"Many researchers … speculate that the information-processing abilities of biological neural systems must follow from highly parallel processes operating on representations that are distributed over many neurons. [Artificial neural networks] capture this kind of highly parallel computation based on distributed representations"
– from Machine Learning (Section 4.1.1; page 82) by Tom M. Mitchell, McGraw Hill Companies, Inc. (1997).
In this tutorial, you will learn how to perform face detection with OpenCV and Haar cascades. I've been an avid reader for PyImageSearch for the last three years, thanks for all the blog posts! My company does a lot of face application work, including face detection, recognition, etc. We just started a new project using embedded hardware. I don't have the luxury of using OpenCV's deep learning face detector which you covered before, it's just too slow on my devices.
For a very long time, humans have been trying to design a machine that has complex capabilities like how human brain does. When artificial intelligence first came into existence, people thought that making a model that imitates humans will be easy. But it took more than five decades for scientists to turn the concept successful. Today, we are running after machines that carry the cognitive capabilities of human brain in it. Why is designing a mechanism that is similar to human brain complex?
Machine learning, artificial intelligence, and other modern statistical methods are providing new opportunities to operationalise previously untapped and rapidly growing sources of data for patient benefit. Despite much promising research currently being undertaken, particularly in imaging, the literature as a whole lacks transparency, clear reporting to facilitate replicability, exploration for potential ethical concerns, and clear demonstrations of effectiveness. Among the many reasons why these problems exist, one of the most important (for which we provide a preliminary solution here) is the current lack of best practice guidance specific to machine learning and artificial intelligence. However, we believe that interdisciplinary groups pursuing research and impact projects involving machine learning and artificial intelligence for health would benefit from explicitly addressing a series of questions concerning transparency, reproducibility, ethics, and effectiveness (TREE). The 20 critical questions proposed here provide a framework for research groups to inform the design, conduct, and reporting; for editors and peer reviewers to evaluate contributions to the literature; and for patients, clinicians and policy makers to critically appraise where new findings may deliver patient benefit. Machine learning (ML), artificial intelligence (AI), and other modern statistical methods are providing new opportunities to operationalise previously untapped and rapidly growing sources of data for patient benefit. The potential uses include improving diagnostic accuracy,1 more reliably predicting prognosis,2 targeting treatments,3 and increasing the operational efficiency of health systems.4 Examples of potentially disruptive technology with early promise include image based diagnostic applications of ML/AI, which have shown the most early clinical promise (eg, deep learning based algorithms improving accuracy in diagnosing retinal pathology compared with that of specialist physicians5), or natural language processing used as a tool to extract information from structured and unstructured (that is, free) text embedded in electronic health records.2 Although we are only just …
Early detection of aortic stenosis (AS) is becoming increasingly important with a better outcome after aortic valve replacement in asymptomatic severe AS patients and a poor outcome in moderate AS. Therefore, researchers of the Mayo Clinic, USA, developed an AI-ECG using a convolutional neural network to identify patients with moderate to severe AS. It was a retrospective study in which researchers identified 258 607 adults [mean age 63 16.3 years; women 122 790 (48%)] with echocardiography and an ECG performed within 180 days using the Mayo Clinic Unified Data Platform (UDP). The researchers tested the use of an AI-ECG to help identify patients with moderate to severe aortic stenosis (AS). Using echocardiography data, the researchers identified moderate to severe AS in 9723 (3.7%) patients. They performed Artificial intelligence training in 129 788 (50%), validation in 25 893 (10%), and testing in 102 926 (40%) in randomly selected subjects.
Deep Learning is a subdivision of machine learning that imitates the working of a human brain with the help of artificial neural networks. It is useful in processing Big Data and can create important patterns that provide valuable insight into important decision making. The manual labeling of unsupervised data is time-consuming and expensive. DeepLearning tutorials help to overcome this with the help of highly sophisticated algorithms that provide essential insights by analyzing and cumulating the data. Deep Learning leverages the different layers of neural networks that enable learning, unlearning, and relearning.
To explore whether generative adversarial networks (GANs) can enable synthesis of realistic medical images that are indiscernible from real images, even by domain experts. In this retrospective study, progressive growing GANs were used to synthesize mammograms at a resolution of 1280 1024 pixels by using images from 90 000 patients (average age, 56 years 9) collected between 2009 and 2019. To evaluate the results, a method to assess distributional alignment for ultra–high-dimensional pixel distributions was used, which was based on moment plots. This method was able to reveal potential sources of misalignment. A total of 117 volunteer participants (55 radiologists and 62 nonradiologists) took part in a study to assess the realism of synthetic images from GANs.
Whatever business a company may be in, software plays an increasingly vital role, from managing inventory to interfacing with customers. Software developers, as a result, are in greater demand than ever, and that's driving the push to automate some of the easier tasks that take up their time. Productivity tools like Eclipse and Visual Studio suggest snippets of code that developers can easily drop into their work as they write. These automated features are powered by sophisticated language models that have learned to read and write computer code after absorbing thousands of examples. But like other deep learning models trained on big datasets without explicit instructions, language models designed for code-processing have baked-in vulnerabilities.
Waymo, Alphabet's self-driving car subsidiary, is reshuffling its top executive lineup. On April 2, John Krafcik, Waymo's CEO since 2015, declared that he will be stepping down from his role. He will be replaced by Tekedra Mawakana and Dmitri Dolgov, the company's former COO and CTO. Krafcik will remain as an advisor to the company. "[With] the fully autonomous Waymo One ride-hailing service open to all in our launch area of Metro Phoenix, and with the fifth generation of the Waymo Driver being prepared for deployment in ride-hailing and goods delivery, it's a wonderful opportunity for me to pass the baton to Tekedra and Dmitri as Waymo's co-CEOs," Krafcik wrote on LinkedIn as he declared his departure.
Nothing tells of the ubiquity of satellite imagery like Google Maps. A completely unpaid service provides anyone with internet access a entire planet's worth of satellite imagery. While Google Maps is free, other paid alternatives exist which take photos of the earth's surface on a more frequent basis for commercial use. World governments also utilize their satellites for many domestic uses. As the availability of satellite imagery outpaces the ability of humans to look through them manually, an automated means to classify them must be developed.