This project is more about the history of machine learning than any current research. In 1943 McCulloch & Pitts created a model of computation based on neurons, rather than electronic logic gates. Their neurons were designed to reason about the kinds of calculations that brains could do, even though they were much more simple then real biological neurons. I read Computation Finite and Infinite, by Marvin Minsky (1967), who is also the author of Percpetrons. In this book he uses this neural net model to explore theory of computation and finite state machines.

ABSTRACT We present a TTS neural network that is able to produce speech in multiple languages. The proposed network is able to transfer a voice, which was presented as a sample in a source language, into one of several target languages. The conversion is based on learning a polyglot network that has multiple perlanguage sub-networksand adding loss terms that preserve the speaker's identity in multiple languages. We evaluate the proposed polyglot neural network for three languages with a total of more than 400 speakers and demonstrate convincing conversion capabilities. Index Terms-- TTS, multilingual, unsupervised learning 1. INTRODUCTION Neural text to speech (TTS) is an emerging technology that is becoming dominant over the alternative TTS technologies, in both quality and flexibility.

In his keynote speech, Zheng explained that the concept of urban computing includes urban perception & data acquisition, big data management, data-driven analysis and services provided. Zheng said that the urban perception & data acquisition process is facing lots of challenges, including acquiring more data with limited resources; insufficient data due to the extreme lack of sensors in cities; and serious data loss issue due to faulty sensors or other factors. To address these challenges, Zheng proposed to unify different types of data in urban spaces, such as crowd flow data, traffic flow, air quality, weather data and social media data into unified spatial-temporal data types, and then optimize the management of those data utilizing multiple specially designed spatial-temporal indexes. Zheng gave an example of how these techniques help with detecting parking violations by mining the massive trajectory data of shared bicycles. When talking about the layer of data-driven analysis in cities, Zheng emphasized that the structure of spatio-temporal data is different from simple text and image data.

They may have no choice, if they wish to survive. Consumers, accustomed to experiences with Amazon, Netflix, and Starbucks, demand rapid fulfillment of requests, personalized solutions, and constant attention from their financial providers. With the wealth of data possessed by banks and credit unions, consumers not surprisingly expect providers to know them, value them, and reward them for their relationships. Given the rise of digital and challenger banks, traditional banks and credit unions must find new ways to maintain their share-of-wallet and customer trust. Technologies that integrate artificial intelligence and big data analytics provide financial institutions with unprecedented visibility into their customers' financial dynamics, enabling the kind of personalized service which they crave.

Speakers powered by smart assistants are becoming dime-a-dozen commodities. In most cases, a smart speaker is good for voice commands, but I haven't found their musical reproduction particularly satisfying. The Riva Concert is part of Riva Audio's new Voice line of smart speakers that also includes the larger Riva Stadium. This sweet-sounding portable speaker is built to satisfy music lovers, with refined high-tech features you don't often see in smart speakers. Unpacking the Riva, I noted its heavy, solid build. Under the hood, you'll find a 50-watt Class D amplifier powering the three active drivers and three passive radiators.

While it is impossible to replicate the exact conditions in which the original photo was taken, it is possible to add colour to the photo to help us imagine what the photographer could have seen in that instant. It is incredible -- almost magical -- how a little bit of colour can bring us that much closer to that specific moment in time. And as such, for our hackathon in January, our team decided to build a deep learning colouriser tool trained specifically for old Singaporean photos. If you have old black and white photos and would like to colourise them, you can do so here: Colourise.sg. We do not store any of the photos that you upload to our colouriser application.

"The financial markets are ahead of others in this," said John Micklethwait, the editor in chief of Bloomberg. In addition to covering company earnings for Bloomberg, robot reporters have been prolific producers of articles on minor league baseball for The Associated Press, high school football for The Washington Post and earthquakes for The Los Angeles Times. MANCHESTER, N.H. (AP) -- Jonathan Davis hit for the cycle, as the New Hampshire Fisher Cats topped the Portland Sea Dogs 10-3 on Tuesday. Last week, The Guardian's Australia edition published its first machine-assisted article, an account of annual political donations to the country's political parties. And Forbes recently announced that it was testing a tool called Birdie to provide reporters with rough drafts and story templates.

I've been spending weeks trying to get an actor-critic reinforcement learning model to work with the OpenAi Pendulum environment, but I haven't been able to solve it, yet. The critic (value) model is predicting the value well and its loss is low. The actor, however, is predicting actions all over the place with it's mean (mu) and variance (sigma) totally not aligned with what they should be. If I limit the mean using a tanh activation then the sigma will keep going up towards infinity. I've tried different activation functions, initializers, and hyper-parameters, but nothing seems to work.

This is an integrative review that address the question, "What makes for a good explanation?" with reference to AI systems. Pertinent literatures are vast. Thus, this review is necessarily selective. That said, most of the key concepts and issues are expressed in this Report. The Report encapsulates the history of computer science efforts to create systems that explain and instruct (intelligent tutoring systems and expert systems). The Report expresses the explainability issues and challenges in modern AI, and presents capsule views of the leading psychological theories of explanation. Certain articles stand out by virtue of their particular relevance to XAI, and their methods, results, and key points are highlighted. It is recommended that AI/XAI researchers be encouraged to include in their research reports fuller details on their empirical or experimental methods, in the fashion of experimental psychology research reports: details on Participants, Instructions, Procedures, Tasks, Dependent Variables (operational definitions of the measures and metrics), Independent Variables (conditions), and Control Conditions.

In low light or short-exposure photography the image is often corrupted by noise. While longer exposure helps reduce the noise, it can produce blurry results due to the object and camera motion. The reconstruction of a noise-less image is an ill posed problem. Recent approaches for image denoising aim to predict kernels which are convolved with a set of successively taken images (burst) to obtain a clear image. We propose a deep neural network based approach called Multi-Kernel Prediction Networks (MKPN) for burst image denoising. MKPN predicts kernels of not just one size but of varying sizes and performs fusion of these different kernels resulting in one kernel per pixel. The advantages of our method are two fold: (a) the different sized kernels help in extracting different information from the image which results in better reconstruction and (b) kernel fusion assures retaining of the extracted information while maintaining computational efficiency. Experimental results reveal that MKPN outperforms state-of-the-art on our synthetic datasets with different noise levels.