"Many researchers … speculate that the information-processing abilities of biological neural systems must follow from highly parallel processes operating on representations that are distributed over many neurons. [Artificial neural networks] capture this kind of highly parallel computation based on distributed representations"
– from Machine Learning (Section 4.1.1; page 82) by Tom M. Mitchell, McGraw Hill Companies, Inc. (1997).
Module 2 Deep Learning with Python Theoretical introduction to Neural Networks for Deep Learning, Python libraries for Deep Learning (Tensorflow), creation of a Neural Network and application to real datasets, Regression applied to different types of structured and unstructured data, such as numerical, categorical, or textual.
Computers run by executing sets of instructions. An algorithm is such a set of instructions, in which a series of tasks are repeated until some particular condition is matched. There are all kinds of algorithms, written for all kinds of purposes, but they are most commonly used for programming tasks like sorting and classification. These tasks are well suited to the algorithm's do/until mentality: Sort these numbers until they are in ascending order. Classify these photographs until they fall neatly into categories.
In order to develop artificial general intelligence (AGI), the sort of all-encompassing AI that we see in science fiction, we might need to merely sit back and let a simple algorithm develop on its own. Reinforcement learning, a kind of gamified AI architecture in which an algorithm "learns" to complete a task by seeking out preprogrammed rewards, could theoretically grow and learn so much that it breaks the theoretical barrier to AGI without any new technological developments, according to research published by the Google-owned DeepMind last month in the journal Artificial Intelligence and spotted by VentureBeat. While reinforcement learning is often overhyped within the AI field, it's interesting to consider that engineers could have already built all the tech needed for AGI and now simply need to let it loose and watch it grow. The kind of artificial intelligence that we encounter every day of our lives, whether it's machine learning or reinforcement learning, is narrow AI: an algorithm designed to accomplish a very specific task like predicting your Google search, spotting objects in a video feed, or mastering a video game. By contrast, AGI -- sometimes called human-level AI intelligence -- would be more along the lines of C-3PO from "Star Wars," in the sense that it could understand context, subtext, and social cues.
The University of Florida, College of Dentistry (UFCD) is recruiting qualified applicants for an Assistant, Associate or Full Professor to be a part of the University of Florida's (UF) Artificial Intelligence (AI) Initiative. The UF AI initiative is supported by a $70 million investment and implementation of the most powerful AI supercomputer in higher education in the world. UF is hiring 100 new faculty members, including one dedicated to applying AI power to help solve oral health challenges in the College of Dentistry. Applicants should specialize in predictive analytics, risk stratification or causal inference methods to develop evidence and tools for decision support in clinical and other public health settings. Applicants will have expertise in the use of large healthcare data and a doctoral degree in the health sciences, computer science, engineering, or related disciplines.
In the summer of 2009, the Israeli neuroscientist Henry Markram strode onto the TED stage in Oxford, England, and made an immodest proposal: Within a decade, he said, he and his colleagues would build a complete simulation of the human brain inside a supercomputer. They'd already spent years mapping the cells in the neocortex, the supposed seat of thought and perception. "It's a bit like going and cataloging a piece of the rain forest," Markram explained. "How many trees does it have? What shapes are the trees?"
I'm a huge fan of Lex Fridman and the awesome content he produces to promote ideas and advances in different sciences. In this regard, I want to share some of the concepts that blew my mind when I first heard them in his Podcasts. He is the President and Chief Scientific Officer of the Allen Institute for Brain Science in Seattle. From 1986 until 2013, he was a professor at CalTech. Cited more than 105,000 times.
As the name suggests Neural Networks can be defined as a set of algorithms that are specifically designed to act like a human brain. In a technological manner, it can be referred to as a computational model that has a network architecture. To get a general idea here I have listed some of its components.
It is not the best of times for self-driving car startups. The past year has seen large tech companies acquire startups that were running out of cash and ride-hailing companies shutter costly self-driving car projects with no prospect of becoming production-ready anytime soon. Yet, in the midst of this downturn, Waabi, a Toronto-based self-driving car startup, has just come out of stealth with an insane amount of $83.5 million in a Series A funding round led by Khosla Ventures, with additional participation from Uber, 8VC, Radical Ventures, OMERS Ventures, BDC, and Aurora Innovation. The company's financial backers also include Geoffrey Hinton, Fei-Fei Li, Peter Abbeel, and Sanja Fidler, artificial intelligence scientists with great influence in the academia and applied AI community. What makes Waabi qualified for such support?
From this article, you will learn how to play Super Mario Bros with Deep Q-Network and Double Deep Q-Network (with code!). Super Mario Bros is a well-known video game title developed and published by Nintendo in the 1980s. It is one of the classical game titles that lived through the years and need no explanations. It is a 2D side-scrolling game, allowing the player to control the main character -- Mario. The gameplay involves moving Mario from left to right, surviving the villains, getting coins, and reaching the flag to clear stages.