We propose a novel memory cell for recurrent neural networks that dynamically maintains information across long windows of time using relatively few resources. The Legendre Memory Unit (LMU) is mathematically derived to orthogonalize its continuous-time history--doing so by solving d coupled ordinary differential equations (ODEs), whose phase space linearly maps onto sliding windows of time via the Legendre polynomials up to degree d 1. Backpropagation across LMUs outperforms equivalently-sized LSTMs on a chaotic time-series prediction task, improves memory capacity by two orders of magnitude, and significantly reduces training and inference times. LMUs can efficiently handle temporal dependencies spanning 100,000 time-steps, converge rapidly, and use few internal state-variables to learn complex functions spanning long windows of time--exceeding state-of-the-art performance among RNNs on permuted sequential MNIST. These results are due to the network's disposition to learn scale-invariant features independently of step size. Backpropagation through the ODE solver allows each layer to adapt its internal time-step, enabling the network to learn task-relevant time-scales.

This is a 30 minute talk from GOTO Copenhagen 2019 by Feynman Liang - Creator of BachBot. I've dropped the full talk abstract below for a read before diving into the talk: Can musical creativity, something believed to be deeply human, be codified into an algorithm? While most music theorists are hesitant to claim a "correct" algorithm for composing music like Bach, recent advances in machine learning and computational musicology may help us reach an answer. In this talk, we describe BachBot: an artificial intelligence which uses deep learning and long short term memory (LSTM) to compose music in the style of Bach. We train BachBot on all known Bach chorale harmonisations and carry out the largest musical Turing test to date.

New Taipei City, Taiwan, Dec. 10, 2019 - Vecow Co., Ltd., a team of embedded experts, today announced the release of her latest GPC-1000 Series Expandable Dual GPU AI Computing System. Powered by workstation-grade Intel C246 chipset, running on dual NVIDIA Tesla /Quadro /GeForce /AMD Radeon Pro/Radeon graphics, Vecow GPC-1000 Series delivers high-performance computing and help to reduce latency and improve efficiency in data process, storage and analysis, making it ideal for robotic control, public surveillance, autonomous vehicles and deep learning applications. Vecow GPC-1000 series is powered by 9th Generation Intel Xeon /Core processor, which offers 37% better performance compared to previous generation Intel Kaby Lake platform. To address the growing AI applications such as autonomous vehicles, factory automation, public surveillance and traffic vision which requires high performance computing capability, Vecow GPC-1000 Series features dual GPUs with options of NVIDIA or AMD graphics and brings the power of dual GPU to accelerate AI solutions development and deployment. Meanwhile, it supports 9V to 55V power input with 80V surge protection, enabling a simple and wide range of applications to deploy for system integrators.

The advent of automation has eliminated paperwork as well as the monotonous workflow resulting in repetitive tasks, thereby minimizing human-borne errors. Moreover, companies can align processes and methodologies in finance, with other functional areas to sustain a comprehensive enterprise system. The modern means of invoice automation and data capture rely on deep learning models that are trained to perform tasks repetitive in nature. The intelligent systems are also trained to understand order management and vendor payment scenarios to detect fallacies or redundancies in the invoice automation system.

When you're creating a chatbot, your goal should be to make one that it requires minimal or no human interference. This can be achieved by two methods. With the first method, the customer service team receives suggestions from AI to improve customer service methods. The second method involves a deep learning chatbot, which handles all of the conversations itself and removes the need for a customer service team. Such is the power of chatbots that the number of chatbots on Facebook Messenger increased from 100K to 300K within just 1 year.

The following topics are covered in this Artificial Intelligence Tutorial: (01:08) History Of AI (03:20) What Is AI? (04:07) Stages Of Artificial Intelligence (06:45) Types Of Artificial Intelligence (09:16) Domains Of Artificial Intelligence Do subscribe to our channel and hit the bell icon to never miss an update from us in the future: https://goo.gl/6ohpTV Check out the entire Machine Learning Playlist: https://bit.ly/2NG9tK4 It includes training on the latest advancements and technical approaches in Artificial Intelligence & Machine Learning such as Deep Learning, Graphical Models and Reinforcement Learning. The Master's Program Covers Topics LIke: Python Programming PySpark HDFS Spark SQL Machine Learning Techniques and Artificial Intelligence Types Tokenization Named Entity Recognition Lemmatization Supervised Algorithms Unsupervised Algorithms Tensor Flow Deep learning Keras Neural Networks Bayesian and Markov's Models Inference Decision Making Bandit Algorithms Bellman Equation Policy Gradient Methods. However, as a goodwill gesture, Edureka offers a complimentary self-paced course in your LMS on SQL Essentials to brush up on your SQL Skills.

Yoshua Bengio is known as one of the "three musketeers" of deep learning, the type of artificial intelligence (AI) that dominates the field today.

Artificial intelligence has evolved to become a revolutionary technology. It is rapidly changing the economy, both by creating new opportunities (it's the backbone of the gig economy) and by bringing venerable institutions, like transportation, into the 21st century. Yet deep at its core something is amiss, and more and more experts are worried: the technology seems to be extremely brittle, a phenomenon epitomized by adversarial examples. Adversarial examples exploit weaknesses in modern AI. Today, most successful AI applications use machine learning (more specifically, supervised learning) by training big neural networks to mimic input-output mappings on sample data.

You might know Moody's Corporation for its credit rating agency, Moody's Investors Service, trusted by bond investors for opinions on credit risk. But the company, founded way back in 1909, grew ever-larger, and in 2007, Moody's Analytics was established to focus on non-rating activities – including economic research, consulting services and software development. Today, the employees of Moody's Analytics include a large number of machine learning and deep learning experts. To find out more about the business, we quizzed one such expert, Ashit Talukder, head of machine learning at Moody's Analytics, who previously spent 12 years at NASA's Jet Propulsion Laboratory and served as the CTO of the US Department of Labor. Q: Most people know Moody's as a credit rating agency.

Some AI systems achieve goals in challenging environments by drawing on representations of the world informed by past experiences. They generalize these to novel situations, enabling them to complete tasks even in settings they haven't encountered before. As it turns out, reinforcement learning -- a training technique that employs rewards to drive software policies toward goals -- is particularly well-suited to learning world models that summarize an agent's experience, and by extension to facilitating the learning of novel behaviors. Researchers hailing from Google, Alphabet subsidiary DeepMind, and the University of Toronto sought to exploit this with an agent -- Dreamer -- designed to internalize a world model and plan ahead to select actions by "imagining" their long-term outcomes. They say that it not only works for any learning objective, but that Dreamer exceeds existing approaches in data efficiency and computation time as well as final performance.