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) …
Google has rolled out a new technology to improve audio quality in Duo calls when the service can't maintain a steady connection called WaveNetEQ. It's based on technology from Google's DeepMind division that aims to replace audio jitter with artificial noise that sounds just like human speech, generated using machine learning. If you've ever made a call over the internet, chances are you've experienced audio jitter. It happens when packets of audio data sent as part of the call get lost along the way or otherwise arrive late or in the wrong order. Google says that 99 percent of Duo calls experience packet loss: 20 percent of these lose over 3 percent of their audio, and 10 percent lose over 8 percent.
The big rub on the first generation of graph databases was that although RDF triple stores were great at storing the simple sentence, they had a hard time with the adverbs, adjectives and clarifying phrases of your data story. If I wanted to store'John is a carpenter since 2001' or'John from Alberta Canada is a carpenter liked by 702 people', the syntax of old-school triple stores had a more tedious, but not impossible way of handling it. It involved creating extra nodes that were confusing to some and a process called reification. Until about a year ago, labeled property graphs (LPG) were better at color and detail than RDF, having a more intuitive syntax for clarifying adverbs, adjectives, and phrases. That was, of course, until recently.
Earn your Master's, learn from pioneering Illinois faculty, and gain the data science skills that are transforming business and society. Illinois Computer Science offers a specialized track that includes both MCS degree requirements and data science-focused coursework. This degree is right for anyone who not only wants to learn to extract knowledge and insights from massive data sets, but also wants full command of the computational infrastructure to do so. The Master of Computer Science in Data Science (MCS-DS) leads the MCS degree through a focus on core competencies in machine learning, data mining, data visualization, and cloud computing, It also includes interdisciplinary data science courses, offered in cooperation with the Department of Statistics and the School of Information Science. Data Visualization: Coursework designed to show you how to create effective and understandable data presentations.
In this course you will learn what Artificial Intelligence (AI) is, explore use cases and applications of AI, understand AI concepts and terms like machine learning, deep learning and neural networks. You will be exposed to various issues and concerns surrounding AI such as ethics and bias, & jobs, and get advice from experts about learning and starting a career in AI. You will also demonstrate AI in action with a mini project. This course does not require any programming or computer science expertise and is designed to introduce the basics of AI to anyone whether you have a technical background or not.
The fast and untraceable virus mutations take lives of thousands of people before the immune system can produce the inhibitory antibody. Recent outbreak of novel coronavirus infected and killed thousands of people in the world. Rapid methods in finding peptides or antibody sequences that can inhibit the viral epitopes of COVID-19 will save the life of thousands. In this paper, we devised a machine learning (ML) model to predict the possible inhibitory synthetic antibodies for Corona virus. We collected 1933 virus-antibody sequences and their clinical patient neutralization response and trained an ML model to predict the antibody response.
Building an AI pipeline is emerging as a critical need across many industries and applications. See and learn how this is being applied when building a smoke detection model using UAS Video Imagery for Prescribed Fire Management. Drones or unmanned aerial systems are likely to be one of the next big changes in fire service and many other use cases. Learn about building a collaborative AI data pipeline to address: • Thermal imaging for hot spots, structural or large commercial fires, natural disaster response; • Applied AI through the lens of seeing how hazard reduction happens through fire authorities, national park staff, and business individual property owners who are using AI to battle the global wildfire crisis; • Algorithms, and how they are deployed to fight future wildfires; and • Smoke detection in UAS Video Imagery for Prescribed Fire Management.
The past few weeks have revealed the worst and the best in human responses to the coronavirus crisis – from the supermarket hoarders clearing the shelves to the neighbourhood groups organising help for elderly and vulnerable people. When it comes to the pharmaceutical companies, how should we judge their response? They, after all, hold the key to ending the pandemic. Yet in one vital respect their behaviour has more in common with the supermarket hoarders than the neighbourhood groups. Our exit strategy from the global lockdown depends on the development of an effective vaccine, as is well-known.
Data lineage is critical to addressing data-driven business requirements such as compliance, change management, data governance, customer experience, and many more. But how do you keep up with data lineage given the volume and scale of data today? "AI-Powered Data Lineage: The New Business Imperative" explains how AI and machine learning, combined with broad metadata connectivity to any source, enables true enterprise-wide visibility into the entire data lifecycle.