What if I tell you that an entire short sci-fi film has been written by an AI bot built on LSTM recurrent neural network, and it has even received positive reviews and critics, Surprised?! well, I'm sure you are because that's what I felt watching "Sunspring" for the first time, I mean I know it can't be compared to Steven Spielberg's or Alex Garland's screenwriting quality but no wonder if in the next few years AI bots will compete against them in the Academy Awards. Indeed, we should no longer be surprised by what artificial intelligence is capable of in order to flip our world upside down, making it a better, "easier", and most comfortable place to live in. From all of the AI subfields, in my opinion, NLP has the coolest and most exciting applications. One of them is text generation that we should have a deep look at it. In this article, I will briefly explain how RNN and LSTM work and how we can generate texts using LSTM in Python.

Powered by Advanced AI, Knowledge Automation overcomes the inherent … for the Internet of Things, Blockchain and Artificial Intelligence.

World's largest E-Scooter factory will be set up in India - to make one scooter in every 2 Seconds!! Cab aggregator Ola aims to set up the world's largest and most advanced scooter factory in Tamil Nadu. Ola has signed MOU with the Tamil Nadu government to set up the factory worth Rs 2,400 crore. Spread across 500 acres, the fully-automated factory will have the capacity to build 10 million electric two-wheeler by June 2022. The facility will be built on Industry 4.0 principles and will be AI-powered with 3,000 robots. There will be one mega block to house all production facilities with 43 acres, 2 million sq ft footprint.

Almost like animated, moving portraits in the Harry Potter franchise, photos once frozen in time are being brought to life with an artificial intelligence (A.I.) program called Deep Nostalgia. The technology, which was released on February 25 by the genealogy website MyHeritage, has since gone viral. Social media users have created lifelike moving portraits of mathematician Alan Turing, abolitionist Frederick Douglass and physicist Marie Curie, reports Mindy Weisberger for Live Science. The historical figures can blink, move their heads side-to-side, and even smile. The tech is also being used to animate artwork, statues and photos of ancestors.

Large-scale transformer-based language models (LMs) demonstrate impressive capabilities in open text generation. However, controlling the generated text's properties such as the topic, style, and sentiment is challenging and often requires significant changes to the model architecture or retraining and fine-tuning the model on new supervised data. This paper presents a novel approach for Topical Language Generation (TLG) by combining a pre-trained LM with topic modeling information. We cast the problem using Bayesian probability formulation with topic probabilities as a prior, LM probabilities as the likelihood, and topical language generation probability as the posterior. In learning the model, we derive the topic probability distribution from the user-provided document's natural structure. Furthermore, we extend our model by introducing new parameters and functions to influence the quantity of the topical features presented in the generated text. This feature would allow us to easily control the topical properties of the generated text. Our experimental results demonstrate that our model outperforms the state-of-the-art results on coherency, diversity, and fluency while being faster in decoding.

Recommendation and ranking systems are known to suffer from popularity bias; the tendency of the algorithm to favor a few popular items while under-representing the majority of other items. Prior research has examined various approaches for mitigating popularity bias and enhancing the recommendation of long-tail, less popular, items. The effectiveness of these approaches is often assessed using different metrics to evaluate the extent to which over-concentration on popular items is reduced. However, not much attention has been given to the user-centered evaluation of this bias; how different users with different levels of interest towards popular items are affected by such algorithms. In this paper, we show the limitations of the existing metrics to evaluate popularity bias mitigation when we want to assess these algorithms from the users' perspective and we propose a new metric that can address these limitations. In addition, we present an effective approach that mitigates popularity bias from the user-centered point of view. Finally, we investigate several state-of-the-art approaches proposed in recent years to mitigate popularity bias and evaluate their performances using the existing metrics and also from the users' perspective. Our experimental results using two publicly-available datasets show that existing popularity bias mitigation techniques ignore the users' tolerance towards popular items. Our proposed user-centered method can tackle popularity bias effectively for different users while also improving the existing metrics.

Several pieces of work have uncovered performance disparities by conducting "disaggregated evaluations" of AI systems. We build on these efforts by focusing on the choices that must be made when designing a disaggregated evaluation, as well as some of the key considerations that underlie these design choices and the tradeoffs between these considerations. We argue that a deeper understanding of the choices, considerations, and tradeoffs involved in designing disaggregated evaluations will better enable researchers, practitioners, and the public to understand the ways in which AI systems may be underperforming for particular groups of people.

In NLP, text classification is one of the primary problems we try to solve and its uses in language analyses are indisputable. The lack of labeled training data made it harder to do these tasks in low resource languages like Amharic. The task of collecting, labeling, annotating, and making valuable this kind of data will encourage junior researchers, schools, and machine learning practitioners to implement existing classification models in their language. In this short paper, we aim to introduce the Amharic text classification dataset that consists of more than 50k news articles that were categorized into 6 classes. This dataset is made available with easy baseline performances to encourage studies and better performance experiments.

The wireless home-audio specialist Sonos has unveiled the Roam, its smaller, cheaper portable speaker with Bluetooth and wifi that works as well at home as it does outdoors. The rugged triangular Roam weighs 430g and is about the size of a water bottle. The aim is that it will avoid ending up stuck in a drawer collecting dust like most portable speakers by sounding good enough and working well enough to warrant being used at home too, connecting to Sonos' smart multi-room wifi system. That means it will work like any other non-battery powered Sonos speaker able to stream music directly via wifi from more than 100 different music services, including Spotify and Apple Music, and be grouped with other speakers to play music all over the home. It also supports Apple's AirPlay 2 and smart speaker functionality with either Google Assistant and Amazon's Alexa. The Roam has separate woofer and tweeter speakers with Sonos's Trueplay technology, which automatically tunes the sound accounting for acoustics, obstacles and position to sound its best at all times.

Ever wish you could listen to documents? In this post, we'll use machine learning to transform PDFs into audiobooks. This project was a collaboration with Kaz Sato. Update: Many of you have asked me what the total cost of this project is, which I've included at the end of this post. These days, you can do anything on foot: listen to the news, take meetings, even write notes (with voice dictation).