Terms like'Data Science', 'Machine Learning', and'Data Analytics' are so infused and embedded in almost every dimension of lifestyle that imagining a day without these smart technologies is next to impossible. With science and technology propelling the world, the digital medium is flooded with data, opening gates to newer job roles that never existed before. However, quite often it is witnessed that beginners get confused over similar terms being used interchangeably, like'Data Science' and'Data Analytics'. This post will give you a clear idea about what some of the prominent concepts and job roles in Data are, and how they differ from each other! The most popular field that has emerged in the wake of digital disruption is'Data Science'. Data being oil and fuel of all the operations, companies are making the most of the accessible data that had never been used before.

An AI speech recognition bot designed to analyze speech and compile it into a database broke when it tried to analyze Trump's speech patterns, its creator told The Los Angeles Times. Factba.se is a project that aims to track every word from Donald Trump available, from speeches and interviews to Facebook posts and his vast catalogue of tweets. Since the project began three years ago, the team have collected over 1,000 hours of video and transcribed over 10,594,000 words from 1976 until now. To do this, CEO of FactSquared Bill Frischling created Margaret, an AI bot for transcription. Frischling tried his AI bot on a short section of a Trump speech commemorating the anniversary of the Battle of the Coral Sea.

Many of the world's most important fisheries are experiencing increases in illegal fishing, undermining efforts to sustainably conserve and manage fish stocks. A major challenge to ending illegal, unreported, and unregulated (IUU) fishing is improving our ability to identify whether a vessel is fishing illegally and where illegal fishing is likely to occur in the ocean. However, monitoring the oceans is costly, time-consuming, and logistically challenging for maritime authorities to patrol. To address this problem, we use vessel tracking data and machine learning to predict illegal fishing on the Patagonian Shelf, one of the world's most productive regions for fisheries. Specifically, we focus on Chinese fishing vessels, which have consistently fished illegally in this region. We combine vessel location data with oceanographic seascapes -- classes of oceanic areas based on oceanographic variables -- as well as other remotely sensed oceanographic variables to train a series of machine learning models of varying levels of complexity. These models are able to predict whether a Chinese vessel is operating illegally with 69-96% confidence, depending on the year and predictor variables used. These results offer a promising step towards preempting illegal activities, rather than reacting to them forensically.

This article was first published on Friday, May 22, 2020 in Kaiser Health News. Dr. Albert Hsiao and his colleagues at the University of California-San Diego health system had been working for 18 months on an artificial intelligence program designed to help doctors identify pneumonia on a chest X-ray. When the coronavirus hit the United States, they decided to see what it could do. The researchers quickly deployed the application, which dots X-ray images with spots of color where there may be lung damage or other signs of pneumonia. It has now been applied to more than 6,000 chest X-rays, and it's providing some value in diagnosis, said Hsiao, the director of UCSD's augmented imaging and artificial intelligence data analytics laboratory.

Data is growing by leaps and bounds, the convergence of extremely large data sets both structured and unstructured define Big Data. The increasing awareness of the Internet of Things (IoT) devices among organizations and volume, variety, velocity and veracity at which data is generated have caught the attention of the enterprise in a bid to enhance digital technologies and guide digital transformation. Analytics Insights eliminates that the big data market size will grow at a CAGR of 10.9%, globally from US$ 193.5 billion in 2020 to US$ 301.5 billion by 2023. This region is witnessing significant developments in the big data market gaining remarkable traction in the BFSI industry vertical. Numerai is the world's first hedge fund, to predict the stock market.

Our second annual list highlights promising, private, U.S.-based companies that are using artificial intelligence in meaningful business-oriented ways.

In After Effects, we can get rid of unwanted objects in our video footage using Adobe Sensei AI. The Content-Aware Fill tool in After Effects simply asks us for the region and the duration for the software to "fill" the video frames to mask things we don't want to see. The tool then samples surrounding contextual pixels to generate pixel patterns in the video frames that "blend in" with the scene -- as if the object never existed. This AI is probably built using Generative Adversarial Networks (GANs) -- the same deep learning algorithms that can create incredibly convincing deepfakes. As a (very) concise overview -- a GAN is composed of two competing neural networks: a generator and a discriminator.

Ever since the companies have realized that the regular software are not going to address the growing competition and that they need something additional to pull them, concepts like Data Science and Machine Learning have started gaining momentum. Whether it is Voice Recognition based searching, Fraud Detection Systems, or a Recommendation System by Amazon or Netflix, Machine Learning has been the most implemented technology over the period of time. This is the reason every company wants to hire Machine Learning Professionals and a huge crowd of aspirants wish to become one. Let's uncover the right way anyone can pursue this field! Well, speaking broadly, Machine Learning is the field that deals with educating the machines to make them able to make decisions like humans.

Dr Albert Hsiao and his colleagues at the UC San Diego health system in the United States had been working for 18 months on an artificial intelligence (AI) program designed to help doctors identify pneumonia on a chest X-ray. When the coronavirus hit the United States, they decided to see what it could do. The researchers quickly deployed their program, which dots X-ray images with spots of colour where there may be lung damage or other signs of pneumonia. It has now been applied to more than 6,000 chest X-rays, and it's providing some value in diagnosis, said Dr Hsiao, the director of UCSD's augmented imaging and artificial intelligence data analytics laboratory. His team is one of several around the country that has pushed AI programs into the Covid-19 crisis to perform tasks like deciding which patients face the greatest risk of complications and which can be safely channeled into lower-intensity care.

Sub-seasonal climate forecasting (SSF) focuses on predicting key climate variables such as temperature and precipitation in the 2-week to 2-month time scales. Skillful SSF would have immense societal value, in areas such as agricultural productivity, water resource management, transportation and aviation systems, and emergency planning for extreme weather events. However, SSF is considered more challenging than either weather prediction or even seasonal prediction. In this paper, we carefully study a variety of machine learning (ML) approaches for SSF over the US mainland. While atmosphere-land-ocean couplings and the limited amount of good quality data makes it hard to apply black-box ML naively, we show that with carefully constructed feature representations, even linear regression models, e.g., Lasso, can be made to perform well. Among a broad suite of 10 ML approaches considered, gradient boosting performs the best, and deep learning (DL) methods show some promise with careful architecture choices. Overall, suitable ML methods are able to outperform the climatological baseline, i.e., predictions based on the 30-year average at a given location and time. Further, based on studying feature importance, ocean (especially indices based on climatic oscillations such as El Nino) and land (soil moisture) covariates are found to be predictive, whereas atmospheric covariates are not considered helpful.