You are in your bed, with a book and a cup of coffee in hand. It's raining, and you are savouring the sound of rain droplets buffeting your window panes while your favourite songs play in the background. And most likely, the song you are listening to is recommended by your music app. Music apps -- that leverages the latest AI, ML technologies -- have become an essential part of our daily routines. The app has over 50 million songs and collects a lot of information about music tastes, search habits, playlists, geographical location, and most-used devices.
The Merriam-Webster dictionary defines artificial intelligence (AI) as "a branch of computer science dealing with the simulation of intelligent behavior in computers" or "the capability of a machine to imitate intelligent human behavior." The layman may think of AI as mere algorithms and programs; however, there is a distinct difference from the usual programs which are task-specific and written to perform repetitive tasks. Machine learning (ML) refers to a computing machine or system's ability to teach or improve itself using experience without explicit programming for each improvement, using methods of forward chaining of algorithms derived from backward chaining of algorithm deduction from data. Deep learning is a subsection within ML focussed on using artificial neural networks to address highly abstract problems;1 however, this is still a primitive form of AI. When fully developed, it will be capable of sentient and recursive or iterative self-improvement.
This is an intermediate-level free artificial intelligence course. This course will teach the basics of modern AI as well as some of the representative applications of AI including machine learning, probabilistic reasoning, robotics, computer vision, and natural language processing. To understand this course, you should have some previous understanding of probability theory and linear algebra.
As per Gartner, 65% of world population's data will be impacted due to privacy regulations by 2023. In fact, it might happen sooner as most countries wish to provide economic nationalism by restricting cross country data transfers and data rationing by global technology businesses. Another Independent trend coupled with the rise of tighter privacy regulations is the volume of unstructured data being collected. Combined, both structured & unstructured data are projected to grow at the rate of 7-12% on an annual basis. Technological advances along with ever falling storage prices have made it quite easy to collect unstructured data from the customers.
Over the last few years, Voice Assistants have become ubiquitous with the popularity of Google Home, Amazon Echo, Siri, Cortana, and others. These are the most well-known examples of Automatic Speech Recognition (ASR). This class of applications starts with a clip of spoken audio in some language and extracts the words that were spoken, as text. For this reason, they are also known as Speech-to-Text algorithms. Of course, applications like Siri and the others mentioned above, go further.
From Hammurabi's stone tablets to papyrus rolls and leather-bound books, the Arab region has a rich history of recordkeeping and transactional systems that closely matches the evolution of data storage mediums. Even modern-day data management concepts like data provenance and lineage have historic roots in the Arab world; generations of scribes meticulously tracked Islamic prophetic narrations from one narrator to the next, forming lineage chains that originated from central Arabia. Database systems research has been part of the academic culture in the Arab world since the 1970s. High-quality computer science and database education was always available at several universities within the Arab region, such as Alexandria University in Egypt. Many students who went through these programs were drawn to database systems research and became globally prominent, such as Ramez Elmasri (professor at University of Texas, Arlington), Amr El Abbadi (professor at University of California, Santa Barbara), and Walid Aref (professor at Purdue University).
TL;DR: motivated to better understand the fundamental tradeoffs in federated learning, we present a probabilistic perspective that generalizes and improves upon federated optimization and enables a new class of efficient federated learning algorithms. Thanks to deep learning, today we can train better machine learning models when given access to massive data. However, the standard, centralized training is impossible in many interesting use-cases--due to the associated data transfer and maintenance costs (most notably in video analytics), privacy concerns (e.g., in healthcare settings), or sensitivity of the proprietary data (e.g., in drug discovery). And yet, different parties that own even a small amount of data want to benefit from access to accurate models. This is where federated learning comes to the rescue!
Artificial intelligence (AI) is the basis for mimicking human intelligence processes through the creation and application of algorithms built into a dynamic computing environment. Stated simply, AI is trying to make computers think and act like humans. The more humanlike the desired outcome, the more data and processing power required. At least since the first century BCE, humans have been intrigued by the possibility of creating machines that mimic the human brain. In modern times, the term artificial intelligence was coined in 1955 by John McCarthy. In 1956, McCarthy and others organized a conference titled the "Dartmouth Summer Research Project on Artificial Intelligence."
A month ago, India's first driverless metro train in the national capital, Delhi, was launched. Yes! Like it or not, automation is happening and will continue to happen in places where you couldn't have imagined before. Artificial Intelligence has swept away the world around us, leading to the natural progression of demand for skilled professionals in the job market. It is one field that will never go outdated and will continue to grow. Wondering how to leverage this opportunity? How can you prepare yourself for such a league of jobs that make the world go around? We have got a repository of questions to help you get ready for your next interview! This article will cover the artificial intelligence interview questions and help you with the much-needed tips and tricks to crack the interview. The article is divided into three parts: basic artificial intelligence questions, intermediate level, and advanced AI questions. AnalytixLabs is India's top-ranked AI & Data Science Institute and is in its tenth year.
Researchers at Rensselaer Polytechnic Institute and IBM Research in New York have recently created pFoodReQ, a system that can recommend recipes tailored around the preferences and dietary needs of individual users. This system is outlined in a paper pre-published on arXiv and set to be presented at the 14th International Conference on Web Search and Data Mining (WSDM) in March. "Our work focuses on personalized food recommendation," Mohammed J. Zaki, one of the researchers who developed the system, told TechXplore. "In particular, given a user query in natural language, we want to retrieve the top matches in a recipe dataset." The short-term goal of the study carried out by Zaki and his colleagues was to help people find healthy recipes that satisfy both their dietary needs and inclinations.