have seemed too futuristic for its time. Fast forward eight decades, and the colossal changes that Artificial Intelligence is catalyzing around us will most likely give the same impression to our future generations. There is one crucial difference though: while those advancements were in movies, what we are seeing today are real. A question that seems to be on everyone's mind is, What is Artificial Intelligence? The pace at which AI is moving, as well as the breadth and scope of the areas it encompasses, ensure that it is going to change our lives beyond the normal.