Models and algorithms for analyzing complex networks are widely used in research and affect society at large through their applications in online social networks, search engines, and recommender systems. According to a new study, however, one widely used algorithmic approach for modeling these networks is fundamentally flawed, failing to capture important properties of real-world complex networks. "It's not that these techniques are giving you absolute garbage. They probably have some information in them, but not as much information as many people believe," said C. "Sesh" Seshadhri, associate professor of computer science and engineering in the Baskin School of Engineering at UC Santa Cruz. Seshadhri is first author of a paper on the new findings published in Proceedings of the National Academy of Sciences.

Every Machine Learning engineer must know how to use Facets for their project -- The No Code AI Tool. Facets, a project from Google Research, is being used to visualise datasets, find interesting relationships, and clean them for machine learning. The No Code movement is on the rise and an increasing number of companies expect their engineers to quickly deliver results using pre-existing tools. From building web pages in minutes to creating mobile apps from a simple spreadsheet, no-code does it all. The proponents of building products quickly are pushing hard for the no-code movement precisely because it lets you get to the state of the art in a matter of hours instead of weeks.

We live in an age where we have unprecedented access to almost any information we need. With the emergence of new technology like artificial intelligence (AI), facial recognition, big data and more, the human experience is being changed forever. Almost anything you need is just a tap away; but this access comes at a price--data for data. A simple online search may seem harmless, but before you know it, you're being bombarded with ads offering you exactly what you were looking for. How exactly does this work?

In response to the coronavirus health crisis, USC researchers have made a hard pivot, adapting labs and lessons learned from treating other diseases to help check the virus and save lives. At their disposal are numerous technologies that give a human advantage, despite the fast-break spread of COVID-19 once it exited central China and spread across the globe. The disease has afflicted thousands of Californians and poses a serious risk to public health and the world economy. Tools such as supercomputers, software apps, virtual reality, big data and algorithms are now in play. They are using the tools to find ways to search and destroy coronavirus DNA, turn smartphones into personal protection devices and use people-friendly simulators to help cope with the crush of medical cases.

Join Roger Magoulas on March 26 for a live and interactive online session exploring recent O'Reilly AI/ML research. O'Reilly online learning is a trove of information about the trends, topics, and issues tech leaders need to know about to do their jobs. We use it as a data source for our annual platform analysis, and we're using it as the basis for this report, where we take a close look at the most-used and most-searched topics in machine learning (ML) and artificial intelligence (AI) on O'Reilly[1]. Our analysis of ML- and AI-related data from the O'Reilly online learning platform indicates: Get a free trial today and find answers on the fly, or master something new and useful. Engagement with the artificial intelligence topic continues to grow, up 88% in 2018 and 58% in 2019 (see Figure 1), outpacing share growth in the much larger machine learning topic ( 14% in 2018, up 5% in 2019).

Applying artificial intelligence and other advanced digital technologies in the real economy isn't a job for tech nerds. Instead, Europe needs more people who can combine a deep knowledge of industry sectors with some expertise in data analytics. That was the verdict of guests at a Science Business roundtable on digital skills, hosted by Sorbonne University's Center for Artificial Intelligence (SCAI) in Paris on 23 January 2020. The event was part of a series of events and publications by Science Business as part of its Digital Skills project. Conversations about AI's impact on the labour market often assume it will create jobs for a small elite of tech nerds, while destroying many more conventional roles.

ResoluteAI, the Connect to Discover company, announced the addition of a News dataset to their Foundation search platform for scientific content. In partnership with FinTech Studios, the leading AI-based intelligent search and analytics platform for Wall Street, the News database provides ResoluteAI's clients with a robust offering of timely scientific content. Foundation is a multi-source research hub that allows public scientific content to be searched as if it's single-source. ResoluteAI applies the most sophisticated artificial intelligence and machine learning to unstructured content. This AI-driven solution creates structured metadata and organizes it into datasets that include Companies, Patents, Grants, Clinical Trials, Technology Transfer, and Publications.

At this point, we've all been changed by COVID-19. Our normal lives have halted and our day-to-day activities have been turned upside down. Here's what we've experienced so far: If you've been laid-off, or stuck working at home, or even have stir-crazy kids that need something to do, we can help. Traditionally, in economic downturns people have gone back to college and reskilled themselves. Moreover, many of the most successful tech businesses are founded in these times.

Machine learning is a nightmare without some kind of structure. You can't build everything from scratch, especially if you're in a business setting. Even if you want to (and if you do, comment here and tell us about it!), You need a framework to help bring your vision to life. Here are a few machine learning frameworks designed to help get those projects off the ground.

Generative adversarial networks (GANs), which were first introduced in 2014, have proven remarkably successful at generating synthetic images. A GAN consists of two networks, one that tries to produce convincing fakes, and one that tries to distinguish fakes from real examples. The two networks are trained together, and the competition between them can converge quickly on a useful generative model. In a paper that was accepted to IEEE's Winter Conference on Applications of Computer Vision, we describe a new use of GANs to generate examples of clothing that match textual product descriptions. The idea is that a shopper could use a visual guide to refine a text query until it reliably retrieved the product for which she or he was looking.