If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
To quantitatively evaluate the generalizability of a deep learning segmentation tool to MRI data from scanners of different MRI manufacturers and to improve the cross-manufacturer performance by using a manufacturer-adaptation strategy. This retrospective study included 150 cine MRI datasets from three MRI manufacturers, acquired between 2017 and 2018 (n 50 for manufacturer 1, manufacturer 2, and manufacturer 3). Three convolutional neural networks (CNNs) were trained to segment the left ventricle (LV), using datasets exclusively from images from a single manufacturer. A generative adversarial network (GAN) was trained to adapt the input image before segmentation. The LV segmentation performance, end-diastolic volume (EDV), end-systolic volume (ESV), LV mass, and LV ejection fraction (LVEF) were evaluated before and after manufacturer adaptation.
The whole backdrop of Artificial intelligence and deep learning is to imitate the human brain, and one of the most notable feature of our brain is it's inherent ability to transfer knowledge across tasks. Which in simple terms means using what you have learnt in kindergarten, adding 2 numbers, to solving matrix addition in high school mathematics. The field of machine learning also makes use of such a concept where a well trained model trained with lots and lots of data can add to the accuracy of our model. Here is my code for the transfer learning project I have implemented. I have made use of open cv to capture real time images of the face and use them as training and test datasets.
A good dataset serves as the backbone of an Artificial Intelligence system. Data assists in various ways as it helps understand how the system is performing, understand meaning insights and others. At the premier annual Computer Vision and Pattern Recognition conference (CVPR 2020), several datasets have been open-sourced in order to help the community achieve higher accuracies and insights. Below here we have listed the top 10 Computer Vision datasets that are open-sourced at the CVPR 2020 conference. About: FaceScape is a large-scale detailed 3D face dataset that includes 18,760 textured 3D face models, which are captured from 938 subjects and each with 20 specific expressions.
The automatic and accurate interlinking of geospatial data poses an important scientific challenge, with direct application in several business fields. The major requirement is achieving high accuracy in identifying similar entities within datasets. For example, in a cadastral database, it is crucial that the land parcels, that were gathered from several different databases, are uniquely and clearly identified. In another example, for a geo-marketing company, it is of high importance to be able to accurately cross-reference the location/addresses of customers and companies, so that they are properly targeted. LinkGeoML aims at researching, developing and extending machine learning methods, utilizing the vast amount of available, open geospatial data, in order to implement automated and highly accurate algorithms for interlinking geospatial entities. The proposed methods will implement novel training features, based on domain knowledge and on the analysis of open and proprietary geospatial datasets.
My aim, as always, was to keep the projects as diverse as possible so you can pick the ones that fit into your data science journey. If you're a beginner, I would suggest starting with the PalmerPenguins dataset as most folks aren't even aware of it right now. A great chance to get a head start. I would love to hear your thoughts on which open source project you found the most useful. Or let me know if you want me to feature any other data science projects here or in next month's edition.
Image classification is not a hard topic anymore. Tensorflow has all the inbuilt functionalities that take care of the complex mathematics for us. Without knowing the details of the neural network, we can use a neural network now. In today's project, I used a Convolutional Neural Network (CNN) which is an advanced version of the neural network. If you worked with the FashionMNIST dataset that contains shirts, shoe handbags, etc., CNN will figure out important portions of the images.
Things are different on the other side of the mirror. Right hands become left hands. Intrigued by how reflection changes images in subtle and not-so-subtle ways, a team of Cornell researchers used artificial intelligence to investigate what sets originals apart from their reflections. Their algorithms learned to pick up on unexpected clues such as hair parts, gaze direction and, surprisingly, beards – findings with implications for training machine learning models and detecting faked images. AI learns to pick up on unexpected clues to differentiate original images from their reflections, the researchers found.
The human brain is an incredibly efficient source of intelligence. Earlier this month, OpenAI announced it had built the biggest AI model in history. This astonishingly large model, known as GPT-3, is an impressive technical achievement. Yet it highlights a troubling and harmful trend in the field of artificial intelligence--one that has not gotten enough mainstream attention. Modern AI models consume a massive amount of energy, and these energy requirements are growing at a breathtaking rate.
I knew SQL long before learning about Pandas, and I was intrigued by the way Pandas faithfully emulates SQL. Stereotypically, SQL is for analysts, who crunch data into informative reports, whereas Python is for data scientists, who use data to build (and overfit) models. Although they are almost functionally equivalent, I'd argue both tools are essential for a data scientist to work efficiently. From my experience with Pandas, I've noticed the following: Those problems are naturally solved when I began feature engineering directly in SQL. If you know a little bit of SQL, it's time to put it into good use.
Artificial intelligence is about to change lead generation and conversion as you know it. In the process, it'll have a transformative impact on companies and careers. AI is a blanket term that covers several different technologies. You might have heard of some of them, like machine learning, computer vision, and natural language processing. Even if you don't know much about it, though, you probably use AI-powered technology dozens or hundreds of times per day.