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Artificial Intelligence (AI) is gaining traction and proving to be a problem solver to many human related tasks. As population across the globe surge, the number of vehicle owners is drastically on the rise. Giant tech Google is striving to deploy Maps in guiding road users to avoid using “confusing lane changes or freeway exits”. In a Google blog dubbed “A smoother ride and a more detailed map thanks to AI”, Google explains how maps will be used to identify and predict when people are striking the brakes. The AI product is tailored to use the available information and AI to detect hard-breaking events. The Giant tech believes that their product will help to eradicate “more than 100 million hard-braking events in routes that are driven with Google Maps every year” and enable drivers to be cautious at certain places. Understanding Google Maps Google Maps are essential to the extent that they assist individuals to explore, navigate and make things done in an easier and mana

Data science is one of the most sought disciplines in the field of technology due to the rapid growth of data in terms of volume, complexity and velocity. The surging demand of data scientists prompted Nvidia Deep Learning Institute to unveil Accelerated Data Science Teaching Kit for trainers . The first version of Accelerated Data Science Teaching Kit for qualified trainers was released by NVIDIA Deep Learning Institute on February 2021 to develop the best data science solutions that meets the increased demand for data skillsets. The kit was co-developed by Polo Chau of the Georgia Institute of Technology and Xishuang Dong from Prairie View A and M University.  In report published by NVIDIA, Chau stated that: “Data Science unlocks the immense potential of data in solving societal challenges and large-scale complex problems across virtually every domain, from business, technology, science and engineering to healthcare, government and many more.” The free teaching content wi

Gone are the days when images with missing parts could never be reconstructed with realistic approach. NVIDIA AI team came up with innovation that can predict and fill missing parts of a photo with realistic results. At least 55,000 incomplete images of different sizes and shapes were generated to train the deep neural network. The team unveiled its state-of-the-art deep learning model in 2018 that has the ability to edit images through reconstruction of incomplete parts. In order to train their deep neural network, the research team generated over 55,000 masks of different sizes and shapes. They then used 25,000 masks with holes to test the dataset. For the enhancement of accuracy, the holes were categorized into six divisions based on the input images. Nvidia’s AI imaging technique can edit and reconstruct images, even if its parts are completely erased. Before Nvidia’s innovation, Adobe Photoshop was widely used to recreate images. Unlike Adobe Photoshop, Nvidia’s techni

Facebook AI is seeking an alternative approach to ensure fairness in decisions made by machine learning systems. The tech giant explores a distinct method to apply algorithmic fairness techniques to complex and large-scale production systems. In a research paper dubbed “Fairness on the Ground: Applying Algorithmic Fairness Approaches to Production Systems,” the Company reconnoiters the expertise of integrating fairness applications into complex and large-scale production systems meant to benefit other disciplines facing similar problems. The research conducted by Facebook AI construes statistical measures of fairness such as calibration and equality of odds to reasonable practical approach. The research isn’t the first one to identify the challenges facing complex systems. According to Facebook AI team, “the challenges of bias in supervised machine learning models are most likely to be studied problem in algorithmic fairness.”  In conclusion, the study presented a holistic

Google Cloud and NVIDIA NGC have revolutionized customer experience by innovating technologies that help to augment employee productivity, improve customer engagement and generate more revenue. Enterprises across different industries are rapidly adopting natural language process (NLP) solutions such as chatbots and audio transcription to upgrade consumer engagement, improve workers’ productivity and drive revenue progression. Chatbots, Audio Transcription Definitions Chatbot is a software program that is automated to conduct online chat conversations through text or text to speech. The technology helps to add convenience for customers. Audio transcription refers to the process of converting speech from audio file into written text.  Challenges Facing NLP The main challenge facing NLP is that it must comprehend the fundamental background of script without unambiguous rubrics in human language. Developing an AI-powered solution from the beginning to the end is a daunting ta

During the pandemic, tech industry reaped big resulting to dramatic technological upheavals. 5G, chatbots, cloud computing and edge computing were some of the tech applications that were on high demand in 2020. Therefore 2020 acted as a precursor of major innovation. 1. Autonomous Ride-Hailing : it is an application that determines the future of uber as it offers a menu of services to get people and services around. Self-driving vehicles were deployed in parts of the US, where vehicles picked and dropped passengers without a human driver. 2. Deep Learning , also referred as deep neural network, is a subset of machine learning in artificial intelligence (AI) that mimics the functioning of human brain in transforming data and developing patterns for decision making. Deep learning is essential in big data sourced from search engines, social media and e-commerce platforms among many others. The data is then shared through fintech applications such as cloud computing. Deep lea

Amazon Web Services (AWS) has rolled out a detailed virtual free training and certification events slated to be held on March. According to AWS, the training is meant “to leverage the power of AWS cloud.” The training targets those who are interested in developing foundational cloud knowledge and those seeking to dive in the technical field. The virtual events will train participants on the following: AWS technical essentials, strategies and tools to perform large-scale migrations, securing your AWS Cloud, machine learning basics, how amazon sagemaker can help, AWS Cloud practitioner essentials and AWS Cloud Practitioner. AWS Technical Essentials will be held on March 01 and  targets individuals who articulate technical benefits of AWS services to customers, individuals who have interest in learning how to get started with AWS, SysOps, solution architects and Developers Online training for Strategies and Tools to Perform Large Scale Migrations will be held on March 5 and

Machine learning (ML) refers to a set of tools that can be used to facilitate making predictions and decisions based on existing data. ML tasks are based on available data that is observed through instructions or experiences. DataCamp Data Scientist Dr Hugo Bowne-Anderson defines ML as “the science and art of giving computers the ability to learn and make decisions from data without being explicitly programmed.” ML can be used in real business to forecast customer attrition. Often businesses are required to analyze market trends and map a better way to predict customer churn. In the era of big data and machine learning, predicting consumer abrasion is not a walk in the park. Businesses are increasingly becoming data-driven making it essential for the adoption of data visualization to make it easier to analyze. Machine learning entails collecting data, cleaning it, training the algorithm and then applying it. To make work easier it’s advisable for businesses to apply four m

Google launched cinematic photos on December 2020 to help people remember some of their most precious moments. Cinematic photos can transform a single 2D photo that was taken in the past “into a more immersive 3D animation.” The conversion of 2D to 3D images involves simulating camera movement and parallax by inferring 3D depictions in an image. Just like the past computational photography, cinematic photos require a depth map for the provision of information regarding the 3D structure of a scene. The technology that sustains depth estimation on smartphone relies on a geometry method and a multi-view stereo to concurrently capture multiple images at different viewpoints in order to solve for the depth of objects. Google AI trained a convolutional neural network to activate cinematic photos on pictures that were not captured in multi-view stereo. The process used an encoder-decoder architecture to speculate a depth map drawn from “a single RGB image.” Median filtering wa