Fujitsu November 17, 2017, announced that it has categorized specific artificial intelligence usage scenarios and introduced 17 offerings that indicate the optimal technology and solutions as well as the implementing effects of Fujitsu Human Centric AI Zinrai, in order to promote its use in a wide range of industries and businesses. These offerings showcases of how AI can resolve issues, and propose Zinrai technology, products, and services that would support such uses, based on a categorization of the various issues facing customers through the approximately 600 business deals relating to AI usage at Fujitsu. Moreover, these offerings predict the impact of deploying these products and services while estimating costs, enabling customers to more concretely envision the use of AI in their business and deploy solutions quickly and easily. Fujitsu will expand the lineup of Zinrai technologies, products, as well as solutions going forward, and search for new usage scenarios across an even broader range of industries and businesses, supporting the customers' digital transformations with the new offerings. Details of these offerings will be introduced at Fujitsu Insight 2017 in Belle Salle Nihonbashi (Tokyo) on November 17.

Machine learning has become the center of discussion in artificial intelligence today. Using the several machine learning libraries available today, machine learning with Python, C, Java, Julia, and R, among others, is easier than ever. Here are some popular machine learning libraries you can start with if you want to venture into this promising career path. Keras is part of TensorFlow's extensive machine learning utilities. Plus it's more human-friendly and written with Python.

Artificial Intelligence and Machine Learning are no longer new concepts in digital marketing and managing the marketing funnel. Automating targeting, automating campaigns, personalized targeting etc have all been enabled by Machine Learning for a few years now. However, with the rapid evolution of Deep Learning, the impact of AI on marketing is now growing insignificance. Deep Learning is a progression of Machine Learning which vastly improved the performance of AI models. A significant step in the evolution of ML was the use of Neural Networks – Models build to mimic the Human Brain, with the ability to process large quantities of data, continuously learn from it and then provide highly accurate outputs – be it Classifications, Predictions or Segmentation/Clustering. Deep Learning is the next step in the evolution of the use of Neural Networks, enabled by the improved processing capacity of computers and the increased quantum of data available.

Did you know that traffic counting is behind the rise of Microsoft and thus modern personal computing? Traffic counting is used by city officials to determine transportation needs. The most popular method is by laying a strip of pneumatic rubber tube on the roadway of interest and analyze metrics like vehicle volume, vehicle types, axle counts, etc. This method, however, is not quite accurate or safe so a better method is researched. In this article, you will find how you can build your own traffic counter using a Deep Learning approach (YOLO V5) using a public dataset from the ground up.

Using image processing, machine learning and deep learning methods to build computer vision applications using popular frameworks such as OpenCV and TensorFlow in Python.

If you are not familiar with the PyCaret library, which was designed for low-code machine learning. I highly recommend checking the Moez Ali articles, who is the main creator of this great framework. Let's assume that we would like to download data and create a simple dashboard for Exploratory Data Analysis. Now, we can analyze basic information about the data and decide which is worth using later in the experiment. We can conclude that many of the variables are useless. All, after all, a typical user doesn't have number of reviews orreviews_per_month .

This problem required the detection of many different objects in a stream of images coming from an industrial camera.

There are a lot of different deep learning models out there that can be used for learning colorization mapping. In this project, I am going to use Pix2Pix to solve the problem. We will use a U-Net and a PatchGAN as the generator and the discriminator respectively as proposed in the Pix2Pix paper. The Pix2Pix model is a GAN, or specifically, it is a type of conditional GAN (cGAN). Just like a GAN, Pix2Pix trains the generator and the discriminator simultaneously. But instead of taking samples from a latent space, the Pix2Pix generator takes images as inputs.

This report from the Montreal AI Ethics Institute covers the most salient progress in research and reporting over the second quarter of 2021 in the field of AI ethics with a special emphasis on "Environment and AI", "Creativity and AI", and "Geopolitics and AI." The report also features an exclusive piece titled "Critical Race Quantum Computer" that applies ideas from quantum physics to explain the complexities of human characteristics and how they can and should shape our interactions with each other. The report also features special contributions on the subject of pedagogy in AI ethics, sociology and AI ethics, and organizational challenges to implementing AI ethics in practice. Given MAIEI's mission to highlight scholars from around the world working on AI ethics issues, the report also features two spotlights sharing the work of scholars operating in Singapore and Mexico helping to shape policy measures as they relate to the responsible use of technology. The report also has an extensive section covering the gamut of issues when it comes to the societal impacts of AI covering areas of bias, privacy, transparency, accountability, fairness, interpretability, disinformation, policymaking, law, regulations, and moral philosophy.

The neuroimage analysis community has neglected the automated segmentation of the olfactory bulb (OB) despite its crucial role in olfactory function. The lack of an automatic processing method for the OB can be explained by its challenging properties. Nonetheless, recent advances in MRI acquisition techniques and resolution have allowed raters to generate more reliable manual annotations. Furthermore, the high accuracy of deep learning methods for solving semantic segmentation problems provides us with an option to reliably assess even small structures. In this work, we introduce a novel, fast, and fully automated deep learning pipeline to accurately segment OB tissue on sub-millimeter T2-weighted (T2w) whole-brain MR images. To this end, we designed a three-stage pipeline: (1) Localization of a region containing both OBs using FastSurferCNN, (2) Segmentation of OB tissue within the localized region through four independent AttFastSurferCNN - a novel deep learning architecture with a self-attention mechanism to improve modeling of contextual information, and (3) Ensemble of the predicted label maps. The OB pipeline exhibits high performance in terms of boundary delineation, OB localization, and volume estimation across a wide range of ages in 203 participants of the Rhineland Study. Moreover, it also generalizes to scans of an independent dataset never encountered during training, the Human Connectome Project (HCP), with different acquisition parameters and demographics, evaluated in 30 cases at the native 0.7mm HCP resolution, and the default 0.8mm pipeline resolution. We extensively validated our pipeline not only with respect to segmentation accuracy but also to known OB volume effects, where it can sensitively replicate age effects.