big data

Researchers use AI, big data and machine learning to find best place in the world to live


Researchers at analytics firm SAS claim to have created an artificial intelligence (AI) program that can rank the best places to live in the world using a range of publicly available data sources. Check out the latest findings on how the hype around artificial intelligence could be sowing damaging confusion. Also, read a number of case studies on how enterprises are using AI to help reach business goals around the world. You forgot to provide an Email Address. This email address doesn't appear to be valid.

IBM Think 2018 Postmortem: Making incumbent enterprises great again


Reimagining business for the digital age is the number-one priority for many of today's top executives. We offer practical advice and examples of how to do it right. IBM Chairman Ginni Rometty issued the clarion call to an arena packed with roughly 20,000 IBM customers during her Think 2018 keynote. Incumbent companies can disrupt their own industries and you don't have to be an Uber to pull it off. Compared to her keynote at the former World of Watson conference almost 18 months ago, Rometty on this go-round was far more specific, showing concrete examples of how legacy companies are adopting next-generation technologies that changes their businesses.

Ushering the Potential of AI and Big Data in Businesses


With the proliferation of smart technologies and analytics, companies are increasingly realizing the potential of artificial intelligence (AI) and big data. Enterprises are witnessing the growing consensus that AI and big data are closely interweaved, thereby the increasing amount and availability of data is empowering AI initiatives in enterprises. Organizations today have huge volumes of data at their disposal that can feed to determine behaviors and detect patterns and anomalies. Business executives observe that investments in the AI and analytics area besides big data have started to yield significant results. Leveraging the melded competence of AI and big data, organizations can now considerably boost their decision-making capabilities, reduce expenses, and achieve a much higher success rate.

The Difference Between AI and Machine Learning -- Exastax


Artificial Intelligence (AI) and Machine Learning used to be heard when the topic was Big Data Analytics – and maybe in some sci-fi movies- before; but now it is impossible to ignore them with the self-driving cars, knowledge navigators, smart home appliances and face/voice recognition solutions in our everyday lives. These terms might be quite widespread but they can lead to confusions as they are very much related and being used interchangeably. Artificial intelligence has a longer history than machine learning. It might sound like a new term but we can say it has been studied and improved over the years since Aristotle introduced syllogism, which was a method of formal and mechanical thought. The real birth of the current understanding however starts in the 1940s and 50s with some scientists from mathematics, engineering, psychology, economics and political science who put the idea of'creating an artificial brain' on the table.

Tutorials for learning R


There are tons of resources to help you learn the different aspects of R, and as a beginner this can be overwhelming. It's also a dynamic language and rapidly changing, so it's important to keep up with the latest tools and technologies. That's why R-bloggers and DataCamp have worked together to bring you a learning path for R. Each section points you to relevant resources and tools to get you started and keep you engaged to continue learning. Just like R, this learning path is a dynamic resource.

Motivating the Greatest Geniuses in AI to Change the World Instead of Destroy It


"The best minds of my generation are thinking about how to make people click ads. That sucks," said data scientist Jeffrey Hammerbacher, founder of Cloudera. What else are many of the top AI folks working on? Instead of solving world hunger or cleaning up the ocean or curing cancer, they're working on killing people and getting people to buy crap they don't really want or need. Sure, the absolute best of the best in the field have the creative freedom to tackle whatever they want but those folks are few and far between. There are only so many pure research positions. A company or college has to achieve incredible success before they have enough money to bet on long term projects that may never work out. Google is one of those companies. The University of Toronto kept the tiny field of neural networks alive for decades when it looked like it might never solve a real world problem. There are others but not many. The fact is to fund real, civilization changing research you need surplus money. And surplus money doesn't come easy.

New Artificial Intelligence Technique Dramatically Improves the Quality of Medical Imaging


Researchers have developed a new technique based on artificial intelligence and machine learning, which enable clinicians to acquire higher quality images without having to collect additional data. A radiologist's ability to make accurate diagnoses from high-quality diagnostic imaging studies directly impacts patient outcome. However, acquiring sufficient data to generate the best quality imaging comes at a cost - increased radiation dose for computed tomography (CT) and positron emission tomography (PET) or uncomfortably long scan times for magnetic resonance imaging (MRI). Now researchers with the Athinoula A. Martinos Center for Biomedical Imaging at Massachusetts General Hospital (MGH) have addressed this challenge with a new technique based on artificial intelligence and machine learning. They describe the technique - dubbed AUTOMAP (automated transform by manifold approximation) - in a paper published today in the journal Nature.

GAN with Keras: Application to Image Deblurring – Sicara Agile Big Data Development


We extract losses at two levels, at the end of the generator and at the end of the full model. The first one is a perceptual loss computed directly on the generator's outputs. This first loss ensures the GAN model is oriented towards a deblurring task. It compares the outputs of the first convolutions of VGG. The second loss is the Wasserstein loss performed on the outputs of the whole model.

Assembles Visionaries and Practitioners in Artificial Intelligence, Data Science, Alternative Data, Blockchain and ALIS - MOV37


Board to help MOV37 find, develop and nurture the new wave of young talent revolutionizing investment management. New York, March 8, 2018 – MOV37, the research and investment platform for Autonomous Learning Investment Strategies (ALIS), has assembled an Advisory Board to help find, develop and nurture the young talent that will revolutionize investment management. "The Advisory Board's primary role is to push us outside our intellectual comfort zones," says Adil Abdulali, Chief Science Officer and President at MOV37. The Board will partner with MOV37 in exploring how technology is fundamentally changing investment management and identifying and supporting the young ALIS managers at the forefront of that disruption. Raphael Douady earned his math PhD in Hamiltonian systems in Paris and holds the Robert Frey Endowed Chair for Quantitative Finance at Stony Brook, New York.

Building an AI that Can Beat You at Your Own Game – Towards Data Science


The full instructions are here, and a sample game is here. AIs are now better than humans at Backgammon, Checkers, Chess, Othello, and Go. See Audrey Keurenkov's A'Brief' History of Game AI Up to AlphaGo for a more in-depth timeline. In 2017, Michael Tucker, Nikhil Prabala, and I set out to create PAI, the world's first AI for Pathwayz. The AIs for Othello and Backgammon were especially relevant to our development of PAI. Othello, like Pathwayz, is a relatively young game -- at least compared to the ancient Backgammon, Checkers, Chess, and Go.