If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
This paper presents a quantitative approach to poetry, based on the use of several statistical measures (entropy, information energy, N-gram, etc.) applied to a few characteristic English writings. We found that English language changes its entropy as time passes, and that entropy depends on the language used and on the author. In order to compare two similar texts, we were able to introduce a statistical method to asses the information entropy between two texts. We also introduced a method of computing the average information conveyed by a group of letters about the next letter in the text. We found a formula for computing the Shannon language entropy and we introduced the concept of N-gram informational energy of a poetry. We also constructed a neural network, which is able to generate Byron-type poetry and to analyze the information proximity to the genuine Byron poetry.
This Python for Machine Learning Tutorial will help you learn the Python programming language from scratch. You'll learn about Classes and Objects in Python. Everything in this course is explained with the relevant example thus you will actually know how to implement the topics that you will learn in this course.
We will use Movie ID and User ID to generate their corresponding embeddings. These embeddings are generated through the model training process along with other parameters. Once we have the embeddings, we build a K-Nearest Neighbor (KNN) model. Then whenever there is a user, we can get that user's embedding from our Neural Network model. We use this embedding to lookup in the KNN database and recommend top -- K movies to this user.
Why is self-driving so hard and so complex? Humans have walked on the moon, split the atom and flown faster than the speed of sound, and yet self-driving continues to elude us. Why is self-driving so hard and so complex? Humans have walked on the moon, split the atom and flown faster than the speed of sound. Yet despite the best efforts of our smartest engineers, backed with many billions of dollars from our wisest VCs and promoted with the passion of our most enthusiastic optimists, self-driving continues to elude us.
The Collective and Augmented Intelligence Against Covid-19 (CAIAC), is a newly formed global alliance of think tanks that are working to deliver a platform, driven by AI to curb the effects of Covid-19 on the economy and public health. The aim is to fight the virus's effects by way of advising policymakers. The collective is backed by the UN and was formed by the Future Society in association with Stanford University's Institute for Human-Centered Artificial Intelligence (HAI). The plan by CAIAC is to create an advisory group that will be a conglomeration of UN entities like UN Global Pulse and UNESCO. They will then use the growing collection of economic, social, and global health data from this pandemic to enable efficient and confident decision-making by policymakers around the globe.
If a business website and mobile app services millions of prospects (who want to buy from you) and customers (servicing existing consumers), AI and ML can help boost the performance through Chatbots. This has massive consequences on any businesses bottom line. Today's businesses are conducted on-the-go and with incredible immediacy. Consumers want solutions and deliveries immediately. In a cut throat world like ours, time management and fast delivery means survival.
Though parts of the world have succeeded in suppressing the coronavirus and are now opening up, it will be some time before we can start traveling to conferences again. I was supposed to attend two meetings this spring and then the Telluride Neuromorphic Engineering Workshop this summer. I enjoy poring through the literature, but was looking forward to hearing from the researchers themselves. So I decided to console myself by putting together a list of (mostly) recent technical neuromorphic video talks available online and have shared these with the neuromorphic community (and now with you). I find conference presentations a much better way into new subject matter than papers: you get a context, explanation, and overview without being bogged down with technical details.
Artificial Intelligence (AI) is like a superhighway, it's moving fast, evolving, and growing quickly. Like most things in life, data scientists are not born with AI and Machine Learning (ML) knowledge. At H2O.ai, we are on a mission to democratize AI. To help every company become an AI company. Companies are also on an AI transformation journey.
Researchers at the US Department of Energy's (DOE's) National Renewable Energy Laboratory (NREL) have developed a novel machine learning approach to quickly enhance the resolution of wind velocity data by 50 times and solar irradiance data by 25 times--an enhancement that has never been achieved before with climate data. The researchers took an alternative approach by using adversarial training, in which the model produces physically realistic details by observing entire fields at a time, providing high-resolution climate data at a much faster rate. This approach will enable scientists to complete renewable energy studies in future climate scenarios faster and with more accuracy. "To be able to enhance the spatial and temporal resolution of climate forecasts hugely impacts not only energy planning, but agriculture, transportation, and so much more," said Ryan King, a senior computational scientist at NREL who specializes in physics-informed deep learning. Recommended AI News: Interlink Electronics Welcomes Aboard Edward Suski As Chief Product Officer King and NREL colleagues Karen Stengel, Andrew Glaws, and Dylan Hettinger authored a new article detailing their approach, titled "Adversarial super-resolution of climatological wind and solar data," which appears in the journal Proceedings of the National Academy of Sciences of the United States …
Fox News Flash top entertainment and celebrity headlines are here. Check out what's clicking today in entertainment. Kristen Doute said she is doing some soul-searching after being fired from "Vanderpump Rules" along with castmate Stassi Schroeder for past racially insensitive actions involving former Black cast member Faith Stowers. The 37-year-old spoke about how she's changing and growing as a person on the "Hollywood Raw" podcast with Dax Holt and Adam Glyn. "It was definitely none of my business to take anything to social media [and] essentially send a mob out to this person. It was really just not my place to go there," she said.