"The field of Machine Learning seeks to answer these questions: How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?"
– from The Discipline of Machine Learning by Tom Mitchell. CMU-ML-06-108, 2006.
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.
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. 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).
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.
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.
IMAGE: Simulated low temperature (left) and high temperature (right) phase of a 2D Ising model, where blue points are spins pointing up, and the red points are spins pointing down. Tokyo, Japan - Researchers from Tokyo Metropolitan University have used machine learning to study spin models, used in physics to study phase transitions. Previous work showed that image/handwriting classifying AI could be applied to distinguish states in the simplest models. The team showed the approach is applicable to more complex models and found that an AI trained on one model and applied to another could reveal key similarities between distinct phases in different systems. Machine learning and artificial intelligence (AI) are revolutionizing how we live, work, play, and drive.
What it does: Persado uses computational linguistics and machine learning to automatically write and design marketing messages for websites and email campaigns. It can automatically run experiments on thousands of potential copy combinations. Why it's cool: The program uses an advanced language database of more than one million tagged words, phrases, and images to create marketing copy that strikes the right "emotional appeal" and has worked with top brands like American Express, Dell, and Comcast.
Creating complex neural networks with different architectures in Python should be a standard practice for any machine learning engineer or data scientist. But a genuine understanding of how a neural network works is equally valuable. In this article, learn the fundamentals of how you can build neural networks without the help of the frameworks that might make it easier to use. While reading the article, you can open the notebook on GitHub and run the code at the same time. In this article, I explain how to make a basic deep neural network by implementing the forward and backward pass (backpropagation). This requires some specific knowledge about the functions of neural networks, which I discuss in this introduction to neural networks.
MADRID – Some political leaders are hailing a potential breakthrough in the fight against COVID-19: simple pin-prick blood tests or nasal swabs that can determine within minutes if someone has, or previously had, the virus. The tests could reveal the true extent of the outbreak and help separate the healthy from the sick. But some scientists have challenged their accuracy. Hopes are hanging on two types of quick tests: antigen tests that use a nose or throat swab to look for the virus, and antibody tests that look in the blood for evidence someone had the virus and recovered. The tests are in short supply, and some of them are considered unreliable.